Climate models capture key features of extreme precipitation probabilities across regions (February 2021)

Quantitative simulation of precipitation in current climate has been an ongoing challenge for global climate models. Despite serious biases in correctly simulating probabilities of extreme rainfall events, model simulations under global warming scenarios are routinely used to provide estimates of future changes in these probabilities. To minimize the impact of model biases, past literature tends to evaluate fractional (instead of absolute) changes in probabilities of precipitation extremes under the assumption that fractional changes would be more reliable. However, formal tests for the validity of this assumption have been lacking. Here we evaluate two measures that address properties important to the correct simulation of future fractional probability changes of precipitation extremes, and that can be assessed with current climate data. The first measure tests climate model performance in simulating the characteristic shape of the probability of occurrence of daily precipitation extremes and the second measure tests whether the key parameter governing the scaling of this shape is well reproduced across regions and seasons in current climate. Contrary to concerns regarding the reliability of global models for extreme precipitation assessment, our results show most models lying within the current range of observational uncertainty in these measures. Thus, most models in the Coupled Model Intercomparison Project Phase 6 ensemble pass two key tests in current climate that support the usefulness of fractional measures to evaluate future changes in the probability of precipitation extremes.

Cristian Martinez-Villalobos and J David Neelin 2021 Environ. Res. Lett. 16 024017,

Figure 1. (a) Probability distributions for the region shown in figure 2(a) calculated from four observational datasets (GPCP, PERSIANN V1, CMORPH v1 and TRMM-3B42; squares) and one CMIP6 model (GFDL-CM4, black circles). Note the different cutoff scales PL in each dataset (big circle markers). (b) As in (a) but rescaled by their respective PL , allowing similarities in the shape of the extreme tail (above $P^{*} = \frac{P}{P_{L}} = 1$) to be evaluated. Big circle markers shows the location of the cutoff-scale in P coordinates ($P_{L}^{*} = 1$ in all cases). In addition to datasets in (a), rescaled distributions in ACCESS-ESM1-5, CanESM5, CESM2, FGOALS-g3, INM-CM5-0 and IPSL-CM6A-LR are also shown. (c) Schematic of the effect of an increase in the cutoff scale $P_{L}^{*}$ on the extreme tail probability. This is illustrated in rescaled coordinates but same fractional changes apply for daily precipitation before rescaling. As an example, we use a fractional increase in $P_{L}^{*}$ of 21%, which (under no changes in dynamical contribution) would correspond to a Clausius–Clapeyron scaling for a 3 °C temperature increase. Using same τP  = 0.5 for both curves yields a fractional increase of the 99.9th daily precipitation percentile $P_{99.9}^{*}$ (over wet days) also of 21%. The actual fractional increase of $P_{99.9}^{*}$ (or P99.9) will have a small dependence on how τP adjusts (see supplementary material).

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