diff --git a/data/emissions/CH4Pledge-rcmip-emissions-ssp245-CLE-MFR-filled.csv b/data/emissions/CH4Pledge-rcmip-emissions-ssp245-CLE-MFR-filled.csv
new file mode 100755
index 0000000..8ed182d
--- /dev/null
+++ b/data/emissions/CH4Pledge-rcmip-emissions-ssp245-CLE-MFR-filled.csv
@@ -0,0 +1,157 @@
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+MESSAGE-GLOBIOM___SSP2-45-CLE-CH4,World,VOC,Mt VOC/yr,60.0218262,59.0881263,58.631997,59.0263494,60.2699452,59.8249337,58.9468293,61.263693,60.4346124,60.4452631,61.0517248,60.9842272,60.0270893,60.1270663,58.4383572,60.6294579,59.8587846,59.8910269,62.3658256,60.9504036,60.9660272,59.4242403,58.724953,58.9165689,61.3133579,60.5408305,59.3811448,60.8934901,61.783487,60.5008149,61.6185957,61.1933065,60.4857215,60.1017127,59.2122599,60.4944794,59.9146824,60.7533571,63.1243305,61.6322924,61.8459918,60.2187392,59.7056857,59.5849395,62.0382245,61.2427321,59.8642424,61.471377,62.197639,61.4962302,62.0032091,61.6656478,61.3261566,60.4043418,60.3647028,61.3199115,61.2234497,61.7064691,64.0819597,62.7100221,62.9735167,61.829606,60.85747,61.1361302,63.4919282,62.8715757,62.1464325,62.941531,64.2676805,63.2575893,64.0766433,63.9874491,63.2272465,62.2607644,62.5634338,63.1857896,62.8159415,63.3899901,65.9053595,64.5728982,64.9407618,63.3368217,62.8708966,63.0695837,65.1049926,64.2354432,64.0801633,64.8224271,65.9472466,65.1113122,64.9229818,65.9454452,64.9216736,63.4240609,63.7624543,64.6855672,64.2440515,65.4436208,67.3982438,66.4270715,66.7830488,64.9500466,63.9578999,64.4455542,66.7946952,65.651564,65.0425347,66.7277988,67.4706038,66.9569473,66.8495349,66.8857555,66.3589602,65.0096354,65.3990666,67.1034735,66.3752816,67.6602645,69.6472938,67.9294956,69.1218769,67.4286236,65.8455371,67.9943705,69.3490801,67.4820886,68.0726649,68.7783807,69.4697957,70.08572,69.1878812,69.7089689,68.7501675,67.0625448,68.5940233,68.9497782,68.8692628,69.7940394,71.4272374,70.6571877,71.4765972,69.5317219,68.6639196,69.487058,71.9080438,71.1517866,69.4226771,71.5467325,71.4566595,72.4607226,71.9065663,71.6326316,71.2414985,70.2537639,72.8843763,73.7097352,73.4617385,76.0044451,76.856238,76.4408771,78.4812434,76.7337057,75.8316082,77.5799616,79.7780734,78.3504006,78.2798516,80.4548181,81.6129481,81.6772439,82.9699211,81.1872552,80.5306943,86.2758342,83.2112311,84.0617442,84.1687708,86.5275353,87.1158634,89.3767025,91.5957386,86.4853187,87.9072537,87.7392644,91.4970397,91.9021407,95.8348454,95.8456743,93.2370606,97.3453542,96.4961289,97.0940735,98.657197,99.4863464,102.0083052,102.3868629,102.6581835,104.1573676,108.0964174,109.1544258,113.8598472,109.0909544,112.6182109,116.7765378,118.7896929,122.5425815,129.82302,135.5915206,129.2085836,131.2104974,133.3368149,143.5652635,140.105892,156.130954,146.4547756,153.4053912,156.4949809,164.7753162,165.0957088,169.6815713,179.6859547,183.5355579,196.6655247,186.5259319,183.6005251,177.5130673,186.2474273,189.2255323,192.9347955,196.0169029,194.391242,189.1686139,206.8844648,183.8358223,189.3335257,190.4568857,195.6652235,203.5851701,203.5322276,200.3165052,168.6335038,210.4186819,164.0086367,170.0742745,198.8296541,173.883414,168.2468326,230.5701039,197.5970855,170.4280537,158.7279109,159.4041271,180.8749421,173.962076,171.8057947,171.7529789,180.2783041,167.017421,158.428355,160.9658855,166.7611017,159.3658689,168.4360541,158.3467615,173.3793778,169.5387636,167.8071854,167.5896475,167.3721095,167.1545715,166.9370335,166.4937731,166.0505127,165.6072523,165.1639919,164.7207314,164.1086343,163.4965372,162.88444,162.2723429,161.6602457,161.4440779,161.2279101,161.0117423,160.7955744,160.5794066,160.3632388,160.147071,159.9309032,159.7147354,159.4985675,159.4576604,159.4167532,159.375846,159.3349389,159.2940317,159.2531245,159.2122174,159.1713102,159.130403,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959,159.0894959
diff --git a/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/calibration/01_tabulate-4xCO2.py b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/calibration/01_tabulate-4xCO2.py
new file mode 100755
index 0000000..d70f826
--- /dev/null
+++ b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/calibration/01_tabulate-4xCO2.py
@@ -0,0 +1,103 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+"""Combine Hege-Beate's data into one file."""
+
+import glob
+import os
+from pathlib import PurePath
+
+import pandas as pd
+from dotenv import load_dotenv
+from fair import __version__
+
+load_dotenv()
+
+print("Making nice 4xCO2 data...")
+
+cal_v = os.getenv("CALIBRATION_VERSION")
+fair_v = os.getenv("FAIR_VERSION")
+constraint_set = os.getenv("CONSTRAINT_SET")
+
+assert fair_v == __version__
+
+available_files = glob.glob(
+    "../../../../../data/cmip6-hbf/cmip_data/*/abrupt-4xCO2/"
+    "*_abrupt-4xCO2_*_anomalies.txt"
+)
+
+maxlen = 0
+
+models = []
+runs = []
+lines = []
+for file in available_files:
+    model = PurePath(file).parts[8]
+    run = PurePath(file).parts[10].split("_")[2]
+    models.append(model)
+    runs.append(run)
+    df = pd.read_csv(file, index_col=0)
+    vars = {}
+    for var in ["tas", "rlut", "rsut", "rsdt"]:
+        vars[var] = df[var].values.squeeze()
+        if len(vars[var]) > maxlen:
+            maxlen = len(vars[var])
+        line = [
+            "CMIP",
+            model,
+            run,
+            "CMIP6",
+            "unspecified",
+            "World",
+            "abrupt-4xCO2",
+            "W m^-2",
+            var,
+        ]
+        line.extend(vars[var][:150])
+        lines.append(line)
+    vars["rndt"] = vars["rsdt"] - vars["rsut"] - vars["rlut"]
+    line = [
+        "CMIP",
+        model,
+        run,
+        "CMIP6",
+        "unspecified",
+        "World",
+        "abrupt-4xCO2",
+        "W m^-2",
+        "rndt",
+    ]
+    line.extend(vars["rndt"][:150])
+    lines.append(line)
+
+df = pd.DataFrame(
+    lines,
+    columns=(
+        [
+            "activity_id",
+            "climate_model",
+            "member_id",
+            "mip_era",
+            "model",
+            "region",
+            "scenario",
+            "unit",
+            "variable",
+        ]
+        + ["X%d" % year for year in range(1850, 2000)]
+    ),
+)
+
+to_remove = [f"X{year}" for year in range(1850, 2000)]
+df.dropna(subset=to_remove, inplace=True)
+
+os.makedirs(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/calibrations/",
+    exist_ok=True,
+)
+
+df.to_csv(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+    "calibrations/4xCO2_cmip6.csv",
+    index=False,
+)
diff --git a/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/calibration/02_calibrate_cummins_3layer.r b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/calibration/02_calibrate_cummins_3layer.r
new file mode 100755
index 0000000..75616d7
--- /dev/null
+++ b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/calibration/02_calibrate_cummins_3layer.r
@@ -0,0 +1,148 @@
+#!/usr/bin/env Rscript
+
+# Goes through each of the models in turn and tunes the parameters of the
+# Cummins three layer model.
+#
+# It will produce an output csv table of Cummins three layer parameters, which
+# we will then put in impulse-response form for FaIR.
+#
+# References:
+# Cummins, Cummins, D. P., Stephenson, D. B., & Stott, P. A. (2020). Optimal
+# Estimation of Stochastic Energy Balance Model Parameters, Journal of Climate,
+# 33(18), 7909-7926, https://doi.org/10.1175/JCLI-D-19-0589.1
+#
+# donaldcummins. (2021). donaldcummins/EBM: Optional quadratic penalty
+# (v1.1.0). Zenodo. https://doi.org/10.5281/zenodo.5217975
+
+message("Running R script for 3 layer model calibrations...")
+
+# Use Donald Cummins' package
+library(EBM)
+
+# Get environment variable describing calibration version
+readRenviron("../../../../../.env")
+cal_v = paste("v", Sys.getenv("CALIBRATION_VERSION"), sep="")
+fair_v = paste("fair-", Sys.getenv("FAIR_VERSION"), sep="")
+constraint_set = Sys.getenv("CONSTRAINT_SET")
+
+# Get the precalculated 4xCO2 N and T data
+input_data = read.csv(file.path("..", "..", "..", "..", "..", "output", fair_v, cal_v, constraint_set, "calibrations", "4xCO2_cmip6.csv"))
+
+# Initial guess for parameter values
+inits3 <- list(
+	gamma = 2,
+	C = c(4, 15, 80),
+	kappa = c(1, 2, 1),
+	epsilon = 1.1,
+	sigma_eta = 0.5,
+	sigma_xi = 0.5,
+	F_4xCO2 = 8
+)
+
+# prepare empty output dataframe
+output <- data.frame(matrix(ncol = 15, nrow = 0))
+
+# grab models
+models = unique(input_data$climate_model)
+
+# iterate through models
+for (model in models) {
+	runs <- input_data[
+		(input_data$climate_model == model) &
+		(input_data$variable == "tas"), 3
+	]
+
+	# iterate through runs of the same model for now, though we will probably
+	# want to combine/downweight this in the final edition
+	for (run in runs) {
+		rndt <- input_data[
+			(input_data$climate_model == model) &
+			(input_data$variable == "rndt") &
+			(input_data$member_id == run), 10:159
+		]
+		rndt <- unname(rndt)
+
+		tas <- input_data[
+			(input_data$climate_model == model) &
+			(input_data$variable ==  "tas") &
+			(input_data$member_id == run), 10:159
+		]
+		tas <- unname(tas)
+
+		tas <- unlist(tas)
+		rndt <- unlist(rndt)
+
+        # Try and achieve convergence: if it fails, use a different alpha and try again.
+		# If it still fails, give up and move on.
+		attempt <- 0
+		success <- FALSE
+		while (attempt < 5) {
+			tryCatch(
+				{
+					check <- capture.output(
+						result <- FitKalman(
+							inits3,
+							T1 = tas,
+							N = rndt,
+							alpha = 1e-05 * 10^attempt,
+							maxeval=20000)$p
+						)
+					success <- TRUE
+					print(check)
+				},
+				error = function(c) {
+					message(paste(model, "did not converge or ran out of iterations"))
+					message("Here's the original error message from FitKalman:")
+					message(c)
+					if (attempt < 4) {
+						message(paste("Trying again with more liberal quadratic penalty (alpha) = ", 1e-05 * 10^attempt))
+					}
+					else {
+						message(paste("This isn't going to work, is it? I'm giving up for", model))
+					}
+				}
+			)
+			if (success) { break }
+			attempt <- attempt + 1
+		}
+		if (!success) {
+			message(paste("I am excluding" , model, " from my table of results."))
+			next
+		}
+
+		conv <- TRUE
+		nit <- strsplit(check, " ")[[1]][5]
+
+		row_out <- c(
+			model,
+			run,
+			conv,
+			nit,
+			result$gamma,
+			result$C[1],
+			result$C[2],
+			result$C[3],
+			result$kappa[1],
+			result$kappa[2],
+			result$kappa[3],
+			result$epsilon,
+			result$sigma_eta,
+			result$sigma_xi,
+			result$F_4xCO2
+		)
+
+		output <- rbind(output, row_out)
+	}
+}
+
+# rename columns away from random defaults
+names = c("model", "run", "conv", "nit", "gamma", "C1", "C2", "C3", "kappa1",
+	"kappa2", "kappa3", "epsilon", "sigma_eta", "sigma_xi", "F_4xCO2")
+colnames(output) <- names
+
+# save output
+write.csv(
+	output,
+	file.path("..", "..", "..", "..", "..", "output", fair_v, cal_v, constraint_set, "calibrations", "4xCO2_cummins_ebm3_cmip6.csv"),
+	row.names=FALSE
+)
diff --git a/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/calibration/03_convert-ebm3-to-impulse-response.py b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/calibration/03_convert-ebm3-to-impulse-response.py
new file mode 100755
index 0000000..bb335fd
--- /dev/null
+++ b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/calibration/03_convert-ebm3-to-impulse-response.py
@@ -0,0 +1,168 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+"""Convert energy balance model to impulse response model."""
+
+# This notebook takes the three-layer energy balance model tunings from Donald Cummins
+# and converts them to a three-layer impulse response function.
+#
+# It will then save these into a CSV file.
+
+import os
+
+import numpy as np
+import pandas as pd
+from dotenv import load_dotenv
+from fair.energy_balance_model import EnergyBalanceModel
+from fair.forcing.ghg import meinshausen2020
+
+load_dotenv()
+
+print("Converting EBM parameters to IRM parameters...")
+
+cal_v = os.getenv("CALIBRATION_VERSION")
+fair_v = os.getenv("FAIR_VERSION")
+constraint_set = os.getenv("CONSTRAINT_SET")
+
+df = pd.read_csv(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/calibrations/"
+    "4xCO2_cummins_ebm3_cmip6.csv"
+)
+
+models = df["model"].unique()
+
+params = {}
+for model in models:
+    params[model] = {}
+    for run in df.loc[df["model"] == model, "run"]:
+        condition = (df["model"] == model) & (df["run"] == run)
+        params[model][run] = {}
+        params[model][run]["gamma_autocorrelation"] = df.loc[condition, "gamma"].values[
+            0
+        ]
+        params[model][run]["ocean_heat_capacity"] = df.loc[
+            condition, "C1":"C3"
+        ].values.squeeze()
+        params[model][run]["ocean_heat_transfer"] = df.loc[
+            condition, "kappa1":"kappa3"
+        ].values.squeeze()
+        params[model][run]["deep_ocean_efficacy"] = df.loc[condition, "epsilon"].values[
+            0
+        ]
+        params[model][run]["sigma_eta"] = df.loc[condition, "sigma_eta"].values[0]
+        params[model][run]["sigma_xi"] = df.loc[condition, "sigma_xi"].values[0]
+        params[model][run]["forcing_4co2"] = df.loc[condition, "F_4xCO2"].values[0]
+
+
+co2 = 284.3169988
+ch4 = 808.2490285
+n2o = 273.021047
+
+double_co2 = co2 * 2
+quadruple_co2 = co2 * 4
+
+rf_4co2 = meinshausen2020(
+    np.array([4 * co2, ch4, n2o]).reshape((1, 1, 1, 3)),
+    np.array([co2, ch4, n2o]).reshape((1, 1, 1, 3)),
+    np.array([1, 1, 1]).reshape((1, 1, 1, 3)),
+    np.ones((1, 1, 1, 3)),
+    0,
+    1,
+    2,
+    [],
+).squeeze()[0]
+
+rf_2co2 = meinshausen2020(
+    np.array([2 * co2, ch4, n2o]).reshape((1, 1, 1, 3)),
+    np.array([co2, ch4, n2o]).reshape((1, 1, 1, 3)),
+    np.array([1, 1, 1]).reshape((1, 1, 1, 3)),
+    np.ones((1, 1, 1, 3)),
+    0,
+    1,
+    2,
+    [],
+).squeeze()[0]
+
+forcing_2co2_4co2_ratio = rf_2co2 / rf_4co2
+
+for model in models:
+    for run in df.loc[df["model"] == model, "run"]:
+        condition = (df["model"] == model) & (df["run"] == run)
+        ebm = EnergyBalanceModel(**params[model][run])
+        ebm.emergent_parameters()
+        params[model][run] = ebm.__dict__
+
+# reconstruct a data table and save
+# df_out = pd.DataFrame(
+#    columns=["model", "run", "ecs", "tcr", "tau1", "tau2", "tau3", "q1", "q2", "q3"]
+# )
+
+rows_to_add = []
+count = 0
+for model in models:
+    for run in df.loc[df["model"] == model, "run"]:
+        values_to_add = {
+            "model": model,
+            "run": run,
+            "ecs": params[model][run]["ecs"],
+            "tcr": params[model][run]["tcr"],
+            "tau1": params[model][run]["timescales"][0],
+            "tau2": params[model][run]["timescales"][1],
+            "tau3": params[model][run]["timescales"][2],
+            "q1": params[model][run]["response_coefficients"][0],
+            "q2": params[model][run]["response_coefficients"][1],
+            "q3": params[model][run]["response_coefficients"][2],
+        }
+        row_to_add = pd.DataFrame(values_to_add, index=[count])
+        # df_out = pd.concat((df_out, row_to_add), axis=1)
+        rows_to_add.append(row_to_add)
+        count = count + 1
+
+# Make LaTeX format table
+# don't make decision on run here for pipeline, but do for paper.
+
+multi_runs = {
+    "GISS-E2-1-G": "r1i1p1f1",
+    "GISS-E2-1-H": "r1i1p3f1",
+    "MRI-ESM2-0": "r1i1p1f1",
+    "EC-Earth3": "r3i1p1f1",
+    "FIO-ESM-2-0": "r1i1p1f1",
+    "CanESM5": "r1i1p2f1",
+    "FGOALS-f3-L": "r1i1p1f1",
+    "CNRM-ESM2-1": "r1i1p1f2",
+}
+
+for model in sorted(list(models)):
+    if model in multi_runs:
+        run = multi_runs[model]
+    else:
+        run = df.loc[df["model"] == model, "run"].values[0]
+    print(
+        f"{model} & {params[model][run]['ocean_heat_transfer'][0]:.2f} & "
+        f"{params[model][run]['ocean_heat_transfer'][1]:.2f} & "
+        f"{params[model][run]['ocean_heat_transfer'][2]:.2f} & "
+        f"{params[model][run]['ocean_heat_capacity'][0]:.2f} & "
+        f"{params[model][run]['ocean_heat_capacity'][1]:.1f} & "
+        f"{params[model][run]['ocean_heat_capacity'][2]:.0f} & "
+        f"{params[model][run]['deep_ocean_efficacy']:.2f} & "
+        f"{params[model][run]['gamma_autocorrelation']:.2f} & "
+        f"{params[model][run]['sigma_xi']:.2f} & "
+        f"{params[model][run]['sigma_eta']:.2f} & "
+        f"{params[model][run]['forcing_4co2']:.2f} & {params[model][run]['ecs']:.2f} & "
+        f"{params[model][run]['tcr']:.2f} \\\\"
+    )
+
+
+df_out = pd.concat(rows_to_add)
+df_out.sort_values(["model", "run"], inplace=True)
+
+os.makedirs(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/calibrations/",
+    exist_ok=True,
+)
+
+df_out.to_csv(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/calibrations/"
+    "4xCO2_impulse_response_ebm3_cmip6.csv",
+    index=False,
+)
diff --git a/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/calibration/04_methane-lifetime-multispecies.py b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/calibration/04_methane-lifetime-multispecies.py
new file mode 100755
index 0000000..0b3d057
--- /dev/null
+++ b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/calibration/04_methane-lifetime-multispecies.py
@@ -0,0 +1,770 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+"""Calibrate methane lifetime from precursors and climate."""
+
+# # What affects methane chemical lifetime?
+#
+# - methane
+# - VOCs
+# - NOx
+# - Ozone
+# - halocarbons (specifically ODSs)
+# - N2O
+# - climate
+#
+# Ozone itself is a function of other precursors: we do not include ozone as a direct
+# influence on methane lifetime, and restrict ourselves to directly emitted
+# anthropogenic species.
+#
+# Gill Thornhill published two papers on methane lifetime: one on the chemical
+# adjustments to lifetime, and one on the climate adjustments. Both effects will be
+# included. We will
+#
+# 1. take AerChemMIP multi-model means from Gill's papers
+# 2. run the lifetime relationship to individual AerChemMIP models in Gill's papers
+# 3. find a least squares fit with reasonable sensitivies across the historical
+# 
+# note here we calibrate to the ECLIPSE emissions supplied by Camilla. For concentration
+# of non-CH4 species we use SSP2-4.5. For observations we use IGCC 2023
+
+import os
+
+import matplotlib.pyplot as pl
+import numpy as np
+import pandas as pd
+import pooch
+import scipy.optimize
+import scipy.stats
+from dotenv import load_dotenv
+
+load_dotenv()
+
+print("Calibrating methane lifetime...")
+
+cal_v = os.getenv("CALIBRATION_VERSION")
+fair_v = os.getenv("FAIR_VERSION")
+constraint_set = os.getenv("CONSTRAINT_SET")
+plots = os.getenv("PLOTS", "False").lower() in ("true", "1", "t")
+progress = os.getenv("PROGRESS", "False").lower() in ("true", "1", "t")
+datadir = os.getenv("DATADIR")
+
+rcmip_file = pooch.retrieve(
+    url="https://zenodo.org/records/4589756/files/"
+    "rcmip-concentrations-annual-means-v5-1-0.csv",
+    known_hash="md5:0d82c3c3cdd4dd632b2bb9449a5c315f",
+    path=datadir,
+    progressbar=progress,
+)
+rcmip_df = pd.read_csv(rcmip_file)
+
+# assert fair_v == __version__
+pl.style.use("../../../../../defaults.mplstyle")
+
+# Temperature data
+# Use observations 1850-2022 from IGCC, then use ssp370 projections from IPCC
+df_temp = pd.read_csv("../../../../../data/forcing/ssp_strawman_warming.csv")
+gmst = df_temp["ssp370"].values
+
+# Get emissions and concentrations: from RCMIP for model tuning stage
+rcmip_emissions_file = pooch.retrieve(
+    url="https://zenodo.org/records/4589756/files/"
+    "rcmip-emissions-annual-means-v5-1-0.csv",
+    known_hash="md5:4044106f55ca65b094670e7577eaf9b3",
+    path=datadir,
+    progressbar=progress,
+)
+
+rcmip_concentration_file = pooch.retrieve(
+    url=(
+        "https://zenodo.org/records/4589756/files/"
+        "rcmip-concentrations-annual-means-v5-1-0.csv"
+    ),
+    known_hash="md5:0d82c3c3cdd4dd632b2bb9449a5c315f",
+    path=datadir,
+    progressbar=progress,
+)
+
+eclipse_file = (
+    "../../../../../data/emissions/CH4Pledge-rcmip-emissions-ssp245-CLE-MFR-filled.csv"
+)
+
+df_rcmip_emis = pd.read_csv(rcmip_emissions_file)
+df_emis = pd.read_csv(eclipse_file)
+df_conc = pd.read_csv(rcmip_concentration_file)
+input = {}
+hc_input = {}
+
+conc_species = ["CH4", "N2O"]
+hc_species = [
+    "CFC-11",
+    "CFC-12",
+    "CFC-113",
+    "CFC-114",
+    "CFC-115",
+    "HCFC-22",
+    "HCFC-141b",
+    "HCFC-142b",
+    "CCl4",
+    "CHCl3",
+    "CH2Cl2",
+    "CH3Cl",
+    "CH3CCl3",
+    "CH3Br",
+    "Halon-1211",
+    "Halon-1301",
+    "Halon-2402",
+]
+
+for species in conc_species:
+    input[species] = (
+        df_conc.loc[
+            (df_conc["Scenario"] == "ssp245")
+            & (df_conc["Variable"].str.endswith(species))
+            & (df_conc["Region"] == "World"),
+            "1750":"2100",
+        ]
+        .interpolate(axis=1)
+        .values.squeeze()
+    )
+
+for species in hc_species:
+    species_rcmip_name = species.replace("-", "")
+    hc_input[species] = (
+        df_conc.loc[
+            (df_conc["Scenario"] == "ssp245")
+            & (df_conc["Variable"].str.endswith(species_rcmip_name))
+            & (df_conc["Region"] == "World"),
+            "1750":"2100",
+        ]
+        .interpolate(axis=1)
+        .values.squeeze()
+    )
+
+# Camilla has fixed the NOx units issue
+print(df_emis)
+emis_species = ["CO", "VOC", "NOx"]
+for species in emis_species:
+    input[species] = (
+        df_emis.loc[
+            (df_emis["scenario"] == "MESSAGE-GLOBIOM___SSP2-45")
+            & (df_emis["variable"].str.endswith(species))
+            & (df_emis["region"] == "World"),
+            "1750.5":"2100.5",
+        ]
+        .interpolate(axis=1)
+        .values.squeeze()
+    )
+
+input["temp"] = gmst
+
+
+def calculate_eesc(
+    concentration,
+    fractional_release,
+    fractional_release_cfc11,
+    cl_atoms,
+    br_atoms,
+    br_cl_ratio=45,
+):
+    # EESC is in terms of CFC11-eq
+    eesc_out = (
+        cl_atoms * (concentration) * fractional_release / fractional_release_cfc11
+        + br_cl_ratio
+        * br_atoms
+        * (concentration)
+        * fractional_release
+        / fractional_release_cfc11
+    ) * fractional_release_cfc11
+    return eesc_out
+
+
+fractional_release = {
+    "CFC-11": 0.47,
+    "CFC-12": 0.23,
+    "CFC-113": 0.29,
+    "CFC-114": 0.12,
+    "CFC-115": 0.04,
+    "HCFC-22": 0.13,
+    "HCFC-141b": 0.34,
+    "HCFC-142b": 0.17,
+    "CCl4": 0.56,
+    "CHCl3": 0,
+    "CH2Cl2": 0,
+    "CH3Cl": 0.44,
+    "CH3CCl3": 0.67,
+    "CH3Br": 0.6,
+    "Halon-1211": 0.62,
+    "Halon-1301": 0.28,
+    "Halon-2402": 0.65,
+}
+
+cl_atoms = {
+    "CFC-11": 3,
+    "CFC-12": 2,
+    "CFC-113": 3,
+    "CFC-114": 2,
+    "CFC-115": 1,
+    "HCFC-22": 1,
+    "HCFC-141b": 2,
+    "HCFC-142b": 1,
+    "CCl4": 4,
+    "CHCl3": 3,
+    "CH2Cl2": 2,
+    "CH3Cl": 1,
+    "CH3CCl3": 3,
+    "CH3Br": 0,
+    "Halon-1211": 1,
+    "Halon-1301": 0,
+    "Halon-2402": 0,
+}
+
+br_atoms = {
+    "CFC-11": 0,
+    "CFC-12": 0,
+    "CFC-113": 0,
+    "CFC-114": 0,
+    "CFC-115": 0,
+    "HCFC-22": 0,
+    "HCFC-141b": 0,
+    "HCFC-142b": 0,
+    "CCl4": 0,
+    "CHCl3": 0,
+    "CH2Cl2": 0,
+    "CH3Cl": 0,
+    "CH3CCl3": 0,
+    "CH3Br": 1,
+    "Halon-1211": 1,
+    "Halon-1301": 1,
+    "Halon-2402": 2,
+}
+
+hc_eesc = {}
+total_eesc = 0
+
+for species in hc_species:
+    hc_eesc[species] = calculate_eesc(
+        hc_input[species],
+        fractional_release[species],
+        fractional_release["CFC-11"],
+        cl_atoms[species],
+        br_atoms[species],
+    )
+    total_eesc = total_eesc + hc_eesc[species]
+
+total_eesc_1850 = total_eesc[100]
+
+input["HC"] = total_eesc
+
+# Use 1850 and 2014 emissions or concentrations corresponding to methane lifetime
+# changes in Thornhill et al. 2021.
