Evaluating precipitation distributions at regional scales: a benchmarking framework and application to CMIP5 and 6 models

Abstract

Abstract. As the resolution of global Earth system models increases, regional-scale evaluations are becoming ever more important. This study presents a framework for quantifying precipitation distributions at regional scales and applies it to evaluate Coupled Model Intercomparison Project (CMIP) 5 and 6 models. We employ the Intergovernmental Panel on Climate Change (IPCC) sixth assessment report (AR6) climate reference regions over land and propose refinements to the oceanic regions based on the homogeneity of precipitation distribution characteristics. The homogeneous regions are identified as heavy-, moderate-, and light-precipitating areas by K-means clustering of Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) version 6 final run product (IMERG) precipitation frequency and amount distributions. With the global domain partitioned into 62 regions, including 46 land and 16 ocean regions, we apply 10 established precipitation distribution metrics. The collection includes metrics focused on the maximum peak, lower 10th percentile, and upper 90th percentile in precipitation amount and frequency distributions; the similarity between observed and modeled frequency distributions; an unevenness measure based on cumulative amount; average total intensity on all days with precipitation; and number of precipitating days each year. We apply our framework to 25 CMIP5 and 41 CMIP6 models, as well as six observation-based products of daily precipitation. Our results indicate that many CMIP5 and 6 models substantially overestimate the observed light-precipitation amount and frequency, as well as the number of precipitating days, especially over midlatitude regions outside of some land regions in the Americas and Eurasia. Improvement from CMIP5 to 6 is shown in some regions, especially in midlatitude regions, but it is not evident globally, and over the tropics most metrics point toward degradation.

A storyline analysis of Hurricane Irma’s precipitation under various levels of climate warming

“…According to the Clausius-Clapeyron relationship, the saturation vapor pressure increases about 7% per K increase in air temperature, and thus this rate is a rough estimate for expected increases in extreme precipitation with climate warming since extreme precipitation tends to happen in saturated atmospheric environments (Allen and Ingram 2002). For TCs, their intensities, precipitation rates, and environmental ocean temperatures are all related (Stansfield and Reed 2021, 2023, Xi et al 2023, which suggests that as climate warming continues TC precipitation rates will increase due to a combination of increasing available atmospheric moisture and increasing TC intensities (Liu et al 2019).…”

Intensification in the Wettest Days to 50 Percent of Annual Precipitation (WD50) Across Europe

“…Under global warming, mean precipitation will also contribute to making “wet regions wetter and dry regions dryer” (Held & Soden, 2006; Norris et al., 2019; Trenberth, 2011). Since WD50 is calculated based on annual precipitation, it is strongly correlated with the number of precipitating days and the Simple Daily Intensity Index (Ahn et al., 2023). As such, changes in WD50 went beyond those in R95p and captured various processes affecting mean precipitation (e.g., more frequent wet extremes, longer dry spells, lesser annual precipitation).…”

“…Pendergrass and Knutti (2018) captured this unevenness in daily precipitation and found that in many parts of the world, it takes around 12 of the Wettest Days to produce 50% of the precipitation in a year (WD50). Since then, the measure of WD50 has been used in limited instances to characterize precipitation distributions in China (Chen, 2020; Han et al., 2021; Wu et al., 2021) and large‐scale Coupled Model Intercomparison Project 6 (CMIP6) projections (Ahn et al., 2023).…”

Objective Evaluation of Earth System Models: PCMDI Metrics Package (PMP) version 3

“…Contributors have helped expand the PMP beyond its traditional large-scale performance summaries of the mean climate (Gleckler et al, 2008). Various evaluation metrics have been implemented to the PMP for climate variability such as El Niño-Southern Oscillation (ENSO) (Planton et al, 2021;Lee et al, 2021a), extratropical modes of variability (Lee et al, 2019(Lee et al, , 2021b, intra-seasonal oscillation (Ahn et al, 2017), monsoons (Sperber and Annamalai, 2014), cloud feedback (Zelinka et al, 2022), and the characteristics of simulated precipitation (Pendergrass et al, 2020;Ahn et al, 2022Ahn et al, , 2023 and extremes (Wehner et al, 2020(Wehner et al, , 2021. This section will provide an overview of each category of the current PMP evaluation metrics with their usage demonstrations.…”

“…In some cases, such as for SAM_SON, the models overestimate the observed amplitude. Other authors have used Portrait plots to synthesize CMIP performance of simulated variability (e.g., Sillmann et al, 2013;Bellenger et al, 2014;Cannon 2020;Kim et al, 2020;Planton et al, 2020;Zhang et al, 2021;Ahn et al, 2022Ahn et al, , 2023. The PMP's ETMoV metrics have been used in several model evaluation studies.…”