Abstract Large biases in subtropical marine low‐cloud cover (LCC) simulated by general circulation models (GCMs) are often attributed to their low‐cloud macrophysical parameterizations. There has been less investigation into other aspects of the simulated climate which may also contribute to biases in GCM LCC. Here, we quantify the contribution of GCM LCC biases from both the macrophysical low‐cloud parameterizations, and the GCM simulation of large‐scale meteorological conditions, which control a large portion of observed LCC variability. We use a machine‐learning model trained to predict observed relationship between LCC and large‐scale conditions to perform the bias decomposition within nine CMIP6 GCMs in both coupled and atmosphere‐only configurations. We find that the biases attributed to the GCM large‐scale conditions are of the same order as those attributed to low‐cloud parameterizations, in contradiction with previous assertions. This has implications for the use of statistical models of low‐clouds to assess their feedback to climate change.
Observations from the Little Bunker
Last updated: August 18, 2025 04:13 PM
Atmospheric CO₂
425.4 ppm
+18.4
ppm/year
Atmospheric CH₄
1934.91 ppb
+9.11
ppb/year
Global Temperature
+1.48 °C
Peak: +1.73°C
World Population
8.1 billion
+67
million/year