Breaching 1.5°C: Give me the odds

Abstract

This is a supplementary notebook for the paper “Breaching 1.5°C: Give me the odds” by J. Eduardo Vera-Valdés and Olivia Kvist. The notebook shows a plot comparing the different temperature anomalies datasets used in the paper.

0. Load Packages

In [1]:

Code

using Pkg
Pkg.activate(pwd())
using Plots, Dates, CSV, DataFrames, Statistics
include("TrendEstimators.jl")
using .TrendEstimators

theme(:ggplot2)

  Activating project at `~/Library/CloudStorage/OneDrive-AalborgUniversitet/Research/CLIMATE/Paris Goal/Odds-of-breaching-1.5C`
WARNING: replacing module TrendEstimators.

1. Load Data

1.1 Load HadCRUT Data

In [2]:

Code

rawtemp_hadcrut = CSV.read("data/HadCRUT.5.0.2.0.analysis.ensemble_series.global.monthly.csv", DataFrame)
first(rawtemp_hadcrut, 5)

5×203 DataFrame

103 columns omitted

Row	Time	Fraction of area represented	Coverage uncertainty (1 sigma)	Realization 1	Realization 2	Realization 3	Realization 4	Realization 5	Realization 6	Realization 7	Realization 8	Realization 9	Realization 10	Realization 11	Realization 12	Realization 13	Realization 14	Realization 15	Realization 16	Realization 17	Realization 18	Realization 19	Realization 20	Realization 21	Realization 22	Realization 23	Realization 24	Realization 25	Realization 26	Realization 27	Realization 28	Realization 29	Realization 30	Realization 31	Realization 32	Realization 33	Realization 34	Realization 35	Realization 36	Realization 37	Realization 38	Realization 39	Realization 40	Realization 41	Realization 42	Realization 43	Realization 44	Realization 45	Realization 46	Realization 47	Realization 48	Realization 49	Realization 50	Realization 51	Realization 52	Realization 53	Realization 54	Realization 55	Realization 56	Realization 57	Realization 58	Realization 59	Realization 60	Realization 61	Realization 62	Realization 63	Realization 64	Realization 65	Realization 66	Realization 67	Realization 68	Realization 69	Realization 70	Realization 71	Realization 72	Realization 73	Realization 74	Realization 75	Realization 76	Realization 77	Realization 78	Realization 79	Realization 80	Realization 81	Realization 82	Realization 83	Realization 84	Realization 85	Realization 86	Realization 87	Realization 88	Realization 89	Realization 90	Realization 91	Realization 92	Realization 93	Realization 94	Realization 95	Realization 96	Realization 97	⋯
	Date	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	⋯
1	1850-01-01	0.53036	0.12529	-0.648141	-0.687369	-0.659397	-0.795922	-0.575723	-0.872706	-0.697845	-0.938079	-0.714489	-0.693846	-0.801877	-0.601654	-0.745235	-0.688707	-0.51959	-0.580684	-0.733635	-0.775802	-0.685629	-0.750843	-0.593316	-0.796245	-0.681935	-0.709096	-0.673113	-0.627142	-0.87027	-0.734045	-0.739397	-0.733723	-0.722726	-0.733075	-0.704837	-0.646199	-0.63273	-0.687739	-0.686664	-0.766938	-0.660519	-0.500427	-0.761633	-0.593218	-0.673419	-0.633932	-0.53979	-0.534376	-0.730824	-0.672585	-0.709929	-0.782612	-0.587371	-0.775013	-0.669417	-0.738468	-0.65991	-0.641778	-0.705	-0.728567	-0.854561	-0.622728	-0.666254	-0.762724	-0.654979	-0.698282	-0.566776	-0.641214	-0.608169	-0.721251	-0.643558	-0.737449	-0.638663	-0.610622	-0.526234	-0.738695	-0.819199	-0.678843	-0.683284	-0.784564	-0.613478	-0.755483	-0.777482	-0.624742	-0.627155	-0.62747	-0.688723	-0.854917	-0.592061	-0.583304	-0.820669	-0.859069	-0.661617	-0.548561	-0.743047	-0.796279	-0.746007	-0.975871	-0.626403	⋯