+#
+# Could we also take into account the fact that there are multiple loss pathways for
+# CH4:
+# - tropospheric OH loss is 560 Tg/yr
+# - chlorine oxidation, 11 Tg/yr, assumed not included in AerChemMIP models
+# - stratospheric loss is 31 Tg/yr, assumed not included in AerChemMIP models
+# - soil uptake, 30 Tg/yr, not included in AerChemMIP models
+#
+# Saunois (2020): 90% of sink is OH chemistry in troposphere and is 553 [476–677] Tg
+# CH4 yr−1, which is close to the IPCC number of 560, (chapter 5)
+#
+# Chapter 6 only give time constants for soil uptake and the combined chemistry loss
+# (trop OH + chlorine + stratosphere).
+
+
+def alpha_scaling_exp(
+    input,
+    baseline,
+    normalisation,
+    beta,
+):
+    log_lifetime_scaling = 0
+    for species in ["CH4", "N2O", "VOC", "HC", "NOx", "temp"]:
+        log_lifetime_scaling = log_lifetime_scaling + (
+            np.log(
+                1
+                + (input[species] - baseline[species])
+                / normalisation[species]
+                * beta[species]
+            )
+        )
+    return np.exp(log_lifetime_scaling)
+
+
+# normalisation = ppb / ppt / Mt yr-1 increase from 1850 to 2014
+normalisation = {}
+for species in ["CH4", "N2O", "VOC", "NOx", "HC"]:
+    normalisation[species] = input[species][264] - input[species][100]
+    print(species, normalisation[species])
+normalisation["temp"] = 1
+
+# baselines are 1850! so "base" lifetime out is for 1750!
+baseline = {}
+for species in ["CH4", "N2O", "VOC", "NOx", "HC"]:
+    baseline[species] = input[species][100]
+baseline["temp"] = 0
+
+# Steps 1 and 2
+
+# Get and tune to AerChemMIP models
+# MRI and GISS both give pretty good historical emulations
+parameters = {}
+
+parameters["AerChemMIP_mean"] = {
+    "base": 10.0,
+    "CH4": +0.22,
+    "NOx": -0.33,
+    "VOC": +0.19,
+    "HC": -0.037,
+    "N2O": -0.02,
+    "temp": -0.006,
+}
+
+parameters["UKESM"] = {
+    "base": 8,
+    "CH4": +0.22,
+    "NOx": -0.25,
+    "VOC": +0.11,
+    "HC": -0.049,
+    "N2O": -0.012,
+    "temp": -0.0408,
+}
+
+# we'll exclude BCC and CESM as they don't have VOC expt and that's important.
+# We can live with a missing N2O from GFDL and a missing temperature feedback from MRI.
+
+parameters["GFDL"] = {
+    "base": 9.6,
+    "CH4": +0.21,
+    "NOx": -0.33,
+    "VOC": +0.15,
+    "HC": -0.075,
+    "N2O": 0,  # missing
+    "temp": -0.0205,
+}
+
+parameters["GISS"] = {
+    "base": 13.4,
+    "CH4": +0.18,
+    "NOx": -0.46,
+    "VOC": +0.27,
+    "HC": -0.006,
+    "N2O": -0.039,
+    "temp": -0.0333,
+}
+
+parameters["MRI"] = {
+    "base": 10.1,
+    "CH4": +0.22,
+    "NOx": -0.26,
+    "VOC": +0.21,
+    "HC": -0.024,
+    "N2O": -0.013,
+    "temp": 0,  # missing
+}
+
+lifetime_scaling = {}
+models = ["UKESM", "GFDL", "GISS", "MRI"]
+
+
+for model in models:
+    print(parameters[model])
+    lifetime_scaling[model] = alpha_scaling_exp(
+        input,
+        baseline,
+        normalisation,
+        parameters[model],
+    )
+
+
+# put this into a simple one box model
+def one_box(
+    emissions,
+    gas_boxes_old,
+    airborne_emissions_old,
+    burden_per_emission,
+    lifetime,
+    alpha_lifetime,
+    partition_fraction,
+    pre_industrial_concentration,
+    timestep=1,
+    natural_emissions_adjustment=0,
+):
+    effective_lifetime = alpha_lifetime * lifetime
+    decay_rate = timestep / (effective_lifetime)
+    decay_factor = np.exp(-decay_rate)
+    gas_boxes_new = (
+        partition_fraction
+        * (emissions - natural_emissions_adjustment)
+        * 1
+        / decay_rate
+        * (1 - decay_factor)
+        * timestep
+        + gas_boxes_old * decay_factor
+    )
+    airborne_emissions_new = gas_boxes_new
+    concentration_out = (
+        pre_industrial_concentration + burden_per_emission * airborne_emissions_new
+    )
+    return concentration_out, gas_boxes_new, airborne_emissions_new
+
+
+emis_ch4 = (
+    df_emis.loc[
+        (df_emis["scenario"] == "MESSAGE-GLOBIOM___SSP2-45-CLE-CH4")
+        & (df_emis["variable"].str.endswith("CH4"))
+        & (df_emis["region"] == "World"),
+        "1750.5":"2100.5",
+    ]
+    .interpolate(axis=1)
+    .values.squeeze()
+)
+
+burden_per_emission = 1 / (5.1352e18 / 1e18 * 16.043 / 28.97)
+partition_fraction = 1
+pre_industrial_concentration = 729.2
+natural_emissions_adjustment = emis_ch4[0]
+
+conc_ch4 = {}
+
+
+for model in models:
+    conc_ch4[model] = np.zeros(351)
+    gas_boxes = 0
+    airborne_emissions = 0
+    for i in range(351):
+        conc_ch4[model][i], gas_boxes, airborne_emissions = one_box(
+            emis_ch4[i],
+            gas_boxes,
+            airborne_emissions,
+            burden_per_emission,
+            parameters[model]["base"],
+            lifetime_scaling[model][i],
+            partition_fraction,
+            pre_industrial_concentration,
+            timestep=1,
+            natural_emissions_adjustment=natural_emissions_adjustment,
+        )
+
+if plots:
+    for model in models:
+        pl.plot(np.arange(1750, 2023), conc_ch4[model][:273], label=model)
+    pl.plot(np.arange(1750, 2023), input["CH4"][:273], color="k", label="CMIP6")
+    pl.legend()
+    os.makedirs(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/", exist_ok=True
+    )
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "aerchemmip_tuning_ch4_conc_1750-2023.png"
+    )
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "aerchemmip_tuning_ch4_conc_1750-2023.pdf"
+    )
+    pl.close()
+
+# Step 3
+#
+# Find least squares sensible historical fit
+invect = np.array(
+    [input["CH4"], input["NOx"], input["VOC"], input["HC"], input["N2O"], input["temp"]]
+)
+
+
+def fit_precursors(x, rch4, rnox, rvoc, rhc, rn2o, rtemp, rbase):
+    conc_ch4 = np.zeros(273)
+    gas_boxes = 0
+    airborne_emissions = 0
+
+    params = {}
+    params["CH4"] = rch4
+    params["NOx"] = rnox
+    params["VOC"] = rvoc
+    params["HC"] = rhc
+    params["N2O"] = rn2o
+    params["temp"] = rtemp
+
+    inp = {}
+    inp["CH4"] = x[0]
+    inp["NOx"] = x[1]
+    inp["VOC"] = x[2]
+    inp["HC"] = x[3]
+    inp["N2O"] = x[4]
+    inp["temp"] = x[5]
+
+    lifetime_scaling = alpha_scaling_exp(
+        inp,
+        baseline,
+        normalisation,
+        params,
+    )
+
+    for i in range(273):
+        conc_ch4[i], gas_boxes, airborne_emissions = one_box(
+            emis_ch4[i],
+            gas_boxes,
+            airborne_emissions,
+            burden_per_emission,
+            rbase,
+            lifetime_scaling[i],
+            partition_fraction,
+            pre_industrial_concentration,
+            timestep=1,
+            natural_emissions_adjustment=natural_emissions_adjustment,
+        )
+    return conc_ch4
+
+
+p, cov = scipy.optimize.curve_fit(
+    fit_precursors,
+    invect[:, :273],
+    input["CH4"][:273],
+    bounds=(  # AerChemMIP min to max range
+        (0.18, -0.46, 0.11, -0.075, -0.039, -0.0408, 6.3),
+        (0.26, -0.25, 0.27, -0.006, -0.012, 0, 13.4),
+    ),
+)
+
+
+parameters["best_fit"] = {
+    "base": p[6],
+    "CH4": p[0],
+    "NOx": p[1],
+    "VOC": p[2],
+    "HC": p[3],
+    "N2O": p[4],
+    "temp": p[5],
+}
+
+# these are the feedback values per ppb / per Mt that go into FaIR
+for specie in ["CH4", "NOx", "VOC", "HC", "N2O"]:
+    print(specie, parameters["best_fit"][specie] / normalisation[specie])
+
+beta_hc_sum = 0
+
+for species in hc_species:
+    beta_hc = p[3] * (
+        (hc_eesc[species][264] - hc_eesc[species][100])
+        / (total_eesc[264] - total_eesc[100])
+    )
+    print(species, beta_hc)
+    beta_hc_sum = beta_hc_sum + beta_hc
+print(beta_hc_sum)
+
+lifetime_scaling["best_fit"] = alpha_scaling_exp(
+    input,
+    baseline,
+    normalisation,
+    parameters["best_fit"],
+)
+
+# if plots:
+#    pl.plot(np.arange(1750, 2101), lifetime_scaling['best_fit'])
+
+print(
+    "methane lifetime 1750:",
+    lifetime_scaling["best_fit"][0] * parameters["best_fit"]["base"],
+)
+print("methane lifetime 1850:", parameters["best_fit"]["base"])
+
+
+if plots:
+    pl.plot(
+        np.arange(1750, 2101),
+        lifetime_scaling["best_fit"] * parameters["best_fit"]["base"],
+        label="best_fit",
+    )
+    pl.legend()
+    pl.ylabel("CH4 chemical lifetime (yr)")
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "ch4_chemical_lifetime_best_fit.png"
+    )
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "ch4_chemical_lifetime_best_fit.pdf"
+    )
+    pl.close()
+
+conc_ch4["best_fit"] = np.zeros(351)
+gas_boxes = 0
+airborne_emissions = 0
+for i in range(351):
+    conc_ch4["best_fit"][i], gas_boxes, airborne_emissions = one_box(
+        emis_ch4[i],
+        gas_boxes,
+        airborne_emissions,
+        burden_per_emission,
+        parameters["best_fit"]["base"],
+        lifetime_scaling["best_fit"][i],
+        partition_fraction,
+        pre_industrial_concentration,
+        timestep=1,
+        natural_emissions_adjustment=natural_emissions_adjustment,
+    )
+
+
+# Compare the SSP3-7.0 fit to other SSPs
+# should do something with the temperature projections here
+
+emis_ch4_ssps = {}
+
+for ssp in [
+    "ssp119",
+    "ssp126",
+    "ssp245",
+    "ssp370",
+    "ssp434",
+    "ssp460",
+    "ssp534-over",
+    "ssp585",
+]:
+    emis_ch4_ssps[ssp] = (
+        df_rcmip_emis.loc[
+            (df_rcmip_emis["Scenario"] == ssp)
+            & (df_rcmip_emis["Variable"].str.endswith("CH4"))
+            & (df_rcmip_emis["Region"] == "World"),
+            "1750":"2100",
+        ]
+        .interpolate(axis=1)
+        .values.squeeze()
+    )
+
+for ssp in [
+    "ssp119",
+    "ssp126",
+    "ssp245",
+    "ssp370",
+    "ssp434",
+    "ssp460",
+    "ssp534-over",
+    "ssp585",
+]:
+    conc_ch4[ssp] = np.zeros(351)
+    gas_boxes = 0
+    airborne_emissions = 0
+    for i in range(351):
+        conc_ch4[ssp][i], gas_boxes, airborne_emissions = one_box(
+            emis_ch4_ssps[ssp][i],
+            gas_boxes,
+            airborne_emissions,
+            burden_per_emission,
+            parameters["best_fit"]["base"],
+            lifetime_scaling["best_fit"][i],
+            partition_fraction,
+            pre_industrial_concentration,
+            timestep=1,
+            natural_emissions_adjustment=natural_emissions_adjustment,
+        )
+
+# Three panel plot
+if plots:
+    ar6_colors = {
+        "ssp119": "#00a9cf",
+        "ssp126": "#003466",
+        "ssp245": "#f69320",
+        "ssp370": "#df0000",
+        "ssp434": "#2274ae",
+        "ssp460": "#b0724e",
+        "ssp534-over": "#92397a",
+        "ssp585": "#980002",
+    }
+
+    fig, ax = pl.subplots(1, 3, figsize=(18 / 2.54, 6 / 2.54))
+    for model in models:
+        ax[0].plot(
+            np.arange(1750, 2101),
+            lifetime_scaling[model] * parameters[model]["base"],
+            label=model,
+            lw=1,
+        )
+    ax[0].plot(
+        np.arange(1750, 2101),
+        lifetime_scaling["best_fit"] * parameters["best_fit"]["base"],
+        color="0.5",
+        label="Best fit",
+        lw=1,
+    )
+    ax[0].set_xlim(1750, 2100)
+    ax[0].set_ylabel("yr")
+    ax[0].set_title("(a) CH$_4$ lifetime")
+
+    for model in models:
+        ax[1].plot(np.arange(1750, 2101), conc_ch4[model], label=model, lw=1)
+    ax[1].plot(
+        np.arange(1750, 2101), conc_ch4["best_fit"], color="0.5", label="Best fit", lw=1
+    )
+    ax[1].plot(
+        np.arange(1750, 2101),
+        input["CH4"],
+        color="k",
+        label="observations +\nMAGICC6",
+        lw=1,
+    )
+    ax[1].set_ylabel("ppb")
+    ax[1].set_xlim(1750, 2100)
+    ax[1].legend(frameon=False)
+    ax[1].set_title("(b) CH$_4$ concentration SSP2-4.5")
+
+    for ssp in [
+        "ssp119",
+        "ssp126",
+        "ssp434",
+        "ssp534-over",
+        "ssp245",
+        "ssp460",
+        "ssp370",
+        "ssp585",
+    ]:
+        gas = (
+            rcmip_df.loc[
+                (rcmip_df["Region"] == "World")
+                & (rcmip_df["Scenario"] == ssp)
+                & (rcmip_df["Variable"].str.endswith("|CH4")),
+                "1750":"2100",
+            ]
+            .interpolate(axis=1)
+            .squeeze()
+        )
+        ax[2].plot(
+            np.arange(1750, 2101), conc_ch4[ssp], label=ssp, color=ar6_colors[ssp], lw=1
+        )
+        ax[2].plot(np.arange(1750, 2101), gas, color=ar6_colors[ssp], lw=0.3)
+    ax[2].set_ylabel("ppb")
+    ax[2].set_title("(c) CH$_4$ projections")
+    ax[2].set_xlim(1750, 2100)
+    ax[2].legend(frameon=False)
+
+    fig.tight_layout()
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "methane_calibrations.png"
+    )
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "methane_calibrations.pdf"
+    )
+    pl.close()
+
+# these are the feedback values per ppb / per Mt that go into FaIR
+out = np.empty((1, 7))
+out[0, 0] = lifetime_scaling["best_fit"][0] * parameters["best_fit"]["base"]
+for i, specie in enumerate(["CH4", "NOx", "VOC", "HC", "N2O"]):
+    out[0, i + 1] = parameters["best_fit"][specie] / normalisation[specie]
+out[0, 6] = parameters["best_fit"]["temp"]
+
+df = pd.DataFrame(
+    out,
+    columns=["base", "CH4", "NOx", "VOC", "HC", "N2O", "temp"],
+    index=["historical_best"],
+)
+os.makedirs(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/calibrations/",
+    exist_ok=True,
+)
+df.to_csv(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/calibrations/"
+    "CH4_lifetime.csv"
+)
diff --git a/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/calibration/05_lapsi_scaling.py b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/calibration/05_lapsi_scaling.py
new file mode 100755
index 0000000..837c13b
--- /dev/null
+++ b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/calibration/05_lapsi_scaling.py
@@ -0,0 +1,47 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+"""Determining the land use scale factor to use."""
+
+# Correlate cumulative CO2 AFOLU emissions to land use forcing in the present
+
+import os
+
+import pandas as pd
+from dotenv import load_dotenv
+
+load_dotenv()
+
+print("Calculating land use scale factor...")
+
+cal_v = os.getenv("CALIBRATION_VERSION")
+fair_v = os.getenv("FAIR_VERSION")
+constraint_set = os.getenv("CONSTRAINT_SET")
+datadir = os.getenv("DATADIR")
+
+target_forcing = +0.08
+base_year = 1750.5
+assessment_year = 2019.5
+
+df_emis = pd.read_csv("../../../../../data/emissions/CH4Pledge-rcmip-emissions-ssp245-CLE-MFR-filled.csv")
+
+bc = (
+    df_emis.loc[df_emis["variable"] == "BC", str(assessment_year)]
+    - df_emis.loc[df_emis["variable"] == "BC", str(base_year)]
+).values[0]
+
+lapsi_scale_factor = target_forcing / bc
+
+df = pd.DataFrame(
+    lapsi_scale_factor,
+    columns=["BC"],
+    index=["historical_best"],
+)
+os.makedirs(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/calibrations/",
+    exist_ok=True,
+)
+df.to_csv(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/calibrations/"
+    "lapsi_scale_factor.csv"
+)
\ No newline at end of file
diff --git a/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/constraining/01_constrain-gsat-rmse-only.py b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/constraining/01_constrain-gsat-rmse-only.py
new file mode 100755
index 0000000..3f14e96
--- /dev/null
+++ b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/constraining/01_constrain-gsat-rmse-only.py
@@ -0,0 +1,268 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+"""First constraint: RMSE < 0.17 K"""
+
+import os
+
+import matplotlib.pyplot as pl
+import numpy as np
+import pandas as pd
+from dotenv import load_dotenv
+from fair import __version__
+from tqdm.auto import tqdm
+
+pl.switch_backend("agg")
+
+load_dotenv()
+pl.style.use("../../../../../defaults.mplstyle")
+
+print("Doing RMSE constraint...")
+
+cal_v = os.getenv("CALIBRATION_VERSION")
+fair_v = os.getenv("FAIR_VERSION")
+constraint_set = os.getenv("CONSTRAINT_SET")
+samples = int(os.getenv("PRIOR_SAMPLES"))
+plots = os.getenv("PLOTS", "False").lower() in ("true", "1", "t")
+progress = os.getenv("PROGRESS", "False").lower() in ("true", "1", "t")
+
+assert fair_v == __version__
+
+
+temp_in = np.load(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/prior_runs/"
+    "temperature_1850-2101.npy"
+)
+
+df_gmst = pd.read_csv("../../../../../data/forcing/IGCC_GMST_1850-2022.csv")
+gmst = df_gmst["gmst"].values
+
+
+def rmse(obs, mod):
+    return np.sqrt(np.sum((obs - mod) ** 2) / len(obs))
+
+
+weights = np.ones(52)
+weights[0] = 0.5
+weights[-1] = 0.5
+
+rmse_temp = np.zeros((samples))
+
+if plots:
+    fig, ax = pl.subplots(figsize=(5, 5))
+    ax.fill_between(
+        np.arange(1850, 2102),
+        np.min(temp_in - np.average(temp_in[:52, :], weights=weights, axis=0), axis=1),
+        np.max(temp_in - np.average(temp_in[:52, :], weights=weights, axis=0), axis=1),
+        color="#000000",
+        alpha=0.2,
+    )
+    ax.fill_between(
+        np.arange(1850, 2102),
+        np.percentile(
+            temp_in - np.average(temp_in[:52, :], weights=weights, axis=0), 5, axis=1
+        ),
+        np.percentile(
+            temp_in - np.average(temp_in[:52, :], weights=weights, axis=0), 95, axis=1
+        ),
+        color="#000000",
+        alpha=0.2,
+    )
+    ax.fill_between(
+        np.arange(1850, 2102),
+        np.percentile(
+            temp_in - np.average(temp_in[:52, :], weights=weights, axis=0), 16, axis=1
+        ),
+        np.percentile(
+            temp_in - np.average(temp_in[:52, :], weights=weights, axis=0), 84, axis=1
+        ),
+        color="#000000",
+        alpha=0.2,
+    )
+    ax.plot(
+        np.arange(1850, 2102),
+        np.median(
+            temp_in - np.average(temp_in[:52, :], weights=weights, axis=0), axis=1
+        ),
+        color="#000000",
+    )
+    ax.plot(np.arange(1850.5, 2023), gmst, color="b")
+
+    ax.set_xlim(1850, 2100)
+    ax.set_ylim(-1, 5)
+    ax.set_ylabel("°C relative to 1850-1900")
+    ax.axhline(0, color="k", ls=":", lw=0.5)
+    pl.title("Prior ensemble")
+    pl.tight_layout()
+    os.makedirs(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/", exist_ok=True
+    )
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/prior_ssp245.png"
+    )
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/prior_ssp245.pdf"
+    )
+    pl.close()
+
+# temperature is on timebounds, and observations are midyears
+# but, this is OK, since we are subtracting a consistent baseline (1850-1900, weighting
+# the bounding timebounds as 0.5)
+# e.g. 1993.0 timebound has big pinatubo hit, timebound 143
+# in obs this is 1992.5, timepoint 142
+# compare the timebound after the obs, since the forcing has had chance to affect both
+# the obs timepoint and the later timebound.
+# the goal of RMSE is as much to match the shape of warming as the magnitude; we do not
+# want to average out internal variability in the model or the obs.
+for i in tqdm(range(samples), disable=1 - progress):
+    rmse_temp[i] = rmse(
+        gmst[:173],
+        temp_in[1:174, i] - np.average(temp_in[:52, i], weights=weights, axis=0),
+    )
+
+accept_temp = rmse_temp < 0.17
+print("Passing RMSE constraint:", np.sum(accept_temp))
+valid_temp = np.arange(samples, dtype=int)[accept_temp]
+
+# get 10 largest (but passing) and 10 smallest RMSEs
+rmse_temp_accept = rmse_temp[accept_temp]
+just_passing = np.argpartition(rmse_temp_accept, -10)[-10:]
+smashing_it = np.argpartition(rmse_temp_accept, 10)[:10]
+print(just_passing)
+print(rmse_temp_accept[just_passing])
+print(rmse_temp_accept[smashing_it])
+
+if plots:
+    # plot top 10 and "just squeaking in 10"
+    fig, ax = pl.subplots(figsize=(5, 5))
+    ax.plot(
+        np.arange(1850.5, 2102),
+        (
+            temp_in[:, valid_temp[just_passing]]
+            - np.average(
+                temp_in[:52, valid_temp[just_passing]], weights=weights, axis=0
+            )
+        ),
+        color="#ff0000",
+        label=[r"RMSE $\approx$ 0.17°C"] + [""] * 9,
+    )
+    ax.plot(
+        np.arange(1850.5, 2102),
+        (
+            temp_in[:, valid_temp[smashing_it]]
+            - np.average(temp_in[:52, valid_temp[smashing_it]], weights=weights, axis=0)
+        ),
+        color="#0000ff",
+        label=[r"RMSE $\approx$ 0.10°C"] + [""] * 9,
+    )
+    ax.axhspan(0.67, 0.99, color="k", alpha=0.15, lw=0)
+    ax.axvspan(1995, 2015, color="k", alpha=0.15, lw=0)
+    ax.plot(np.arange(1850.5, 2023), gmst, color="k", label="Best estimate historical")
+    ax.set_xlim(1850, 2100)
+    ax.set_ylim(-1, 4)
+    ax.set_ylabel("°C relative to 1850-1900")
+    ax.axhline(0, color="k", ls=":", lw=0.5)
+    ax.text(1860, 0.83, "IPCC AR6 5--95% range", va="center")
+    ax.legend(loc="upper left")
+    pl.title("Historical + SSP2-4.5 GMST")
+    pl.tight_layout()
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "post_rsme_top10_bottom10_ssp245.png"
+    )
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "post_rsme_top10_bottom10_ssp245.pdf"
+    )
+    pl.close()
+
+    # ensemble wide
+    fig, ax = pl.subplots(figsize=(5, 5))
+    ax.fill_between(
+        np.arange(1850, 2102),
+        np.min(
+            temp_in[:, accept_temp]
+            - np.average(temp_in[:52, accept_temp], weights=weights, axis=0),
+            axis=1,
+        ),
+        np.max(
+            temp_in[:, accept_temp]
+            - np.average(temp_in[:52, accept_temp], weights=weights, axis=0),
+            axis=1,
+        ),
+        color="#000000",
+        alpha=0.2,
+    )
+    ax.fill_between(
+        np.arange(1850.5, 2102),
+        np.percentile(
+            temp_in[:, accept_temp]
+            - np.average(temp_in[:52, accept_temp], weights=weights, axis=0),
+            5,
+            axis=1,
+        ),
+        np.percentile(
+            temp_in[:, accept_temp]
+            - np.average(temp_in[:52, accept_temp], weights=weights, axis=0),
+            95,
+            axis=1,
+        ),
+        color="#000000",
+        alpha=0.2,
+    )
+    ax.fill_between(
+        np.arange(1850.5, 2102),
+        np.percentile(
+            temp_in[:, accept_temp]
+            - np.average(temp_in[:52, accept_temp], weights=weights, axis=0),
+            16,
+            axis=1,
+        ),
+        np.percentile(
+            temp_in[:, accept_temp]
+            - np.average(temp_in[:52, accept_temp], weights=weights, axis=0),
+            84,
+            axis=1,
+        ),
+        color="#000000",
+        alpha=0.2,
+    )
+    ax.plot(
+        np.arange(1850.5, 2102),
+        np.median(
+            temp_in[:, accept_temp]
+            - np.average(temp_in[:52, accept_temp], weights=weights, axis=0),
+            axis=1,
+        ),
+        color="#000000",
+    )
+
+    ax.plot(np.arange(1850.5, 2023), gmst, color="b")
+
+    ax.set_xlim(1850, 2100)
+    ax.set_ylim(-1, 5)
+    ax.set_ylabel("°C relative to 1850-1900")
+    ax.axhline(0, color="k", ls=":", lw=0.5)
+    pl.title("After RMSE constraint")
+    pl.tight_layout()
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "post_rsme_ssp245.png"
+    )
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "post_rsme_ssp245.pdf"
+    )
+    pl.close()
+
+valid_temp = np.arange(samples, dtype=int)[accept_temp]
+os.makedirs(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/posteriors",
+    exist_ok=True,
+)
+np.savetxt(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/posteriors/"
+    "runids_rmse_pass.csv",
+    valid_temp.astype(int),
+    fmt="%d",
+)
diff --git a/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/constraining/02_run-1pct.py b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/constraining/02_run-1pct.py
new file mode 100755
index 0000000..3f358ae
--- /dev/null
+++ b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/constraining/02_run-1pct.py
@@ -0,0 +1,148 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+"""Run 1pct CO2 concentration driven runs where RMSE passes"""
+
+import multiprocessing
+import os
+from concurrent.futures import ProcessPoolExecutor
+
+import numpy as np
+import pandas as pd
+from dotenv import load_dotenv
+from fair import __version__
+from parallel_1pct import run_fair
+from utils import _parallel_process
+
+if __name__ == "__main__":
+    print("Running 1pctCO2 scenarios...")
