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arxiv: 2511.00731 · v2 · submitted 2025-11-01 · ⚛️ physics.ao-ph

Quantifying the radiative response to surface temperature variability: A critical comparison of current methods

Leif Fredericks , Maria Rugenstein , David W. J. Thompson , Senne Van Loon , Fabrizio Falasca , Rory Basinski-Ferris , Paulo Ceppi , Quran Wu
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Jonah Bloch-Johnson Marc Alessi Sarah M. Kang
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Pith reviewed 2026-05-18 02:07 UTC · model grok-4.3

classification ⚛️ physics.ao-ph
keywords pattern effectradiative feedbacksurface temperature patternsclimate model simulationsstatistical methodsinternal variabilityCO2 forcing
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The pith

Different methods for quantifying the pattern effect agree on internal variability but produce very different results for CO2-forced temperature patterns.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper compares several statistical methods for estimating the pattern effect, in which the radiative response to surface temperature change depends on the spatial structure of the temperature field. Each method is tested on the same climate model data to predict how radiation responds to temperature variations. The methods mostly agree when the temperature changes come from internal climate variability. They disagree substantially when the temperature patterns come from simulations with steadily increasing CO2. The comparison highlights where each approach is reliable and where it may need refinement for understanding climate feedbacks.

Core claim

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 CO2 concentrations. Most methods indicate large negative feedbacks over the western Pacific, but over other regions the methods frequently disagree on feedback sign and spatial homogeneity.

What carries the argument

The pattern effect: the dependence of radiative response to surface temperature change on the spatially varying structure of the temperature field.

If this is right

  • Most methods identify large negative feedbacks over the western Pacific.
  • Methods disagree on the sign of feedbacks and on whether they are spatially uniform in other regions.
  • Discrepancies between methods are larger when applied to CO2-forced temperature patterns than to internal variability.
  • The choice of method affects estimates of how radiative feedbacks respond to future warming patterns.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Testing the methods directly against Green's function experiments could show which statistical approach best captures causal influences from specific temperature patches.
  • The method differences may affect how historical observations are used to infer long-term climate sensitivity.
  • Resolving the disagreements could improve regional projections of cloud and energy-balance responses to patterned warming.

Load-bearing premise

That applying each method to the same temperature patterns and radiative data produces a fair comparison in which observed differences arise from the methods themselves rather than from implementation details or dataset choices.

What would settle it

Applying all methods to an independent collection of climate model runs with known radiative responses and checking whether the agreement on internal variability and the divergence on CO2-forced patterns both reappear.

Figures

Figures reproduced from arXiv: 2511.00731 by David W. J. Thompson, Fabrizio Falasca, Jonah Bloch-Johnson, Leif Fredericks, Marc Alessi, Maria Rugenstein, Paulo Ceppi, Quran Wu, Rory Basinski-Ferris, Sarah M. Kang, Senne Van Loon.

Figure 1
Figure 1. Figure 1: b) Patches outlined at the half-patch width for the global protocol, c) sensitivity map produced by using [PITH_FULL_IMAGE:figures/full_fig_p019_1.png] view at source ↗
read the original abstract

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 temperatures 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 CO2 concentrations. We discuss reasons for the discrepancies between methods and recommend paths towards using them in the future to enhance physical understanding of the pattern effect.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 3 minor

Summary. The paper compares several statistical methods for estimating the pattern effect, i.e., the dependence of the radiative response on the spatial structure of surface temperature changes. The authors apply each method to the same prediction task on climate model data and report that most methods indicate large negative feedbacks over the western Pacific, with frequent disagreements on sign and homogeneity elsewhere. All methods produce similar global radiative response predictions for temperature variations driven by internal variability, but yield very different predictions when applied to surface temperature patterns from CO2-forced simulations. The manuscript discusses reasons for discrepancies and recommends paths for future use to improve physical understanding.

Significance. If the central comparison holds under controlled conditions, the work is significant for climate feedback research because the pattern effect influences estimates of equilibrium climate sensitivity. Highlighting where methods converge on internal variability but diverge on forced responses can help the community select and refine techniques. The explicit head-to-head application to identical tasks is a strength, though the absence of quantitative agreement metrics limits the immediate utility for model evaluation or observational constraints.

