Quantifying CMIP6 Model Uncertainties in Extreme Precipitation Projections

From Weather and Climate Extremes

AmalJohnacHervéDouville aAurélienRibesa PascalYiou b

a Centre National de Recherches Météorologiques, Météo-France, CNRS, Toulouse, France
b Laboratoire des Sciences du Climat et de l’Environnement, UMR 8212 CEA-CNRS-UVSQ, IPSL & U Paris-Saclay, 91191 Gif-sur-Yvette, France
c Université de Toulouse, France

Received 8 October 2021, Revised 9 February 2022, Accepted 10 March 2022, Available online 21 March 2022, Version of Record 26 March 2022.

https://doi.org/10.1016/j.wace.2022.100435 Get rights and content
Under a Creative Commons license Open access


Projected changes in precipitation extremes and their uncertainties are evaluated using an ensemble of global climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP). They are scaled by corresponding changes either in global mean surface temperature (ΔGSAT) or in local surface temperature (ΔT) and are expressed in terms of 20-yr return values (RV20) of annual maximum one-day precipitation. Our main objective is to quantify the model response uncertainty and to highlight the regions where changes may not be consistent with the widely used assumption of a Clausius–Clapeyron (CC) rate of ≈7%/K. When using a single realization for each model, as in the latest report from the Intergovernmental Panel on Climate Change (IPCC), the assessed inter-model spread includes both model uncertainty and internal variability, which can be however assessed separately using a large ensemble. Despite the overestimated inter-model spread, our results show a robust enhancement of extreme precipitation with more than 90% of models simulating an increase of RV20. Moreover, this increase is consistent with the CC rate of ≈7%/K over about 83% of the global land domain when scaled by (ΔGSAT). Our results also advocate for producing multiple single model initial condition ensembles in the next CMIP projections, to better filter internal variability out in estimating the response of extreme events.

1. Introduction

Global climate models provide an increasingly comprehensive representation of the climate system and are used as a primary tool for understanding and projecting changes in climate mean, variability and extremes due to human activities. The Intergovernmental Panel on Climate Change (IPCC) in its sixth assessment report (AR6) has re-estimated an increase in the observed global mean surface temperature of 1.09 °C in 2011–2020 relative to the beginning of the industrial revolution (1850–1900), which can be fully attributed to a human influence (IPCC AR6 SPM Masson-Delmotte et al. (2021)). This anthropogenic global warming is reckoned to have long-term consequences on all components of the climate system, including changes in the daily precipitation distribution. Several generations of multi-model simulations contributing to the Coupled Model Intercomparison Project (CMIP), supported by observational evidence, show that both the frequency and intensity of extreme daily precipitation events have increased over recent decades (Allen and Ingram, 2002Asadieh and Krakauer, 2015Scherrer et al., 2016Karl and Easterling, 1999Kharin et al., 2013Min et al., 2011O’Gorman, 2015). This is also documented in the IPCC special report on Managing the Risks of Extremes Events to Advance Climate Change Adaptation (SREX, Seneviratne (2012)).

In the absence of moisture limitation and of significant dynamical response, the extreme precipitation intensity is expected to increase exponentially with the atmospheric temperature at a rate determined by the Clausius–Clapeyron (CC) relationship. A robust scaling of daily precipitation extremes with global warming across scenarios was confirmed by Li et al. (2020) who found that changes in precipitation extremes follow changes in global warming at roughly the CC rate of ≈7%/°C in the latest-generation CMIP6 models. Several studies based on climate model simulations show a future increase of precipitation extremes with temperature at a rate comparable to or higher than the CC rate (Li et al., 2020Kharin et al., 2007Pall et al., 2007Allan and Soden, 2008Sugiyama et al., 2010Kao and Ganguly, 2011Muller et al., 2011). However, wet extremes are not expected to intensify in all regions (Trenberth, 2011Pfahl et al., 2017).