+    load_dotenv()
+
+    cal_v = os.getenv("CALIBRATION_VERSION")
+    fair_v = os.getenv("FAIR_VERSION")
+    constraint_set = os.getenv("CONSTRAINT_SET")
+    samples = int(os.getenv("PRIOR_SAMPLES"))
+    batch_size = int(os.getenv("BATCH_SIZE"))
+    WORKERS = int(os.getenv("WORKERS"))
+
+    # number of processors
+    WORKERS = min(multiprocessing.cpu_count(), WORKERS)
+
+    assert fair_v == __version__
+
+    df_cc = pd.read_csv(
+        f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/priors/"
+        "carbon_cycle.csv"
+    )
+    df_cr = pd.read_csv(
+        f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/priors/"
+        "climate_response_ebm3.csv"
+    )
+    df_scaling = pd.read_csv(
+        f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/priors/"
+        "forcing_scaling.csv"
+    )
+
+    seedgen = 1355763
+    seedstep = 399
+
+    # we also only want to run ensembles that passed RMSE test
+    rmse_pass = np.loadtxt(
+        f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/posteriors/"
+        "runids_rmse_pass.csv"
+    ).astype(int)
+
+    # we only care about temperature and airborne fraction in years 70 and 140
+    temp_2x4x_out = np.ones((2, len(rmse_pass))) * np.nan
+    af_out = np.ones((2, len(rmse_pass))) * np.nan
+    temp_1000_out = np.ones(len(rmse_pass)) * np.nan
+
+    config = []
+    for ibatch, batch_start in enumerate(range(0, len(rmse_pass), batch_size)):
+        config.append({})
+        batch_end = min(batch_start + batch_size, len(rmse_pass))
+        config[ibatch]["batch_start"] = batch_start
+        config[ibatch]["batch_end"] = batch_end
+        config[ibatch]["c1"] = df_cr.loc[rmse_pass[batch_start:batch_end], "c1"].values
+        config[ibatch]["c2"] = df_cr.loc[rmse_pass[batch_start:batch_end], "c2"].values
+        config[ibatch]["c3"] = df_cr.loc[rmse_pass[batch_start:batch_end], "c3"].values
+        config[ibatch]["kappa1"] = df_cr.loc[
+            rmse_pass[batch_start:batch_end], "kappa1"
+        ].values
+        config[ibatch]["kappa2"] = df_cr.loc[
+            rmse_pass[batch_start:batch_end], "kappa2"
+        ].values
+        config[ibatch]["kappa3"] = df_cr.loc[
+            rmse_pass[batch_start:batch_end], "kappa3"
+        ].values
+        config[ibatch]["epsilon"] = df_cr.loc[
+            rmse_pass[batch_start:batch_end], "epsilon"
+        ].values
+        config[ibatch]["gamma"] = df_cr.loc[
+            rmse_pass[batch_start:batch_end], "gamma"
+        ].values
+        config[ibatch]["forcing_4co2"] = df_cr.loc[
+            rmse_pass[batch_start:batch_end], "F_4xCO2"
+        ]
+        config[ibatch]["iirf_0"] = df_cc.loc[
+            rmse_pass[batch_start:batch_end], "r0"
+        ].values.squeeze()
+        config[ibatch]["iirf_airborne"] = df_cc.loc[
+            rmse_pass[batch_start:batch_end], "rA"
+        ].values.squeeze()
+        config[ibatch]["iirf_uptake"] = df_cc.loc[
+            rmse_pass[batch_start:batch_end], "rU"
+        ].values.squeeze()
+        config[ibatch]["iirf_temperature"] = df_cc.loc[
+            rmse_pass[batch_start:batch_end], "rT"
+        ].values.squeeze()
+        config[ibatch]["scaling_CO2"] = df_scaling.loc[
+            rmse_pass[batch_start:batch_end], "CO2"
+        ].values.squeeze()
+        config[ibatch]["scaling_CH4"] = df_scaling.loc[
+            rmse_pass[batch_start:batch_end], "CH4"
+        ].values.squeeze()
+        config[ibatch]["scaling_N2O"] = df_scaling.loc[
+            rmse_pass[batch_start:batch_end], "N2O"
+        ].values.squeeze()
+
+    parallel_process_kwargs = dict(
+        func=run_fair,
+        configuration=config,
+        config_are_kwargs=False,
+    )
+
+    with ProcessPoolExecutor(WORKERS) as pool:
+        res = _parallel_process(
+            **parallel_process_kwargs,
+            pool=pool,
+        )
+
+    for ibatch, batch_start in enumerate(range(0, len(rmse_pass), batch_size)):
+        batch_end = min(batch_start + batch_size, len(rmse_pass))
+        temp_2x4x_out[:, batch_start:batch_end] = res[ibatch][0]
+        af_out[:, batch_start:batch_end] = res[ibatch][1]
+        temp_1000_out[batch_start:batch_end] = res[ibatch][2]
+
+    os.makedirs(
+        f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/prior_runs/",
+        exist_ok=True,
+    )
+    np.save(
+        f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/prior_runs/"
+        "temperature_1pctCO2_y70_y140.npy",
+        temp_2x4x_out,
+        allow_pickle=True,
+    )
+    np.save(
+        f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/prior_runs/"
+        "airborne_fraction_1pctCO2_y70_y140.npy",
+        af_out,
+        allow_pickle=True,
+    )
+    np.save(
+        f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/prior_runs/"
+        "temperature_1pctCO2_1000GtC.npy",
+        temp_1000_out,
+        allow_pickle=True,
+    )
diff --git a/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/constraining/03_reweight-rmse-posterior-multiple-constraints.py b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/constraining/03_reweight-rmse-posterior-multiple-constraints.py
new file mode 100755
index 0000000..ca966ee
--- /dev/null
+++ b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/constraining/03_reweight-rmse-posterior-multiple-constraints.py
@@ -0,0 +1,809 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+"""Apply posterior weighting"""
+
+# mention in paper: skew-normal distribution
+# this is where Zeb earns his corn
+
+import os
+
+import matplotlib.pyplot as pl
+import numpy as np
+import pandas as pd
+import scipy.optimize
+import scipy.stats
+from dotenv import load_dotenv
+from fair import __version__
+from fair.earth_params import mass_atmosphere, molecular_weight_air
+from matplotlib.lines import Line2D
+from tqdm.auto import tqdm
+
+pl.switch_backend("agg")
+
+load_dotenv()
+
+cal_v = os.getenv("CALIBRATION_VERSION")
+fair_v = os.getenv("FAIR_VERSION")
+constraint_set = os.getenv("CONSTRAINT_SET")
+samples = int(os.getenv("PRIOR_SAMPLES"))
+output_ensemble_size = int(os.getenv("POSTERIOR_SAMPLES"))
+plots = os.getenv("PLOTS", "False").lower() in ("true", "1", "t")
+pl.style.use("../../../../../defaults.mplstyle")
+progress = os.getenv("PROGRESS", "False").lower() in ("true", "1", "t")
+
+assert fair_v == __version__
+
+print("Doing reweighting...")
+
+
+NINETY_TO_ONESIGMA = scipy.stats.norm.ppf(0.95)
+
+valid_temp = np.loadtxt(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/posteriors/"
+    "runids_rmse_pass.csv"
+).astype(np.int64)
+
+input_ensemble_size = len(valid_temp)
+
+assert input_ensemble_size > output_ensemble_size
+
+temp_in = np.load(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/prior_runs/"
+    "temperature_1850-2101.npy"
+)
+ohc_in = np.load(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/prior_runs/"
+    "ocean_heat_content_2020_minus_1971.npy"
+)
+fari_in = np.load(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/prior_runs/"
+    "forcing_ari_2005-2014_mean.npy"
+)
+faci_in = np.load(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/prior_runs/"
+    "forcing_aci_2005-2014_mean.npy"
+)
+co2_in = np.load(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/prior_runs/"
+    "concentration_co2_2022.npy"
+)
+ecs_in = np.load(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/prior_runs/ecs.npy"
+)
+tcr_in = np.load(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/prior_runs/tcr.npy"
+)
+af_in = np.load(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/prior_runs/"
+    "airborne_fraction_1pctCO2_y70_y140.npy"
+)
+faer_in = fari_in + faci_in
+
+
+def opt(x, q05_desired, q50_desired, q95_desired):
+    "x is (a, loc, scale) in that order."
+    q05, q50, q95 = scipy.stats.skewnorm.ppf(
+        (0.05, 0.50, 0.95), x[0], loc=x[1], scale=x[2]
+    )
+    return (q05 - q05_desired, q50 - q50_desired, q95 - q95_desired)
+
+
+ecs_params = scipy.optimize.root(opt, [1, 1, 1], args=(2, 3, 5)).x
+gsat_params = scipy.optimize.root(opt, [1, 1, 1], args=(0.87, 1.03, 1.13)).x
+
+samples = {}
+samples["ECS"] = scipy.stats.skewnorm.rvs(
+    ecs_params[0],
+    loc=ecs_params[1],
+    scale=ecs_params[2],
+    size=10**5,
+    random_state=91603,
+)
+samples["TCR"] = scipy.stats.norm.rvs(
+    loc=1.8, scale=0.6 / NINETY_TO_ONESIGMA, size=10**5, random_state=18196
+)
+# note fair produces, and we here report, total earth energy uptake, not just ocean
+# this value from IGCC 2023. Use new uncertainties for ocean, assume same uncertainties
+# for land, atmosphere and cryopshere.
+samples["OHC"] = scipy.stats.norm.rvs(
+    loc=465.3, scale=108.5 / NINETY_TO_ONESIGMA, size=10**5, random_state=43178
+)
+samples["temperature 2003-2022"] = scipy.stats.skewnorm.rvs(
+    gsat_params[0],
+    loc=gsat_params[1],
+    scale=gsat_params[2],
+    size=10**5,
+    random_state=19387,
+)
+samples["ERFari"] = scipy.stats.norm.rvs(
+    loc=-0.3, scale=0.3 / NINETY_TO_ONESIGMA, size=10**5, random_state=70173
+)
+samples["ERFaci"] = scipy.stats.norm.rvs(
+    loc=-1.0, scale=0.7 / NINETY_TO_ONESIGMA, size=10**5, random_state=91123
+)
+samples["ERFaer"] = scipy.stats.norm.rvs(
+    loc=-1.3,
+    scale=np.sqrt(0.7**2 + 0.3**2) / NINETY_TO_ONESIGMA,
+    size=10**5,
+    random_state=3916153,
+)
+# IGCC paper: 417.1 +/- 0.4
+# IGCC dataset: 416.9
+# my assessment 417.0 +/- 0.5
+samples["CO2 concentration"] = scipy.stats.norm.rvs(
+    loc=417.0, scale=0.5, size=10**5, random_state=81693
+)
+
+ar_distributions = {}
+for constraint in [
+    "ECS",
+    "TCR",
+    "OHC",
+    "temperature 2003-2022",
+    "ERFari",
+    "ERFaci",
+    "ERFaer",
+    "CO2 concentration",
+]:
+    ar_distributions[constraint] = {}
+    ar_distributions[constraint]["bins"] = np.histogram(
+        samples[constraint], bins=100, density=True
+    )[1]
+    ar_distributions[constraint]["values"] = samples[constraint]
+
+weights_20yr = np.ones(21)
+weights_20yr[0] = 0.5
+weights_20yr[-1] = 0.5
+weights_51yr = np.ones(52)
+weights_51yr[0] = 0.5
+weights_51yr[-1] = 0.5
+
+co2_1850 = 284.3169988
+co2_1920 = co2_1850 * 1.01**70  # NOT 2x (69.66 yr), per definition of TCRE
+mass_factor = 12.011 / molecular_weight_air * mass_atmosphere / 1e21
+
+accepted = pd.DataFrame(
+    {
+        "ECS": ecs_in[valid_temp],
+        "TCR": tcr_in[valid_temp],
+        "OHC": ohc_in[valid_temp] / 1e21,
+        "temperature 2003-2022": np.average(
+            temp_in[153:174, valid_temp], weights=weights_20yr, axis=0
+        )
+        - np.average(temp_in[:52, valid_temp], weights=weights_51yr, axis=0),
+        "ERFari": fari_in[valid_temp],
+        "ERFaci": faci_in[valid_temp],
+        "ERFaer": faer_in[valid_temp],
+        "CO2 concentration": co2_in[valid_temp],
+    },
+    index=valid_temp,
+)
+
+
+def calculate_sample_weights(distributions, samples, niterations=50):
+    weights = np.ones(samples.shape[0])
+    gofs = []
+    gofs_full = []
+
+    unique_codes = list(distributions.keys())  # [::-1]
+
+    for k in tqdm(
+        range(niterations), desc="Iterations", leave=False, disable=1 - progress
+    ):
+        gofs.append([])
+        if k == (niterations - 1):
+            weights_second_last_iteration = weights.copy()
+            weights_to_average = []
+
+        for j, unique_code in enumerate(unique_codes):
+            unique_code_weights, our_values_bin_idx = get_unique_code_weights(
+                unique_code, distributions, samples, weights, j, k
+            )
+            if k == (niterations - 1):
+                weights_to_average.append(unique_code_weights[our_values_bin_idx])
+
+            weights *= unique_code_weights[our_values_bin_idx]
+
+            gof = ((unique_code_weights[1:-1] - 1) ** 2).sum()
+            gofs[-1].append(gof)
+
+            gofs_full.append([unique_code])
+            for unique_code_check in unique_codes:
+                unique_code_check_weights, _ = get_unique_code_weights(
+                    unique_code_check, distributions, samples, weights, 1, 1
+                )
+                gof = ((unique_code_check_weights[1:-1] - 1) ** 2).sum()
+                gofs_full[-1].append(gof)
+
+    weights_stacked = np.vstack(weights_to_average).mean(axis=0)
+    weights_final = weights_stacked * weights_second_last_iteration
+
+    gofs_full.append(["Final iteration"])
+    for unique_code_check in unique_codes:
+        unique_code_check_weights, _ = get_unique_code_weights(
+            unique_code_check, distributions, samples, weights_final, 1, 1
+        )
+        gof = ((unique_code_check_weights[1:-1] - 1) ** 2).sum()
+        gofs_full[-1].append(gof)
+
+    return (
+        weights_final,
+        pd.DataFrame(np.array(gofs), columns=unique_codes),
+        pd.DataFrame(np.array(gofs_full), columns=["Target marginal"] + unique_codes),
+    )
+
+
+def get_unique_code_weights(unique_code, distributions, samples, weights, j, k):
+    bin_edges = distributions[unique_code]["bins"]
+    our_values = samples[unique_code].copy()
+
+    our_values_bin_counts, bin_edges_np = np.histogram(our_values, bins=bin_edges)
+    np.testing.assert_allclose(bin_edges, bin_edges_np)
+    assessed_ranges_bin_counts, _ = np.histogram(
+        distributions[unique_code]["values"], bins=bin_edges
+    )
+
+    our_values_bin_idx = np.digitize(our_values, bins=bin_edges)
+
+    existing_weighted_bin_counts = np.nan * np.zeros(our_values_bin_counts.shape[0])
+    for i in range(existing_weighted_bin_counts.shape[0]):
+        existing_weighted_bin_counts[i] = weights[(our_values_bin_idx == i + 1)].sum()
+
+    if np.equal(j, 0) and np.equal(k, 0):
+        np.testing.assert_equal(
+            existing_weighted_bin_counts.sum(), our_values_bin_counts.sum()
+        )
+
+    unique_code_weights = np.nan * np.zeros(bin_edges.shape[0] + 1)
+
+    # existing_weighted_bin_counts[0] refers to samples outside the
+    # assessed range's lower bound. Accordingly, if `our_values` was
+    # digitized into a bin idx of zero, it should get a weight of zero.
+    unique_code_weights[0] = 0
+    # Similarly, if `our_values` was digitized into a bin idx greater
+    # than the number of bins then it was outside the assessed range
+    # so get a weight of zero.
+    unique_code_weights[-1] = 0
+
+    for i in range(1, our_values_bin_counts.shape[0] + 1):
+        # the histogram idx is one less because digitize gives values in the
+        # range bin_edges[0] <= x < bin_edges[1] a digitized index of 1
+        histogram_idx = i - 1
+        if np.equal(assessed_ranges_bin_counts[histogram_idx], 0):
+            unique_code_weights[i] = 0
+        elif np.equal(existing_weighted_bin_counts[histogram_idx], 0):
+            # other variables force this box to be zero so just fill it with
+            # one
+            unique_code_weights[i] = 1
+        else:
+            unique_code_weights[i] = (
+                assessed_ranges_bin_counts[histogram_idx]
+                / existing_weighted_bin_counts[histogram_idx]
+            )
+
+    return unique_code_weights, our_values_bin_idx
+
+
+weights, gofs, gofs_full = calculate_sample_weights(
+    ar_distributions, accepted, niterations=30
+)
+
+effective_samples = int(np.floor(np.sum(np.minimum(weights, 1))))
+print("Number of effective samples:", effective_samples)
+
+assert effective_samples >= output_ensemble_size
+
+draws = []
+drawn_samples = accepted.sample(
+    n=output_ensemble_size, replace=False, weights=weights, random_state=10099
+)
+draws.append((drawn_samples))
+
+target_ecs = scipy.stats.gaussian_kde(samples["ECS"])
+prior_ecs = scipy.stats.gaussian_kde(ecs_in)
+post1_ecs = scipy.stats.gaussian_kde(ecs_in[valid_temp])
+post2_ecs = scipy.stats.gaussian_kde(draws[0]["ECS"])
+
+target_tcr = scipy.stats.gaussian_kde(samples["TCR"])
+prior_tcr = scipy.stats.gaussian_kde(tcr_in)
+post1_tcr = scipy.stats.gaussian_kde(tcr_in[valid_temp])
+post2_tcr = scipy.stats.gaussian_kde(draws[0]["TCR"])
+
+target_temp = scipy.stats.gaussian_kde(samples["temperature 2003-2022"])
+prior_temp = scipy.stats.gaussian_kde(
+    np.average(temp_in[153:174, :], weights=weights_20yr, axis=0)
+    - np.average(temp_in[:52, :], weights=weights_51yr, axis=0)
+)
+post1_temp = scipy.stats.gaussian_kde(
+    np.average(temp_in[153:174, valid_temp], weights=weights_20yr, axis=0)
+    - np.average(temp_in[:52, valid_temp], weights=weights_51yr, axis=0)
+)
+post2_temp = scipy.stats.gaussian_kde(draws[0]["temperature 2003-2022"])
+
+target_ohc = scipy.stats.gaussian_kde(samples["OHC"])
+prior_ohc = scipy.stats.gaussian_kde(ohc_in / 1e21)
+post1_ohc = scipy.stats.gaussian_kde(ohc_in[valid_temp] / 1e21)
+post2_ohc = scipy.stats.gaussian_kde(draws[0]["OHC"])
+
+target_aer = scipy.stats.gaussian_kde(samples["ERFaer"])
+prior_aer = scipy.stats.gaussian_kde(faer_in)
+post1_aer = scipy.stats.gaussian_kde(faer_in[valid_temp])
+post2_aer = scipy.stats.gaussian_kde(draws[0]["ERFaer"])
+
+target_aci = scipy.stats.gaussian_kde(samples["ERFaci"])
+prior_aci = scipy.stats.gaussian_kde(faci_in)
+post1_aci = scipy.stats.gaussian_kde(faci_in[valid_temp])
+post2_aci = scipy.stats.gaussian_kde(draws[0]["ERFaci"])
+
+target_ari = scipy.stats.gaussian_kde(samples["ERFari"])
+prior_ari = scipy.stats.gaussian_kde(fari_in)
+post1_ari = scipy.stats.gaussian_kde(fari_in[valid_temp])
+post2_ari = scipy.stats.gaussian_kde(draws[0]["ERFari"])
+
+target_co2 = scipy.stats.gaussian_kde(samples["CO2 concentration"])
+prior_co2 = scipy.stats.gaussian_kde(co2_in)
+post1_co2 = scipy.stats.gaussian_kde(co2_in[valid_temp])
+post2_co2 = scipy.stats.gaussian_kde(draws[0]["CO2 concentration"])
+
+colors = {"prior": "#207F6E", "post1": "#684C94", "post2": "#EE696B", "target": "black"}
+
+if plots:
+    os.makedirs(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/", exist_ok=True
+    )
+    fig, ax = pl.subplots(3, 3, figsize=(18 / 2.54, 18 / 2.54))
+    start = 0
+    stop = 8
+    ax[0, 0].plot(
+        np.linspace(start, stop, 1000),
+        prior_ecs(np.linspace(start, stop, 1000)),
+        color=colors["prior"],
+        label="Prior",
+        lw=2,
+    )
+    ax[0, 0].plot(
+        np.linspace(start, stop, 1000),
+        post1_ecs(np.linspace(start, stop, 1000)),
+        color=colors["post1"],
+        label="Temperature RMSE",
+        lw=2,
+    )
+    ax[0, 0].plot(
+        np.linspace(start, stop, 1000),
+        post2_ecs(np.linspace(start, stop, 1000)),
+        color=colors["post2"],
+        label="All constraints",
+        lw=2,
+    )
+    ax[0, 0].plot(
+        np.linspace(start, stop, 1000),
+        target_ecs(np.linspace(start, stop, 1000)),
+        color=colors["target"],
+        label="Target",
+        lw=2,
+    )
+    ax[0, 0].set_xlim(start, stop)
+    ax[0, 0].set_ylim(0, 0.6)
+    ax[0, 0].set_title("ECS")
+    ax[0, 0].set_yticklabels([])
+    ax[0, 0].set_xlabel("°C")
+    ax[0, 0].set_ylabel("Probability density")
+
+    start = 0
+    stop = 4
+    ax[0, 1].plot(
+        np.linspace(start, stop, 1000),
+        prior_tcr(np.linspace(start, stop, 1000)),
+        color=colors["prior"],
+        label="Prior",
+        lw=2,
+    )
+    ax[0, 1].plot(
+        np.linspace(start, stop, 1000),
+        post1_tcr(np.linspace(start, stop, 1000)),
+        color=colors["post1"],
+        label="Temperature RMSE",
+        lw=2,
+    )
+    ax[0, 1].plot(
+        np.linspace(start, stop, 1000),
+        post2_tcr(np.linspace(start, stop, 1000)),
+        color=colors["post2"],
+        label="All constraints",
+        lw=2,
+    )
+    ax[0, 1].plot(
+        np.linspace(start, stop, 1000),
+        target_tcr(np.linspace(start, stop, 1000)),
+        color=colors["target"],
+        label="Target",
+        lw=2,
+    )
+    ax[0, 1].set_xlim(start, stop)
+    ax[0, 1].set_ylim(0, 1.5)
+    ax[0, 1].set_title("TCR")
+    ax[0, 1].set_yticklabels([])
+    ax[0, 1].set_xlabel("°C")
+
+    start = 0.6
+    stop = 1.4
+    ax[0, 2].plot(
+        np.linspace(start, stop, 1000),
+        target_temp(np.linspace(start, stop, 1000)),
+        color=colors["target"],
+        label="Target",
+        lw=2,
+    )
+    ax[0, 2].plot(
+        np.linspace(start, stop, 1000),
+        prior_temp(np.linspace(start, stop, 1000)),
+        color=colors["prior"],
+        label="Prior",
+        lw=2,
+    )
+    ax[0, 2].plot(
+        np.linspace(start, stop, 1000),
+        post1_temp(np.linspace(start, stop, 1000)),
+        color=colors["post1"],
+        label="Temperature RMSE",
+        lw=2,
+    )
+    ax[0, 2].plot(
+        np.linspace(start, stop, 1000),
+        post2_temp(np.linspace(start, stop, 1000)),
+        color=colors["post2"],
+        label="All constraints",
+        lw=2,
+    )
+    ax[0, 2].set_xlim(start, stop)
+    ax[0, 2].set_ylim(0, 6)
+    ax[0, 2].set_title("Temperature anomaly")
+    ax[0, 2].set_yticklabels([])
+    ax[0, 2].set_xlabel("°C, 2003-2022 minus 1850-1900")
+
+    start = -1.0
+    stop = 0.4
+    ax[1, 0].plot(
+        np.linspace(start, stop, 1000),
+        target_ari(np.linspace(start, stop, 1000)),
+        color=colors["target"],
+        label="Target",
+        lw=2,
+    )
+    ax[1, 0].plot(
+        np.linspace(start, stop, 1000),
+        prior_ari(np.linspace(start, stop, 1000)),
+        color=colors["prior"],
+        label="Prior",
+        lw=2,
+    )
+    ax[1, 0].plot(
+        np.linspace(start, stop, 1000),
+        post1_ari(np.linspace(start, stop, 1000)),
+        color=colors["post1"],
+        label="Temperature RMSE",
+        lw=2,
+    )
+    ax[1, 0].plot(
+        np.linspace(start, stop, 1000),
+        post2_ari(np.linspace(start, stop, 1000)),
+        color=colors["post2"],
+        label="All constraints",
+        lw=2,
+    )
+    ax[1, 0].set_xlim(start, stop)
+    ax[1, 0].set_ylim(0, 2.5)
+    ax[1, 0].set_title("Aerosol ERFari")
+    ax[1, 0].set_yticklabels([])
+    ax[1, 0].set_xlabel("W m$^{-2}$, 2005-2014 minus 1750")
+    ax[1, 0].set_ylabel("Probability density")
+
+    start = -2.25
+    stop = 0.25
+    ax[1, 1].plot(
+        np.linspace(start, stop, 1000),
+        target_aci(np.linspace(start, stop, 1000)),
+        color=colors["target"],
+        label="Target",
+        lw=2,
+    )
+    ax[1, 1].plot(
+        np.linspace(start, stop, 1000),
+        prior_aci(np.linspace(start, stop, 1000)),
+        color=colors["prior"],
+        label="Prior",
+        lw=2,
+    )
+    ax[1, 1].plot(
+        np.linspace(start, stop, 1000),
+        post1_aci(np.linspace(start, stop, 1000)),
+        color=colors["post1"],
+        label="Temperature RMSE",
+        lw=2,
+    )
+    ax[1, 1].plot(
+        np.linspace(start, stop, 1000),
+        post2_aci(np.linspace(start, stop, 1000)),
+        color=colors["post2"],
+        label="All constraints",
+        lw=2,
+    )
+    ax[1, 1].set_xlim(start, stop)
+    ax[1, 1].set_ylim(0, 1.1)
+    ax[1, 1].set_title("Aerosol ERFaci")
+    ax[1, 1].set_yticklabels([])
+    ax[1, 1].set_xlabel("W m$^{-2}$, 2005-2014 minus 1750")
+
+    start = -3
+    stop = 0.4
+    ax[1, 2].plot(
+        np.linspace(start, stop, 1000),
+        target_aer(np.linspace(start, stop, 1000)),
+        color=colors["target"],
+        label="Target",
+        lw=2,
+    )
+    ax[1, 2].plot(
+        np.linspace(start, stop, 1000),
+        prior_aer(np.linspace(start, stop, 1000)),
+        color=colors["prior"],
+        label="Prior",
+        lw=2,
+    )
+    ax[1, 2].plot(
+        np.linspace(start, stop, 1000),
+        post1_aer(np.linspace(start, stop, 1000)),
+        color=colors["post1"],
+        label="Temperature RMSE",
+        lw=2,
+    )
+    ax[1, 2].plot(
+        np.linspace(start, stop, 1000),
+        post2_aer(np.linspace(start, stop, 1000)),
+        color=colors["post2"],
+        label="All constraints",
+        lw=2,
+    )
+    ax[1, 2].set_xlim(start, stop)
+    ax[1, 2].set_ylim(0, 1.1)
+    ax[1, 2].set_title("Aerosol ERF")
+    ax[1, 2].set_yticklabels([])
+    ax[1, 2].set_xlabel("W m$^{-2}$, 2005-2014 minus 1750")
+
+    start = 413
+    stop = 421
+    ax[2, 0].plot(
+        np.linspace(start, stop, 1000),
+        target_co2(np.linspace(start, stop, 1000)),
+        color=colors["target"],
+        label="Target",
+        lw=2,
+    )
+    ax[2, 0].plot(
+        np.linspace(start, stop, 1000),
+        prior_co2(np.linspace(start, stop, 1000)),
+        color=colors["prior"],
+        label="Prior",
+        lw=2,
+    )
+    ax[2, 0].plot(
+        np.linspace(start, stop, 1000),
+        post1_co2(np.linspace(start, stop, 1000)),
+        color=colors["post1"],
+        label="Temperature RMSE",
+        lw=2,
+    )
+    ax[2, 0].plot(
+        np.linspace(start, stop, 1000),
+        post2_co2(np.linspace(start, stop, 1000)),
+        color=colors["post2"],
+        label="All constraints",
+        lw=2,
+    )
+    ax[2, 0].set_xlim(start, stop)
+    ax[2, 0].set_ylim(0, 1.0)
+    ax[2, 0].set_ylabel("Probability density")
+    ax[2, 0].set_title("CO$_2$ concentration")
+    ax[2, 0].set_yticklabels([])
+    ax[2, 0].set_xlabel("ppm, 2022")
+
+    start = 100
+    stop = 900
+    ax[2, 1].plot(
+        np.linspace(start, stop),
+        target_ohc(np.linspace(start, stop)),
+        color=colors["target"],
+        label="Target",
+        lw=2,
+    )
+    ax[2, 1].plot(
+        np.linspace(start, stop),
+        prior_ohc(np.linspace(start, stop)),
+        color=colors["prior"],
+        label="Prior",
+        lw=2,
+    )
+    ax[2, 1].plot(
+        np.linspace(start, stop),
+        post1_ohc(np.linspace(start, stop)),
+        color=colors["post1"],
+        label="Temperature RMSE",
+        lw=2,
+    )
+    ax[2, 1].plot(
+        np.linspace(start, stop),
+        post2_ohc(np.linspace(start, stop)),
+        color=colors["post2"],
+        label="All constraints",
+        lw=2,
+    )
+    ax[2, 1].set_xlim(start, stop)
+    ax[2, 1].set_ylim(0, 0.007)
+    ax[2, 1].set_title("Ocean heat content change")
+    ax[2, 1].set_yticklabels([])
+    ax[2, 1].set_xlabel("ZJ, 2020 minus 1971")
+
+    ax[2, 2].axis("off")
+    legend_lines = [
+        Line2D([0], [0], color=colors["prior"], lw=2),
+        Line2D([0], [0], color=colors["post1"], lw=2),
+        Line2D([0], [0], color=colors["post2"], lw=2),
+        Line2D([0], [0], color=colors["target"], lw=2),
+    ]
+    legend_labels = ["Prior", "Temperature RMSE", "All constraints", "Target"]
+    ax[2, 2].legend(legend_lines, legend_labels, frameon=False, loc="upper left")
+
+    fig.tight_layout()
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "constraints.png"
+    )
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "constraints.pdf"
+    )
+    pl.close()
+
+
+if plots:
+    pl.scatter(draws[0]["TCR"], draws[0]["ECS"])
+    pl.xlabel("TCR, °C")
+    pl.ylabel("ECS, °C")
+    pl.tight_layout()
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "ecs_tcr_constrained.png"
+    )
+    pl.close()
+
+
+if plots:
+    pl.scatter(draws[0]["TCR"], draws[0]["ERFaci"] + draws[0]["ERFari"])
+    pl.xlabel("TCR, °C")
+    pl.ylabel("Aerosol ERF, W m$^{-2}$, 2005-2014 minus 1750")
+    pl.tight_layout()
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "tcr_aer_constrained.png"
+    )
+    pl.close()
+
+
+# move these to the validation script
+print("Constrained, reweighted parameters:")
+print("ECS:", np.percentile(draws[0]["ECS"], (5, 50, 95)))
+print("TCR:", np.percentile(draws[0]["TCR"], (5, 50, 95)))
+print(
+    "CO2 concentration 2022:", np.percentile(draws[0]["CO2 concentration"], (5, 50, 95))
+)
+print(
+    "Temperature 2003-2022 rel. 1850-1900:",
+    np.percentile(draws[0]["temperature 2003-2022"], (5, 50, 95)),
+)
+print(
+    "Aerosol ERFari 2005-2014 rel. 1750:",
+    np.percentile(draws[0]["ERFari"], (5, 50, 95)),
+)
+print(
+    "Aerosol ERFaci 2005-2014 rel. 1750:",
+    np.percentile(draws[0]["ERFaci"], (5, 50, 95)),
+)
+print(
+    "Aerosol ERF 2005-2014 rel. 1750:",
+    np.percentile(draws[0]["ERFaci"] + draws[0]["ERFari"], (5, 50, 95)),
+)
+print("OHC change 2020 rel. 1971:", np.percentile(draws[0]["OHC"], (5, 50, 95)))
+
+print("*likely range")
+
+if plots:
+    df_gmst = pd.read_csv("../../../../../data/forcing/IGCC_GMST_1850-2022.csv")
+    gmst = df_gmst["gmst"].values
+
+    fig, ax = pl.subplots(figsize=(5, 5))
+    ax.fill_between(
+        np.arange(1850, 2102),
+        np.min(
+            temp_in[:, draws[0].index]
+            - np.average(temp_in[:52, draws[0].index], weights=weights_51yr, axis=0),
+            axis=1,
+        ),
+        np.max(
+            temp_in[:, draws[0].index]
+            - np.average(temp_in[:52, draws[0].index], weights=weights_51yr, axis=0),
+            axis=1,
+        ),
+        color="#000000",
+        alpha=0.2,
+    )
+    ax.fill_between(
+        np.arange(1850, 2102),
+        np.percentile(
+            temp_in[:, draws[0].index]
+            - np.average(temp_in[:52, draws[0].index], weights=weights_51yr, axis=0),
+            5,
+            axis=1,
+        ),
+        np.percentile(
+            temp_in[:, draws[0].index]
+            - np.average(temp_in[:52, draws[0].index], weights=weights_51yr, axis=0),
+            95,
+            axis=1,
+        ),
+        color="#000000",
+        alpha=0.2,
+    )
+    ax.fill_between(
+        np.arange(1850, 2102),
+        np.percentile(
+            temp_in[:, draws[0].index]
+            - np.average(temp_in[:52, draws[0].index], weights=weights_51yr, axis=0),
+            16,
+            axis=1,
+        ),
+        np.percentile(
+            temp_in[:, draws[0].index]
+            - np.average(temp_in[:52, draws[0].index], weights=weights_51yr, axis=0),
+            84,
+            axis=1,
+        ),
+        color="#000000",
+        alpha=0.2,
+    )
+    ax.plot(
+        np.arange(1850, 2102),
+        np.median(
+            temp_in[:, draws[0].index]
+            - np.average(temp_in[:52, draws[0].index], weights=weights_51yr, axis=0),
+            axis=1,
+        ),
+        color="#000000",
+    )
+
+    ax.plot(np.arange(1850.5, 2023), gmst, color="b", label="Observations")
+
+    ax.legend(frameon=False, loc="upper left")
+
+    ax.set_xlim(1850, 2100)
+    ax.set_ylim(-1, 5)
+    ax.set_ylabel("°C relative to 1850-1900")
+    ax.axhline(0, color="k", ls=":", lw=0.5)
+    pl.title("Constrained, reweighted posterior")
+    pl.tight_layout()
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "final_reweighted_ssp245.png"
+    )
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "final_reweighted_ssp245.pdf"
+    )
+    pl.close()
+
+np.savetxt(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/posteriors/"
+    "runids_rmse_reweighted_pass.csv",
+    sorted(draws[0].index),
+    fmt="%d",
+)
diff --git a/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/constraining/04_dump-calibration.py b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/constraining/04_dump-calibration.py
new file mode 100755
index 0000000..26bf9de
--- /dev/null
+++ b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/constraining/04_dump-calibration.py
@@ -0,0 +1,85 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+"""Takes constrained runs and dumps parameters into the output file"""
+
+import os
+
+import numpy as np
+import pandas as pd
+from dotenv import load_dotenv
+from fair import __version__
+
+load_dotenv()
+
+print("Dumping output...")
