The Role of Zonally Averaged Climate Change in Contributing to Intermodel Spread in CMIP5 Predicted Local Precipitation Changes

Abstract

While CMIP5 models robustly project drying of the subtropics and more precipitation in the tropics and subpolar latitudes by the end of the century, the magnitude of these changes in precipitation varies widely across models: for example, some models simulate no drying in the eastern Mediterranean while others simulate more than a 50% reduction in precipitation relative to the model-simulated present-day value. Furthermore, the factors leading to changes in local subtropical precipitation remain unclear. The importance of zonal-mean changes in atmospheric structure for local precipitation changes is explored in 42 CMIP5 models. It is found that up to half of the local intermodel spread over the Mediterranean, northern Mexico, East Asia, southern Africa, southern Australia, and southern South America is related to the intermodel spread in large-scale processes such as the magnitude of globally averaged surface temperature increases, Hadley cell widening, polar amplification, stabilization of the tropical upper troposphere, or changes in the polar stratosphere. Globally averaged surface temperature increases account for intermodel spread in land subtropical drying in the Southern Hemisphere but are not important for land drying adjacent to the Mediterranean. The factors associated with drying over the eastern Mediterranean and western Mediterranean differ, with stabilization of the tropical upper troposphere being a crucial factor for the former only. Differences in precipitation between the western and eastern Mediterranean are also evident on interannual time scales. In contrast, the global factors examined here are unimportant over most of the United States, and more generally over the interior of continents. Much of the rest of the spread can be explained by variations in local relative humidity, a proxy also for zonally asymmetric circulation and thermodynamic changes.

Storyline description of Southern Hemisphere midlatitude circulation and precipitation response to greenhouse gas forcing

“…Zappa and Shepherd (2017) thus used Arctic warming, tropical upper-tropospheric warming, and stratospheric vortex change, to construct storylines of European wintertime regional climate change. More recently, Garfinkel et al (2020) has shown how such zonally averaged drivers can statistically account for a substantial portion of the spread in the annually averaged precipitation response across the midlatitudes of both hemispheres.…”

Refining projected multidecadal hydroclimate uncertainty in East-Central Europe using CMIP5 and single-model large ensemble simulations

“…However, to abandon "model democracy" and weight or give preference to certain models in a multi-model ensemble based on performance, ranking was also proposed (Knutti 2010;Merrifield et al 2019). The latter has proved effective in constraining model uncertainty (Knutti et al 2017) and is of particular importance when studying climatic variables (e.g., precipitation) whose future projections show large spread between different models (Garfinkel et al 2020).…”

“…In addition, the concern of interdependency of CMIP models (Sanderson et al 2015) has been re-evaluated recently (Olson et al 2019). Regarding the target area of model evaluation Garfinkel et al (2020) studied the sources of CMIP5 intermodel spread in precipitation changes globally, however, ample analyses are targeted at more regional areas, e.g., the North-Atlantic (Perez et al 2014), parts of Europe (Coppola et al 2010;Pieczka et al 2017), Africa (Brands et al 2013;Dyer et al 2019;Yapo et al 2020), South-America (Lovino et al 2018), or Asia (Ahmed et al 2019).…”

Water Budgets of Managed Forests in Northeast Germany under Climate Change—Results from a Model Study on Forest Monitoring Sites

“…MPI/Remo show increasing precipitation under RCP8.5, which partly compensates for increasing water losses due to evapotranspiration and leads to an increase of seepage. Due to the dependency of precipitation to atmospheric circulation and their changes, e.g., [11,95], uncertainty in the simulation of regional precipitation in climate change models is high, and the models show high variability in simulated precipitation, e.g., [46,96,97].…”