1.5°C Long-term Temperature Methods

	A	B	C	D	E	F	G	H	I	J	K	L	M	N	O	P	Q	R	S
1	Section	Method Name	Citation	Uncertainty from internal variability samplings	Uncertainty from climate sensitivity	Uncertainty from temp observation uncertainty?	Uncertainty from forcing	Can isolate volcanic, climate modes?	Uncertainty from standard fit error	Attribution	Can isolate volcanic, climate modes?	Needed addl. inputs	Smoothness ## 2nd deriv (TO BE CALCULATED)	Back - updated?	Code status	Evolving States	Const. DOF (approx - specific method implementation)	Parsimony	Universality

2	4.2.1	20-yr centred		y		y		no		no	no	!! Future GMST		no	1	1	0
3	4.2.1 $	30-yr centred		y		y		no		no	no	!! Future GMST		no	1	1	0
4	4.2.1	5-yr lagging		y		y		no		no	no			no	1	1	0
5	4.2.1 $	10-yr lagging	AR6	y		y		no		no	no			no	1	1	0
6	4.2.2 $	OLS refitting	AR5			n		no		no	no			yes**	1	2	0
7	4.2.2	OLS AR5 all	AR5			n		no		no	no			no	1	0	2
8	4.2.3	OLS AR5 split				n				no	no			no	1	0	4
9	4.2.2	Theil Sen slope after hinge fit 1975	Duan et al., 2021			n		no		no	no			yes	0.5	2	0
10	4.2.2	OLS Hinge fit 1975 (refitting mt+b)	Livezey et. al. 2007			n		no		no	no			yes	0.8	2	0
11	4.2.2 $	Hinge fit meet (refitting mt, b fixed)	(to make lines meet)			n		no		no	no			yes	1	1	1
12	4.2.2	quartic polynomial	Hawkins and Sutton 2009	y		y		no		no	no			yes	0.5	4	0
13	4.2.2	Bayesian change-point	Yu and Ruggieri, 2019					no		no	no			yes	1	2
14	4.2.3 $	11-yr offset	Trewin (2022)					no		no	no			no	1	1	1
15	4.2.3 $	15-yr trend endpoint	SR15 Allen et al., 2018					no		no	no			no**	1	2	0
16		End of 30-year trend (met office)
17		End of 30-year trend 3CS (met office)	Unclear what data they are referring to. Possibly ERA5Land? Over just 60N to 60S
18		IPCC Assessed warming trend. CGWL
19	4.2.3	LOWESS linear	Clarke & Richardson (2021)					no		no	no				0.5	2
20	(4.2.3)	LOWESS quadratic						no		no	no				0.5	3
21		LOESS (other Met Office)... (wt, span, correct)
22	4.2.3 $	Butterworth	Mann (2008)					no		no	no			yes	1
23	4.2.3	Empirical Mode Decomposition	Wu 2011
24	(4.2.3)	Optimal climate normal	Livezey et. al. 2007					no		no	no				0.8 (check)
25	4.2.4 $	GAM AR1 residuals	AR5, box 2.2 (Stephenson?)					no		no	no			yes	1	1	10
26	(4.2.2)	GAM AR0 (standard)						no		no	no			yes	1	1	10
27	4.2.5	Kalman: Std Random Walk	Shumway and Stoffer (2016)					no		no	no	SST (HADSST4)		yes	1	1	4
28	4.2.5 $	Kalman: Integrated Rand Walk	Visser 2018					no		no	no	SST (HADSST4)		yes	1	2	6
29	4.2.6 $	Remove MEI	(Foster and Rahmstorf 2011)					yes		no	yes	MEI (1880 at earliest)	poor	no	1
30	(4.3.2)	Remove MEI, volcanic AOD, solar	(Foster and Rahmstorf 2011)					yes		possible?	yes	MEI, AOD, TSI	poor	no	1
31	4.2.6	Atlantic and Pacific modes of variability	Wu et al. 2019 ??2017?					yes		no	yes	??? specific varability indices		no
32	4.2.6	Pattern Recognition on TS	Wills et al. 2020					yes		no	yes	TS field		no	S/NP filtering: github.com/rcjwills/forced-patterns. LFCA: github.com/rcjwills/lfca.
33	4.2.6	Spatial EOFs of TS	Chen and Tung, 2018					yes		no	yes	TS field		no	Emailed Sept 22, awaiting response
34	4.2.6	Reg. Linear Models of SLP	(Sippel et al. 2019)					?		no	?	SLP field		no	https://github.com/sebastian-sippel/dynamical_adjustment_elasticnet
35	4.2.6	Greens Functions on TS	Samset et al. 2023					yes		no	yes	TS field		no	Bjorn working on code, sent hadcrut5 file
36	4.4	UKCIP18 RCP4.5 CGWL	Betts et. al. (2023)					partially		yes	partially	CMIP model futures		yes -> 21-yr cent	0	>10^8 / GCM	50 / GCM
37	4.4	HadGEM3 CGWL	Betts et. al. (2023)
38	4.4	Weighted CMIP RCP4.5 CGWL
39	4.3.1	Effective Rad. Forcings	AR6 Chapter 7					yes		yes	yes	TSI, GHG, anth. aerosols, CFC, land use		no	https://github.com/chrisroadmap/temperature-attribution.	1	5
40	4.3.2	Nonlinear EBM within Kalman Filter	Nicklas (2024)					yes		yes	yes	TSI, GHG, anth. aerosols, AOD		no** (can be)	1	2	17
41	4.3.2	3-layer linear heat Kalman Filter	Cummins (2020)					no		possible?	no	TOA net forcing			https://github.com/donaldcummins/EBM2	3	11
42	4.3.3	GWI: Human-induced warming / Multifingerprinting	Otto et al. 2015, Haustein et al., 2017, Forster et al. 2023					yes		yes	yes	at least ERF. But also including samplings of internal variability of climate and a range of climate parameterisations increases uncertainty inclusion			https://github.com/ClimateIndicator/anthropogenic-warming-assessment - since this paper goes beyond IPCC-style assessment, here is where the more general anslysis happens: https://github.com/tristramwalsh/global-warming-index
43	4.3.3	Kriging for Climate Change	Qasmi and Ribes 2022					?		yes	?	ERF			https://gitlab.com/saidqasmi/kcc_notebook/-/blob/master/KCC_notebook.ipynb?ref_type=heads
44	4.3.3	Regularized Optimal Fingerprinting	Gillett et al. 2021					yes		yes	yes	ERF			https://github.com/ESMValGroup/ESMValTool/tree/forster23/esmvaltool/diag_scripts/attribute
45		Neural Network (CMIP trained)	https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022MS003475
46		UNet (preprint)	https://essopenarchive.org/users/653004/articles/660203-separation-of-internal-and-forced-variability-of-climate-using-a-u-net
47		NN - small, trained on obs
48		NN - metalearning, retrained on obs
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