+
+cal_v = os.getenv("CALIBRATION_VERSION")
+fair_v = os.getenv("FAIR_VERSION")
+constraint_set = os.getenv("CONSTRAINT_SET")
+samples = int(os.getenv("PRIOR_SAMPLES"))
+output_ensemble_size = int(os.getenv("POSTERIOR_SAMPLES"))
+
+assert fair_v == __version__
+
+df_cc = pd.read_csv(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/priors/"
+    "carbon_cycle.csv"
+)
+df_cr = pd.read_csv(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/priors/"
+    "climate_response_ebm3.csv"
+)
+df_aci = pd.read_csv(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/priors/"
+    "aerosol_cloud.csv"
+)
+df_ari = pd.read_csv(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/priors/"
+    "aerosol_radiation.csv"
+)
+df_ozone = pd.read_csv(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/priors/ozone.csv"
+)
+df_scaling = pd.read_csv(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/priors/"
+    "forcing_scaling.csv"
+)
+df_1750co2 = pd.read_csv(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/priors/"
+    "co2_concentration_1750.csv"
+)
+
+valid_all = np.loadtxt(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/posteriors/"
+    "runids_rmse_reweighted_pass.csv"
+).astype(
+    np.int64
+)  # [:1000]
+valid_all
+
+seed = 1355763 + 399 * valid_all
+seed
+
+# concatenate each param dataframe and prefix with its model element to avoid
+# namespace conflicts (and a bit of user intuivity)
+params_out = pd.concat(
+    (
+        df_cr.loc[valid_all, :].rename(columns=lambda x: "clim_" + x),
+        df_cc.loc[valid_all, :].rename(columns=lambda x: "cc_" + x),
+        df_ari.loc[valid_all, :].rename(columns=lambda x: "ari_" + x),
+        df_aci.loc[valid_all, :].rename(columns=lambda x: "aci_" + x),
+        df_ozone.loc[valid_all, :].rename(columns=lambda x: "o3_" + x),
+        df_scaling.loc[valid_all, :].rename(columns=lambda x: "fscale_" + x),
+        df_1750co2.loc[valid_all, :].rename(
+            columns={"co2_concentration": "cc_co2_concentration_1750"}
+        ),
+        pd.DataFrame(seed, index=valid_all, columns=["seed"]),
+    ),
+    axis=1,
+)
+
+params_out.to_csv(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/posteriors/"
+    "calibrated_constrained_parameters.csv"
+)
diff --git a/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/constraining/05_constrained-projections.py b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/constraining/05_constrained-projections.py
new file mode 100755
index 0000000..8d3c7e8
--- /dev/null
+++ b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/constraining/05_constrained-projections.py
@@ -0,0 +1,744 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+"""Run constrained projections for SSPs"""
+
+import os
+
+import matplotlib.pyplot as pl
+import numpy as np
+import pandas as pd
+import pooch
+import xarray as xr
+from dotenv import load_dotenv
+from fair import FAIR
+from fair.interface import fill, initialise
+from fair.io import read_properties
+
+pl.switch_backend("agg")
+
+load_dotenv()
+
+pl.style.use("../../../../../defaults.mplstyle")
+
+print("Running SSP scenarios...")
+
+cal_v = os.getenv("CALIBRATION_VERSION")
+fair_v = os.getenv("FAIR_VERSION")
+constraint_set = os.getenv("CONSTRAINT_SET")
+output_ensemble_size = int(os.getenv("POSTERIOR_SAMPLES"))
+plots = os.getenv("PLOTS", "False").lower() in ("true", "1", "t")
+progress = os.getenv("PROGRESS", "False").lower() in ("true", "1", "t")
+datadir = os.getenv("DATADIR")
+
+scenarios = [
+    "MESSAGE-GLOBIOM___SSP2-45",
+    "MESSAGE-GLOBIOM___SSP2-45-MFR-CH4",
+    "MESSAGE-GLOBIOM___SSP2-45-CLE-CH4",
+]
+
+df_solar = pd.read_csv(
+    "../../../../../data/forcing/solar_erf_timebounds.csv", index_col="year"
+)
+df_volcanic = pd.read_csv(
+    "../../../../../data/forcing/volcanic_ERF_1750-2101_timebounds.csv",
+    index_col="timebounds",
+)
+
+volcanic_forcing = df_volcanic["erf"].loc[1750:2101].values
+solar_forcing = df_solar["erf"].loc[1750:2101].values
+
+df_methane = pd.read_csv(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/calibrations/"
+    "CH4_lifetime.csv",
+    index_col=0,
+)
+df_configs = pd.read_csv(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/posteriors/"
+    "calibrated_constrained_parameters.csv",
+    index_col=0,
+)
+df_lapsi = pd.read_csv(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/calibrations/"
+    "lapsi_scale_factor.csv",
+    index_col=0,
+)
+valid_all = df_configs.index
+
+trend_shape = np.ones(551)
+trend_shape[:271] = np.linspace(0, 1, 271)
+
+f = FAIR(ch4_method="Thornhill2021")
+f.define_time(1750, 2101, 1)
+f.define_scenarios(scenarios)
+f.define_configs(valid_all)
+species, properties = read_properties()
+species.remove("NOx aviation")
+species.remove("Contrails")
+f.define_species(species, properties)
+f.allocate()
+
+# run with harmonized emissions
+da_emissions = xr.load_dataarray(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/emissions/"
+    "emissions_1750-2100.nc"
+)
+
+da = da_emissions.loc[dict(config="unspecified")]
+fe = da.expand_dims(dim=["config"], axis=(2))
+f.emissions = fe.drop("config") * np.ones((1, 1, output_ensemble_size, 1))
+
+# solar and volcanic forcing
+fill(
+    f.forcing,
+    volcanic_forcing[:, None, None] * df_configs["fscale_Volcanic"].values.squeeze(),
+    specie="Volcanic",
+)
+fill(
+    f.forcing,
+    solar_forcing[:, None, None] * df_configs["fscale_solar_amplitude"].values.squeeze(),
+    specie="Solar",
+)
+
+# climate response
+fill(
+    f.climate_configs["ocean_heat_capacity"],
+    df_configs.loc[:, "clim_c1":"clim_c3"].values,
+)
+fill(
+    f.climate_configs["ocean_heat_transfer"],
+    df_configs.loc[:, "clim_kappa1":"clim_kappa3"].values,
+)  # not massively robust, since relies on kappa1, kappa2, kappa3 being in adjacent cols
+fill(
+    f.climate_configs["deep_ocean_efficacy"],
+    df_configs["clim_epsilon"].values.squeeze(),
+)
+fill(
+    f.climate_configs["gamma_autocorrelation"],
+    df_configs["clim_gamma"].values.squeeze(),
+)
+fill(f.climate_configs["sigma_eta"], df_configs["clim_sigma_eta"].values.squeeze())
+fill(f.climate_configs["sigma_xi"], df_configs["clim_sigma_xi"].values.squeeze())
+fill(f.climate_configs["seed"], df_configs["seed"])
+fill(f.climate_configs["stochastic_run"], True)
+fill(f.climate_configs["use_seed"], True)
+fill(f.climate_configs["forcing_4co2"], df_configs["clim_F_4xCO2"])
+
+# species level
+f.fill_species_configs()
+
+# carbon cycle
+fill(f.species_configs["iirf_0"], df_configs["cc_r0"].values.squeeze(), specie="CO2")
+fill(
+    f.species_configs["iirf_airborne"],
+    df_configs["cc_rA"].values.squeeze(),
+    specie="CO2",
+)
+fill(
+    f.species_configs["iirf_uptake"], df_configs["cc_rU"].values.squeeze(), specie="CO2"
+)
+fill(
+    f.species_configs["iirf_temperature"],
+    df_configs["cc_rT"].values.squeeze(),
+    specie="CO2",
+)
+
+# aerosol indirect
+fill(f.species_configs["aci_scale"], df_configs["aci_beta"].values.squeeze())
+fill(
+    f.species_configs["aci_shape"],
+    df_configs["aci_shape_so2"].values.squeeze(),
+    specie="Sulfur",
+)
+fill(
+    f.species_configs["aci_shape"],
+    df_configs["aci_shape_bc"].values.squeeze(),
+    specie="BC",
+)
+fill(
+    f.species_configs["aci_shape"],
+    df_configs["aci_shape_oc"].values.squeeze(),
+    specie="OC",
+)
+
+# methane lifetime baseline and sensitivity
+fill(
+    f.species_configs["unperturbed_lifetime"],
+    df_methane.loc["historical_best", "base"],
+    specie="CH4",
+)
+fill(
+    f.species_configs["ch4_lifetime_chemical_sensitivity"],
+    df_methane.loc["historical_best", "CH4"],
+    specie="CH4",
+)
+fill(
+    f.species_configs["ch4_lifetime_chemical_sensitivity"],
+    df_methane.loc["historical_best", "N2O"],
+    specie="N2O",
+)
+fill(
+    f.species_configs["ch4_lifetime_chemical_sensitivity"],
+    df_methane.loc["historical_best", "VOC"],
+    specie="VOC",
+)
+fill(
+    f.species_configs["ch4_lifetime_chemical_sensitivity"],
+    df_methane.loc["historical_best", "NOx"],
+    specie="NOx",
+)
+fill(
+    f.species_configs["ch4_lifetime_chemical_sensitivity"],
+    df_methane.loc["historical_best", "HC"],
+    specie="Equivalent effective stratospheric chlorine",
+)
+fill(
+    f.species_configs["lifetime_temperature_sensitivity"],
+    df_methane.loc["historical_best", "temp"],
+)
+
+# correct LAPSI scale factor terms
+fill(
+    f.species_configs["lapsi_radiative_efficiency"],
+    df_lapsi.loc["historical_best", "BC"],
+    specie="BC",
+)
+
+
+# emissions adjustments for N2O and CH4
+fill(f.species_configs["baseline_emissions"], 19.019783117809567, specie="CH4")
+fill(f.species_configs["baseline_emissions"], 0.08602230754, specie="N2O")
+fill(f.species_configs["baseline_emissions"], 19.423526730206152, specie="NOx")
+
+# aerosol direct
+for specie in [
+    "BC",
+    "CH4",
+    "N2O",
+    "NH3",
+    "NOx",
+    "OC",
+    "Sulfur",
+    "VOC",
+    "Equivalent effective stratospheric chlorine",
+]:
+    fill(
+        f.species_configs["erfari_radiative_efficiency"],
+        df_configs[f"ari_{specie}"],
+        specie=specie,
+    )
+
+# forcing scaling
+for specie in [
+    "CO2",
+    "CH4",
+    "N2O",
+    "Stratospheric water vapour",
+    "Light absorbing particles on snow and ice",
+    "Land use",
+]:
+    fill(
+        f.species_configs["forcing_scale"],
+        df_configs[f"fscale_{specie}"].values.squeeze(),
+        specie=specie,
+    )
+
+for specie in [
+    "CFC-11",
+    "CFC-12",
+    "CFC-113",
+    "CFC-114",
+    "CFC-115",
+    "HCFC-22",
+    "HCFC-141b",
+    "HCFC-142b",
+    "CCl4",
+    "CHCl3",
+    "CH2Cl2",
+    "CH3Cl",
+    "CH3CCl3",
+    "CH3Br",
+    "Halon-1202",
+    "Halon-1211",
+    "Halon-1301",
+    "Halon-2402",
+    "CF4",
+    "C2F6",
+    "C3F8",
+    "c-C4F8",
+    "C4F10",
+    "C5F12",
+    "C6F14",
+    "C7F16",
+    "C8F18",
+    "NF3",
+    "SF6",
+    "SO2F2",
+    "HFC-125",
+    "HFC-134a",
+    "HFC-143a",
+    "HFC-152a",
+    "HFC-227ea",
+    "HFC-23",
+    "HFC-236fa",
+    "HFC-245fa",
+    "HFC-32",
+    "HFC-365mfc",
+    "HFC-4310mee",
+]:
+    fill(
+        f.species_configs["forcing_scale"],
+        df_configs["fscale_minorGHG"].values.squeeze(),
+        specie=specie,
+    )
+
+# ozone
+for specie in [
+    "CH4",
+    "N2O",
+    "Equivalent effective stratospheric chlorine",
+    "CO",
+    "VOC",
+    "NOx",
+]:
+    fill(
+        f.species_configs["ozone_radiative_efficiency"],
+        df_configs[f"o3_{specie}"],
+        specie=specie,
+    )
+
+# initial condition of CO2 concentration (but not baseline for forcing calculations)
+fill(
+    f.species_configs["baseline_concentration"],
+    df_configs["cc_co2_concentration_1750"].values.squeeze(),
+    specie="CO2",
+)
+
+# initial conditions
+initialise(f.concentration, f.species_configs["baseline_concentration"])
+initialise(f.forcing, 0)
+initialise(f.temperature, 0)
+initialise(f.cumulative_emissions, 0)
+initialise(f.airborne_emissions, 0)
+
+f.run(progress=progress)
+
+fancy_titles = {
+    "MESSAGE-GLOBIOM___SSP2-45-CLE-CH4": "CLE",
+    "MESSAGE-GLOBIOM___SSP2-45-MFR-CH4": "MFR",
+    "MESSAGE-GLOBIOM___SSP2-45": "SSP2-4.5",
+}
+
+ar6_colors = {
+    "MESSAGE-GLOBIOM___SSP2-45-MFR-CH4": "#00a9cf",
+    "MESSAGE-GLOBIOM___SSP2-45": "#f69320",
+    "MESSAGE-GLOBIOM___SSP2-45-CLE-CH4": "#980002",
+}
+
+df_gmst = pd.read_csv("../../../../../data/forcing/IGCC_GMST_1850-2022.csv")
+gmst = df_gmst["gmst"].values
+
+if plots:
+    fig, ax = pl.subplots(1, 3, figsize=(18 / 2.54, 6 / 2.54))
+    for i in range(3):
+        ax[i].fill_between(
+            np.arange(1750.5, 2102),
+            np.min(
+                f.temperature[:, i, :, 0]
+                - f.temperature[100:151, i, :, 0].mean(axis=0),
+                axis=1,
+            ),
+            np.max(
+                f.temperature[:, i, :, 0]
+                - f.temperature[100:151, i, :, 0].mean(axis=0),
+                axis=1,
+            ),
+            color=ar6_colors[scenarios[i]],
+            alpha=0.2,
+            lw=0,
+        )
+        ax[i].fill_between(
+            np.arange(1750.5, 2102),
+            np.percentile(
+                f.temperature[:, i, :, 0]
+                - f.temperature[100:151, i, :, 0].mean(axis=0),
+                5,
+                axis=1,
+            ),
+            np.percentile(
+                f.temperature[:, i, :, 0]
+                - f.temperature[100:151, i, :, 0].mean(axis=0),
+                95,
+                axis=1,
+            ),
+            color=ar6_colors[scenarios[i]],
+            alpha=0.2,
+            lw=0,
+        )
+        ax[i].fill_between(
+            np.arange(1750.5, 2102),
+            np.percentile(
+                f.temperature[:, i, :, 0]
+                - f.temperature[100:151, i, :, 0].mean(axis=0),
+                16,
+                axis=1,
+            ),
+            np.percentile(
+                f.temperature[:, i, :, 0]
+                - f.temperature[100:151, i, :, 0].mean(axis=0),
+                84,
+                axis=1,
+            ),
+            color=ar6_colors[scenarios[i]],
+            alpha=0.2,
+            lw=0,
+        )
+        ax[i].plot(
+            np.arange(1750.5, 2102),
+            np.median(
+                f.temperature[:, i, :, 0]
+                - f.temperature[100:151, i, :, 0].mean(axis=0),
+                axis=1,
+            ),
+            color=ar6_colors[scenarios[i]],
+            lw=1,
+        )
+        ax[i].plot(np.arange(1850.5, 2023), gmst, color="k", lw=1)
+        ax[i].set_xlim(1950, 2100)
+        ax[i].set_ylim(-1, 5)
+        ax[i].axhline(0, color="k", ls=":", lw=0.5)
+        ax[i].set_title(fancy_titles[scenarios[i]])
+
+    ax[0].set_ylabel("°C since 1850-1900")
+
+    fig.tight_layout()
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "final_ssp_temperatures.png"
+    )
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "final_ssp_temperatures.pdf"
+    )
+    pl.close()
+
+# # Temperature diffs w.r.t. 1995-2014
+# Future periods are 2021-2040, 2041-2060, 2081-2100. Values are 5th, 50th, 95th
+
+# as temperatures are timebounds, we should weight first and last by 0.5
+
+weight_20yr = np.ones(21)
+weight_20yr[0] = 0.5
+weight_20yr[-1] = 0.5
+
+weight_51yr = np.ones(52)
+weight_51yr[0] = 0.5
+weight_51yr[-1] = 0.5
+
+temp_model_19952014 = np.zeros((9, 3))
+temp_model_18501900 = np.zeros((9, 3))
+
+periodmap = {0: slice(271, 292), 1: slice(291, 312), 2: slice(331, 352)}
+for irow in range(9):
+    scenmap = irow // 3
+    temp_model_19952014[irow, :] = np.percentile(
+        np.average(
+            f.temperature[periodmap[irow % 3], scenmap, :, 0],
+            weights=weight_20yr,
+            axis=0,
+        )
+        - np.average(
+            f.temperature[245:266, scenmap, :, 0], weights=weight_20yr, axis=0
+        ),
+        (5, 50, 95),
+    )
+    temp_model_18501900[irow, :] = np.percentile(
+        np.average(
+            f.temperature[periodmap[irow % 3], scenmap, :, 0],
+            weights=weight_20yr,
+            axis=0,
+        )
+        - np.average(
+            f.temperature[100:152, scenmap, :, 0], weights=weight_51yr, axis=0
+        ),
+        (5, 50, 95),
+    )
+
+print("Anomalies rel. 1995-2014:")
+print((temp_model_19952014))
+print()
+print("Anomalies rel. 1850-1900:")
+print((temp_model_18501900))
+print()
+print(
+    "Methane forcing 2019:",
+    np.percentile(f.forcing[269:271, 2, :, 3].mean(axis=0), (5, 50, 95)),
+)
+print(
+    "Methane concentration 2019:",
+    np.percentile(f.concentration[269:271, 2, :, 3].mean(axis=0), (5, 50, 95)),
+)
+print(
+    "WMGHG forcing 2019:",
+    np.percentile(
+        (
+            f.forcing[269:271, 2, :, 2:5].sum(axis=2)
+            + f.forcing[269:271, 2, :, 12:53].sum(axis=2)
+        ).mean(axis=0),
+        (5, 50, 95),
+    ),
+)
+
+
+rcmip_concentration_file = pooch.retrieve(
+    url=("doi:10.5281/zenodo.4589756/" "rcmip-concentrations-annual-means-v5-1-0.csv"),
+    known_hash="md5:0d82c3c3cdd4dd632b2bb9449a5c315f",
+)
+
+
+df_conc = pd.read_csv(rcmip_concentration_file)
+conc_ch4 = {}
+conc_co2 = {}
+for scenario in scenarios:
+    conc_ch4[scenario] = (
+        df_conc.loc[
+            (df_conc["Scenario"] == scenario)
+            & (df_conc["Variable"].str.endswith("|CH4"))
+            & (df_conc["Region"] == "World"),
+            "1750":"2100",
+        ]
+        .interpolate(axis=1)
+        .values.squeeze()
+    )
+    conc_co2[scenario] = (
+        df_conc.loc[
+            (df_conc["Scenario"] == scenario)
+            & (df_conc["Variable"].str.endswith("|CO2"))
+            & (df_conc["Region"] == "World"),
+            "1750":"2100",
+        ]
+        .interpolate(axis=1)
+        .values.squeeze()
+    )
+
+
+if plots:
+    pl.plot(
+        df_methane.loc["historical_best", "base"]
+        * np.percentile(f.alpha_lifetime[:, 2, :, 3], (50), axis=1)
+    )
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "ch4lifetime_ssp245.png"
+    )
+    pl.close()
+
+    pl.plot(np.percentile(f.concentration[:, 2, :, 60], (50), axis=1))
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/eesc_ssp245.png"
+    )
+    pl.close()
+
+    pl.plot(
+        np.arange(1750, 2102),
+        np.percentile(f.concentration[:, 2, :, 2], (50), axis=1),
+        label="fair2.1 median",
+    )
+    pl.legend()
+    pl.xlim(1750, 2100)
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "co2_historical.png"
+    )
+    pl.close()
+
+    pl.fill_between(
+        np.arange(1950, 2101),
+        np.percentile(
+            f.toa_imbalance[200:351, 2, :],
+            5,
+            axis=1,
+        ),
+        np.percentile(
+            f.toa_imbalance[200:351, 2, :],
+            95,
+            axis=1,
+        ),
+        color="k",
+        alpha=0.3,
+    )
+    pl.plot(
+        np.arange(1950, 2101),
+        np.median(
+            f.toa_imbalance[200:351, 2, :],
+            axis=1,
+        ),
+        color="k",
+    )
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "toa_imbalance_ssp245.png"
+    )
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "toa_imbalance_ssp245.pdf"
+    )
+    pl.close()
+
+    pl.fill_between(
+        np.arange(1750, 2102),
+        np.percentile(
+            f.forcing[:, 2, :, 2:5].sum(axis=2)
+            + f.forcing[:, 2, :, 12:53].sum(axis=2),
+            5,
+            axis=1,
+        ),
+        np.percentile(
+            f.forcing[:, 2, :, 2:5].sum(axis=2)
+            + f.forcing[:, 2, :, 12:53].sum(axis=2),
+            95,
+            axis=1,
+        ),
+        color="k",
+        alpha=0.3,
+    )
+    pl.plot(
+        np.arange(1750, 2102),
+        np.median(
+            f.forcing[:, 2, :, 2:5].sum(axis=2)
+            + f.forcing[:, 2, :, 12:53].sum(axis=2),
+            axis=1,
+        ),
+        color="k",
+    )
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "ghg_forcing_ssp245.png"
+    )
+    pl.close()
+
+    pl.fill_between(
+        np.arange(1750, 2101),
+        np.percentile(f.forcing[:351, 2, :, 55:57].sum(axis=2), 5, axis=1),
+        np.percentile(f.forcing[:351, 2, :, 55:57].sum(axis=2), 95, axis=1),
+        color="k",
+        alpha=0.3,
+    )
+    pl.plot(
+        np.arange(1750, 2101),
+        np.median(
+            f.forcing[:351, 2, :, 55:57].sum(axis=2),
+            axis=1,
+        ),
+        color="k",
+    )
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "aerosol_forcing_ssp245.png"
+    )
+    pl.close()
+
+    pl.fill_between(
+        np.arange(1750, 2101),
+        np.percentile(f.forcing[:351, 2, :, 2], 5, axis=1),
+        np.percentile(f.forcing[:351, 2, :, 2], 95, axis=1),
+        color="k",
+        alpha=0.3,
+    )
+    pl.plot(
+        np.arange(1750, 2101),
+        np.median(
+            f.forcing[:351, 2, :, 2],
+            axis=1,
+        ),
+        color="k",
+    )
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "co2_forcing_ssp245.png"
+    )
+    pl.close()
+
+    pl.fill_between(
+        np.arange(1750, 2101),
+        np.percentile(f.forcing[:351, 2, :, 57], 5, axis=1),
+        np.percentile(f.forcing[:351, 2, :, 57], 95, axis=1),
+        color="k",
+        alpha=0.3,
+    )
+    pl.plot(
+        np.arange(1750, 2101),
+        np.median(
+            f.forcing[:351, 2, :, 57],
+            axis=1,
+        ),
+        color="k",
+    )
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "ozone_ssp245.png"
+    )
+    pl.close()
+
+    pl.fill_between(
+        np.arange(1750, 2101),
+        np.percentile(f.forcing[:351, 2, :, 58], 5, axis=1),
+        np.percentile(f.forcing[:351, 2, :, 58], 95, axis=1),
+        color="k",
+        alpha=0.3,
+    )
+    pl.plot(
+        np.arange(1750, 2101),
+        np.median(
+            f.forcing[:351, 2, :, 58],
+            axis=1,
+        ),
+        color="k",
+    )
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "lapsi_ssp245.png"
+    )
+    pl.close()
+
+    pl.fill_between(
+        np.arange(1750, 2101),
+        np.percentile(f.forcing[:351, 2, :, 59], 5, axis=1),
+        np.percentile(f.forcing[:351, 2, :, 59], 95, axis=1),
+        color="k",
+        alpha=0.3,
+    )
+    pl.plot(
+        np.arange(1750, 2101),
+        np.median(
+            f.forcing[:351, 2, :, 59],
+            axis=1,
+        ),
+        color="k",
+    )
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "stratH2O_ssp245.png"
+    )
+    pl.close()
+
+    pl.fill_between(
+        np.arange(1750, 2101),
+        np.percentile(f.forcing[:351, 2, :, 60], 5, axis=1),
+        np.percentile(f.forcing[:351, 2, :, 60], 95, axis=1),
+        color="k",
+        alpha=0.3,
+    )
+    pl.plot(
+        np.arange(1750, 2101),
+        np.median(
+            f.forcing[:351, 2, :, 60],
+            axis=1,
+        ),
+        color="k",
+    )
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "landuse_ssp245.png"
+    )
+    pl.close()
diff --git a/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/constraining/06_check-tcre-af.py b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/constraining/06_check-tcre-af.py
new file mode 100755
index 0000000..8f0c552
--- /dev/null
+++ b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/constraining/06_check-tcre-af.py
@@ -0,0 +1,61 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+"""Spit out the TCRE and airborne fraction."""
