Laboratoire de Météorologie Dynamique Institut Pierre Simon Laplace (LMD IPSL), Sorbonne Université, CNRS 75005, Paris, France.
University of Hamburg, 20148 Hamburg, Germany.
Proc Natl Acad Sci U S A. 2023 Feb 21;120(8):e2209805120. doi: 10.1073/pnas.2209805120. Epub 2023 Feb 13.
The response of trade cumulus clouds to warming remains a major source of uncertainty for climate sensitivity. Recent studies have highlighted the role of the cloud-convection coupling in explaining this spread in future warming estimates. Here, using observations from an instrumented site and an airborne field campaign, together with high-frequency climate model outputs, we show that i) over the course of the daily cycle, a cloud transition is observed from deeper cumuli during nighttime to shallower cumuli during daytime, ii) the cloud evolution that models predict from night to day reflects the strength of cloud sensitivity to convective mass flux and exhibits many similarities with the cloud evolution they predict under global warming, and iii) those models that simulate a realistic cloud transition over the daily cycle tend to predict weak trade cumulus feedback. Our findings thus show that the daily cycle is a particularly relevant testbed, amenable to process studies and anchored by observations, to assess and improve the model representation of cloud-convection coupling and thus make climate projections more reliable.
贸易积云对变暖的响应仍然是气候敏感性的一个主要不确定性来源。最近的研究强调了云-对流耦合在解释未来变暖预估差异方面的作用。在这里，我们利用仪器站点和机载野外考察的观测资料以及高频气候模型输出，表明：i）在一天的周期内，观测到云从夜间的深积云向白天的浅积云转变；ii）模型预测的从夜间到白天的云演化反映了云对对流质量通量的敏感性的强度，并与它们在全球变暖下预测的云演化具有许多相似之处；iii）那些模拟了一天周期中真实云转变的模型往往预测贸易积云反馈较弱。因此，我们的研究结果表明，一天的周期是一个特别相关的测试平台，适合进行过程研究，并以观测为依据，以评估和改进云-对流耦合的模型表示，从而使气候预测更加可靠。
