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.

使用机器学习模型作为仿真器来进行气候预测是当前研究中的一个主要领域，可以帮助决策者做出明智决策。本研究侧重于评估使用非线性回归模型进行气候仿真的能力，并比较了三种非线性回归模型的效果。高斯过程回归模型在气候仿真研究中展现了卓越的性能，但在计算资源方面存在一些问题。另外，支持向量和核岭模型也取得了竞争性的结果，但需要解决一些权衡问题。此外，正在积极研究复合核和变分推断等技术，以进一步提高回归模型的性能，有效地模拟包括降水在内的复杂非线性模式。