+
+# we don't constrain on these as they are model-based assessments, but we want to add
+# to the table.
+
+import os
+
+import numpy as np
+from dotenv import load_dotenv
+from fair.earth_params import mass_atmosphere, molecular_weight_air
+
+load_dotenv()
+
+cal_v = os.getenv("CALIBRATION_VERSION")
+fair_v = os.getenv("FAIR_VERSION")
+constraint_set = os.getenv("CONSTRAINT_SET")
+
+af = np.load(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+    "prior_runs/airborne_fraction_1pctCO2_y70_y140.npy"
+)
+temp = np.load(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+    "prior_runs/temperature_1pctCO2_y70_y140.npy"
+)
+temp1000 = np.load(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+    "prior_runs/temperature_1pctCO2_1000GtC.npy"
+)
+pass1 = np.loadtxt(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+    "posteriors/runids_rmse_pass.csv",
+    dtype=int,
+)
+pass2 = np.loadtxt(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+    "posteriors/runids_rmse_reweighted_pass.csv",
+    dtype=int,
+)
+
+co2_1850 = 284.3169988
+co2_1920 = co2_1850 * 1.01**70  # NOT 2x (69.66 yr), per definition of TCRE
+mass_factor = 12.011 / molecular_weight_air * mass_atmosphere / 1e21
+# mass_factor converts ppm CO2 to (1000 Gt C)
+
+idx = np.in1d(pass1, pass2).nonzero()[0]
+print("temperature 2xCO2:", np.percentile(temp[0, idx], (5, 50, 95)))
+print("temperature 4xCO2:", np.percentile(temp[1, idx], (5, 50, 95)))
+print("TCRE @1000GtC:", np.percentile(temp1000[idx], (5, 50, 95)))
+print("AF 2xCO2*:", np.percentile(af[0, idx], (16, 50, 84)))
+print("AF 4xCO2*:", np.percentile(af[1, idx], (16, 50, 84)))
+print(
+    "TCRE (IPCC method)*:",
+    np.percentile(
+        af[0, idx] * temp[0, idx] / ((co2_1920 - co2_1850) * mass_factor), (16, 50, 84)
+    ),
+)
+print("*likely range")
diff --git a/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/constraining/parallel_1pct.py b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/constraining/parallel_1pct.py
new file mode 100644
index 0000000..2c52ecb
--- /dev/null
+++ b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/constraining/parallel_1pct.py
@@ -0,0 +1,133 @@
+# put imports outside: we don't have a lot of overhead here, and it looks nicer.
+import os
+import warnings
+
+import numpy as np
+import xarray as xr
+from dotenv import load_dotenv
+from fair import FAIR
+from fair.interface import fill, initialise
+from fair.io import read_properties
+from scipy.interpolate import interp1d
+
+load_dotenv()
+
+cal_v = os.getenv("CALIBRATION_VERSION")
+fair_v = os.getenv("FAIR_VERSION")
+constraint_set = os.getenv("CONSTRAINT_SET")
+
+
+def run_fair(cfg):
+    scenarios = ["1pctCO2"]
+    batch_start = cfg["batch_start"]
+    batch_end = cfg["batch_end"]
+    batch_size = batch_end - batch_start
+
+    species, properties = read_properties()
+
+    da_concentration = xr.load_dataarray(
+        f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "concentration/1pctCO2_concentration_1850-1990.nc"
+    )
+
+    f = FAIR()
+    f.define_time(1850, 1990, 1)
+    f.define_scenarios(scenarios)
+    species = ["CO2", "CH4", "N2O"]
+    properties = {
+        "CO2": {
+            "type": "co2",
+            "input_mode": "concentration",
+            "greenhouse_gas": True,
+            "aerosol_chemistry_from_emissions": False,
+            "aerosol_chemistry_from_concentration": False,
+        },
+        "CH4": {
+            "type": "ch4",
+            "input_mode": "concentration",
+            "greenhouse_gas": True,
+            "aerosol_chemistry_from_emissions": False,
+            "aerosol_chemistry_from_concentration": False,
+        },
+        "N2O": {
+            "type": "n2o",
+            "input_mode": "concentration",
+            "greenhouse_gas": True,
+            "aerosol_chemistry_from_emissions": False,
+            "aerosol_chemistry_from_concentration": False,
+        },
+    }
+    f.define_configs(list(range(batch_start, batch_end)))
+    f.define_species(species, properties)
+    f.allocate()
+
+    da = da_concentration.loc[dict(config="unspecified", scenario="1pctCO2")]
+    fe = da.expand_dims(dim=["scenario", "config"], axis=(1, 2))
+    f.concentration = fe.drop("config") * np.ones((1, 1, batch_size, 1))
+
+    # climate response
+    fill(
+        f.climate_configs["ocean_heat_capacity"],
+        np.array([cfg["c1"], cfg["c2"], cfg["c3"]]).T,
+    )
+    fill(
+        f.climate_configs["ocean_heat_transfer"],
+        np.array([cfg["kappa1"], cfg["kappa2"], cfg["kappa3"]]).T,
+    )
+    fill(f.climate_configs["deep_ocean_efficacy"], cfg["epsilon"])
+    fill(f.climate_configs["gamma_autocorrelation"], cfg["gamma"])
+    fill(f.climate_configs["stochastic_run"], False)
+    fill(f.climate_configs["forcing_4co2"], cfg["forcing_4co2"])
+
+    # species level
+    f.fill_species_configs()
+
+    # carbon cycle
+    fill(f.species_configs["iirf_0"], cfg["iirf_0"], specie="CO2")
+    fill(f.species_configs["iirf_airborne"], cfg["iirf_airborne"], specie="CO2")
+    fill(f.species_configs["iirf_uptake"], cfg["iirf_uptake"], specie="CO2")
+    fill(f.species_configs["iirf_temperature"], cfg["iirf_temperature"], specie="CO2")
+
+    # forcing scaling
+    fill(f.species_configs["forcing_scale"], cfg["scaling_CO2"], specie="CO2")
+    fill(f.species_configs["forcing_scale"], cfg["scaling_CH4"], specie="CH4")
+    fill(f.species_configs["forcing_scale"], cfg["scaling_N2O"], specie="N2O")
+
+    # initial condition of CO2 concentration (but not baseline for forcing calculations)
+    fill(f.species_configs["baseline_concentration"], 284.3169988, specie="CO2")
+    fill(f.species_configs["baseline_concentration"], 808.2490285, specie="CH4")
+    fill(f.species_configs["baseline_concentration"], 273.021047, specie="N2O")
+
+    fill(
+        f.species_configs["forcing_reference_concentration"], 284.3169988, specie="CO2"
+    )
+    fill(
+        f.species_configs["forcing_reference_concentration"], 808.2490285, specie="CH4"
+    )
+    fill(f.species_configs["forcing_reference_concentration"], 273.021047, specie="N2O")
+
+    # initial conditions
+    initialise(f.concentration, f.species_configs["baseline_concentration"])
+    initialise(f.forcing, 0)
+    initialise(f.temperature, 0)
+    initialise(f.cumulative_emissions, 0)
+    initialise(f.airborne_emissions, 0)
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        f.run(progress=False)
+
+    # interpolate warming at 1000 GtC
+    t1000 = np.ones(batch_size) * np.nan
+    ttco2 = 1000 * 44.009 / 12.011
+    for ibatch in range(batch_size):
+        interpolator = interp1d(
+            f.cumulative_emissions[:, 0, ibatch, 0], f.temperature[:, 0, ibatch, 0]
+        )
+        t1000[ibatch] = interpolator(ttco2)
+
+    return (
+        np.array((f.temperature[70, 0, :, 0], f.temperature[140, 0, :, 0])),
+        np.array((f.airborne_fraction[70, 0, :, 0], f.airborne_fraction[140, 0, :, 0])),
+        np.array(t1000),
+    )
diff --git a/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/constraining/utils.py b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/constraining/utils.py
new file mode 100644
index 0000000..0a9e5bb
--- /dev/null
+++ b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/constraining/utils.py
@@ -0,0 +1,170 @@
+import logging
+import os
+import time
+from concurrent.futures import as_completed
+
+from dotenv import load_dotenv
+from tqdm.auto import tqdm
+
+load_dotenv()
+
+front_serial = int(os.getenv("FRONT_SERIAL"))
+front_parallel = int(os.getenv("FRONT_PARALLEL"))
+progress = os.getenv("PROGRESS", "False").lower() in ("true", "1", "t")
+
+LOGGER = logging.getLogger(__name__)
+
+# TODO: handle configuration in a consistent manner
+_default_tqdm_params = {
+    "mininterval": 5,
+    "unit": "it",
+    "unit_scale": True,
+    "disable": 1 - progress,
+}
+
+
+def progress(*args, **kwargs):
+    kwargs = {**_default_tqdm_params, **kwargs}
+    return tqdm(*args, **kwargs)
+
+
+def _run_serial(func, configs, config_are_kwargs, desc):
+    LOGGER.debug("Entering _run_serial")
+
+    if config_are_kwargs:
+        LOGGER.debug("Treating config as kwargs")
+        res = [func(**a) for a in progress(configs, desc=desc)]
+    else:
+        LOGGER.debug("Treating config as args")
+        res = [func(a) for a in progress(configs, desc=desc)]
+
+    LOGGER.debug("Exiting _run_serial")
+    return res
+
+
+def _run_parallel(  # pylint:disable=too-many-arguments
+    pool, timeout, func, configs, config_are_kwargs, desc, bar_start
+):
+    LOGGER.debug("Entering _run_parallel")
+
+    if config_are_kwargs:
+        LOGGER.debug("Treating config as kwargs")
+        futures = [pool.submit(func, **a) for a in configs]
+    else:
+        LOGGER.debug("Treating config as args")
+        futures = [pool.submit(func, a) for a in configs]
+
+    LOGGER.debug("Waiting for jobs to complete")
+    for i, future in progress(
+        enumerate(as_completed(futures, timeout=timeout)), total=len(futures), desc=desc
+    ):
+        if future.exception() is not None:
+            time.sleep(2)  # let buffer flush out
+            print(
+                "One of the processes failed, see error below (was something "
+                "unable to be pickled?)"
+            )
+            raise future.exception()
+
+        LOGGER.debug("Job %s completed", i + bar_start)
+
+    res = []
+
+    LOGGER.debug("Collecting results")
+    for i, future in enumerate(futures):
+        try:
+            res.append(future.result())
+            LOGGER.debug("Retrived result %s", i + bar_start)
+        except Exception as exc:  # pylint:disable=broad-except
+            LOGGER.debug("Retrieving result %s failed", i + bar_start)
+            res.append(exc)
+
+    LOGGER.debug("Exiting _run_parallel")
+    return res
+
+
+def _parallel_process(  # pylint:disable=too-many-arguments
+    func,
+    configuration,
+    pool=None,
+    config_are_kwargs=False,
+    front_serial=front_serial,
+    front_parallel=front_parallel,
+    timeout=None,
+):
+    """
+    Run a process in parallel with a progress bar.
+    Adapted from http://danshiebler.com/2016-09-14-parallel-progress-bar/
+    Parameters
+    ----------
+    func : function
+        A function to apply to each set of arguments in ``configuration``
+    configuration : sequence
+        An array of configuration with which to run ``func``.
+    pool : :obj:`concurrent.futures.ProcessPoolExecutor`
+        Pool in which to execute the jobs. If ``None``, the jobs will be executed
+        serially in a single process (useful for debugging and benchmarking).
+    config_are_kwargs : bool
+        Are the elements of ``configuration`` intended to be used as keyword arguments
+        when calling ``func``.
+    front_serial : int
+        The number of iterations to run serially before kicking off the parallel job.
+        Useful for debugging.
+    front_parallel : int
+        The number of initial iterations to run parallel before kicking off the rest
+        of the parallel jobs. Useful for debugging (especially if pickling is
+        possible).
+    timeout : float
+        How long to wait for processes to complete before timing out. If
+        ``None``, there is no timeout limit.
+    Returns
+    -------
+    sequence
+        Results of calling ``func`` with each configuration in ``configuration``
+    """
+    front_serial_res = []
+    if front_serial > 0:
+        LOGGER.debug("Running front serial jobs")
+        front_serial_res = _run_serial(
+            func=func,
+            configs=configuration[:front_serial],
+            config_are_kwargs=config_are_kwargs,
+            desc="Front serial",
+        )
+
+    if pool is None:
+        LOGGER.info("No pool provided, running rest of the jobs serially and returning")
+        rest = _run_serial(
+            func=func,
+            configs=configuration[front_serial:],
+            config_are_kwargs=config_are_kwargs,
+            desc="Serial runs",
+        )
+
+        return rest + front_serial_res
+
+    front_parallel_res = []
+    if front_parallel > 0:
+        LOGGER.debug("Running front parallel jobs")
+        front_parallel_res = _run_parallel(
+            pool=pool,
+            timeout=timeout,
+            func=func,
+            configs=configuration[front_serial : front_serial + front_parallel],
+            config_are_kwargs=config_are_kwargs,
+            desc="Front parallel",
+            bar_start=front_serial,
+        )
+
+    LOGGER.debug("Running rest of parallel jobs")
+    rest = _run_parallel(
+        pool=pool,
+        timeout=timeout,
+        func=func,
+        configs=configuration[front_serial + front_parallel :],
+        config_are_kwargs=config_are_kwargs,
+        desc="Parallel runs",
+        bar_start=front_serial + front_parallel,
+    )
+
+    return front_serial_res + front_parallel_res + rest
diff --git a/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/01_climate-response-sampling-ebm3.py b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/01_climate-response-sampling-ebm3.py
new file mode 100755
index 0000000..68e2516
--- /dev/null
+++ b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/01_climate-response-sampling-ebm3.py
@@ -0,0 +1,215 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+"""Climate response calibrations"""
+# The purpose here is to provide correlated calibrations to the climate response in
+# CMIP6 models.
+#
+# We will apply a very naive model weighting to the 4xCO2 results. We won't downweight
+# for similar models*, but we will only select one ensemble member from models that
+# provide multiple runs (the ensemble member that I deem the most reliable).
+#
+# *maybe the same model at different resolution should be downweighted.
+
+import os
+import warnings
+
+import matplotlib.pyplot as pl
+import numpy as np
+import pandas as pd
+import scipy.linalg
+import scipy.stats
+from dotenv import load_dotenv
+from fair.energy_balance_model import EnergyBalanceModel
+from tqdm import tqdm
+
+warnings.simplefilter("error", RuntimeWarning)
+
+load_dotenv()
+
+print("Making climate response calibrations...")
+
+cal_v = os.getenv("CALIBRATION_VERSION")
+fair_v = os.getenv("FAIR_VERSION")
+constraint_set = os.getenv("CONSTRAINT_SET")
+samples = int(os.getenv("PRIOR_SAMPLES"))
+plots = os.getenv("PLOTS", "False").lower() in ("true", "1", "t")
+pl.style.use("../../../../../defaults.mplstyle")
+progress = os.getenv("PROGRESS", "False").lower() in ("true", "1", "t")
+
+df = pd.read_csv(
+    os.path.join(
+        f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/calibrations/"
+        "4xCO2_cummins_ebm3_cmip6.csv"
+    )
+)
+models = df["model"].unique()
+
+# NorESM2-LM is currently INCLUDED. Comment below left in for train-of-thought.
+# Executive decision: remove NorESM2-LM. It is always incredibly difficult to calibrate.
+# Sometimes it fails completely, sometimes it gives nonsense values that wreck the rest
+# of the distribution.
+#
+# if "NorESM2-LM" in models:
+#     noresm2lm_index = np.argwhere(models=="NorESM2-LM")
+#     np.delete(models, noresm2lm_index)
+#     print("NorESM2-LM is being removed.")
+
+for model in models:
+    print(model, df.loc[df["model"] == model, "run"].values)
+
+n_models = len(models)
+
+multi_runs = {
+    "GISS-E2-1-G": "r1i1p1f1",
+    "GISS-E2-1-H": "r1i1p3f1",
+    "MRI-ESM2-0": "r1i1p1f1",
+    "EC-Earth3": "r3i1p1f1",
+    "FIO-ESM-2-0": "r1i1p1f1",
+    "CanESM5": "r1i1p2f1",
+    "FGOALS-f3-L": "r1i1p1f1",
+    "CNRM-ESM2-1": "r1i1p1f2",
+}
+
+params = {}
+
+params[r"$\gamma$"] = np.ones(n_models) * np.nan
+params["$c_1$"] = np.ones(n_models) * np.nan
+params["$c_2$"] = np.ones(n_models) * np.nan
+params["$c_3$"] = np.ones(n_models) * np.nan
+params[r"$\kappa_1$"] = np.ones(n_models) * np.nan
+params[r"$\kappa_2$"] = np.ones(n_models) * np.nan
+params[r"$\kappa_3$"] = np.ones(n_models) * np.nan
+params[r"$\epsilon$"] = np.ones(n_models) * np.nan
+params[r"$\sigma_{\eta}$"] = np.ones(n_models) * np.nan
+params[r"$\sigma_{\xi}$"] = np.ones(n_models) * np.nan
+params[r"$F_{4\times}$"] = np.ones(n_models) * np.nan
+
+for im, model in enumerate(models):
+    if model in multi_runs:
+        condition = (df["model"] == model) & (df["run"] == multi_runs[model])
+    else:
+        condition = df["model"] == model
+    params[r"$\gamma$"][im] = df.loc[condition, "gamma"].values[0]
+    params["$c_1$"][im], params["$c_2$"][im], params["$c_3$"][im] = df.loc[
+        condition, "C1":"C3"
+    ].values.squeeze()
+    (
+        params[r"$\kappa_1$"][im],
+        params[r"$\kappa_2$"][im],
+        params[r"$\kappa_3$"][im],
+    ) = df.loc[condition, "kappa1":"kappa3"].values.squeeze()
+    params[r"$\epsilon$"][im] = df.loc[condition, "epsilon"].values[0]
+    params[r"$\sigma_{\eta}$"][im] = df.loc[condition, "sigma_eta"].values[0]
+    params[r"$\sigma_{\xi}$"][im] = df.loc[condition, "sigma_xi"].values[0]
+    params[r"$F_{4\times}$"][im] = df.loc[condition, "F_4xCO2"].values[0]
+
+params = pd.DataFrame(params)
+print(params.corr())
+
+if plots:
+    fig = pl.figure(figsize=(18 / 2.54, 13 / 2.54))
+    pd.plotting.scatter_matrix(params)
+    pl.suptitle("Distributions and correlations of CMIP6 calibrations")
+    pl.tight_layout()
+    pl.subplots_adjust(wspace=0, hspace=0)
+    os.makedirs(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/", exist_ok=True
+    )
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "ebm3_distributions.png"
+    )
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "ebm3_distributions.pdf"
+    )
+    pl.close()
+
+NINETY_TO_ONESIGMA = scipy.stats.norm.ppf(0.95)
+
+kde = scipy.stats.gaussian_kde(params.T)
+ebm_sample = kde.resample(size=int(samples * 4), seed=2181882)
+
+# remove unphysical combinations
+for col in range(10):
+    ebm_sample[:, ebm_sample[col, :] <= 0] = np.nan
+ebm_sample[:, ebm_sample[0, :] <= 0.5] = np.nan  # gamma
+ebm_sample[:, ebm_sample[1, :] <= 1.8] = np.nan  # C1
+ebm_sample[:, ebm_sample[2, :] <= ebm_sample[1, :]] = np.nan  # C2
+ebm_sample[:, ebm_sample[3, :] <= ebm_sample[2, :]] = np.nan  # C3
+ebm_sample[:, ebm_sample[4, :] <= 0.3] = np.nan  # kappa1 = lambda
+
+mask = np.all(np.isnan(ebm_sample), axis=0)
+ebm_sample = ebm_sample[:, ~mask]
+
+# check that covariance matrix is positive semidefinite and if not, remove param combo.
+# to do: change away from sparse, once we move away from R
+for isample in tqdm(range(len(ebm_sample.T)), disable=1 - progress):
+    ebm = EnergyBalanceModel(
+        ocean_heat_capacity=ebm_sample[1:4, isample],
+        ocean_heat_transfer=ebm_sample[4:7, isample],
+        deep_ocean_efficacy=ebm_sample[7, isample],
+        gamma_autocorrelation=ebm_sample[0, isample],
+        sigma_xi=ebm_sample[9, isample],
+        sigma_eta=ebm_sample[8, isample],
+        forcing_4co2=ebm_sample[10, isample],
+        stochastic_run=True,
+    )
+    eb_matrix = ebm._eb_matrix()
+    q_mat = np.zeros((4, 4))
+    q_mat[0, 0] = ebm.sigma_eta**2
+    q_mat[1, 1] = (ebm.sigma_xi / ebm.ocean_heat_capacity[0]) ** 2
+    h_mat = np.zeros((8, 8))
+    h_mat[:4, :4] = -eb_matrix
+    h_mat[:4, 4:] = q_mat
+    h_mat[4:, 4:] = eb_matrix.T
+    g_mat = scipy.sparse.linalg.expm(h_mat)
+    q_mat_d = g_mat[4:, 4:].T @ g_mat[:4, 4:]
+    q_mat_d = q_mat_d.astype(np.float64)
+
+    # I can't work out exactly what checks scipy is doing to decide the param
+    # set is a fail. Best to just let it tell me if it likes it or not.
