TY - JOUR
T1 - Recent cloud controlling factor analyses indicate higher climate sensitivity
AU - Wilson Kemsley, Sarah
AU - Nowack, Peer
AU - Ceppi, Paulo
N1 - Data Availability Statement:
All data used in this research is freely available. ERA5 meteorological reanalysis data are from the Copernicus Climate Change Service (Hersbach et al., 2023a, 2023b). Combined MODIS Aqua–Terra data are freely available and downloaded with monthly resolution (NASA, 2022). CMIP5/6 data are obtained from the UK Center for Environmental Data Analysis portal (CEDA). Cloud radiative kernels are freely available at Zelinka (2022), and cloud-radiative sensitivities were calculated using code adapted from Nowack (2021). We used ECS values from Zelinka (2024). Code for reproducing Sherwood et al. (2020)'s Bayesian analysis was taken from Webb (2022).
PY - 2026/2/16
Y1 - 2026/2/16
N2 - Cloud feedback is a dominant source of uncertainty in climate model estimates of equilibrium climate sensitivity (ECS). Cloud controlling factor analysis can observationally constrain cloud feedback. For the first time, we use separate rather than unified frameworks to assess high- and low-cloud feedbacks and constrain the net cloud feedback and subsequently, the ECS. We find a robustly positive cloud feedback (i.e., a negative feedback is (Formula presented.) % probable), indicating that clouds amplify global warming. We assess the individual and combined impacts of our cloud feedback constraints on ECS using three approaches. Two indicate an upward ECS shift with reduced uncertainty, preserving ECS–feedback correlations but using cloud feedback as a single line of evidence. The third, a Bayesian framework combining multiple lines of evidence, also suggests a higher ECS but with a smaller increase and broader confidence range.
AB - Cloud feedback is a dominant source of uncertainty in climate model estimates of equilibrium climate sensitivity (ECS). Cloud controlling factor analysis can observationally constrain cloud feedback. For the first time, we use separate rather than unified frameworks to assess high- and low-cloud feedbacks and constrain the net cloud feedback and subsequently, the ECS. We find a robustly positive cloud feedback (i.e., a negative feedback is (Formula presented.) % probable), indicating that clouds amplify global warming. We assess the individual and combined impacts of our cloud feedback constraints on ECS using three approaches. Two indicate an upward ECS shift with reduced uncertainty, preserving ECS–feedback correlations but using cloud feedback as a single line of evidence. The third, a Bayesian framework combining multiple lines of evidence, also suggests a higher ECS but with a smaller increase and broader confidence range.
KW - climate sensitivity
KW - cloud feedback
UR - https://www.scopus.com/pages/publications/105029042246
U2 - 10.1029/2025GL118366
DO - 10.1029/2025GL118366
M3 - Letter
SN - 0094-8276
VL - 53
JO - Geophysical Research Letters
JF - Geophysical Research Letters
IS - 3
M1 - e2025GL118366
ER -