Description:

Long-term earth observations (EO) play a significant role in climate research. With nearly five decades of satellite data available for some variables, Climate Data Records (CDRs) provide trustworthy information on how, where, and to what extent the Earth system is changing. These include data sets from NOAA, ESA Climate Change Initiative, and NASA. Although these long-term observations have shown the potential to monitor the change of the Earth system and its impacts on society from space, their application to advance Earth system models (ESM) has not reached its full potential. Recent advancements in Earth system modeling at finer cloud-resolving, km-scale resolutions require higher resolution observations for initialisation, benchmarking, and validation. Additionally, machine learning and AI approaches are now often integrated into ESM to improve the representation of complex atmospheric, land surface, and biogeochemical processes across the ESM landscape and these have their own observation requirements. The trend towards increased resolution and more complex process representation provides a unique opportunity to better integrate EO CDRs with ESM efforts. This session is convened by WCRP Earth System Modeling and Observations (ESMO) and looking for research relevant but not limited to the following topics:

1. Leveraging existing and/or developing new long-term EO datasets to advance process-oriented diagnosis for ESMs;
2. Improving the usability and accessibility of long-term EOs using emerging technologies (e.g., AI, cloud computing) for the ESM community;
3. Integrating long-term EOs with ESMs to improve Earth system predictability;
4. AI applications that leverage long-term EOs to improve Earth system predictability;
5. Novel methods to integrate EO datasets and other observations relevant for ESMs (e.g., in situ observations, airborne data, and uncrewed systems)