PNNL

The Science                                

The location, timing, frequency, and quantity of precipitation has significant implications for the Earth system’s energy, water, and biogeochemical cycles. Current weather and climate models have limited skill in simulating precipitation, motivating a need to explore new diagnostics and metrics that better connect model precipitation errors to their sources to improve models. This study illustrates examples of exploratory diagnostics and metrics that together delineate the multifaceted and multiscale nature of precipitation, its relationship with the environment, and its generation mechanisms. The metrics were applied to climate simulations from phases 5 and 6 of the Coupled Model Intercomparison Project, revealing possible relationships among subsets of metrics of interest for model development.

The Impact

Precipitation sustains life and supports human activities, making its prediction one of the most societally relevant challenges in weather and climate modeling. By focusing on metrics related to the spatiotemporal characteristics of precipitation—including the probability of daily precipitation and the duration of dry spells, the moisture and temperature environments of precipitation, and precipitation associated with different weather phenomena—this study advances metrics for broader use by the weather and climate modeling community. It aims to improve precipitation modeling, support new and more diverse uses of precipitation from climate models, and improve scientists’ ability to communicate climate model performance by connecting precipitation to commonly understood weather phenomena.

Summary

This study represents a collaborative effort to develop more advanced precipitation metrics and use them to benchmark diverse aspects of precipitation from climate simulations that grew from the “Benchmarking Simulated Precipitation in Earth System Models” workshop. It presents three types of precipitation diagnostics and metrics: 1) spatiotemporal characteristics metrics, such as the diurnal variability, probability of extremes, duration of dry spells, spectral characteristics, and spatiotemporal coherence of precipitation; 2) process-oriented metrics, based on the rainfall–moisture coupling and temperature–water vapor environments of precipitation; and 3) phenomena-based metrics, focusing on precipitation associated with weather phenomena, such as low pressure systems, mesoscale convective systems, frontal systems, and atmospheric rivers. These diagnostics and metrics take advantage of analysis built on advances in understanding the thermodynamic environments of precipitation and their role in patterns of climate variability and in tracking weather features, such as mesoscale convective systems and atmospheric rivers. This study illustrates the use of diagnostics and metrics to evaluate broader aspects of precipitation in climate simulations and explore insights gained through comparative analysis of multiple metrics. The metrics provide useful information to support new and more diverse uses of precipitation from climate models and improve scientists’ ability to communicate climate model performance to users of model precipitation output by connecting precipitation to commonly understood weather phenomena.

PNNL Contact

L. Ruby Leung, Pacific Northwest National Laboratory, Ruby.Leung@pnnl.gov

Funding

This study represents a collaborative effort as an outgrowth of a workshop on “Benchmarking Simulated Precipitation in Earth System Models” sponsored by the Office of Science of the Department of Energy Biological and Environmental Research through the Regional and Global Model Analysis program area. Additionally, other sources of funding are acknowledged by the authors and listed in the paper, including the World Climate Research Programme, which coordinated and promoted CMIP6 through the Working Group on Coupled Modelling. The authors also thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving and providing access to the data, and the multiple funding agencies that support CMIP6 and ESGF. The authors thank the Department of Energy’s Regional and Global Model Analysis program area, the Data Management program, and NERSC for making this coordinated CMIP6 analysis activity possible.