high performance computing

Climate change research today relies on climate information from the past. Historical climate records of temperature observations form global gridded datasets that are examined, for example, in IPCC reports. However, the datasets combining measurement records are sparse in the past. Even today, they contain missing values. We found that recently successful image inpainting technologies, such as those found on smartphones to get rid of unwanted objects or people in photos, are useful here. The derived AI networks are able to reconstruct artificially cropped versions in the grid space for any given month using the missing values observation mask. So herewith we have found with AI a technique that gives us data from the past that we never measured with instruments.  Other important datasets used in the Assessment Report 6 of the IPCC to study climate change, as well as advanced applications such as downscaling in atmosphere and ocean, a hybrid (AI&ESM) data assimilation approach within ICON, or precipitation in broken radar fields are shown in this presentation.

Climate research, including the study mentioned in the previous paragraph, often requires substantial technical expertise. This involves managing data standards, various file formats, software engineering, and high-performance computing. Translating scientific questions into code that can answer them demands significant effort. The question is, why? Data analysis platforms like Freva (Kadow et al. 2021, e.g., gems.dkrz.de) aim to enhance user convenience, yet programming expertise is still required. In this context, we introduce a large language model setup and chat bot interface based on GPT-4/ChatGPT, which enables climate analysis without technical obstacles, including language barriers. This approach is tailored to the needs of the broader climate community, which deals with massive data sets from kilometer-scale modeling and requires a processing environment utilizing modern technologies, but addressing society after all - such as those in the Earth Virtualization Engines (EVE eve4climate.org).

Kadow, C., Hall, D.M. & Ulbrich, U. Artificial intelligence reconstructs missing climate information. Nat. Geosci. 13, 408-413 (2020)