2	1850-02-01	0.471513	0.15873	-0.198692	-0.379979	-0.353424	-0.499135	-0.307929	-0.437846	-0.226076	-0.577648	-0.405545	-0.392731	-0.597314	-0.396609	-0.332673	-0.431029	-0.186967	-0.323219	-0.320514	-0.585462	-0.386137	-0.31327	-0.374146	-0.393756	-0.377606	-0.270782	-0.281058	-0.297886	-0.440194	-0.272709	-0.375954	-0.316262	-0.395188	-0.45457	-0.310405	-0.380035	-0.414555	-0.284795	-0.391644	-0.302887	-0.429657	-0.208564	-0.397717	-0.427922	-0.29149	-0.208944	-0.232016	-0.34283	-0.303425	-0.159931	-0.409084	-0.379061	-0.441772	-0.384035	-0.168103	-0.374769	-0.331487	-0.281074	-0.473583	-0.308901	-0.519065	-0.37427	-0.373581	-0.392881	-0.259453	-0.358303	-0.189542	-0.268094	-0.19915	-0.513153	-0.427336	-0.380227	-0.389459	-0.32174	-0.139402	-0.278627	-0.342224	-0.304565	-0.209553	-0.431765	-0.262323	-0.418606	-0.363407	-0.292048	-0.373431	-0.360892	-0.476022	-0.406109	-0.389504	-0.239712	-0.329308	-0.478804	-0.303512	-0.305987	-0.337012	-0.644136	-0.404883	-0.496024	-0.366001	⋯
3	1850-03-01	0.443069	0.146062	-0.559715	-0.58127	-0.613536	-0.770914	-0.649228	-0.674574	-0.422177	-0.66901	-0.633439	-0.52613	-0.742085	-0.673435	-0.500491	-0.750384	-0.441357	-0.66936	-0.684599	-0.871666	-0.528859	-0.450323	-0.676369	-0.747881	-0.515235	-0.564603	-0.517356	-0.65774	-0.799563	-0.562602	-0.7321	-0.708126	-0.601581	-0.750345	-0.491204	-0.521375	-0.63429	-0.650293	-0.695271	-0.567457	-0.661922	-0.402203	-0.729825	-0.630483	-0.591241	-0.595347	-0.520872	-0.534415	-0.675771	-0.521642	-0.579516	-0.71082	-0.538983	-0.637005	-0.613244	-0.527521	-0.518286	-0.549176	-0.646482	-0.474831	-0.749775	-0.593508	-0.65672	-0.616531	-0.641366	-0.579132	-0.353847	-0.593789	-0.379657	-0.577604	-0.665159	-0.552994	-0.700469	-0.621582	-0.519006	-0.692925	-0.651994	-0.63646	-0.483905	-0.629932	-0.463239	-0.664408	-0.699684	-0.507791	-0.731221	-0.577734	-0.688031	-0.647881	-0.681086	-0.607305	-0.700216	-0.774448	-0.562562	-0.489596	-0.625427	-0.771725	-0.605319	-0.642851	-0.665689	⋯
4	1850-04-01	0.477059	0.116737	-0.582562	-0.630568	-0.667448	-0.662525	-0.535898	-0.639695	-0.530702	-0.777102	-0.586981	-0.560966	-0.728786	-0.682735	-0.672664	-0.766407	-0.453648	-0.560511	-0.602442	-0.735504	-0.63805	-0.540513	-0.739323	-0.585814	-0.718682	-0.603517	-0.559599	-0.594016	-0.883967	-0.482028	-0.72607	-0.738021	-0.665697	-0.593461	-0.588228	-0.420146	-0.570728	-0.525597	-0.762746	-0.651996	-0.67501	-0.645	-0.770939	-0.635276	-0.575136	-0.474133	-0.439831	-0.45318	-0.473134	-0.518875	-0.570078	-0.59986	-0.661941	-0.67698	-0.512728	-0.550911	-0.497807	-0.615813	-0.453946	-0.432336	-0.790955	-0.860717	-0.531102	-0.672874	-0.730638	-0.601917	-0.429681	-0.665624	-0.535866	-0.594661	-0.656524	-0.685286	-0.49377	-0.605827	-0.551834	-0.556864	-0.688364	-0.615699	-0.659199	-0.586911	-0.499775	-0.698917	-0.642701	-0.524081	-0.586376	-0.729377	-0.701497	-0.737507	-0.544223	-0.426124	-0.786389	-0.706019	-0.53647	-0.61126	-0.617072	-0.755983	-0.614728	-0.72169	-0.544256	⋯
5	1850-05-01	0.487233	0.0959004	-0.405015	-0.493155	-0.550864	-0.544453	-0.47662	-0.672841	-0.49744	-0.602508	-0.587425	-0.501821	-0.602731	-0.661768	-0.543273	-0.610651	-0.416928	-0.468399	-0.599917	-0.601003	-0.517457	-0.532942	-0.559357	-0.613025	-0.540389	-0.424855	-0.623499	-0.473184	-0.582298	-0.488883	-0.597144	-0.580549	-0.596275	-0.515859	-0.479709	-0.451944	-0.450419	-0.513041	-0.654336	-0.641418	-0.526376	-0.440389	-0.501845	-0.485711	-0.625926	-0.433709	-0.496531	-0.421105	-0.541942	-0.478641	-0.559323	-0.551123	-0.677664	-0.617393	-0.537903	-0.498957	-0.40096	-0.499489	-0.400485	-0.450071	-0.640857	-0.553566	-0.649817	-0.483871	-0.56162	-0.587049	-0.408533	-0.486738	-0.515691	-0.4793	-0.607593	-0.468211	-0.523822	-0.490501	-0.496283	-0.508472	-0.526525	-0.486144	-0.449892	-0.609757	-0.281833	-0.624157	-0.528959	-0.380567	-0.620688	-0.520108	-0.608274	-0.551075	-0.464028	-0.444602	-0.423735	-0.538749	-0.574072	-0.397351	-0.445786	-0.760367	-0.51173	-0.664402	-0.439849	⋯

