Low-level clouds have a strong cooling effect on climate because they reflect more solar radiation back to space than the surface while exerting nearly the same greenhouse effect as the clear…

This federal contract award, issued by the National Aeronautics and Space Administration (NASA), provides a $430,063.00 grant to the University of California San Diego (UCSD) Scripps Institution of Oceanography. The grant funds a research project to better understand how low-level clouds respond to changes in atmospheric and ocean conditions associated with global warming. The project leverages multiple satellite datasets to quantify the relationships between low-level cloud properties and key meteorological factors across different climate regimes. This work aims to improve climate model simulations and reduce uncertainty around the cloud feedback effect on climate change. The grant does not have a set-aside designation, reflecting Scripps' status as a large educational institution. As a renowned center for ocean and earth science research, Scripps has a long history of supporting federal agencies like NASA through both grants and contracts.

Low-level clouds have a strong cooling effect on climate because they reflect more solar radiation back to space than the surface while exerting nearly the same greenhouse effect as the clear atmosphere. One major question is how low-level clouds will respond to global warming, and whether the cloud response will exacerbate (positive feedback) or mitigate (negative feedback) the warming. IT is crucial to develop better physical understanding of meteorological factors controlling low-level clouds and to quantify the observed cloud response to changes in these factors. Previous research, mainly for subtropical stratocumulus, shows that low-level clouds respond to sea surface temperature, inversion strength, temperature advection, subsidence, free-tropospheric humidity, and related parameters on time scales from days to decades. What is lacking, however, is a comprehensive and quantitative understanding of how low-level clouds will change across multiple climate regimes in response to changes in the atmosphere and ocean associated with global warming. Global climate models fail to reproduce the magnitudes and even signs of multiple observed relationships between low-level cloud and meteorological controlling factors. Incorrect and inconsistent cloud simulation by models results in widespread disagreement over what cloud changes will occur with global warming even as the models exhibit widespread agreement over what changes in controlling factors will occur. Some models predict an increase in cloud whereas other models predict varying amounts of decrease, and the divergence of model low-level cloud feedback values is one of the biggest contributors to global warming uncertainty. Interestingly, for the subset of models with the most realistic low-level cloud simulations, the long-term cloud change in a particular model can be successfully predicted by multiplying the long-term changes in controlling factors by cloud response coefficients obtained via multilinear regression on interannual variability in that particular model. This suggests that the range of cloud feedback uncertainty could be substantially reduced by replacing model cloud response coefficients with observed cloud response coefficients. Recent studies have calculated observed cloud coefficients, but they suffer from various critical shortcomings. These include lack of robust quantification of the radiative impact of cloud changes, examination of only one or two cloud-controlling factors while ignoring several others, and focus on only limited geographical areas or a single climate regime. The proposed project will overcome such drawbacks by combining ceres radiative fluxes, modis cloud properties, and airs temperature and moisture profiles with merra vertical velocity and horizontal wind to obtain a complete picture of cloud response to changes in controlling factors. This will be done across subtropical stratocumulus, trade cumulus, and midlatitude climate regimes and across day to decadal time scales by use of multilinear regression, multi-parameter compositing, and related techniques. The results will constrain model cloud simulations and reduce uncertainty in global low-level cloud feedback. Additionally, the role of horizontal temperature advection in controlling low-level cloud will be investigated. Although observationally demonstrated to have great impact on cloud variability on daily to decadal time scales, almost all climate models fail to reproduce the observed cloud-advection relationship. Regression, compositing, and similar methods will be employed to observationally determine the specific mechanisms by which advection most influences cloud, and the results can inform model parameterization efforts. The proposed project is responsive to the solicitation by combining datasets from multiple instruments on the terra, aqua, and suomi NPP satellites to develop new understanding of cloud processes highly relevant to climate change.