+    try:
+        scipy.stats.multivariate_normal.rvs(size=1, mean=np.zeros(4), cov=q_mat_d)
+    except:  # noqa: E722
+        ebm_sample[:, isample] = np.nan
+
+mask = np.all(np.isnan(ebm_sample), axis=0)
+ebm_sample = ebm_sample[:, ~mask]
+
+print("Total number of retained samples:", len(ebm_sample.T))
+
+ebm_sample_df = pd.DataFrame(
+    data=ebm_sample[:, :samples].T,
+    columns=[
+        "gamma",
+        "c1",
+        "c2",
+        "c3",
+        "kappa1",
+        "kappa2",
+        "kappa3",
+        "epsilon",
+        "sigma_eta",
+        "sigma_xi",
+        "F_4xCO2",
+    ],
+)
+
+assert len(ebm_sample_df) >= samples
+
+os.makedirs(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/priors/",
+    exist_ok=True,
+)
+
+ebm_sample_df.to_csv(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/priors/"
+    "climate_response_ebm3.csv",
+    index=False,
+)
+
+# what we do want to do is to scale the variability in 4xCO2 (correlated with the other
+# EBM parameters)
+# to feed into the effective radiative forcing scaling factor.
diff --git a/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/02_erfaci-sampling-smith2023.py b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/02_erfaci-sampling-smith2023.py
new file mode 100755
index 0000000..210e2b2
--- /dev/null
+++ b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/02_erfaci-sampling-smith2023.py
@@ -0,0 +1,309 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+"""Sample aerosol indirect."""
+
+# **Note also** the uniform prior from -2 to 0. A lot of the sublteties here might also
+# want to go into the paper.
+
+
+import glob
+import os
+
+import matplotlib.pyplot as pl
+import numpy as np
+import pandas as pd
+import pooch
+import scipy.stats
+from dotenv import load_dotenv
+from scipy.optimize import curve_fit
+from tqdm import tqdm
+
+load_dotenv()
+
+pl.style.use("../../../../../defaults.mplstyle")
+
+cal_v = os.getenv("CALIBRATION_VERSION")
+fair_v = os.getenv("FAIR_VERSION")
+constraint_set = os.getenv("CONSTRAINT_SET")
+samples = int(os.getenv("PRIOR_SAMPLES"))
+plots = os.getenv("PLOTS", "False").lower() in ("true", "1", "t")
+progress = os.getenv("PROGRESS", "False").lower() in ("true", "1", "t")
+datadir = os.getenv("DATADIR")
+
+print("Sampling aerosol cloud interactions...")
+
+
+files = glob.glob("../../../../../data/smith2023aerosol/*.csv")
+
+ari = {}
+aci = {}
+models = []
+models_runs = {}
+years = {}
+for file in files:
+    model = os.path.split(file)[1].split("_")[0]
+    run = os.path.split(file)[1].split("_")[1]
+    models.append(model)
+    if run not in models_runs:
+        models_runs[model] = []
+    models_runs[model].append(run)
+
+models = list(models_runs.keys())
+
+for model in models:
+    nruns = 0
+    for run in models_runs[model]:
+        file = f"../../../../../data/smith2023aerosol/{model}_{run}_aerosol_forcing.csv"
+        df = pd.read_csv(file, index_col=0)
+        if nruns == 0:
+            ari_temp = df["ERFari"].values.squeeze()
+            aci_temp = df["ERFaci"].values.squeeze()
+        else:
+            ari_temp = ari_temp + df["ERFari"].values.squeeze()
+            aci_temp = aci_temp + df["ERFaci"].values.squeeze()
+        years[model] = df.index + 0.5
+        nruns = nruns + 1
+    ari[model] = ari_temp / nruns
+    aci[model] = aci_temp / nruns
+
+
+# Calibrate on RCMIP
+rcmip_emissions_file = pooch.retrieve(
+    url=(
+        "https://zenodo.org/records/4589756/files/"
+        "rcmip-emissions-annual-means-v5-1-0.csv"
+    ),
+    known_hash="md5:4044106f55ca65b094670e7577eaf9b3",
+    progressbar=progress,
+    path=datadir,
+)
+
+emis_df = pd.read_csv(rcmip_emissions_file)
+
+bc = (
+    emis_df.loc[
+        (emis_df["Scenario"] == "ssp245")
+        & (emis_df["Region"] == "World")
+        & (emis_df["Variable"] == "Emissions|BC"),
+        "1750":"2100",
+    ]
+    .interpolate(axis=1)
+    .squeeze()
+    .values
+)
+
+oc = (
+    emis_df.loc[
+        (emis_df["Scenario"] == "ssp245")
+        & (emis_df["Region"] == "World")
+        & (emis_df["Variable"] == "Emissions|OC"),
+        "1750":"2100",
+    ]
+    .interpolate(axis=1)
+    .squeeze()
+    .values
+)
+
+so2 = (
+    emis_df.loc[
+        (emis_df["Scenario"] == "ssp245")
+        & (emis_df["Region"] == "World")
+        & (emis_df["Variable"] == "Emissions|Sulfur"),
+        "1750":"2100",
+    ]
+    .interpolate(axis=1)
+    .squeeze()
+    .values
+)
+
+
+def aci_log(x, beta, n0, n1, n2):
+    aci = beta * np.log(1 + x[0] * n0 + x[1] * n1 + x[2] * n2)
+    aci_1850 = beta * np.log(1 + so2[100] * n0 + bc[100] * n1 + oc[100] * n2)
+    return aci - aci_1850
+
+
+param_fits = {}
+
+for model in models:
+    ist = int(np.floor(years[model][0] - 1750))
+    ien = int(np.ceil(years[model][-1] - 1750))
+    param_fits[model], cov = curve_fit(
+        aci_log,
+        [so2[ist:ien], bc[ist:ien], oc[ist:ien]],
+        aci[model],
+        bounds=((-np.inf, 0, 0, 0), (0, np.inf, np.inf, np.inf)),
+        max_nfev=10000,
+    )
+
+
+def aci_log_nocorrect(x, beta, n0, n1, n2):
+    aci = beta * np.log(1 + x[0] * n0 + x[1] * n1 + x[2] * n2)
+    return aci
+
+
+if plots:
+    colors = {
+        "CanESM5": "red",
+        "CNRM-CM6-1": "orangered",
+        "E3SM-2-0": "darkorange",
+        "GFDL-ESM4": "yellowgreen",
+        "GFDL-CM4": "yellow",
+        "GISS-E2-1-G": "green",
+        "HadGEM3-GC31-LL": "turquoise",
+        "IPSL-CM6A-LR": "teal",
+        "MIROC6": "blue",
+        "MPI-ESM-1-2-HAM": "darkslateblue",
+        "MRI-ESM2-0": "blueviolet",
+        "NorESM2-LM": "purple",
+        "UKESM1-0-LL": "crimson",
+        "mean": "black",
+    }
+
+    fig, ax = pl.subplots(4, 4, figsize=(18 / 2.54, 12 / 2.54), squeeze=False)
+    for imodel, model in enumerate(sorted(models, key=str.lower)):
+        i = imodel // 4
+        j = imodel % 4
+        ax[i, j].plot(years[model], aci[model], color="k", ls="-", alpha=0.5, lw=1)
+        ax[i, j].plot(
+            np.arange(1750.5, 2101),
+            aci_log([so2, bc, oc], *param_fits[model]),
+            color=colors[model],
+            zorder=7,
+        )
+
+        ax[i, j].set_xlim(1750, 2100)
+        ax[i, j].set_ylim(-1.7, 0.5)
+        ax[i, j].axhline(0, lw=0.5, ls=":", color="k")
+        ax[i, j].fill_between(
+            np.arange(1850, 2015), -10, 10, color="#e0e0e0", zorder=-20
+        )
+        ax[i, j].get_xticklabels()[-1].set_ha("right")
+        if model == "HadGEM3-GC31-LL":
+            modlab = "HadGEM3"
+        elif model == "MPI-ESM-1-2-HAM":
+            modlab = "MPI-ESM1-2"
+        else:
+            modlab = model
+        ax[i, j].text(
+            0.03, 0.05, modlab, transform=ax[i, j].transAxes, fontweight="bold"
+        )
+
+    ax[0, 0].set_ylabel("W m$^{-2}$")
+    ax[1, 0].set_ylabel("W m$^{-2}$")
+    ax[2, 0].set_ylabel("W m$^{-2}$")
+    ax[3, 0].set_ylabel("W m$^{-2}$")
+    ax[3, 1].axis("off")
+    ax[3, 2].axis("off")
+    ax[3, 3].axis("off")
+
+    pl.suptitle("Aerosol-cloud interactions parameterisations")
+
+    fig.tight_layout()
+    os.makedirs(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}", exist_ok=True
+    )
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "aci_calibration.png"
+    )
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "aci_calibration.pdf"
+    )
+    pl.close()
+
+df_params = pd.DataFrame(param_fits, index=["aci_scale", "Sulfur", "BC", "OC"]).T
+
+df_params.to_csv(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/calibrations/"
+    "aerosol_cloud.csv"
+)
+
+print("Correlation coefficients between aci parameters")
+print(df_params.corr())
+
+beta_samp = df_params["aci_scale"]
+n0_samp = df_params["Sulfur"]
+n1_samp = df_params["BC"]
+n2_samp = df_params["OC"]
+
+kde = scipy.stats.gaussian_kde(
+    [np.log(n0_samp), np.log(n1_samp), np.log(n2_samp)], bw_method=0.1
+)
+aci_sample = kde.resample(size=samples * 1, seed=63648708)
+
+# trapezoid distribution [-2.2, -1.6, -1.0, -0.4, +0.2]
+erfaci_sample = scipy.stats.trapezoid.rvs(
+    0.25, 0.75, size=samples, loc=-2.2, scale=2.4, random_state=71271
+)
+
+# Sampling with updated emissions.
+df_emis_obs = pd.read_csv(
+    "../../../../../data/emissions/"
+    "CH4Pledge-rcmip-emissions-ssp245-CLE-MFR-filled.csv",
+)
+
+# overwrite RCMIP
+so2 = df_emis_obs.loc[
+    (df_emis_obs["scenario"] == "MESSAGE-GLOBIOM___SSP2-45-CLE-CH4")
+    & (df_emis_obs["variable"] == "Sulfur")
+    & (df_emis_obs["region"] == "World"),
+    "1750.5":"2100.5",
+].interpolate(axis=1).values.squeeze()
+
+bc = df_emis_obs.loc[
+    (df_emis_obs["scenario"] == "MESSAGE-GLOBIOM___SSP2-45-CLE-CH4")
+    & (df_emis_obs["variable"] == "BC")
+    & (df_emis_obs["region"] == "World"),
+    "1750.5":"2100.5",
+].interpolate(axis=1).values.squeeze()
+
+oc = df_emis_obs.loc[
+    (df_emis_obs["scenario"] == "MESSAGE-GLOBIOM___SSP2-45-CLE-CH4")
+    & (df_emis_obs["variable"] == "OC")
+    & (df_emis_obs["region"] == "World"),
+    "1750.5":"2100.5",
+].interpolate(axis=1).values.squeeze()
+
+beta = np.zeros(samples)
+# erfaci = np.zeros((273, samples))
+for i in tqdm(range(samples), desc="aci samples", disable=1 - progress):
+    ts2010 = np.mean(
+        aci_log_nocorrect(
+            [so2[255:265], bc[255:265], oc[255:265]],
+            1,
+            np.exp(aci_sample[0, i]),
+            np.exp(aci_sample[1, i]),
+            np.exp(aci_sample[2, i]),
+        )
+    )
+    ts1750 = aci_log_nocorrect(
+        [so2[0], bc[0], oc[0]],
+        1,
+        np.exp(aci_sample[0, i]),
+        np.exp(aci_sample[1, i]),
+        np.exp(aci_sample[2, i]),
+    )
+    beta[i] = erfaci_sample[i] / (ts2010 - ts1750)
+
+df = pd.DataFrame(
+    {
+        "shape_so2": np.exp(aci_sample[0, :samples]),
+        "shape_bc": np.exp(aci_sample[1, :samples]),
+        "shape_oc": np.exp(aci_sample[2, :samples]),
+        "beta": beta,
+    }
+)
+
+os.makedirs(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/priors/",
+    exist_ok=True,
+)
+
+df.to_csv(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/priors/"
+    "aerosol_cloud.csv",
+    index=False,
+)
\ No newline at end of file
diff --git a/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/03_erfari-sampling-ar6ch6.py b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/03_erfari-sampling-ar6ch6.py
new file mode 100755
index 0000000..4cc62f2
--- /dev/null
+++ b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/03_erfari-sampling-ar6ch6.py
@@ -0,0 +1,325 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+"""Aerosol-radiation calibration."""
+# In AR6, ERFari was based on emissions to forcing coefficients from Myhre et al (2013)
+# https://acp.copernicus.org/articles/13/1853/2013/. At the time, I deemed there not
+# sufficient evidence from CMIP6 AerChemMIP models or any other sources to update these.
+# The uncertainty ranges from each precursor were expanded slightly compared to Myhre et
+# al., in order to reproduce the overall ERFari uncertainty assessment (assumed that
+# uncertainties in individual components are uncorrelated).
+
+# Following AR6 and a re-calibration of FaIR, I now use Bill Collins/Terje Bertnsen/
+# Sara Blichner/Sophie Szopa's chapter 6 correspondences of emissions or concentrations
+# to forcing.
+
+# Rescale to the assessed forcings of -0.3 W/m2 for ERFari 2005-14
+
+import os
+
+import numpy as np
+import pandas as pd
+import pooch
+import scipy.stats
+from dotenv import load_dotenv
+
+load_dotenv()
+
+cal_v = os.getenv("CALIBRATION_VERSION")
+fair_v = os.getenv("FAIR_VERSION")
+constraint_set = os.getenv("CONSTRAINT_SET")
+samples = int(os.getenv("PRIOR_SAMPLES"))
+progress = os.getenv("PROGRESS", "False").lower() in ("true", "1", "t")
+datadir = os.getenv("DATADIR")
+
+rcmip_concentration_file = pooch.retrieve(
+    url=("doi:10.5281/zenodo.4589756/" "rcmip-concentrations-annual-means-v5-1-0.csv"),
+    known_hash="md5:0d82c3c3cdd4dd632b2bb9449a5c315f",
+    progressbar=progress,
+    path=datadir,
+)
+
+df_emis = pd.read_csv("../../../../../data/emissions/CH4Pledge-rcmip-emissions-ssp245-CLE-MFR-filled.csv")
+df_conc = pd.read_csv(rcmip_concentration_file)
+
+# these are the present day ERFari which comes from AR6 WG1
+# source: https://github.com/sarambl/AR6_CH6_RCMIPFIGS/blob/master/ar6_ch6_rcmipfigs/
+# data_out/fig6_12_ts15_historic_delta_GSAT/2019_ERF_est.csv
+# they sum to exactly -0.22 W/m2, for 2019
+# Calculate a radiative efficiency for each species from CEDS and updated
+# concentrations.
+df_ari_emitted_mean = pd.read_csv(
+    "../../../../../data/forcing/table_mean_thornhill_collins_orignames.csv",
+    index_col=0,
+)
+erfari_emitted = pd.Series(df_ari_emitted_mean["Aerosol"])
+erfari_emitted.rename_axis(None, inplace=True)
+erfari_emitted.rename(
+    {"HC": "Equivalent effective stratospheric chlorine", "SO2": "Sulfur"}, inplace=True
+)
+
+df_ari_emitted_std = pd.read_csv(
+    "../../../../../data/forcing/table_std_thornhill_collins_orignames.csv", index_col=0
+)
+erfari_emitted_std = pd.Series(df_ari_emitted_std["Aerosol_sd"])
+erfari_emitted_std.rename_axis(None, inplace=True)
+erfari_emitted_std.rename(
+    {"HC": "Equivalent effective stratospheric chlorine", "SO2": "Sulfur"}, inplace=True
+)
+
+emitted_species = [
+    "Sulfur",
+    "BC",
+    "OC",
+    "NH3",
+    "NOx",
+    "VOC",
+]
+
+concentration_species = [
+    "CH4",
+    "N2O",
+    "CFC-11",
+    "CFC-12",
+    "CFC-113",
+    "CFC-114",
+    "CFC-115",
+    "HCFC-22",
+    "HCFC-141b",
+    "HCFC-142b",
+    "CCl4",
+    "CHCl3",
+    "CH2Cl2",
+    "CH3Cl",
+    "CH3CCl3",
+    "CH3Br",
+    "Halon-1211",
+    "Halon-1301",
+    "Halon-2402",
+]
+
+
+hc_species = [
+    "CFC-11",
+    "CFC-12",
+    "CFC-113",
+    "CFC-114",
+    "CFC-115",
+    "HCFC-22",
+    "HCFC-141b",
+    "HCFC-142b",
+    "CCl4",
+    "CHCl3",
+    "CH2Cl2",
+    "CH3Cl",
+    "CH3CCl3",
+    "CH3Br",
+    "Halon-1211",
+    "Halon-1301",
+    "Halon-2402",
+]
+
+species_out = {}
+for ispec, species in enumerate(emitted_species):
+    emis_in = (
+        df_emis.loc[
+            (df_emis["scenario"] == "MESSAGE-GLOBIOM___SSP2-45-CLE-CH4")
+            & (df_emis["variable"] == species)
+            & (df_emis["region"] == "World"),
+            "1750.5":"2100.5",
+        ]
+        .interpolate(axis=1)
+        .values.squeeze()
+    )
+    species_out[species] = emis_in
+
+for ispec, species in enumerate(concentration_species):
+    species_rcmip_name = species.replace("-", "")
+    conc_in = (
+        df_conc.loc[
+            (df_conc["Scenario"] == "ssp245")
+            & (df_conc["Variable"].str.endswith("|" + species_rcmip_name))
+            & (df_conc["Region"] == "World"),
+            "1750":"2100",
+        ]
+        .interpolate(axis=1)
+        .values.squeeze()
+    )
+    species_out[species] = conc_in
+
+species_df = pd.DataFrame(species_out, index=range(1750, 2101))
+
+
+def calculate_eesc(
+    concentration,
+    fractional_release,
+    fractional_release_cfc11,
+    cl_atoms,
+    br_atoms,
+    br_cl_ratio=45,
+):
+    # EESC is in terms of CFC11-eq
+    eesc_out = (
+        cl_atoms * (concentration) * fractional_release / fractional_release_cfc11
+        + br_cl_ratio
+        * br_atoms
+        * (concentration)
+        * fractional_release
+        / fractional_release_cfc11
+    ) * fractional_release_cfc11
+    return eesc_out
+
+
+fractional_release = {
+    "CFC-11": 0.47,
+    "CFC-12": 0.23,
+    "CFC-113": 0.29,
+    "CFC-114": 0.12,
+    "CFC-115": 0.04,
+    "HCFC-22": 0.13,
+    "HCFC-141b": 0.34,
+    "HCFC-142b": 0.17,
+    "CCl4": 0.56,
+    "CHCl3": 0,
+    "CH2Cl2": 0,
+    "CH3Cl": 0.44,
+    "CH3CCl3": 0.67,
+    "CH3Br": 0.6,
+    "Halon-1211": 0.62,
+    "Halon-1301": 0.28,
+    "Halon-2402": 0.65,
+}
+
+cl_atoms = {
+    "CFC-11": 3,
+    "CFC-12": 2,
+    "CFC-113": 3,
+    "CFC-114": 2,
+    "CFC-115": 1,
+    "HCFC-22": 1,
+    "HCFC-141b": 2,
+    "HCFC-142b": 1,
+    "CCl4": 4,
+    "CHCl3": 3,
+    "CH2Cl2": 2,
+    "CH3Cl": 1,
+    "CH3CCl3": 3,
+    "CH3Br": 0,
+    "Halon-1211": 1,
+    "Halon-1301": 0,
+    "Halon-2402": 0,
+}
+
+br_atoms = {
+    "CFC-11": 0,
+    "CFC-12": 0,
+    "CFC-113": 0,
+    "CFC-114": 0,
+    "CFC-115": 0,
+    "HCFC-22": 0,
+    "HCFC-141b": 0,
+    "HCFC-142b": 0,
+    "CCl4": 0,
+    "CHCl3": 0,
+    "CH2Cl2": 0,
+    "CH3Cl": 0,
+    "CH3CCl3": 0,
+    "CH3Br": 1,
+    "Halon-1211": 1,
+    "Halon-1301": 1,
+    "Halon-2402": 2,
+}
+
+
+hc_eesc = {}
+total_eesc = np.zeros(351)
+for species in cl_atoms:
+    hc_eesc[species] = calculate_eesc(
+        species_df.loc[:, species],
+        fractional_release[species],
+        fractional_release["CFC-11"],
+        cl_atoms[species],
+        br_atoms[species],
+    )
+    total_eesc = total_eesc + hc_eesc[species]
+
+# erfari radiative efficiency per Mt or ppb or ppt
+re = erfari_emitted / (species_df.loc[2019, :] - species_df.loc[1750, :])
+re.dropna(inplace=True)
+
+re["Equivalent effective stratospheric chlorine"] = erfari_emitted[
+    "Equivalent effective stratospheric chlorine"
+] / (total_eesc.loc[2019] - total_eesc.loc[1750])
+
+re_std = erfari_emitted_std / (species_df.loc[2019, :] - species_df.loc[1750, :])
+re_std.dropna(inplace=True)
+re_std["Equivalent effective stratospheric chlorine"] = erfari_emitted_std[
+    "Equivalent effective stratospheric chlorine"
+] / (total_eesc.loc[2019] - total_eesc.loc[1750])
+
+print(re)
+print(re_std)
+
+erfari_best = (
+    pd.concat(
+        (
+            (re * species_df)[["BC", "OC", "Sulfur", "NOx", "VOC", "NH3", "CH4", "N2O"]]
+            - (
+                re
+                * species_df.loc[
+                    1750, ["BC", "OC", "Sulfur", "NOx", "VOC", "NH3", "CH4", "N2O"]
+                ]
+            ),
+            re["Equivalent effective stratospheric chlorine"]
+            * (total_eesc - total_eesc.loc[1750]),
+        ),
+        axis=1,
+    )
+    .dropna(axis=1)
+    .sum(axis=1)
+)
+
+
+# we need to map the 2019 mean and stdev to -0.3 +/- 0.3 for 2005-2014 which is the
+# IPCC AR6 assessment
+NINETY_TO_ONESIGMA = scipy.stats.norm.ppf(0.95)
+best_scale = -0.3 / erfari_best.loc[2005:2014].mean()
+unc_scale = 0.3 / (
+    (erfari_best.loc[2005:2014].mean() / -0.22)
+    * np.sqrt((erfari_emitted_std**2).sum())
+    * NINETY_TO_ONESIGMA
+)
+
+# convert to numpy for efficiency
+erfari_re_samples = pd.DataFrame(
+    scipy.stats.norm.rvs(
+        re * best_scale,
+        re_std * unc_scale,
+        size=(samples, 9),
+        random_state=3729329,
+    ),
+    columns=re.index,
+)[
+    [
+        "BC",
+        "OC",
+        "Sulfur",
+        "NOx",
+        "VOC",
+        "NH3",
+        "CH4",
+        "N2O",
+        "Equivalent effective stratospheric chlorine",
+    ]
+]
+
+os.makedirs(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/priors/",
+    exist_ok=True,
+)
+
+erfari_re_samples.to_csv(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/priors/"
+    "aerosol_radiation.csv",
+    index=False,
+)
diff --git a/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/04_carbon-cycle-sampling-leach2021.py b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/04_carbon-cycle-sampling-leach2021.py
new file mode 100755
index 0000000..5b99c17
--- /dev/null
+++ b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/04_carbon-cycle-sampling-leach2021.py
@@ -0,0 +1,131 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+"""Check and run carbon cycle calibrations."""
+#
+# Carbon cycle tunings for 11 C4MIP models are from FaIR 2.0 paper (Leach et al. 2021),
+# calibrated on CMIP6 1pct runs. Let's see if they give reasonable concentrations in
+# emissions-driven mode.
+
+import os
+
+import matplotlib.pyplot as pl
+import numpy as np
+import pandas as pd
+import scipy.stats
+from dotenv import load_dotenv
+from fair import __version__
+from fair.structure.units import compound_convert
+
+load_dotenv()
+
+cal_v = os.getenv("CALIBRATION_VERSION")
+fair_v = os.getenv("FAIR_VERSION")
+constraint_set = os.getenv("CONSTRAINT_SET")
+samples = int(os.getenv("PRIOR_SAMPLES"))
+plots = os.getenv("PLOTS", "False").lower() in ("true", "1", "t")
+pl.style.use("../../../../../defaults.mplstyle")
+
+
+print("Making carbon cycle calibrations...")
+
+assert fair_v == __version__
+
+
+# NB: rU and rA are in GtC units, we need to convert to GtCO2
+data = np.array(
+    [
+        [
+            36.73854601035055,
+            25.589821019851797,
+            40.704695982343765,
+            38.09182601398885,
+            35.70573492682388,
+            34.26732262345922,
+            32.223599635483424,
+            33.39478016647172,
+            33.33937342916488,
+            40.735872526405046,
+            37.91594456570033,
+        ],
+        [
+            0.0349535801301073,
+            0.00597614250950862,
+            0.010664893971021883,
+            0.0005810081769186404,
+            -0.005958784801017192,
+            0.021861410870304354,
+            0.016608701817126814,
+            0.013104461258272693,
+            0.031043773610946346,
+            0.009471296196005063,
+            0.020138272127751655,
+        ],
+        [
+            3.036651884848311,
+            5.196160258410032,
+            1.2786398011433562,
+            2.472206604249436,
+            -0.10385375927186047,
+            4.855081881723322,
+            1.0693983052255476,
+            3.4644393974775767,
+            1.499323874009292,
+            1.5631932779473914,
+            2.6714005898495543,
+        ],
+        [
+            -0.0006603263192310749,
+            0.004393751681079472,
+            0.004211308668836011,
+            0.009783189726962682,
+            0.018116906645659014,
+            -0.004242277713558451,
+            0.012336113500092338,
+            0.003993779169272571,
+            -0.002570300844565665,
+            0.004887468785878646,
+            0.0018119017134572424,
+        ],
+    ]
+)
+data[1, :] = data[1, :] * compound_convert["CO2"]["C"]
+data[3, :] = data[3, :] * compound_convert["CO2"]["C"]
+
+models = [
+    "ACCESS-ESM1-5",
+    "BCC-CSM2-MR",
+    "CESM2",
+    "CNRM-ESM2-1",
+    "CanESM5",
+    "GFDL-ESM4",
+    "IPSL-CM6A-LR",
+    "MIROC-ES2L",
+    "MPI-ESM1-2-LR",
+    "NorESM2-LM",
+    "UKESM1-0-LL",
+]
+
+
+params = pd.DataFrame(data.T, columns=["r0", "rU", "rT", "rA"], index=models)
+
+
+kde = scipy.stats.gaussian_kde(params.T)
+cc_sample = kde.resample(size=int(samples), seed=2421911)
+
+mask = np.all(np.isnan(cc_sample), axis=0)
+cc_sample = cc_sample[:, ~mask]
+cc_sample_df = pd.DataFrame(
+    data=cc_sample[:, :samples].T, columns=["r0", "rU", "rT", "rA"]
+)
+
+os.makedirs(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/priors/",
+    exist_ok=True,
+)
+
+cc_sample_df.to_csv(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/priors/"
+    "carbon_cycle.csv",
+    index=False,
+)
diff --git a/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/05_ozone-sampling-ar6.py b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/05_ozone-sampling-ar6.py
new file mode 100755
index 0000000..65a2897
--- /dev/null
+++ b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/05_ozone-sampling-ar6.py
@@ -0,0 +1,441 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+"""Ozone calibration"""
+
+# concentrations are midyear rather than endyear so are six months out, but it won't
+# be a biggie.