Saving the mean of the ensemble to be used later.

In [3]:

Code

menstemp_hadcrut = reduce(+, eachcol(rawtemp_hadcrut[:, 4:203])) ./ ncol(rawtemp_hadcrut[:, 4:203]);
temp_hadcrut = DataFrame("Dates" => rawtemp_hadcrut.Time, "Temperature" => menstemp_hadcrut[:]);
first(temp_hadcrut, 5)

5×2 DataFrame

Row	Dates	Temperature
	Date	Float64
1	1850-01-01	-0.674564
2	1850-02-01	-0.333416
3	1850-03-01	-0.591323
4	1850-04-01	-0.588721
5	1850-05-01	-0.508817

Baseline

In [4]:

Code

oldbase_hadcrut = mean(temp_hadcrut.Temperature[(temp_hadcrut.Dates.>=Date(1850, 1, 1)).&(temp_hadcrut.Dates.<Date(1900, 1, 1))]);
temp_hadcrut.Temperature = temp_hadcrut.Temperature .- oldbase_hadcrut;
first(temp_hadcrut, 5)

5×2 DataFrame

Row	Dates	Temperature
	Date	Float64
1	1850-01-01	-0.31563
2	1850-02-01	0.0255188
3	1850-03-01	-0.232388
4	1850-04-01	-0.229786
5	1850-05-01	-0.149882

1.2 GISTEMP Data

In [5]:

Code

widetemp = CSV.read("data/GLB.Ts+dSST.csv", DataFrame)
rawtemp_gistemp = TrendEstimators.longseries(widetemp)
T = size(rawtemp_gistemp, 1);
dates = collect(Date(1880, 1, 1):Month(1):(Date(1880, 1, 1)+Dates.Month(T - 1)));
temp_gistemp = DataFrame("Dates" => dates, "Temperature" => rawtemp_gistemp[:]);
first(temp_gistemp, 5)

5×2 DataFrame

Row	Dates	Temperature
	Date	Float64
1	1880-01-01	-0.2
2	1880-02-01	-0.26
3	1880-03-01	-0.09
4	1880-04-01	-0.17
5	1880-05-01	-0.1

Baseline

In [6]:

Code

oldbase_gistemp = mean(temp_gistemp.Temperature[(temp_gistemp.Dates.>=Date(1850, 1, 1)).&(temp_gistemp.Dates.<Date(1901, 1, 1))]);
temp_gistemp.Temperature = temp_gistemp.Temperature .- oldbase_gistemp;

