@article{Fredericks:2026:10.1175/JCLI-D-25-0612.1,
author = {Fredericks, L and Rugenstein, M and Thompson, DWJ and Van, Loon S and Falasca, F and Basinski-Ferris, R and Ceppi, P and Wu, Q and Bloch-Johnson, J and Alessi, MJ and Kang, SM},
doi = {10.1175/JCLI-D-25-0612.1},
journal = {Journal of Climate},
pages = {4101--4102},
title = {Quantifying the Radiative Response to Surface Temperature Variability: A Critical Comparison of Current Methods},
url = {http://dx.doi.org/10.1175/JCLI-D-25-0612.1},
volume = {39},
year = {2026}
}

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TY  - JOUR
AB  - Over the past decade, it has become clear that the radiative response to surface temperature change depends on the spatially varying structure in the temperature field, a phenomenon known as the “pattern effect.” The pattern effect is commonly estimated from dedicated climate model simulations forced with local surface temperature patches (Green's function experiments). Green's function experiments capture causal influences from temperature perturbations but are computationally expensive to run. Recently, however, several methods have been proposed that estimate the pattern effect through statistical means. These methods can accurately predict the radiative response to temperature variations in climate model simulations. The goal of this paper is to compare methods used to quantify the pattern effect. We apply each method to the same prediction task and discuss its advantages and disadvantages. Most methods indicate large negative feedbacks over the western Pacific. Over other regions, the methods frequently disagree on feedback sign and spatial homogeneity. While all methods yield similar predictions of the global radiative response to surface temperature variations driven by internal variability, they produce very different predictions from the patterns of surface temperature change in simulations forced with increasing carbon dioxide (CO?) concentrations. We discuss reasons for the discrepancies between methods and recommend paths toward using them in the future to enhance physical understanding of the pattern effect.
AU  - Fredericks,L
AU  - Rugenstein,M
AU  - Thompson,DWJ
AU  - Van,Loon S
AU  - Falasca,F
AU  - Basinski-Ferris,R
AU  - Ceppi,P
AU  - Wu,Q
AU  - Bloch-Johnson,J
AU  - Alessi,MJ
AU  - Kang,SM
DO  - 10.1175/JCLI-D-25-0612.1
EP  - 4102
PY  - 2026///
SN  - 0894-8755
SP  - 4101
TI  - Quantifying the Radiative Response to Surface Temperature Variability: A Critical Comparison of Current Methods
T2  - Journal of Climate
UR  - http://dx.doi.org/10.1175/JCLI-D-25-0612.1
VL  - 39
ER  - 

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