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<p>In this contribution, a statistical learning technique is used to quantify the response of cloud radiative effects to changes in a large number of environmental factors in spatial observation data.</p> <p>Clouds play a key role for the Earth’s energy balance; however, their response to climatic and anthropogenic aerosol emission changes is not clear, yet. Here, 20 years of satellite observations of cloud radiative effects (CRE) are analysed together with reanalysis data sets in a (regularised) ridge regression framework to quantitatively link the variability of observed CREs to changes in environmental factors, or cloud-controlling factors (CCFs). In the literature the meteorological kernels of such CCF analyses are typically established in regime-specific regression frameworks based on a low (2-8) number of CCFs. In our data-driven approach, the capabilities of the regularised regression to deal with collinearities in a large number of predictors are exploited to establish a regime-independent CCF framework based on a large number of CCFs. Using a reference 7-CCF framework, we show that ridge regression produces nearly identical patterns of CCF sensitivities when compared to the traditional regression. In the data-driven framework, however, the traditional regression fails at producing consistent results due to overfitting. The data-driven analysis reveals distinct regional patterns of CCF importance for shortwave and longwave CRE: </p> <ul> <li aria-level="1">Sea surface temperatures and inversion strength are important for shortwave CRE in stratocumulus regions, in agreement with existing studies. However, zonal wind speeds in the free troposphere and surface fluxes are also shown to be important.</li> <li aria-level="1">Free tropospheric meridional winds are important drivers of CRE in the subtropical belts (20°-40°) in both hemispheres, likely capturing aspects of Rossby Wave-related CRE variability. </li> <li aria-level="1">Aerosols are shown to be most important for shortwave CRE in the regions of stratocumulus to cumulus transition. </li> </ul> <p>While the multivariate method aims at limiting the influence of confounding factors on the estimated sensitivities, particularly the aerosol-CRE sensitivity may still be confounded to a degree. Future analyses of interactions between different CCFs and comparisons to global climate models are outlined.</p>

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