Strong cloud–circulation coupling explains weak trade cumulus feedback

Abstract

Shallow cumulus clouds in the trade-wind regions cool the planet by reflecting solar radiation. The response of trade cumulus clouds to climate change is a key uncertainty in climate projections1–4. Trade cumulus feedbacks in climate models are governed by changes in cloud fraction near cloud base5,6, with high-climate-sensitivity models suggesting a strong decrease in cloud-base cloudiness owing to increased lower-tropospheric mixing5–7. Here we show that new observations from the EUREC4A (Elucidating the role of cloud-circulation coupling in climate) field campaign8,9 refute this mixing-desiccation hypothesis. We find the dynamical increase of cloudiness through mixing to overwhelm the thermodynamic control through humidity. Because mesoscale motions and the entrainment rate contribute equally to variability in mixing but have opposing effects on humidity, mixing does not desiccate clouds. The magnitude, variability and coupling of mixing and cloudiness differ markedly among climate models and with the EUREC4A observations. Models with large trade cumulus feedbacks tend to exaggerate the dependence of cloudiness on relative humidity as opposed to mixing and also exaggerate variability in cloudiness. Our observational analyses render models with large positive feedbacks implausible and both support and explain at the process scale a weak trade cumulus feedback. Our findings thus refute an important line of evidence for a high climate sensitivity10,11.

Cloud transition across the daily cycle illuminates model responses of trade cumuli to warming

“…We use all hourly model outputs to construct these composites, which we evaluate against observed hourly covariability between clouds, mass flux, and relative humidity from the EUREC 4 ​A field campaign ( Materials and Methods and ref. 21 ).…”

“…3 A and D , see also ref. 21 ). Overall, with increased mass flux, clouds deepen with enhanced coverage near the base level.…”

“…Moreover, the large sensitivity of trade cumuli to environmental changes predicted by some General Circulation Models (GCMs) ( 8 ) seems at odds with the small sensitivity of trade cumuli to surface warming inferred from satellite observations at the interannual timescale ( 7 , 9 ) or predicted by high-resolution numerical models in idealized experiments ( 10 , 11 ). The diversity and uncertainty of trade cumulus feedback across models are thought to relate to the representation of the strength of cloud–convection coupling ( 12 – 21 ). Measurements from the recent EUREC 4 ​A (Elucidating the Role of Cloud-Circulation Coupling in Climate) field campaign ( 22 , 23 ) demonstrated that i) the cloud–convection coupling observed in nature is stronger than what is simulated by most climate models, and ii) models with no or weak cloud–convection coupling exhibit a larger sensitivity of cloud-base cloud fraction to relative humidity than to convection, and they simulate more positive trade cumulus cloud feedback under global warming ( 21 ).…”

“…The diversity and uncertainty of trade cumulus feedback across models are thought to relate to the representation of the strength of cloud–convection coupling ( 12 – 21 ). Measurements from the recent EUREC 4 ​A (Elucidating the Role of Cloud-Circulation Coupling in Climate) field campaign ( 22 , 23 ) demonstrated that i) the cloud–convection coupling observed in nature is stronger than what is simulated by most climate models, and ii) models with no or weak cloud–convection coupling exhibit a larger sensitivity of cloud-base cloud fraction to relative humidity than to convection, and they simulate more positive trade cumulus cloud feedback under global warming ( 21 ). Climate models with weaker cloud–convection coupling are thus less credible with respect to observational constraints, falsifying an important line of evidence in support of a large trade cumulus feedback and high climate sensitivity ( 14 , 16 , 18 ).…”

A Colorful look at Climate Sensitivity

“…This assertion is at odds with literature that does not account for the ability of clouds to unmask the spectral response to warming. Even if their coverage does change, the assessed feedback from cloud amount changes is only 0.2 W m −2 K −1 (Forster et al, 2021), with recent work suggesting that it may be even smaller (Myers et al, 2021;Vogel et al, 2022) Calculating S cs based on the clear-sky Simpsonian response, and distinguishing it from S, is justified by the strong theoretical foundation for the Simpsonian response, and an ability to estimate it from existing measurements with quantifiable uncertainty.…”