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Martinez-Villalobos, C., Neelin, J. D., & Pendergrass, A. G. (2022). Metrics for Evaluating CMIP6 Representation of Daily Precipitation Probability Distributions. J. Clim., 35(17), 5719–5743.
Abstract: The performance of GCMs in simulating daily precipitation probability distributions is investigated by comparing 35 CMIP6 models against observational datasets (TRMM-3B42 and GPCP). In these observational datasets, PDFs on wet days follow a power-law range for low and moderate intensities below a characteristic precipitation cutoff scale. Beyond the cutoff scale, the probability drops much faster, hence controlling the size of extremes in a given climate. In the satellite products analyzed, PDFs have no interior peak. Contributions to the first and second moments tend to be single-peaked, implying a single dominant precipitation scale; the relationship to the cutoff scale and log-precipitation coordinate and normalization of frequency density are outlined. Key metrics investigated include the fraction of wet days, PDF power-law exponent, cutoff scale, shape of probability distributions, and number of probability peaks. The simulated power-law exponent and cutoff scale generally fall within observational bounds, although these bounds are large; GPCP systematically displays a smaller exponent and cutoff scale than TRMM-3B42. Most models simulate a more complex PDF shape than these observational datasets, with both PDFs and contributions exhibiting additional peaks in many regions. In most of these instances, one peak can be attributed to large-scale precipitation and the other to convective precipitation. Similar to previous CMIP phases, most models also rain too often and too lightly. These differences in wet-day fraction and PDF shape occur primarily over oceans and may relate to deterministic scales in precipitation parameterizations. It is argued that stochastic parameterizations may contribute to simplifying simulated distributions.
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Leung, L. R., Boos, W. R., Catto, J. L., DeMott, C., Martin, G. M., Neelin, J. D., et al. (2022). Exploratory precipitation metrics: spatiotemporal characteristics, process-oriented, and phenomena-based. J. Clim., 35(12), 3659–3686.
Abstract: Precipitation sustains life and supports human activities, making its prediction one of the most societally relevant challenges in weather and climate modeling. Limitations in modeling precipitation underscore the need for diagnostics and metrics to evaluate precipitation in simulations and predictions. While routine use of basic metrics is important for documenting model skill, more sophisticated diagnostics and metrics aimed at connecting model biases to their sources and revealing precipitation characteristics relevant to how model precipitation is used are critical for improving models and their uses. This paper illustrates examples of exploratory diagnostics and metrics including: (1) spatiotemporal characteristics such as 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 including low pressure systems, mesoscale convective systems, frontal systems, and atmospheric rivers. Together, these diagnostics and metrics delineate the multifaceted and multiscale nature of precipitation, its relations with the environments, and its generation mechanisms. The metrics are applied to historical simulations from the Coupled Model Intercomparison Project Phase 5 and Phase 6. Models exhibit diverse skill as measured by the suite of metrics, with very few models consistently ranked as top or bottom performers compared to other models in multiple metrics. Analysis of model skill across metrics and models suggests possible relationships among subsets of metrics, motivating the need for more systematic analysis to understand model biases for informing model development.
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Chang, M., Liu, B., Wang, B., Martinez-Villalobos, C., Ren, G., & Zhou, T. (2022). Understanding future increases in precipitation extremes in global land monsoon regions. J. Clim., 35, 1839–1851.
Abstract: This study investigates future changes in daily precipitation extremes and the involved physics over the global land monsoon (GM) region using climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The daily precipitation extreme is identified by the cutoff scale, measuring the extreme tail of the precipitation distribution. Compared to the historical period, multi-model results reveal a continuous increase in precipitation extremes under four scenarios, with a progressively higher fraction of precipitation exceeding the historical cutoff scale when moving into the future. The rise of the cutoff-scale by the end of the century is reduced by 57.8% in the moderate emission scenario relative to the highest scenario, underscoring the social benefit in reducing emissions. The cutoff scale sensitivity, defined by the increasing rates of the cutoff scale over the GM region to the global mean surface temperature increase, is nearly independent of the projected periods and emission scenarios, roughly 8.0% K−1 by averaging all periods and scenarios. To understand the cause of the changes, we applied a physical scaling diagnostic to decompose them into thermodynamic and dynamic contributions. We find that thermodynamics and dynamics have comparable contributions to the intensified precipitation extremes in the GM region. Changes in thermodynamic scaling contribute to a spatially uniform increase pattern, while changes in dynamic scaling dominate the regional differences in the increased precipitation extremes. Furthermore, the large inter-model spread of the projection is primarily attributed to variations of dynamic scaling among models.
Keywords: Precipitation; Extreme events; Monsoons; Climate prediction; Thermodynamics; Dynamics
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