Climate Model Driven Seasonal Forecasting Approach with Deep Learning

TL;DR

This paper introduces a deep learning approach using UNet++ trained on CMIP6 models and ERA5 data to forecast global temperatures a month ahead, demonstrating improved accuracy over traditional models.

Contribution

The study develops a novel deep learning framework combining UNet++ with climate model data and finetuning techniques for seasonal temperature forecasting.

Findings

UNet++ with CMIP6 + elevation + ERA5 finetuning achieved the lowest MAE of 0.7.

The AI model shows high agreement with ERA5 data, with slope and R^2 close to 1.

The model outperforms mean CMIP6 ensemble predictions, especially in summer months.

Abstract

Understanding seasonal climatic conditions is critical for better management of resources such as water, energy and agriculture. Recently, there has been a great interest in utilizing the power of artificial intelligence methods in climate studies. This paper presents a cutting-edge deep learning model (UNet++) trained by state-of-the-art global CMIP6 models to forecast global temperatures a month ahead using the ERA5 reanalysis dataset. ERA5 dataset was also used for finetuning as well performance analysis in the validation dataset. Three different setups (CMIP6; CMIP6 + elevation; CMIP6 + elevation + ERA5 finetuning) were used with both UNet and UNet++ algorithms resulting in six different models. For each model 14 different sequential and non-sequential temporal settings were used. The Mean Absolute Error (MAE) analysis revealed that UNet++ with CMIP6 with elevation and ERA5 finetuning model with "Year 3 Month 2" temporal case provided the best outcome with an MAE of 0.7. Regression analysis over the validation dataset between the ERA5 data values and the corresponding AI model predictions revealed slope and $R^2$ values close to 1 suggesting a very good agreement. The AI model predicts significantly better than the mean CMIP6 ensemble between 2016 and 2021. Both models predict the summer months more accurately than the winter months.