Estimated cloud-top entrainment index explains positive low-cloud-cover feedback

Abstract

How subtropical marine low cloud cover (LCC) will respond to global warming is a major source of uncertainty in future climate change. Although the estimated inversion strength (EIS) is a good predictive index of LCC, it has a serious limitation when applied to evaluate LCC changes due to warming: The LCC decreases despite increases in EIS in future climate simulations of global climate models (GCMs). In this work, using state-of-the-art GCMs, we show that the recently proposed estimated cloud-top entrainment index (ECTEI) decreases consistently with LCC in warmer sea surface temperature (SST) climates. For the patterned SST warming predicted by coupled GCMs, ECTEI can constrain the subtropical marine LCC feedback to –0.41 ± 0.28% K −1 (90% CI), implying virtually certain positive feedback. ECTEI physically explains the heuristic model for LCC changes based on a linear combination of EIS and SST changes in previous studies in terms of cloud-top entrainment processes.

Circus Tents, Convective Thresholds, and the Non‐Linear Climate Response to Tropical SSTs

“…In regions unstable to deep convection, we expect this quantity to be positive, whereas in regions stable against deep convection (e.g., subsiding regions) we expect this to be negative and to act as a measure of the inversion strength (similar to Wood and Bretherton (2006), but also accounting for moisture differences as in Koshiro et al. (2022)).…”

“…Using this observation, we follow previous work (e.g., I. N. Williams & Pierrehumbert, 2017) in defining a "convective instability index," 𝐴𝐴 𝐴0 − 𝐴 * 500 , (taking the 500 hPa level to be representative of the free-troposphere). In regions unstable to deep convection, we expect this quantity to be positive, whereas in regions stable against deep convection (e.g., subsiding regions) we expect this to be negative and to act as a measure of the inversion strength (similar to Wood and Bretherton (2006), but also accounting for moisture differences as in Koshiro et al (2022)).…”

Circus tents, convective thresholds and the non-linear climate response to tropical SSTs

“…1 but with q replaced by its value at saturation, q * ) of the free-troposphere, h level to be representative of the free-troposphere). In regions unstable to deep convection, we expect this quantity to be positive, whereas in regions stable against deep convection (e.g., subsiding regions) we expect this to be negative and to act as a measure of the inversion strength (similar to Wood and Bretherton (2006), but also accounting for moisture differences as in Koshiro et al (2022)).…”

What Are the Main Causes of Positive Subtropical Low Cloud Feedbacks in Climate Models?

“…For the present study we have concentrated on those that we are most familiar with and which we are able to interpret causally. In future work we hope to consider additional hypothesized positive low cloud feedback mechanisms as explanations for stratocumulus/trade cumulus transition cloud feedbacks in climate models, for example, those discussed in Brient and Bony (2012), Blossey et al (2013), Jones et al (2014), Blossey et al (2016), Hirota et al (2021), Koshiro et al (2022), andSchiro et al (2022). We would also like to consider the details of the parametrizations in the models in more detail, to see if any mechanisms can be ruled out because they rely on processes that are not represented.…”

“…Despite the progress in constraining the magnitudes of subtropical low cloud feedbacks described above, there remain many competing hypotheses to potentially explain the physical mechanisms responsible for them, given that stratocumulus and stratocumulus to trade cumulus transition region feedbacks may well be as important as trade cumulus feedbacks in some models (e.g., Blossey et al., 2013; Bretherton & Blossey, 2014; Bretherton et al., 2013; Brient & Bony, 2012, 2013; Brient et al., 2016; Hirota et al., 2021; Jones et al., 2014; Koshiro et al., 2022; Rieck et al., 2012; S. C. Sherwood et al., 2014; Vial et al., 2016, 2021, 2023; Vogel et al., 2022; M. J. Webb & Lock, 2013; Zhang et al., 2013). This presents a challenge when it comes to improving climate models, as there are multiple parametrizations involved which represent processes such as surface‐atmosphere heat and moisture exchange, atmospheric convection, turbulence, cloud microphysics, and cloud cover.…”