All these studies either show the multi-model mean or median and have not yet assessed the uncertainties in global CMIP6 projections. A suite of different model projections often exhibits a large spread (Lehner et al., 2020) and can even disagree on a particular region becoming wetter or drier (sign change in the future). Even where there is an overall consensus among the models on the sign of changes in the projected extremes due to a warmer climate, the magnitude of such changes can differ considerably. Though the climate models have improved over recent decades (Wyser et al., 2020Zelinka et al., 2020), these improvements do not necessarily result in a reduced spread among the projections (Douville et al., 2021). Thus, the main focus of this paper is to quantify the model uncertainties in extreme precipitation projections based on CMIP6 models. We also aim to provide a blueprint on using these projections to identify regions where the projected changes in daily precipitation extremes are consistent with the CC rate and those where they are not.

Changes in extreme precipitation against a backdrop of warming climate arise both due to thermodynamic and dynamic effects (Pfahl et al., 2017). A sub-CC relation or even negative dependence on global mean temperature has been found for precipitation extremes over some regions, especially over the climatologically dry oceanic regions in the subtropics, presumably as a result of decreasing moisture availability and enhanced large-scale subsidence (Berg et al., 2009Hardwick Jones et al., 2010Utsumi et al., 2011Pfahl et al., 2017). But the question of an appropriate choice of temperature for scaling extreme precipitation is still an open question and the available studies differ in scope (Zhang et al., 2019Schroeer and Kirchengast, 2018Sun et al., 2021). There is a large-scale warming contrast between the continental landmass and the oceans with certain regions over the ocean experiencing a negligible or limited change in the projected surface temperature. The larger warming observed over land may result in a lower scaling with local mean temperature, which may not be considered as a sub-CC scaling rate (Wang et al., 2017). Any departure from the CC rate can be an indication of a dynamical response which may be either amplified or offset by a thermodynamic response regionally (Pfahl et al., 2017Sherwood et al., 2010O’Gorman, 2015). Thus here we explore changes in extreme precipitation simply scaled by either global mean or local surface air temperature changes.

Several studies (Alexander et al., 2006Tebaldi et al., 2006Sillmann et al., 2013aSillmann et al., 2013b) have used various indices as a proxy for different features of precipitation extremes. Here we focus on extreme events with typical return periods of 20 years (or 20-year return values, RV20) as estimated from the annual maximum one-day precipitation (RX1DAY). Projected long-period RX1DAY return value changes are larger than changes in mean RX1DAY and increase with increasing rarity (Mizuta and Endo, 2020Wehner, 2020). Here we did not explore longer (e.g., 50 or 100 years) return periods since the associated uncertainties would be even stronger than for our RV20 estimations due to the limited sampling.

The goal of this study is to assess the uncertainties of projected changes in extreme precipitation based on the multi-model CMIP6 ensemble, to discuss the limitations of assessing the inter-model spread using such ensembles of opportunity, and to highlight the regions where projected changes may not be consistent with the widely used assumption of a Clausius–Clapeyron rate of ≈7%/K (Kharin et al., 2013Westra et al., 2013Seneviratne et al., 2021). For this purpose, we use the SSP5-8.5 scenario from 35 CMIP6 models. The total spread in this ensemble is therefore a combination of both model response uncertainty and internal variability. Therefore, we also assess the potential contribution of internal variability to the inter-model spread by analyzing the projected changes of the RV20 in the CanESM5 model with 25 realizations, with the same concentration scenario.

The rest of the paper is structured as follows. We start by introducing in Section 2 the models and methods used in this study. Turning to the results in Section 3, we address the uncertainties in the model projections along with a discussion on the role of internal variability using the ensemble simulations from CanESM5. The role of local versus global temperature scaling is also assessed. Section 4 summarizes the main findings. Other supporting figures and tables are available in the online supplementary material.

Read the full paper here.

via Watts Up With That?


April 14, 2022 at 08:57PM

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