1.3 Berkeley Data

In [7]:

Code

rawtemp_berkeley = CSV.read("data/BerkeleyEarth.csv", DataFrame)
T = size(rawtemp_berkeley, 1);
dates = collect(Date(1850, 1, 1):Month(1):(Date(1850, 1, 1)+Dates.Month(T - 1)));
rawtemp_berkeley = DataFrame("Dates" => dates, "Temperature" => rawtemp_berkeley[!, 3]);

Baseline

In [8]:

Code

oldbase_berkeley = mean(rawtemp_berkeley.Temperature[(rawtemp_berkeley.Dates.>=Date(1850, 1, 1)).&(rawtemp_berkeley.Dates.<Date(1900, 1, 1))]);
temp_berkeley = DataFrame("Dates" => dates, "Temperature" => rawtemp_berkeley.Temperature .- oldbase_berkeley);

1.4 NOAA Data

In [9]:

Code

rawtemp_noaa = CSV.read("data/NOAA.csv", DataFrame)
T = size(rawtemp_noaa, 1);
dates = collect(Date(1850, 1, 1):Month(1):(Date(1850, 1, 1)+Dates.Month(T - 1)));
temp_noaa = DataFrame("Dates" => dates, "Temperature" => rawtemp_noaa[!, 3]);

Baseline

In [10]:

Code

oldbase_noaa = mean(temp_noaa.Temperature[(temp_noaa.Dates.>=Date(1850, 1, 1)).&(temp_noaa.Dates.<Date(1900, 1, 1))]);
temp_noaa.Temperature = temp_noaa.Temperature .- oldbase_noaa;
last(temp_noaa, 5)

5×2 DataFrame

Row	Dates	Temperature
	Date	Float64
1	2024-04-01	1.34532
2	2024-05-01	1.39105
3	2024-06-01	1.39492
4	2024-07-01	1.44785
5	2024-08-01	1.45617

Plots

In [11]:

Code

plot(temp_hadcrut.Dates, temp_hadcrut.Temperature, label="HadCRUT (pre-industrial baseline)", xlabel="Date (monthly)", ylabel="°C", linewidth=0.8, linestyle= :dot, xticks=(temp_hadcrut.Dates[372:240:end], Dates.format.(temp_hadcrut.Dates[372:240:end], "Y")), title="Temperature anomalies", color=1)
plot!(temp_gistemp.Dates, temp_gistemp.Temperature, label="GISTEMP (pre-industrial baseline)", linewidth=0.8, linestyle= :dot, color=2)
plot!(temp_berkeley.Dates, temp_berkeley.Temperature, label="Berkeley Earth (pre-industrial baseline)", linewidth=0.8, linestyle= :dot, color=3, xlims=(Date(1990, 1, 1), Date(2024, 10, 1)))
plot!(temp_noaa.Dates, temp_noaa.Temperature, label="NOAA (pre-industrial baseline)", linewidth=0.7, linestyle= :dot, color=4, ylims=(-0.2, 1.8))
plot!(fontfamily="Computer Modern", legendfontsize=12, tickfontsize=12, titlefontfamily="Computer Modern", legendfontfamily="Computer Modern", tickfontfamily="Computer Modern", ylabelfontsize=12, xlabelfontsize=12, titlefontsize=12, legend=:bottomright)

Figure 1: Temperature anomalies (°C) in the HadCRUT5 (Morice et al. 2021), GISTEMP (GISTEMP 2020), Berkeley Earth (Rohde and Hausfather 2020), and NOAAGlobalTemp (Huang et al. 2024) datasets. All datasets are presented with the pre-industrial baseline period (Before 1900).

