Climate pattern-scaling set for an ensemble of 22 GCMs – adding uncertainty to the IMOGEN version 2.0 impact system

Abstract

Abstract. Global circulation models (GCMs) are the best tool to understand climate change, as they attempt to represent all the important Earth system processes, including anthropogenic perturbation through fossil fuel burning. However, GCMs are computationally very expensive, which limits the number of simulations that can be made. Pattern scaling is an emulation technique that takes advantage of the fact that local and seasonal changes in surface climate are often approximately linear in the rate of warming over land and across the globe. This allows interpolation away from a limited number of available GCM simulations, to assess alternative future emissions scenarios. In this paper, we present a climate pattern-scaling set consisting of spatial climate change patterns along with parameters for an energybalance model that calculates the amount of global warming. The set, available for download, is derived from 22 GCMs of the WCRP CMIP3 database, setting the basis for similar eventual pattern development for the CMIP5 and forthcoming CMIP6 ensemble. Critically, it extends the use of the IMOGEN (Integrated Model Of Global Effects of climatic aNomalies) framework to enable scanning across full uncertainty in GCMs for impact studies. Across models, the presented climate patterns represent consistent global mean trends, with a maximum of 4 (out of 22) GCMs exhibiting the opposite sign to the global trend per variable (relative humidity). The described new climate regimes are generally warmer, wetter (but with less snowfall), cloudier and windier, and have decreased relative humidity. Overall, when averaging individual performance across all variables, and without considering co-variance, the patterns explain one-third of regional change in decadal averages (mean percentage variance explained, PVE, 34.25±5.21), but the signal in some models exhibits much more linearity (e.g. MIROC3.2(hires): 41.53) than in others (GISS_ER: 22.67). The two most often considered variables, near-surface temperature and precipitation, have a PVE of 85.44±4.37 and 14.98±4.61, respectively. We also provide an example assessment of a terrestrial impact (changes in mean runoff) and compare projections by the IMOGEN system, which has one land surface model, against direct GCM outputs, which all have alternative representations of land functioning. The latter is noted as an additional source of uncertainty. Finally, current and potential future applications of the IMOGEN version 2.0 modelling system in the areas of ecosystem modelling and climate change impact assessment are presented and discussed.

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

“…These results are based on the assumption that the changes in all fields and remote drivers scale linearly with climate sensitivity. Although pattern scaling has been shown to be a useful approximation (Zappa and Shepherd 2017;Zelazowski et al 2018), it can certainly be improved (Tebaldi and Arblaster 2014;Herger et al 2015). For example, it has been shown that the circulation response can be sensitive to the rate of CO 2 emissions or aerosol radiative responses independently from global warming (Grise and Polvani 2014) and that the stratospheric vortex also exhibits a weaker "direct" response to greenhouse gas forcing (Ceppi and Shepherd 2019).…”

A new precipitation emulator (PREMU v1.0) for lower complexity models

“…Some more complex LCMs, such as IMOGEN and OSCAR, additionally emulate the spatial pattern of temperature through the pattern-scaling method. Pattern-scaling multiplies global temperature change by spatial patterns to give regional information (Zelazowski et al, 2018;Gasser et al, 2017;Huntingford et al, 2010). Recently, a spatially resolved emulator (MESMER) coupled with the emission-driven LCM (MAGICC) has been developed, which is found to reproduce the spatial pattern of temperature in ESMs better (Beusch et al, 2022).…”

“…Accurately emulating precipitation change is necessary to determine the climate response to different emission scenarios and to understand feedbacks to global warming via rainfall-dependent vegetation net primary productivity (Gasser et al, 2017). Until now, however, only two LCMs (IMOGEN and OSCAR) can emulate precipitation (Zelazowski et al, 2018;Gasser et al, 2017). IMOGEN emulates the gridded precipitation based on the regression relationship (by month and location) between gridded precipitation and global land average temperature (Zelazowski et al, 2018).…”

“…Until now, however, only two LCMs (IMOGEN and OSCAR) can emulate precipitation (Zelazowski et al, 2018;Gasser et al, 2017). IMOGEN emulates the gridded precipitation based on the regression relationship (by month and location) between gridded precipitation and global land average temperature (Zelazowski et al, 2018). OSCAR constructs the emulator by establishing the relationship between global average precipitation and global average temperature and radiative forcing, and then using the pattern-scaling method to deduce the gridded precipitation (Gasser et al, 2017).…”

“…OSCAR constructs the emulator by establishing the relationship between global average precipitation and global average temperature and radiative forcing, and then using the pattern-scaling method to deduce the gridded precipitation (Gasser et al, 2017). Nevertheless, the gridded precipitation estimated by the simple linear method is not reliable either in IMOGEN or in OSCAR; the gridded precipitation predicted by IMOGEN only explains less than 20% of variance of seasonal precipitation in most regions (Zelazowski et al, 2018) and OSCAR cannot capture the interannual variations of gridded precipitation across the globe at all (Gasser et al, 2017). This may be because 1) the global average temperature could not fully characterize local temperature and moisture recycling (Prein et al, 2017) and large-scale circulation (Shepherd, 2014;Fereday et al, 2018;Heinze-Deml et al, 2021); 2) there is at any given location a nonlinear relationship between local rainfall features and global warming (Allen and Ingram., 2002;Collins et al, 2013;Chadwick and Good, 2013).…”