+
+import copy
+import os
+
+import matplotlib.pyplot as pl
+import numpy as np
+import pandas as pd
+import pooch
+import scipy.stats
+from dotenv import load_dotenv
+from fair import __version__
+from scipy.interpolate import interp1d
+from scipy.optimize import curve_fit
+
+load_dotenv()
+
+pl.style.use("../../../../../defaults.mplstyle")
+
+cal_v = os.getenv("CALIBRATION_VERSION")
+fair_v = os.getenv("FAIR_VERSION")
+constraint_set = os.getenv("CONSTRAINT_SET")
+samples = int(os.getenv("PRIOR_SAMPLES"))
+plots = os.getenv("PLOTS", "False").lower() in ("true", "1", "t")
+progress = os.getenv("PROGRESS", "False").lower() in ("true", "1", "t")
+datadir = os.getenv("DATADIR")
+
+print("Doing ozone sampling...")
+
+# now include temperature feedback
+Tobs = pd.read_csv(
+    "../../../../../data/forcing/IGCC_GMST_1850-2022.csv", index_col=0
+).values
+
+delta_gmst = [
+    0,
+    Tobs[65:76].mean(),
+    Tobs[75:86].mean(),
+    Tobs[85:96].mean(),
+    Tobs[95:106].mean(),
+    Tobs[105:116].mean(),
+    Tobs[115:126].mean(),
+    Tobs[125:136].mean(),
+    Tobs[135:146].mean(),
+    Tobs[145:156].mean(),
+    Tobs[152:163].mean(),
+    Tobs[155:166].mean(),
+    Tobs[159:170].mean(),
+    Tobs[167].mean(),  # we don't use this
+    Tobs[168].mean(),
+]
+warming_pi_pd = Tobs[159:170].mean()
+
+assert fair_v == __version__
+
+good_models = [
+    "BCC-ESM1",
+    "CESM2(WACCM6)",
+    "GFDL-ESM4",
+    "GISS-E2-1-H",
+    "MRI-ESM2-0",
+    "OsloCTM3",
+]
+skeie_trop = pd.read_csv(
+    "../../../../../data/forcing/skeie_ozone_trop.csv", index_col=0
+)
+skeie_trop = skeie_trop.loc[good_models]
+skeie_trop.insert(0, 1850, 0)
+skeie_trop.columns = pd.to_numeric(skeie_trop.columns)
+skeie_trop.interpolate(axis=1, method="values", limit_area="inside", inplace=True)
+
+skeie_strat = pd.read_csv(
+    "../../../../../data/forcing/skeie_ozone_strat.csv", index_col=0
+)
+skeie_strat = skeie_strat.loc[good_models]
+skeie_strat.insert(0, 1850, 0)
+skeie_strat.columns = pd.to_numeric(skeie_strat.columns)
+skeie_strat.interpolate(axis=1, method="values", limit_area="inside", inplace=True)
+
+skeie_total = skeie_trop + skeie_strat
+
+coupled_models = copy.deepcopy(good_models)
+coupled_models.remove("OsloCTM3")
+
+skeie_total.loc[coupled_models] = skeie_total.loc[coupled_models] - (-0.037) * np.array(
+    delta_gmst
+)
+skeie_ssp245 = skeie_total.mean()
+skeie_ssp245[1750] = -0.03
+skeie_ssp245.sort_index(inplace=True)
+skeie_ssp245 = skeie_ssp245 + 0.03
+skeie_ssp245.drop([2014, 2017, 2020], inplace=True)
+skeie_ssp245 = pd.concat(
+    (
+        skeie_ssp245,
+        skeie_total.loc["OsloCTM3", 2014:]
+        - skeie_total.loc["OsloCTM3", 2010]
+        + skeie_ssp245[2010],
+    )
+)
+
+f = interp1d(
+    skeie_ssp245.index, skeie_ssp245, bounds_error=False, fill_value="extrapolate"
+)
+years = np.arange(1750, 2021)
+o3total = f(years)
+
+print("2014-1750 ozone ERF from Skeie:", o3total[264])
+print("2019-1750 ozone ERF from Skeie:", o3total[269])
+print("2014-1850 ozone ERF from Skeie:", o3total[264] - o3total[100])
+
+rcmip_concentration_file = pooch.retrieve(
+    url=("doi:10.5281/zenodo.4589756/" "rcmip-concentrations-annual-means-v5-1-0.csv"),
+    known_hash="md5:0d82c3c3cdd4dd632b2bb9449a5c315f",
+    progressbar=progress,
+    path=datadir,
+)
+
+df_emis = pd.read_csv("../../../../../data/emissions/CH4Pledge-rcmip-emissions-ssp245-CLE-MFR-filled.csv")
+df_conc = pd.read_csv(rcmip_concentration_file)
+
+emitted_species = [
+    "NOx",
+    "VOC",
+    "CO",
+]
+
+concentration_species = [
+    "CH4",
+    "N2O",
+    "CFC-11",
+    "CFC-12",
+    "CFC-113",
+    "CFC-114",
+    "CFC-115",
+    "HCFC-22",
+    "HCFC-141b",
+    "HCFC-142b",
+    "CCl4",
+    "CHCl3",
+    "CH2Cl2",
+    "CH3Cl",
+    "CH3CCl3",
+    "CH3Br",
+    "Halon-1211",
+    "Halon-1301",
+    "Halon-2402",
+]
+
+
+hc_species = [
+    "CFC-11",
+    "CFC-12",
+    "CFC-113",
+    "CFC-114",
+    "CFC-115",
+    "HCFC-22",
+    "HCFC-141b",
+    "HCFC-142b",
+    "CCl4",
+    "CHCl3",
+    "CH2Cl2",
+    "CH3Cl",
+    "CH3CCl3",
+    "CH3Br",
+    "Halon-1211",
+    "Halon-1301",
+    "Halon-2402",
+]
+
+species_out = {}
+for ispec, species in enumerate(emitted_species):
+    emis_in = (
+        df_emis.loc[
+            (df_emis["scenario"] == "MESSAGE-GLOBIOM___SSP2-45-CLE-CH4")
+            & (df_emis["variable"] == species)
+            & (df_emis["region"] == "World"),
+            "1750.5":"2020.5",
+        ]
+        .interpolate(axis=1)
+        .values.squeeze()
+    )
+    species_out[species] = emis_in[:-1]
+
+for ispec, species in enumerate(concentration_species):
+    species_rcmip_name = species.replace("-", "")
+    conc_in = (
+        df_conc.loc[
+            (df_conc["Scenario"] == "ssp245")
+            & (df_conc["Variable"].str.endswith("|" + species_rcmip_name))
+            & (df_conc["Region"] == "World"),
+            "1750":"2020",
+        ]
+        .interpolate(axis=1)
+        .values.squeeze()
+    )
+    species_out[species] = conc_in[:-1]
+
+print(species_out.keys())
+
+species_df = pd.DataFrame(species_out, index=range(1750, 2020))
+
+
+def calculate_eesc(
+    concentration,
+    fractional_release,
+    fractional_release_cfc11,
+    cl_atoms,
+    br_atoms,
+    br_cl_ratio=45,
+):
+    # EESC is in terms of CFC11-eq
+    eesc_out = (
+        cl_atoms * (concentration) * fractional_release / fractional_release_cfc11
+        + br_cl_ratio
+        * br_atoms
+        * (concentration)
+        * fractional_release
+        / fractional_release_cfc11
+    ) * fractional_release_cfc11
+    return eesc_out
+
+
+fractional_release = {
+    "CFC-11": 0.47,
+    "CFC-12": 0.23,
+    "CFC-113": 0.29,
+    "CFC-114": 0.12,
+    "CFC-115": 0.04,
+    "HCFC-22": 0.13,
+    "HCFC-141b": 0.34,
+    "HCFC-142b": 0.17,
+    "CCl4": 0.56,
+    "CHCl3": 0,
+    "CH2Cl2": 0,
+    "CH3Cl": 0.44,
+    "CH3CCl3": 0.67,
+    "CH3Br": 0.6,
+    "Halon-1211": 0.62,
+    "Halon-1301": 0.28,
+    "Halon-2402": 0.65,
+}
+
+cl_atoms = {
+    "CFC-11": 3,
+    "CFC-12": 2,
+    "CFC-113": 3,
+    "CFC-114": 2,
+    "CFC-115": 1,
+    "HCFC-22": 1,
+    "HCFC-141b": 2,
+    "HCFC-142b": 1,
+    "CCl4": 4,
+    "CHCl3": 3,
+    "CH2Cl2": 2,
+    "CH3Cl": 1,
+    "CH3CCl3": 3,
+    "CH3Br": 0,
+    "Halon-1211": 1,
+    "Halon-1301": 0,
+    "Halon-2402": 0,
+}
+
+br_atoms = {
+    "CFC-11": 0,
+    "CFC-12": 0,
+    "CFC-113": 0,
+    "CFC-114": 0,
+    "CFC-115": 0,
+    "HCFC-22": 0,
+    "HCFC-141b": 0,
+    "HCFC-142b": 0,
+    "CCl4": 0,
+    "CHCl3": 0,
+    "CH2Cl2": 0,
+    "CH3Cl": 0,
+    "CH3CCl3": 0,
+    "CH3Br": 1,
+    "Halon-1211": 1,
+    "Halon-1301": 1,
+    "Halon-2402": 2,
+}
+
+hc_eesc = {}
+total_eesc = 0
+for species in hc_species:
+    hc_eesc[species] = calculate_eesc(
+        species_df.loc[:, species],
+        fractional_release[species],
+        fractional_release["CFC-11"],
+        cl_atoms[species],
+        br_atoms[species],
+    )
+    total_eesc = total_eesc + hc_eesc[species].values
+
+delta_Cch4 = species_out["CH4"][264] - species_out["CH4"][0]
+delta_Cn2o = species_out["N2O"][264] - species_out["N2O"][0]
+delta_Cods = total_eesc[264] - total_eesc[0]
+delta_Eco = species_out["CO"][264] - species_out["CO"][0]
+delta_Evoc = species_out["VOC"][264] - species_out["VOC"][0]
+delta_Enox = species_out["NOx"][264] - species_out["NOx"][0]
+
+# best estimate radiative efficienices from 2014 - 1850 from AR6 here
+radeff_ch4 = 0.14 / delta_Cch4
+radeff_n2o = 0.03 / delta_Cn2o
+radeff_ods = -0.11 / delta_Cods
+radeff_co = 0.067 / delta_Eco  # stevenson CMIP5 scaled to CO + VOC total
+radeff_voc = 0.043 / delta_Evoc  # stevenson CMIP5 scaled to CO + VOC total
+radeff_nox = 0.20 / delta_Enox
+
+
+fac_cmip6_skeie = (
+    radeff_ch4 * delta_Cch4
+    + radeff_n2o * delta_Cn2o
+    + radeff_ods * delta_Cods
+    + radeff_co * delta_Eco
+    + radeff_voc * delta_Evoc
+    + radeff_nox * delta_Enox
+) / (o3total[264] - o3total[0])
+ts = np.vstack(
+    (
+        species_out["CH4"],
+        species_out["N2O"],
+        total_eesc,
+        species_out["CO"],
+        species_out["VOC"],
+        species_out["NOx"],
+    )
+).T
+
+
+def fit_precursors(x, rch4, rn2o, rods, rco, rvoc, rnox):
+    return (
+        rch4 * x[0] + rn2o * x[1] + rods * x[2] + rco * x[3] + rvoc * x[4] + rnox * x[5]
+    )
+
+
+p, cov = curve_fit(
+    fit_precursors,
+    ts[:270, :].T - ts[0:1, :].T,
+    o3total[:270] - o3total[0],
+    bounds=(  # very likely range from Thornhill - maybe could be wider?
+        (
+            0.09 / delta_Cch4 / fac_cmip6_skeie,
+            0.01 / delta_Cn2o / fac_cmip6_skeie,
+            -0.21 / delta_Cods / fac_cmip6_skeie,
+            0.010 / delta_Eco / fac_cmip6_skeie,
+            0 / delta_Evoc / fac_cmip6_skeie,
+            0.09 / delta_Enox / fac_cmip6_skeie,
+        ),
+        (
+            0.19 / delta_Cch4 / fac_cmip6_skeie,
+            0.05 / delta_Cn2o / fac_cmip6_skeie,
+            -0.01 / delta_Cods / fac_cmip6_skeie,
+            0.124 / delta_Eco / fac_cmip6_skeie,
+            0.086 / delta_Evoc / fac_cmip6_skeie,
+            0.31 / delta_Enox / fac_cmip6_skeie,
+        ),
+    ),
+)
+
+forcing = (
+    p[0] * (species_out["CH4"] - species_out["CH4"][0])
+    + p[1] * (species_out["N2O"] - species_out["N2O"][0])
+    + p[2] * (total_eesc - total_eesc[0])
+    + p[3] * (species_out["CO"] - species_out["CO"][0])
+    + p[4] * (species_out["VOC"] - species_out["VOC"][0])
+    + p[5] * (species_out["NOx"] - species_out["NOx"][0])
+)
+
+if plots:
+    pl.figure(figsize=(9 / 2.54, 9 / 2.54))
+    pl.plot(np.arange(1750.5, 2020), forcing, label="best estimate fit", color="0.5")
+    pl.plot(np.arange(1750.5, 2021), o3total, label="Skeie et al. 2020 mean", color="k")
+    pl.legend()
+    pl.title("Ozone forcing (no feedbacks)")
+    pl.ylabel("W m$^{-2}$")
+    pl.xlim(1750, 2020)
+    pl.tight_layout()
+    os.makedirs(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}", exist_ok=True
+    )
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "ozone_calibration.png"
+    )
+    pl.savefig(
+        f"../../../../../plots/fair-{fair_v}/v{cal_v}/{constraint_set}/"
+        "ozone_calibration.pdf"
+    )
+    pl.close()
+
+print(p)  # these coefficients we export to the ERF time series
+# print(radeff_ch4, radeff_n2o, radeff_ods, radeff_co, radeff_voc, radeff_nox)
+
+NINETY_TO_ONESIGMA = scipy.stats.norm.ppf(0.95)
+
+scalings = scipy.stats.norm.rvs(
+    loc=np.array(p),
+    scale=np.array(
+        [
+            0.05 / delta_Cch4 / fac_cmip6_skeie,
+            0.02 / delta_Cn2o / fac_cmip6_skeie,
+            0.10 / delta_Cods / fac_cmip6_skeie,
+            0.057 / delta_Eco / fac_cmip6_skeie,
+            0.043 / delta_Evoc / fac_cmip6_skeie,
+            0.11 / delta_Enox / fac_cmip6_skeie,
+        ]
+    )
+    # scale=np.array([0.000062, 0.000471, 0.000113, 0.000131, 0.000328, 0.000983])
+    / NINETY_TO_ONESIGMA,
+    size=(samples, 6),
+    random_state=52,
+)
+
+df = pd.DataFrame(
+    scalings,
+    columns=[
+        "CH4",
+        "N2O",
+        "Equivalent effective stratospheric chlorine",
+        "CO",
+        "VOC",
+        "NOx",
+    ],
+)
+
+os.makedirs(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/priors/",
+    exist_ok=True,
+)
+df.to_csv(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/priors/ozone.csv",
+    index=False,
+)
diff --git a/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/06_forcing-uncertainty-ar6.py b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/06_forcing-uncertainty-ar6.py
new file mode 100755
index 0000000..ce979f0
--- /dev/null
+++ b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/06_forcing-uncertainty-ar6.py
@@ -0,0 +1,95 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+"""ERF scaling factors"""
+#
+# Based on AR6 Chapter 7 ERF uncertainty
+#
+# We do not modify forcing scale factors for ozone and aerosols, because we adjust the
+# precursor species to span the forcing uncertainty this way.
+
+import os
+
+import pandas as pd
+import scipy.stats
+from dotenv import load_dotenv
+from fair import __version__
+from sklearn.preprocessing import QuantileTransformer
+
+load_dotenv()
+
+print("Doing forcing uncertainty sampling...")
+
+
+cal_v = os.getenv("CALIBRATION_VERSION")
+fair_v = os.getenv("FAIR_VERSION")
+constraint_set = os.getenv("CONSTRAINT_SET")
+samples = int(os.getenv("PRIOR_SAMPLES"))
+
+assert fair_v == __version__
+
+NINETY_TO_ONESIGMA = scipy.stats.norm.ppf(0.95)
+NINETY_TO_ONESIGMA
+
+forcing_u90 = {
+    "CH4": 0.20,  # CH4: updated value from etminan 2016
+    "N2O": 0.14,  # N2O
+    "minorGHG": 0.19,  # other WMGHGs
+    "Stratospheric water vapour": 1.00,
+    "Land use": 0.50,  # land use change
+    "Volcanic": 5.0 / 20.0,  # needs to be way bigger?
+    "solar_amplitude": 0.50,
+    "solar_trend": 0.07,
+}
+
+seedgen = 380133900
+scalings = {}
+for forcer in forcing_u90:
+    scalings[forcer] = scipy.stats.norm.rvs(
+        1, forcing_u90[forcer] / NINETY_TO_ONESIGMA, size=samples, random_state=seedgen
+    )
+    seedgen = seedgen + 112
+
+
+def opt(x, q05_desired, q50_desired, q95_desired):
+    "x is (a, loc, scale) in that order."
+    q05, q50, q95 = scipy.stats.skewnorm.ppf(
+        (0.05, 0.50, 0.95), x[0], loc=x[1], scale=x[2]
+    )
+    return (q05 - q05_desired, q50 - q50_desired, q95 - q95_desired)
+
+
+# Asymmetric distributions we use skew-normal, fitting quantiles
+lapsi_params = scipy.optimize.root(opt, [1, 1, 1], args=(0, 1, 2.25)).x
+contrails_params = scipy.optimize.root(opt, [1, 1, 1], args=(19 / 57, 1, 98 / 57)).x
+
+scalings["Light absorbing particles on snow and ice"] = scipy.stats.skewnorm.rvs(
+    lapsi_params[0],
+    loc=lapsi_params[1],
+    scale=lapsi_params[2],
+    size=samples,
+    random_state=3701584,
+)
+
+# Solar trend is absolute, not scaled
+scalings["solar_trend"] = scalings["solar_trend"] - 1
+
+# CO2 scaling is quantile mapping from ERF 4xCO2 and +/- 12%
+df_ebm = pd.read_csv(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/priors/"
+    "climate_response_ebm3.csv"
+)
+
+qt = QuantileTransformer(output_distribution="normal", random_state=70601701)
+f4xco2 = df_ebm["F_4xCO2"].values.reshape(-1, 1)
+trans = qt.fit(f4xco2).transform(f4xco2)
+trans = 1 + trans * 0.12 / NINETY_TO_ONESIGMA
+scalings["CO2"] = trans.squeeze()
+
+df_out = pd.DataFrame(scalings, columns=scalings.keys())
+
+df_out.to_csv(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/priors/"
+    "forcing_scaling.csv",
+    index=False,
+)
diff --git a/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/07_CO2-1750-sampling-ar6ch2.py b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/07_CO2-1750-sampling-ar6ch2.py
new file mode 100755
index 0000000..d7136f0
--- /dev/null
+++ b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/07_CO2-1750-sampling-ar6ch2.py
@@ -0,0 +1,41 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+"""Sampling CO2 concentration in 1750"""
+#
+# 1750 concentration was 278.3 ppm +/- 2.9 ppm (data provided by Jinho Ahn, IPCC AR6 WG1
+# Ch2).
+
+import os
+
+import pandas as pd
+import scipy.stats
+from dotenv import load_dotenv
+from fair import __version__
+
+load_dotenv()
+
+print("Sampling 1750 CO2 concentration...")
+
+# Get environment variables
+load_dotenv()
+
+cal_v = os.getenv("CALIBRATION_VERSION")
+fair_v = os.getenv("FAIR_VERSION")
+constraint_set = os.getenv("CONSTRAINT_SET")
+samples = int(os.getenv("PRIOR_SAMPLES"))
+
+assert fair_v == __version__
+
+NINETY_TO_ONESIGMA = scipy.stats.norm.ppf(0.95)
+co2_1750_conc = scipy.stats.norm.rvs(
+    size=samples, loc=278.3, scale=2.9 / NINETY_TO_ONESIGMA, random_state=1067061
+)
+
+df = pd.DataFrame({"co2_concentration": co2_1750_conc})
+
+df.to_csv(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/priors/"
+    "co2_concentration_1750.csv",
+    index=False,
+)
diff --git a/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/08_make-emissions-binary.py b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/08_make-emissions-binary.py
new file mode 100755
index 0000000..529fed3
--- /dev/null
+++ b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/08_make-emissions-binary.py
@@ -0,0 +1,83 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+"""Make emissions binary file."""
+#
+# SSPs from RCMIP
+
+import os
+
+import pandas as pd
+import pooch
+from dotenv import load_dotenv
+from fair import FAIR, __version__
+from fair.io import read_properties
+from fair.interface import fill
+
+load_dotenv()
+
+
+cal_v = os.getenv("CALIBRATION_VERSION")
+fair_v = os.getenv("FAIR_VERSION")
+constraint_set = os.getenv("CONSTRAINT_SET")
+samples = int(os.getenv("PRIOR_SAMPLES"))
+progress = os.getenv("PROGRESS", "False").lower() in ("true", "1", "t")
+datadir = os.getenv("DATADIR")
+
+assert fair_v == __version__
+
+print("Making emissions binary...")
+
+scenarios = [
+    "MESSAGE-GLOBIOM___SSP2-45",
+    "MESSAGE-GLOBIOM___SSP2-45-MFR-CH4",
+    "MESSAGE-GLOBIOM___SSP2-45-CLE-CH4",
+]
+
+species, properties = read_properties()
+species.remove("NOx aviation")
+species.remove("Contrails")
+
+f = FAIR(ch4_method="thornhill2021")
+f.define_time(1750, 2101, 1)
+f.define_configs(["unspecified"])
+f.define_scenarios(scenarios)
+f.define_species(species, properties)
+
+f.allocate()
+
+
+df_in = pd.read_csv(
+    "../../../../../data/emissions/CH4Pledge-rcmip-emissions-ssp245-CLE-MFR-filled.csv"
+)
+variables = list(df_in["variable"])
+units = list(df_in["unit"])
+var_units = {var: unit for var, unit in zip(variables, units)}
+
+# fill emissions
+for scenario in scenarios:
+    for specie in variables:
+        data = df_in.loc[
+            (df_in["scenario"] == scenario)
+            & (df_in["variable"] == specie),
+            "1750.5":,
+        ].values.squeeze()
+        fill(
+            f.emissions,
+            data,
+            config="unspecified",
+            scenario=scenario,
+            #        timepoints=np.arange(1750.5, 2021),
+            specie=specie,
+        )
+
+
+os.makedirs(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/emissions/",
+    exist_ok=True,
+)
+
+f.emissions.to_netcdf(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/emissions/"
+    "emissions_1750-2100.nc"
+)
diff --git a/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/09_make-1pctCO2-concentration-binary.py b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/09_make-1pctCO2-concentration-binary.py
new file mode 100755
index 0000000..1d1aca4
--- /dev/null
+++ b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/09_make-1pctCO2-concentration-binary.py
@@ -0,0 +1,99 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+"""Make concentration binary file."""
+#
+# 1pctCO2 from RCMIP
+
+import os
+
+import pandas as pd
+import pooch
+from dotenv import load_dotenv
+from fair import FAIR, __version__
+from fair.interface import fill
+
+load_dotenv()
+
+
+cal_v = os.getenv("CALIBRATION_VERSION")
+fair_v = os.getenv("FAIR_VERSION")
+constraint_set = os.getenv("CONSTRAINT_SET")
+samples = int(os.getenv("PRIOR_SAMPLES"))
+progress = os.getenv("PROGRESS", "False").lower() in ("true", "1", "t")
+datadir = os.getenv("DATADIR")
+
+assert fair_v == __version__
+
+print("Making 1pctCO2 concentration binary...")
+
+scenarios = ["1pctCO2"]
+
+species = ["CO2", "CH4", "N2O"]
+properties = {
+    "CO2": {
+        "type": "co2",
+        "input_mode": "concentration",
+        "greenhouse_gas": True,
+        "aerosol_chemistry_from_emissions": False,
+        "aerosol_chemistry_from_concentration": False,
+    },
+    "CH4": {
+        "type": "ch4",
+        "input_mode": "concentration",
+        "greenhouse_gas": True,
+        "aerosol_chemistry_from_emissions": False,
+        "aerosol_chemistry_from_concentration": False,
+    },
+    "N2O": {
+        "type": "n2o",
+        "input_mode": "concentration",
+        "greenhouse_gas": True,
+        "aerosol_chemistry_from_emissions": False,
+        "aerosol_chemistry_from_concentration": False,
+    },
+}
+
+f = FAIR()
+f.define_time(1850, 1990, 1)
+f.define_configs(["unspecified"])
+f.define_scenarios(scenarios)
+f.define_species(species, properties)
+
+f.allocate()
+
+rcmip_concentration_file = pooch.retrieve(
+    url=(
+        "https://zenodo.org/records/4589756/files/"
+        "rcmip-concentrations-annual-means-v5-1-0.csv"
+    ),
+    known_hash="md5:0d82c3c3cdd4dd632b2bb9449a5c315f",
+    progressbar=progress,
+    path=datadir,
+)
+
+df_conc = pd.read_csv(rcmip_concentration_file)
+
+input = {}
+for specie in species:
+    input[specie] = (
+        df_conc.loc[
+            (df_conc["Scenario"] == "1pctCO2")
+            & (df_conc["Variable"].str.endswith(specie))
+            & (df_conc["Region"] == "World"),
+            "1850":"1990",
+        ]
+        .interpolate(axis=1)
+        .values.squeeze()
+    )
+    fill(f.concentration, input[specie][:, None, None], specie=specie)
+
+os.makedirs(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/concentration/",
+    exist_ok=True,
+)
+
+f.concentration.to_netcdf(
+    f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/concentration/"
+    "1pctCO2_concentration_1850-1990.nc"
+)
diff --git a/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/10_run-fair-ssp-prior-ensemble-ebm3-intvar.py b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/10_run-fair-ssp-prior-ensemble-ebm3-intvar.py
new file mode 100755
index 0000000..bf5f753
--- /dev/null
+++ b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/10_run-fair-ssp-prior-ensemble-ebm3-intvar.py
@@ -0,0 +1,300 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+"""AR6 calibration of FaIR 2.1"""
+#
+# - use logsum aerosol indirect forcing relationship.
+# - aerosol direct is from AR6.
+# - three layer model for climate response.
+# - include overlap of the major GHGs.
+# - prognostic equation for land use related forcing (e.g. from FaIR 1.6).
+# - ozone relationship from FaIR 1.6 used in AR6.
+# - interactive methane lifetime
+# - internal variability (here included)
+# - NEW: volcanic forcing data from IGCC, updated to end 2022
+#
+# We have to do this slightly differently to the examples so far. 1.5 million ensemble
+# members is going to take up too much memory, so we run in batches of 1000,
+# initialising a new FaIR instance for each batch, and saving the output as we go.
+
+
+import multiprocessing
+import os
+from concurrent.futures import ProcessPoolExecutor
+
+import numpy as np
+import pandas as pd
+from dotenv import load_dotenv
+from fair import __version__
+from parallel import run_fair
+from utils import _parallel_process
+
+if __name__ == "__main__":
+    print("Running the priors (could take a while)...")
