Investigating the State Dependence of Cloud Feedback Using a Suite of Perturbed Parameter Ensembles
Abstract The state dependence of cloud feedback—its variation with the mean state climate—has been found in many paleoclimate and contemporary climate simulations. Previous results have shown inconsistencies in the sign, magnitude, and underlying mechanisms of state dependence. To address this, we utilize a perturbed parameter ensemble (PPE) approach with fixed sea surface temperature (SST) in the Community Atmosphere Model, version 6. Our suites of PPEs span a wide range of global mean surface temperatures (GMSTs), with spatially uniform SST perturbations of −4, 0, 4, 8, 12, and 16 K from the preindustrial. The results reveal a nonmonotonic variation with GMSTs: Cloud feedback increases under both cooler and warmer-than-preindustrial conditions, with a rise of ∼0.1 W m −2 K −1 under a 4-K colder climate and ∼0.4 W m −2 K −1 under a 12-K warmer climate. This complexity arises from differing cloud feedback responses in high and low latitudes. In high latitudes, cloud feedback consistently rises with warming, likely driven by a moist adiabatic mechanism that influences cloud liquid water. The low-latitude feedback increases under both cooler and warmer conditions, likely influenced by changes in the lower-tropospheric stability. This stability shift is tied to nonlinearity in thermodynamic responses, particularly in the tropical latent heating, alongside potential state-dependent changes in tropical circulations. Under warmer-than-preindustrial conditions, the increase in cloud feedback with warming is negatively correlated with its preindustrial value. Our PPE approach takes the model parameter uncertainty into account and emphasizes the critical role of state dependence in understanding past and predicting future climates. Significance Statement This study focuses on how cloud feedback—one of the most uncertain aspects of climate change—varies as global temperatures rise. We found that the cloud feedback decreases at first with warming and then increases, showing significant variation. This complexity stems from nonlinear thermodynamics, such as the Clapeyron–Clausius relationship, which describes how temperature affects moisture in the atmosphere. Our results indicate that the cloud feedback depends on the level of global warming, which is a significant factor rooted in fundamental physics. Recognizing this dependence is important for studies that aim to interpret past climates and predict future climate changes.
Authors
- Andrew Gettelman (ORCID: https://orcid.org/0000-0002-8284-2599)
- Esther C. Brady (ORCID: https://orcid.org/0000-0001-7833-2249)
- Bette L. Otto‐Bliesner (ORCID: https://orcid.org/0000-0003-1911-1598)
- Trude Eidhammer (ORCID: https://orcid.org/0000-0003-2281-9351)
- Jiang Zhu (ORCID: https://orcid.org/0000-0002-0908-5130)
- Christina S. McCluskey (ORCID: https://orcid.org/0000-0003-3778-4112)
- Ran Feng (ORCID: https://orcid.org/0000-0002-4433-4745)
Institutions
- NSF National Center for Atmospheric Research (US)
- University of Connecticut (US)
- Pacific Northwest National Laboratory (US)
Publication Details
- Journal
- Journal of Climate
- Published
- 2025-06-13
- DOI
- https://doi.org/10.1175/jcli-d-24-0686.1
- Primary Topic
- Neural Networks and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00