Simulation of global sea surface temperature maps using Pix2Pix GAN

Simulated data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) has been very important for climate science research, as they can provide wide spatio-temporal coverage to address data deficiencies in both present and future scenarios. However, these physics-based models require a huge amount of high-performance computing (HPC) resources. As an alternative approach, researchers are exploring if such simulated data can be generated by Generative Machine Learning models. In this work, we develop a model based on Pix2Pix conditional Generative Adversarial Network (cGAN), which can generate high-resolution spatial maps of global sea surface temperature (SST) using comparatively less computing power and time. We have shown that the maps generated by these models have similar statistical characteristics as the CMIP6 model simulations. Notably, we trained and validated our cGAN model on completely distinct time periods across all ensemble members of the EC-Earth3-CC and CMCC-CM2-SR5 CMIP6 models, demonstrating satisfactory results and confirming the generalizability of our proposed model.