  Climate projections using data driven machine learning models acting as
emulators, is one of the prevailing areas of research to enable policy makers
make informed decisions. Use of machine learning emulators as surrogates for
computationally heavy GCM simulators reduces time and carbon footprints. In
this direction, ClimateBench [1] is a recently curated benchmarking dataset for
evaluating the performance of machine learning emulators designed for climate
data. Recent studies have reported that despite being considered fundamental,
regression models offer several advantages pertaining to climate emulations. In
particular, by leveraging the kernel trick, regression models can capture
complex relationships and improve their predictive capabilities. This study
focuses on evaluating non-linear regression models using the aforementioned
dataset. Specifically, we compare the emulation capabilities of three
non-linear regression models. Among them, Gaussian Process Regressor
demonstrates the best-in-class performance against standard evaluation metrics
used for climate field emulation studies. However, Gaussian Process Regression
suffers from being computational resource hungry in terms of space and time
complexity. Alternatively, Support Vector and Kernel Ridge models also deliver
competitive results and but there are certain trade-offs to be addressed.
Additionally, we are actively investigating the performance of composite
kernels and techniques such as variational inference to further enhance the
performance of the regression models and effectively model complex non-linear
patterns, including phenomena like precipitation.