In [12]:

Code

savefig("figures/Temperature-Anomalies-All-Datasets.png")

"/Users/jeddy/Library/CloudStorage/OneDrive-AalborgUniversitet/Research/CLIMATE/Paris Goal/Odds-of-breaching-1.5C/figures/Temperature-Anomalies-All-Datasets.png"

In [13]:

Code


[ last(temp_hadcrut.Temperature, 5), last(temp_gistemp.Temperature, 5), last(temp_berkeley.Temperature, 5), last(temp_noaa.Temperature, 5) ]

4-element Vector{Vector{Float64}}:
 [1.4334640716722102, 1.4743430876722106, 1.4969345246722099, 1.5985191671722112, 1.4999801771722105]
 [1.3774603174603173, 1.4574603174603173, 1.4174603174603173, 1.5174603174603174, 1.4474603174603173]
 [1.513775, 1.531775, 1.530775, 1.683775, 1.573775]
 [1.3453185816666666, 1.3910525816666666, 1.3949165816666667, 1.4478515816666666, 1.4561725816666666]

Probability paths

In [14]:

Code

paths_hadcrut = CSV.read("tables/ProbabilityPathsHadCRUT5-Extensive.csv", DataFrame);
paths_gistemp = CSV.read("tables/ProbabilityPathsGISTEMP-Extensive.csv", DataFrame);
paths_berkeley = CSV.read("tables/ProbabilityPathsBerkeley-Extensive.csv", DataFrame);
paths_noaa = CSV.read("tables/ProbabilityPathsNOAA_breakmodel-Extensive.csv", DataFrame);

In [15]:

Code

plot(paths_hadcrut."Date (month)", [paths_hadcrut."1.5°C Threshold"], label="HadCRUT", xlabel="Date (monthly)", ylabel="Probability", linewidth=4, linestyle= :dashdot, title="", color=:darkorange)
plot!(paths_gistemp."Date (month)", [paths_gistemp."1.5°C Threshold"], label="GISTEMP", linewidth=4, linestyle= :dot, color=:darkorange2)
plot!(paths_berkeley."Date (month)", [paths_berkeley."1.5°C Threshold"], label="Berkeley Earth", linewidth=4, linestyle= :dash, color=:darkorange3)
plot!(paths_noaa."Date (month)", [paths_noaa."1.5°C Threshold"], label="NOAA", linewidth=4, linestyle= :solid, color=:darkorange4)
plot!(xlims= (Date(2020, 1, 1), Date(2053, 1, 1)), ylims=(0, 1), xticks=(paths_hadcrut."Date (month)"[168:60:end-336], Dates.format.(paths_hadcrut."Date (month)"[168:60:end-336], "Y")), fontfamily="Computer Modern", legendfontsize=12, tickfontsize=12, titlefontfamily="Computer Modern", legendfontfamily="Computer Modern", tickfontfamily="Computer Modern", ylabelfontsize=12, xlabelfontsize=12, titlefontsize=12, legend=:bottomright)

Figure 2: Probability of exceeding the 1.5°C threshold in the HadCRUT5 (Morice et al. 2021), GISTEMP (GISTEMP 2020), Berkeley Earth (Rohde and Hausfather 2020), and NOAAGlobalTemp (Huang et al. 2024) datasets.

In [16]:

Code

savefig("figures/Coverage-All-Datasets.png")

"/Users/jeddy/Library/CloudStorage/OneDrive-AalborgUniversitet/Research/CLIMATE/Paris Goal/Odds-of-breaching-1.5C/figures/Coverage-All-Datasets.png"

GISTEMP. 2020. “GISS Surface Temperature Analysis (GISTEMP), version 4.” https://data.giss.nasa.gov/gistemp/.

Huang, Boyin, Xungang Yin, Matthew J. Menne, Russell S. Vose, and Huai-Min Zhang. 2024. “NOAA Global Surface Temperature Dataset (NOAAGlobalTemp).” NOAA National Centers for Environmental Information. https://doi.org/10.25921/rzxg-p717.

Morice, Colin P, John J Kennedy, Nick A Rayner, JP Winn, Emma Hogan, RE Killick, RJH Dunn, TJ Osborn, PD Jones, and IR Simpson. 2021. “An Updated Assessment of Near-Surface Temperature Change from 1850: The HadCRUT5 Data Set.” Journal of Geophysical Research: Atmospheres 126 (3): e2019JD032361.

Rohde, Robert A, and Zeke Hausfather. 2020. “The Berkeley Earth Land/Ocean Temperature Record.” Earth System Science Data 12 (4): 3469–79. https://doi.org/10.5194/essd-12-3469-2020.

Supplementary notebook (temperature datasets comparison)

0. Load Packages

1. Load Data

1.1 Load HadCRUT Data

1.2 GISTEMP Data

1.3 Berkeley Data

1.4 NOAA Data

Plots

Probability paths