+    load_dotenv()
+
+    cal_v = os.getenv("CALIBRATION_VERSION")
+    fair_v = os.getenv("FAIR_VERSION")
+    constraint_set = os.getenv("CONSTRAINT_SET")
+    samples = int(os.getenv("PRIOR_SAMPLES"))
+    batch_size = int(os.getenv("BATCH_SIZE"))
+    WORKERS = int(os.getenv("WORKERS"))
+
+    # number of processors
+    WORKERS = min(multiprocessing.cpu_count(), WORKERS)
+
+    assert fair_v == __version__
+
+    df_solar = pd.read_csv(
+        "../../../../../data/forcing/solar_erf_timebounds.csv", index_col="year"
+    )
+    df_volcanic = pd.read_csv(
+        "../../../../../data/forcing/volcanic_ERF_1750-2101_timebounds.csv",
+        index_col="timebounds",
+    )
+
+    volcanic_forcing = df_volcanic["erf"].loc[1750:2101].values
+    solar_forcing = df_solar["erf"].loc[1750:2101].values
+
+    df_cc = pd.read_csv(
+        f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/priors/"
+        "carbon_cycle.csv"
+    )
+    df_cr = pd.read_csv(
+        f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/priors/"
+        "climate_response_ebm3.csv"
+    )
+    df_aci = pd.read_csv(
+        f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/priors/"
+        "aerosol_cloud.csv"
+    )
+    df_ari = pd.read_csv(
+        f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/priors/"
+        "aerosol_radiation.csv"
+    )
+    df_ozone = pd.read_csv(
+        f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/priors/"
+        "ozone.csv"
+    )
+    df_scaling = pd.read_csv(
+        f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/priors/"
+        "forcing_scaling.csv"
+    )
+    df_1750co2 = pd.read_csv(
+        f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/priors/"
+        "co2_concentration_1750.csv"
+    )
+    df_methane = pd.read_csv(
+        f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/calibrations/"
+        "CH4_lifetime.csv",
+        index_col=0,
+    )
+    df_lapsi = pd.read_csv(
+        f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/calibrations/"
+        "lapsi_scale_factor.csv",
+        index_col=0,
+    )
+
+    seedgen = 1355763
+    seedstep = 399
+
+    # for all except temperature, the full time series is not important so we can save
+    # a bit of space
+    temp_out = np.ones((252, samples)) * np.nan
+    ohc_out = np.ones((samples)) * np.nan
+    fari_out = np.ones((samples)) * np.nan
+    faci_out = np.ones((samples)) * np.nan
+    co2_out = np.ones((samples)) * np.nan
+    fo3_out = np.ones((samples)) * np.nan
+    ecs = np.ones(samples) * np.nan
+    tcr = np.ones(samples) * np.nan
+
+    config = []
+    for ibatch, batch_start in enumerate(range(0, samples, batch_size)):
+        config.append({})
+        batch_end = batch_start + batch_size
+        config[ibatch]["batch_start"] = batch_start
+        config[ibatch]["batch_end"] = batch_start + batch_size
+        config[ibatch]["volcanic_forcing"] = volcanic_forcing
+        config[ibatch]["solar_forcing"] = solar_forcing
+        config[ibatch]["scaling_Volcanic"] = df_scaling.loc[
+            batch_start : batch_end - 1, "Volcanic"
+        ].values.squeeze()
+        config[ibatch]["scaling_solar_amplitude"] = df_scaling.loc[
+            batch_start : batch_end - 1, "solar_amplitude"
+        ].values.squeeze()
+        config[ibatch]["c1"] = df_cr.loc[batch_start : batch_end - 1, "c1"].values
+        config[ibatch]["c2"] = df_cr.loc[batch_start : batch_end - 1, "c2"].values
+        config[ibatch]["c3"] = df_cr.loc[batch_start : batch_end - 1, "c3"].values
+        config[ibatch]["kappa1"] = df_cr.loc[
+            batch_start : batch_end - 1, "kappa1"
+        ].values
+        config[ibatch]["kappa2"] = df_cr.loc[
+            batch_start : batch_end - 1, "kappa2"
+        ].values
+        config[ibatch]["kappa3"] = df_cr.loc[
+            batch_start : batch_end - 1, "kappa3"
+        ].values
+        config[ibatch]["epsilon"] = df_cr.loc[
+            batch_start : batch_end - 1, "epsilon"
+        ].values
+        config[ibatch]["gamma"] = df_cr.loc[batch_start : batch_end - 1, "gamma"].values
+        config[ibatch]["sigma_eta"] = df_cr.loc[
+            batch_start : batch_end - 1, "sigma_eta"
+        ].values
+        config[ibatch]["sigma_xi"] = df_cr.loc[
+            batch_start : batch_end - 1, "sigma_xi"
+        ].values
+        config[ibatch]["seed"] = np.arange(
+            seedgen + batch_start * seedstep,
+            seedgen + batch_end * seedstep,
+            seedstep,
+            dtype=int,
+        )
+        config[ibatch]["forcing_4co2"] = df_cr.loc[
+            batch_start : batch_end - 1, "F_4xCO2"
+        ]
+        config[ibatch]["iirf_0"] = df_cc.loc[
+            batch_start : batch_end - 1, "r0"
+        ].values.squeeze()
+        config[ibatch]["iirf_airborne"] = df_cc.loc[
+            batch_start : batch_end - 1, "rA"
+        ].values.squeeze()
+        config[ibatch]["iirf_uptake"] = df_cc.loc[
+            batch_start : batch_end - 1, "rU"
+        ].values.squeeze()
+        config[ibatch]["iirf_temperature"] = df_cc.loc[
+            batch_start : batch_end - 1, "rT"
+        ].values.squeeze()
+        config[ibatch]["beta"] = df_aci.loc[
+            batch_start : batch_end - 1, "beta"
+        ].values.squeeze()
+        config[ibatch]["shape_so2"] = df_aci.loc[
+            batch_start : batch_end - 1, "shape_so2"
+        ].values.squeeze()
+        config[ibatch]["shape_bc"] = df_aci.loc[
+            batch_start : batch_end - 1, "shape_bc"
+        ].values.squeeze()
+        config[ibatch]["shape_oc"] = df_aci.loc[
+            batch_start : batch_end - 1, "shape_oc"
+        ].values.squeeze()
+        config[ibatch]["scaling_CO2"] = df_scaling.loc[
+            batch_start : batch_end - 1, "CO2"
+        ].values.squeeze()
+        config[ibatch]["scaling_CH4"] = df_scaling.loc[
+            batch_start : batch_end - 1, "CH4"
+        ].values.squeeze()
+        config[ibatch]["scaling_N2O"] = df_scaling.loc[
+            batch_start : batch_end - 1, "N2O"
+        ].values.squeeze()
+        config[ibatch]["scaling_minorGHG"] = df_scaling.loc[
+            batch_start : batch_end - 1, "minorGHG"
+        ].values.squeeze()
+        config[ibatch]["scaling_stwv"] = df_scaling.loc[
+            batch_start : batch_end - 1, "Stratospheric water vapour"
+        ].values.squeeze()
+        config[ibatch]["scaling_lapsi"] = df_scaling.loc[
+            batch_start : batch_end - 1, "Light absorbing particles on snow and ice"
+        ].values.squeeze()
+        config[ibatch]["scaling_landuse"] = df_scaling.loc[
+            batch_start : batch_end - 1, "Land use"
+        ].values.squeeze()
+        config[ibatch]["ari_BC"] = df_ari.loc[batch_start : batch_end - 1, "BC"]
+        config[ibatch]["ari_CH4"] = df_ari.loc[batch_start : batch_end - 1, "CH4"]
+        config[ibatch]["ari_N2O"] = df_ari.loc[batch_start : batch_end - 1, "N2O"]
+        config[ibatch]["ari_NH3"] = df_ari.loc[batch_start : batch_end - 1, "NH3"]
+        config[ibatch]["ari_NOx"] = df_ari.loc[batch_start : batch_end - 1, "NOx"]
+        config[ibatch]["ari_OC"] = df_ari.loc[batch_start : batch_end - 1, "OC"]
+        config[ibatch]["ari_Sulfur"] = df_ari.loc[batch_start : batch_end - 1, "Sulfur"]
+        config[ibatch]["ari_VOC"] = df_ari.loc[batch_start : batch_end - 1, "VOC"]
+        config[ibatch]["ari_EESC"] = df_ari.loc[
+            batch_start : batch_end - 1, "Equivalent effective stratospheric chlorine"
+        ]
+        config[ibatch]["ozone_CH4"] = df_ozone.loc[batch_start : batch_end - 1, "CH4"]
+        config[ibatch]["ozone_N2O"] = df_ozone.loc[batch_start : batch_end - 1, "N2O"]
+        config[ibatch]["ozone_NOx"] = df_ozone.loc[batch_start : batch_end - 1, "NOx"]
+        config[ibatch]["ozone_VOC"] = df_ozone.loc[batch_start : batch_end - 1, "VOC"]
+        config[ibatch]["ozone_CO"] = df_ozone.loc[batch_start : batch_end - 1, "CO"]
+        config[ibatch]["ozone_EESC"] = df_ozone.loc[
+            batch_start : batch_end - 1, "Equivalent effective stratospheric chlorine"
+        ]
+        config[ibatch]["CO2_1750"] = df_1750co2.loc[
+            batch_start : batch_end - 1, "co2_concentration"
+        ].values.squeeze()
+        config[ibatch]["ch4_base"] = df_methane.loc["historical_best", "base"]
+        config[ibatch]["ch4_CH4"] = df_methane.loc["historical_best", "CH4"]
+        config[ibatch]["ch4_NOx"] = df_methane.loc["historical_best", "NOx"]
+        config[ibatch]["ch4_VOC"] = df_methane.loc["historical_best", "VOC"]
+        config[ibatch]["ch4_EESC"] = df_methane.loc["historical_best", "HC"]
+        config[ibatch]["ch4_N2O"] = df_methane.loc["historical_best", "N2O"]
+        config[ibatch]["ch4_temp"] = df_methane.loc["historical_best", "temp"]
+        config[ibatch]["lapsi_factor"] = df_lapsi.loc["historical_best", "BC"]
+
+    parallel_process_kwargs = dict(
+        func=run_fair,
+        configuration=config,
+        config_are_kwargs=False,
+    )
+
+    with ProcessPoolExecutor(WORKERS) as pool:
+        res = _parallel_process(
+            **parallel_process_kwargs,
+            pool=pool,
+        )
+
+    for ibatch, batch_start in enumerate(range(0, samples, batch_size)):
+        batch_end = batch_start + batch_size
+        temp_out[:, batch_start:batch_end] = res[ibatch][0]
+        ohc_out[batch_start:batch_end] = res[ibatch][1]
+        co2_out[batch_start:batch_end] = res[ibatch][2]
+        fari_out[batch_start:batch_end] = res[ibatch][3]
+        faci_out[batch_start:batch_end] = res[ibatch][4]
+        ecs[batch_start:batch_end] = res[ibatch][5]
+        tcr[batch_start:batch_end] = res[ibatch][6]
+
+    os.makedirs(
+        f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/prior_runs/",
+        exist_ok=True,
+    )
+    np.save(
+        f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/prior_runs/"
+        "temperature_1850-2101.npy",
+        temp_out,
+        allow_pickle=True,
+    )
+    np.save(
+        f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/prior_runs/"
+        "ocean_heat_content_2020_minus_1971.npy",
+        ohc_out,
+        allow_pickle=True,
+    )
+    np.save(
+        f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/prior_runs/"
+        "concentration_co2_2022.npy",
+        co2_out,
+        allow_pickle=True,
+    )
+    np.save(
+        f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/prior_runs/"
+        "forcing_ari_2005-2014_mean.npy",
+        fari_out,
+        allow_pickle=True,
+    )
+    np.save(
+        f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/prior_runs/"
+        "forcing_aci_2005-2014_mean.npy",
+        faci_out,
+        allow_pickle=True,
+    )
+    np.save(
+        f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/prior_runs/"
+        "ecs.npy",
+        ecs,
+        allow_pickle=True,
+    )
+    np.save(
+        f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/prior_runs/"
+        "tcr.npy",
+        tcr,
+        allow_pickle=True,
+    )
diff --git a/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/parallel.py b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/parallel.py
new file mode 100644
index 0000000..bd31087
--- /dev/null
+++ b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/parallel.py
@@ -0,0 +1,262 @@
+# put imports outside: we don't have a lot of overhead here, and it looks nicer.
+import os
+import warnings
+
+import numpy as np
+import xarray as xr
+from dotenv import load_dotenv
+from fair import FAIR
+from fair.interface import fill, initialise
+from fair.io import read_properties
+
+load_dotenv()
+
+cal_v = os.getenv("CALIBRATION_VERSION")
+fair_v = os.getenv("FAIR_VERSION")
+constraint_set = os.getenv("CONSTRAINT_SET")
+
+
+def run_fair(cfg):
+    scenarios = ["MESSAGE-GLOBIOM___SSP2-45-CLE-CH4"]
+    batch_start = cfg["batch_start"]
+    batch_end = cfg["batch_end"]
+    batch_size = batch_end - batch_start
+
+    species, properties = read_properties()
+    species.remove("NOx aviation")
+    species.remove("Contrails")
+
+    da_emissions = xr.load_dataarray(
+        f"../../../../../output/fair-{fair_v}/v{cal_v}/{constraint_set}/emissions/"
+        "emissions_1750-2100.nc"
+    )
+
+    f = FAIR(ch4_method="Thornhill2021")
+    f.define_time(1750, 2101, 1)
+    f.define_scenarios(scenarios)
+    f.define_configs(list(range(batch_start, batch_end)))
+    f.define_species(species, properties)
+    f.allocate()
+
+    da = da_emissions.loc[dict(config="unspecified", scenario="MESSAGE-GLOBIOM___SSP2-45-CLE-CH4")][:351, ...]
+    fe = da.expand_dims(dim=["scenario", "config"], axis=(1, 2))
+    f.emissions = fe.drop("config") * np.ones((1, 1, batch_size, 1))
+
+    # solar and volcanic forcing
+    fill(
+        f.forcing,
+        cfg["volcanic_forcing"][:, None, None] * cfg["scaling_Volcanic"],
+        specie="Volcanic",
+    )
+    fill(
+        f.forcing,
+        cfg["solar_forcing"][:, None, None] * cfg["scaling_solar_amplitude"],
+        specie="Solar",
+    )
+
+    # climate response
+    fill(
+        f.climate_configs["ocean_heat_capacity"],
+        np.array([cfg["c1"], cfg["c2"], cfg["c3"]]).T,
+    )
+    fill(
+        f.climate_configs["ocean_heat_transfer"],
+        np.array([cfg["kappa1"], cfg["kappa2"], cfg["kappa3"]]).T,
+    )
+    fill(f.climate_configs["deep_ocean_efficacy"], cfg["epsilon"])
+    fill(f.climate_configs["gamma_autocorrelation"], cfg["gamma"])
+    fill(f.climate_configs["sigma_eta"], cfg["sigma_eta"])
+    fill(f.climate_configs["sigma_xi"], cfg["sigma_xi"])
+    fill(f.climate_configs["seed"], cfg["seed"])
+    fill(f.climate_configs["stochastic_run"], True)
+    fill(f.climate_configs["use_seed"], True)
+    fill(f.climate_configs["forcing_4co2"], cfg["forcing_4co2"])
+
+    # species level
+    f.fill_species_configs()
+
+    # carbon cycle
+    fill(f.species_configs["iirf_0"], cfg["iirf_0"], specie="CO2")
+    fill(f.species_configs["iirf_airborne"], cfg["iirf_airborne"], specie="CO2")
+    fill(f.species_configs["iirf_uptake"], cfg["iirf_uptake"], specie="CO2")
+    fill(f.species_configs["iirf_temperature"], cfg["iirf_temperature"], specie="CO2")
+
+    # methane lifetime baseline and sensitivity
+    fill(f.species_configs["unperturbed_lifetime"], cfg["ch4_base"], specie="CH4")
+    fill(
+        f.species_configs["ch4_lifetime_chemical_sensitivity"],
+        cfg["ch4_CH4"],
+        specie="CH4",
+    )
+    fill(
+        f.species_configs["ch4_lifetime_chemical_sensitivity"],
+        cfg["ch4_N2O"],
+        specie="N2O",
+    )
+    fill(
+        f.species_configs["ch4_lifetime_chemical_sensitivity"],
+        cfg["ch4_VOC"],
+        specie="VOC",
+    )
+    fill(
+        f.species_configs["ch4_lifetime_chemical_sensitivity"],
+        cfg["ch4_NOx"],
+        specie="NOx",
+    )
+    fill(
+        f.species_configs["ch4_lifetime_chemical_sensitivity"],
+        cfg["ch4_EESC"],
+        specie="Equivalent effective stratospheric chlorine",
+    )
+    fill(f.species_configs["lifetime_temperature_sensitivity"], cfg["ch4_temp"])
+
+    # correct LAPSI scale factor terms
+    fill(
+        f.species_configs["lapsi_radiative_efficiency"],
+        cfg["lapsi_factor"],
+        specie="BC",
+    )
+
+    # emissions adjustments for N2O and CH4
+    fill(f.species_configs["baseline_emissions"], 19.019783117809567, specie="CH4")
+    fill(f.species_configs["baseline_emissions"], 0.08602230754, specie="N2O")
+    fill(f.species_configs["baseline_emissions"], 19.423526730206152, specie="NOx")
+
+    # aerosol indirect
+    fill(f.species_configs["aci_scale"], cfg["beta"])
+    fill(f.species_configs["aci_shape"], cfg["shape_so2"], specie="Sulfur")
+    fill(f.species_configs["aci_shape"], cfg["shape_bc"], specie="BC")
+    fill(f.species_configs["aci_shape"], cfg["shape_oc"], specie="OC")
+
+    # forcing scaling
+    fill(f.species_configs["forcing_scale"], cfg["scaling_CO2"], specie="CO2")
+    fill(f.species_configs["forcing_scale"], cfg["scaling_CH4"], specie="CH4")
+    fill(f.species_configs["forcing_scale"], cfg["scaling_N2O"], specie="N2O")
+    fill(
+        f.species_configs["forcing_scale"],
+        cfg["scaling_stwv"],
+        specie="Stratospheric water vapour",
+    )
+    fill(
+        f.species_configs["forcing_scale"],
+        cfg["scaling_lapsi"],
+        specie="Light absorbing particles on snow and ice",
+    )
+    fill(f.species_configs["forcing_scale"], cfg["scaling_landuse"], specie="Land use")
+
+    for specie in [
+        "CFC-11",
+        "CFC-12",
+        "CFC-113",
+        "CFC-114",
+        "CFC-115",
+        "HCFC-22",
+        "HCFC-141b",
+        "HCFC-142b",
+        "CCl4",
+        "CHCl3",
+        "CH2Cl2",
+        "CH3Cl",
+        "CH3CCl3",
+        "CH3Br",
+        "Halon-1202",
+        "Halon-1211",
+        "Halon-1301",
+        "Halon-2402",
+        "CF4",
+        "C2F6",
+        "C3F8",
+        "c-C4F8",
+        "C4F10",
+        "C5F12",
+        "C6F14",
+        "C7F16",
+        "C8F18",
+        "NF3",
+        "SF6",
+        "SO2F2",
+        "HFC-125",
+        "HFC-134a",
+        "HFC-143a",
+        "HFC-152a",
+        "HFC-227ea",
+        "HFC-23",
+        "HFC-236fa",
+        "HFC-245fa",
+        "HFC-32",
+        "HFC-365mfc",
+        "HFC-4310mee",
+    ]:
+        fill(f.species_configs["forcing_scale"], cfg["scaling_minorGHG"], specie=specie)
+
+    # aerosol radiation interactions
+    fill(f.species_configs["erfari_radiative_efficiency"], cfg["ari_BC"], specie="BC")
+    fill(f.species_configs["erfari_radiative_efficiency"], cfg["ari_CH4"], specie="CH4")
+    fill(f.species_configs["erfari_radiative_efficiency"], cfg["ari_N2O"], specie="N2O")
+    fill(f.species_configs["erfari_radiative_efficiency"], cfg["ari_NH3"], specie="NH3")
+    fill(f.species_configs["erfari_radiative_efficiency"], cfg["ari_NOx"], specie="NOx")
+    fill(f.species_configs["erfari_radiative_efficiency"], cfg["ari_OC"], specie="OC")
+    fill(
+        f.species_configs["erfari_radiative_efficiency"],
+        cfg["ari_Sulfur"],
+        specie="Sulfur",
+    )
+    fill(f.species_configs["erfari_radiative_efficiency"], cfg["ari_VOC"], specie="VOC")
+    fill(
+        f.species_configs["erfari_radiative_efficiency"],
+        cfg["ari_EESC"],
+        specie="Equivalent effective stratospheric chlorine",
+    )
+
+    # Ozone
+    fill(
+        f.species_configs["ozone_radiative_efficiency"], cfg["ozone_CH4"], specie="CH4"
+    )
+    fill(
+        f.species_configs["ozone_radiative_efficiency"], cfg["ozone_N2O"], specie="N2O"
+    )
+    fill(f.species_configs["ozone_radiative_efficiency"], cfg["ozone_CO"], specie="CO")
+    fill(
+        f.species_configs["ozone_radiative_efficiency"], cfg["ozone_VOC"], specie="VOC"
+    )
+    fill(
+        f.species_configs["ozone_radiative_efficiency"], cfg["ozone_NOx"], specie="NOx"
+    )
+    fill(
+        f.species_configs["ozone_radiative_efficiency"],
+        cfg["ozone_EESC"],
+        specie="Equivalent effective stratospheric chlorine",
+    )
+
+    # CO2 in 1750
+    fill(f.species_configs["baseline_concentration"], cfg["CO2_1750"], specie="CO2")
+
+    # initial conditions
+    initialise(f.concentration, f.species_configs["baseline_concentration"])
+    initialise(f.forcing, 0)
+    initialise(f.temperature, 0)
+    initialise(f.cumulative_emissions, 0)
+    initialise(f.airborne_emissions, 0)
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        f.run(progress=False)
+
+    return (
+        f.temperature[100:, 0, :, 0],
+        f.ocean_heat_content_change[270:272, 0, :].mean(axis=0)
+        - f.ocean_heat_content_change[221:223, 0, :].mean(axis=0),
+        f.concentration[272:274, 0, :, 2].mean(axis=0),
+        np.average(
+            f.forcing[255:266, 0, :, 55],
+            weights=np.array([0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5]),
+            axis=0,
+        ),
+        np.average(
+            f.forcing[255:266, 0, :, 56],
+            weights=np.array([0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5]),
+            axis=0,
+        ),
+        f.ebms.ecs,
+        f.ebms.tcr,
+    )
diff --git a/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/utils.py b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/utils.py
new file mode 100644
index 0000000..0a9e5bb
--- /dev/null
+++ b/input/fair-2.1.3/v1.4/AR6-updated_no-contrails_eclipse/sampling/utils.py
@@ -0,0 +1,170 @@
+import logging
+import os
+import time
+from concurrent.futures import as_completed
+
+from dotenv import load_dotenv
+from tqdm.auto import tqdm
+
+load_dotenv()
+
+front_serial = int(os.getenv("FRONT_SERIAL"))
+front_parallel = int(os.getenv("FRONT_PARALLEL"))
+progress = os.getenv("PROGRESS", "False").lower() in ("true", "1", "t")
+
+LOGGER = logging.getLogger(__name__)
+
+# TODO: handle configuration in a consistent manner
+_default_tqdm_params = {
+    "mininterval": 5,
+    "unit": "it",
+    "unit_scale": True,
+    "disable": 1 - progress,
+}
+
+
+def progress(*args, **kwargs):
+    kwargs = {**_default_tqdm_params, **kwargs}
+    return tqdm(*args, **kwargs)
+
+
+def _run_serial(func, configs, config_are_kwargs, desc):
+    LOGGER.debug("Entering _run_serial")
+
+    if config_are_kwargs:
+        LOGGER.debug("Treating config as kwargs")
+        res = [func(**a) for a in progress(configs, desc=desc)]
+    else:
+        LOGGER.debug("Treating config as args")
+        res = [func(a) for a in progress(configs, desc=desc)]
+
+    LOGGER.debug("Exiting _run_serial")
+    return res
+
+
+def _run_parallel(  # pylint:disable=too-many-arguments
+    pool, timeout, func, configs, config_are_kwargs, desc, bar_start
+):
+    LOGGER.debug("Entering _run_parallel")
+
+    if config_are_kwargs:
+        LOGGER.debug("Treating config as kwargs")
+        futures = [pool.submit(func, **a) for a in configs]
+    else:
+        LOGGER.debug("Treating config as args")
+        futures = [pool.submit(func, a) for a in configs]
+
+    LOGGER.debug("Waiting for jobs to complete")
+    for i, future in progress(
+        enumerate(as_completed(futures, timeout=timeout)), total=len(futures), desc=desc
+    ):
+        if future.exception() is not None:
+            time.sleep(2)  # let buffer flush out
+            print(
+                "One of the processes failed, see error below (was something "
+                "unable to be pickled?)"
+            )
+            raise future.exception()
+
+        LOGGER.debug("Job %s completed", i + bar_start)
+
+    res = []
+
+    LOGGER.debug("Collecting results")
+    for i, future in enumerate(futures):
+        try:
+            res.append(future.result())
+            LOGGER.debug("Retrived result %s", i + bar_start)
+        except Exception as exc:  # pylint:disable=broad-except
+            LOGGER.debug("Retrieving result %s failed", i + bar_start)
+            res.append(exc)
+
+    LOGGER.debug("Exiting _run_parallel")
+    return res
+
+
+def _parallel_process(  # pylint:disable=too-many-arguments
+    func,
+    configuration,
+    pool=None,
+    config_are_kwargs=False,
+    front_serial=front_serial,
+    front_parallel=front_parallel,
+    timeout=None,
+):
+    """
+    Run a process in parallel with a progress bar.
+    Adapted from http://danshiebler.com/2016-09-14-parallel-progress-bar/
+    Parameters
+    ----------
+    func : function
+        A function to apply to each set of arguments in ``configuration``
+    configuration : sequence
+        An array of configuration with which to run ``func``.
+    pool : :obj:`concurrent.futures.ProcessPoolExecutor`
+        Pool in which to execute the jobs. If ``None``, the jobs will be executed
+        serially in a single process (useful for debugging and benchmarking).
+    config_are_kwargs : bool
+        Are the elements of ``configuration`` intended to be used as keyword arguments
+        when calling ``func``.
+    front_serial : int
+        The number of iterations to run serially before kicking off the parallel job.
+        Useful for debugging.
+    front_parallel : int
+        The number of initial iterations to run parallel before kicking off the rest
+        of the parallel jobs. Useful for debugging (especially if pickling is
+        possible).
+    timeout : float
+        How long to wait for processes to complete before timing out. If
+        ``None``, there is no timeout limit.
+    Returns
+    -------
+    sequence
+        Results of calling ``func`` with each configuration in ``configuration``
+    """
+    front_serial_res = []
+    if front_serial > 0:
+        LOGGER.debug("Running front serial jobs")
+        front_serial_res = _run_serial(
+            func=func,
+            configs=configuration[:front_serial],
+            config_are_kwargs=config_are_kwargs,
+            desc="Front serial",
+        )
+
+    if pool is None:
+        LOGGER.info("No pool provided, running rest of the jobs serially and returning")
+        rest = _run_serial(
+            func=func,
+            configs=configuration[front_serial:],
+            config_are_kwargs=config_are_kwargs,
+            desc="Serial runs",
+        )
+
+        return rest + front_serial_res
+
+    front_parallel_res = []
+    if front_parallel > 0:
+        LOGGER.debug("Running front parallel jobs")
+        front_parallel_res = _run_parallel(
+            pool=pool,
+            timeout=timeout,
+            func=func,
+            configs=configuration[front_serial : front_serial + front_parallel],
+            config_are_kwargs=config_are_kwargs,
+            desc="Front parallel",
+            bar_start=front_serial,
+        )
+
+    LOGGER.debug("Running rest of parallel jobs")
+    rest = _run_parallel(
+        pool=pool,
+        timeout=timeout,
+        func=func,
+        configs=configuration[front_serial + front_parallel :],
+        config_are_kwargs=config_are_kwargs,
+        desc="Parallel runs",
+        bar_start=front_serial + front_parallel,
+    )
+
+    return front_serial_res + front_parallel_res + rest