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.

本文介绍了一种基于深度学习模型（UNet++），利用 ERA5 重分析数据集和全球 CMIP6 模型进行训练和验证，以预测未来一个月的全球温度，发现该模型可以显著地提高预测准确度。

基于深度学习的气候模型驱动季节预测方法

Climate Model Driven Seasonal Forecasting Approach with Deep Learning

One of the most pressing questions in climate science is that of the effect
of anthropogenic aerosol on the Earth's energy balance. Aerosols provide the
`seeds' on which cloud droplets form, and changes in the amount of aerosol
available to a cloud can change its brightness and other physical properties
such as optical thickness and spatial extent. Clouds play a critical role in
moderating global temperatures and small perturbations can lead to significant
amounts of cooling or warming. Uncertainty in this effect is so large it is not
currently known if it is negligible, or provides a large enough cooling to
largely negate present-day warming by CO2. This work uses deep convolutional
neural networks to look for two particular perturbations in clouds due to
anthropogenic aerosol and assess their properties and prevalence, providing
valuable insights into their climatic effects.

本研究使用深度卷积神经网络寻找人为气溶胶引起的云层扰动，以评估它们的特性和普遍性，为气候效应提供有价值的洞见。