The Dependence of Radiative Forcing and Feedback on Evolving Patterns of Surface Temperature Change in Climate Models

By T. Andrews, J. Gregory, M. Webb. 2015

319 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/26e48d9507d50c54d1975660b25d8a22a2c9a8e0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

In Global and Planetary Change

Citations in the corpus

Cloud feedback mechanisms and their representation in global climate models (2017, unknown): “In particular, most CMIP5 models subjected to an abrupt quadrupling of CO2 concentrations indicate that the SW cloud feedback parameter increases after about two decades, and this is a direct consequence of changes in the SST warming pattern.(179) As future patterns of SST increase are uncertain in ”
Presentation and Evaluation of the IPSL‐CM6A‐LR Climate Model (2020, background): “Results are sensitive to the length of the simulation because of the spatial and temporal dependence of the feedback parameters (Andrews et al., 2012, 2015; Knutti et al., 2017; Rugenstein et al., 2020).”
Analyzing the dependence of global cloud feedback on the spatial pattern of sea surface temperature change with a Green's function approach (2017, background): “…the CO2 concentration increases abruptly [Williams et al., 2008; Held et al., 2010; Armour et al., 2013; Andrews et al., 2015; Rugenstein et al., 2016; Proistosescu and Huybers, 2017; Armour, 2017], which is at least in some models caused by an increase in the cloud feedback [Andrews et al., 2015].”
The DOE E3SM Coupled Model Version 1: Overview and Evaluation at Standard Resolution (2019, background): “Because the surface temperature versus TOA energy imbalance slope weakens with time in most models (Andrews et al., 2015; Armour et al., 2013; Ceppi & Gregory, 2017), ECS computed this way is best described as “effective climate sensitivity.””
The Dependence of Global Cloud and Lapse Rate Feedbacks on the Spatial Structure of Tropical Pacific Warming (2018, background): “…in feedback strength under an abrupt quadrupling of CO2 is close to that of phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel-mean change (Andrews et al. 2015); thus, the variation in feedback strength in HadGEM2 is not unusual in comparison to other contemporary models.”
The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6. (2016, background): “Recent studies have shown significant time variation in climate feedbacks in response to CO2 quadrupling (e.g. Andrews et al., 2012, 2015; Geoffroy et al., 2013; Armour et al., 2013).”
Low-Cloud Feedbacks from Cloud-Controlling Factors: A Review (2017, background): “7 which is a typical value for climate model simulations of climate warming (Andrews et al. 2015).”
The Radiative Forcing Model Intercomparison Project (RFMIP): Experimental Protocol for CMIP6 (in open review: 10.5194/gmd-2016-88) (2016, background): “But this is not true in practice: models participating in exercises like CMIP are subject to surprisingly large differences in radiative forcing (Andrews et al., 2012; Forster et al., 2013; Chung and Soden, 2015) even when the perturbations applied to the physical system are the same.”
Recommendations for diagnosing effective radiative forcing from climate models for CMIP6 (2016, methodology): “, 2014] and temporal evolution of feedback [Andrews et al., 2015; Gregory and Andrews, 2016] have all been mooted as a possible cause of differences between ECS estimates.”
Evaluating Emergent Constraints on Equilibrium Climate Sensitivity (2018, background): “2 of Andrews et al. (2015)] and simulations that would allow us to compute something more akin to ‘‘true’’ ECS (e.g., coupled 2 3 CO 2 simulations extending thousands of years) are not available for most CMIP5 models.”
Causes of Higher Climate Sensitivity in CMIP6 Models (2020, methodology): “ECS diagnosed ignoring the first 20 years of abrupt-4xCO2 anomalies (Andrews et al., 2015) is 6% larger on average, roughly half as large as the CMIP5 inflation factor of 11% (Figure S11).”
High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections (2023, result): “The TCR quantifies the change in global-mean near-surface air temperature in the year when CO 2 concentrations have doubled after continuously increasing by 1% every year 31,61,62 (Table 3 and Fig.”