A2 - Clouds and Tropical Circulation
The tropics are the engine of the atmosphere’s general circulation, and their response to warming dominates Earth’s climate sensitivity. While the range of the thermodynamic climate response (the warming) is well established, our understanding of the dynamical response is poor. It is this dynamical response -- the evolution of the tropical circulation and the associated precipitation patterns -- which will affect the lives of billions of people in the tropics. In A2 we address the question to which extent the tropics warm and how the circulation systems respond to warming. Towards this objective, we develop and apply novel methods for filtering waves and coherent flows in tropical observations and high-resolution simulations. Our goal is to better understand the processes that govern how the tropics responds to radiative forcing, in ways that can guide adaptation efforts on the one hand, or help anticipate catastrophes on the other.
A field campaign to understand shallow, rainy clouds in the tropics
To understand how tropical boundary-layer clouds change with warming remains a challenge. In January and February 2020, the field campaign EUREC4A took place in the western tropical Atlantic with the aim to advance understanding of the interplay between clouds, convection and circulation and their role in climate change. A2 scientists have played manifold roles in conducting major components of the campaign (Stevens et al., 2021), including contributions to the deployment of the German research aircraft HALO and its dropsonde observations, as well as support for the ship-based (R/V Meteor) and Barbados based near-surface measurements. A focus of our post-campaign analysis has been to analyze radar data collected during EUREC4A to better understand how precipitation is associated with clustering. We show that clustering is important for precipitation formation in dry environments, but across all regimes it is of second order importance (Radtke et al, 2022).
Global storm-resolving models to explore the coupling between clouds and circulation
The EUREC4A measurements are also being used to evaluate and refine a new generation of climate models. These global storm-revolving models resolve the coupling between clouds and the circulation systems, but their computational intensity makes them unwieldy. In A2, we refined and developed new methods, which take observational limitations into account to enable a fair comparison between simulations and observations (Naumann and Kiemle, 2020). Applying this method to airborne lidar measurements, we illustrate the fragility of simulated cloudiness at kilometer-scale resolution and how a model's ability to properly capture the water vapor distribution does not need to translate into an adequate representation of shallow cumulus clouds that live at the tail of the water vapor distribution.
In the dry subsidence regions of the tropics water vapor is especially important in regulating Earth’s clear-sky energy budget. In conventional climate models, inter-model differences in tropical humidity limit our ability to predict the clear-sky sensitivity of the climate system to warming. Making use of the first global storm-resolving multi-model ensemble, we show that the model spread in tropical free-tropospheric humidity, while still a substantial source of bias, is reduced in global storm-resolving models compared to conventional models (Lang et al., 2021).
Filtering waves signals to diagnose tropical patterns of variability
To help link changes in clouds and water vapor to circulation, we have developed new tools for diagnosing equatorial waves in models in wavenumber and frequency domains, which can also help to quantify their horizontal propagation in relation to their vertical structures. Analytical and numerical methods for frequency filtering of time series of complex wave signals have been developed and applied on reanalysis data. It was shown that wave signals filtered from highly nonlinear time series may be assigned excessive variance at larger spatial and temporal scales (Žagar et al., 2022).