2020-02-27 All-Hands Presentation Meeting notes

Evaluation of climate models (e.g., E3SM) needs to go beyond the simple use of an individual variable (e.g., precipitation, cloud radiative forcing) to include relationships. Evaluation of relationships provides insights for climate model improvement and requires rethinking of how observation-based datasets be used. For instance, the usual approach for using datasets for a single variable (e.g., precipitation) is to take the multi-product ensemble mean as the "ground truth" with the deviations as the product uncertainty. However, it is unclear if such an ensemble mean is appropriate for relationships or processes (e.g., surface water balance over Amazon).

In this talk, we will review our recent work on data analysis and modeling of relationships related to land-atmosphere interactions:

a) We have used an innovative method to develop a high-quality daily 4km snow water equivalent data over contiguous U.S., and we have used the relationship of snow water equivalent with precipitation and temperature to evaluate climate models (including E3SM).

b) We have evaluated various datasets of all variables of the surface water balance over the Amazon River basin, and proposed a new approach to decide which products are most appropriate for model evaluations. We are doing some preliminary evaluations of E3SM results on the integrated water cycle.

c) We have used the ARM SGP data to evaluate the soil moisture effect on afternoon rainfall in summer and have expanded it to global analysis. We will use such relationships to evaluate climate models in the near future.