major comments (3)
  1. [Abstract and §3] Abstract and §3 (Results): The claims that 'all methods yield similar predictions' for internal variability and 'produce very different predictions' for CO2-forced patterns are presented without quantitative metrics (e.g., pattern correlations, RMSE, or global-mean differences with uncertainty ranges). This makes the degree of agreement/disagreement impossible to assess objectively and weakens the contrast that forms the paper's central result.
  2. [§2 and §3] §2 (Methods) and §3: The central claim that discrepancies on CO2-forced patterns reflect intrinsic methodological differences requires that every method received exactly the same surface temperature fields (same grid, same ensemble mean or individual runs, same definition of the forced pattern) and the same radiative response target. The manuscript does not explicitly confirm that processing steps such as pattern extraction, temporal averaging, or flux calculation were held fixed across methods; any unstated variation would render the reported differences non-diagnostic.
  3. [§3 and §4] §3 and §4: No error bars, bootstrap uncertainties, or sensitivity tests to implementation choices (e.g., regression window, spatial smoothing) are reported for the global or regional feedback estimates. This is especially load-bearing for the claim of method disagreement on forced patterns, as small differences in data preparation could produce the observed spread.
minor comments (3)
  1. [Introduction] The introduction would benefit from a concise table listing the specific methods compared, their key assumptions, and original references.
  2. [Figures] Figure captions should explicitly state the data source, ensemble, and time period used for each panel to allow readers to reproduce the comparison.
  3. [§4] A short discussion of computational cost or data requirements for each method would help readers evaluate practical advantages beyond accuracy.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed review. The comments correctly identify opportunities to strengthen the objectivity and transparency of our comparisons. We have revised the manuscript accordingly and respond to each major comment below.

read point-by-point responses

  1. Referee: [Abstract and §3] Abstract and §3 (Results): The claims that 'all methods yield similar predictions' for internal variability and 'produce very different predictions' for CO2-forced patterns are presented without quantitative metrics (e.g., pattern correlations, RMSE, or global-mean differences with uncertainty ranges). This makes the degree of agreement/disagreement impossible to assess objectively and weakens the contrast that forms the paper's central result.

    Authors: We agree that quantitative metrics are needed for an objective assessment. In the revised manuscript we have added pattern correlations, RMSE values, and global-mean differences (with uncertainty ranges) to the abstract and Section 3. These metrics show high agreement for internal variability (pattern correlations typically >0.8) while confirming substantially larger discrepancies for CO2-forced patterns. revision: yes

  2. Referee: [§2 and §3] §2 (Methods) and §3: The central claim that discrepancies on CO2-forced patterns reflect intrinsic methodological differences requires that every method received exactly the same surface temperature fields (same grid, same ensemble mean or individual runs, same definition of the forced pattern) and the same radiative response target. The manuscript does not explicitly confirm that processing steps such as pattern extraction, temporal averaging, or flux calculation were held fixed across methods; any unstated variation would render the reported differences non-diagnostic.

    Authors: All methods were applied to identical surface temperature fields on the same grid, using the same ensemble means, the same definition of the forced pattern, and the same radiative response target. Processing steps including pattern extraction, temporal averaging, and flux calculation were held fixed. We have added explicit statements in Section 2 confirming these details so that the reported differences can be attributed to methodological choices. revision: yes

  3. Referee: [§3 and §4] §3 and §4: No error bars, bootstrap uncertainties, or sensitivity tests to implementation choices (e.g., regression window, spatial smoothing) are reported for the global or regional feedback estimates. This is especially load-bearing for the claim of method disagreement on forced patterns, as small differences in data preparation could produce the observed spread.

    Authors: We acknowledge that the original submission did not include uncertainty estimates or sensitivity tests. In the revision we have added bootstrap uncertainties for both global and regional estimates in Sections 3 and 4. We also report sensitivity tests to regression window length and spatial smoothing; these tests show that the primary finding of larger method disagreement on CO2-forced patterns is robust to these choices. revision: yes

Circularity Check

0 steps flagged

No circularity: comparative evaluation of existing methods on shared inputs

full rationale

The paper performs a head-to-head comparison by applying several pre-existing statistical methods to identical temperature patterns and radiative response data for the same prediction tasks. No derivation chain is claimed; the central results are empirical outcomes of running each method on the fixed dataset rather than any quantity being redefined in terms of itself or fitted parameters being relabeled as predictions. The abstract and described approach contain no self-definitional steps, no uniqueness theorems imported from prior self-citations, and no ansatz smuggled via citation. The observed agreement on internal variability and disagreement on CO2-forced patterns are presented as diagnostic of methodological differences, with the shared-input protocol serving as the independent benchmark. This structure is self-contained against external data and does not reduce any reported result to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no explicit free parameters, axioms, or invented entities are identifiable from the provided text.

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Works this paper leans on

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