Changes in extreme temperatures of the Earth's desert regions over the next 100 years

Abstract

We quantify changes DeltaQ in 100-year return values for regional annual maxima and minima of near-surface atmospheric temperature from output of five CMIP6 models, for five of the Earth's desert regions, over the interval (2025,2125). We use generalised extreme value (GEV) regression to characterise changes in extremes, considering a range of different parametric forms for the variation of GEV parameters with time, and coupling models for different scenarios so that they provide a common GEV tail in the first year of observation. Parameters are estimated using Bayesian inference. We perform a simulation study using ground truth models generating data qualitatively similar to the CMIP6 output, to assess the relative performance of different information criteria in selecting models from a set of candidates, to minimise error in predictions of DeltaQ. The Bayesian information criterion (BIC) provides best performance, out-performing the divergence and widely-applicable information criteria in particular. Using BIC-selected GEV regression models, we estimate joint posterior distributions of DeltaQ over three forcing scenarios, for different combinations of region, GCM and climate ensemble. Estimates show a consistent trend across regions, GCMs and climate ensembles, of DeltaQ increasing with climate scenario for both regional annual maxima and minima. Aggregating posterior distributions over climate ensembles and GCMs, we find evidence for significant increases in DeltaQ for regional annual maxima under more severe forcing scenarios for all desert regions. Similar but weaker and less significant trends are observed for regional annual minima.

Figures (13)

• Figure 1: World map showing desert regions considered. Details of longitude-latitude bounding boxes in Table \ref{['Tbl:LctRgn']}. Note that Mojave (MO) and Dasht-e Lut (DA) are small geographic regions consisting of small numbers of GCM grid locations.
• Figure 2: Regional annual maxima (left) and minima (right) time series of tas (K) for the UK "control" region from the UKESM1-0-LL GCM. Climate scenarios are distinguished by colour: SSP126 (green), SSP245 (orange), SSP585 (grey). Climate ensemble runs for each scenario, as listed in Table \ref{['Tbl:GcmDat']}, are distinguished by line style.
• Figure 3: Time series of regional annual maxima of tas (K) by GCM (rows: ACCESS-CM2, CESM2, EC-Earth3, MRI-ESM2-0 and UKESM1-0-LL) and region (column: : Antarctic, Dasht-e Lut, Mojave, Sahara, Simpson and UK). In each panel, climate scenarios are distinguished by colour: SSP126 (green), SSP245 (orange), SSP126 (grey). Climate ensemble runs for each scenario, as listed in Table \ref{['Tbl:GcmDat']}, are distinguished by line style. Corresponding spatial annual minima time series are given in Figure SM1.
• Figure 4: Simulation study design. Variation of GEV location ($\mu$; top), scale ($\sigma$; middle) and shape ($\xi$; bottom) in time for three scenarios (red, green and blue), for each of 11 data-generating processes (CCC to QQQ; columns left to right).
• Figure 5: RMSE estimates (see Equation \ref{['Eqt:RMSE']}) from the simulation study, for different combinations of data-generating process (CCC to QQQ; x-axis) and model selection information criterion (AIC1 to WAIC; coloured lines). Note that for data-generating process CCC, all of BIC1, BIC2 and BIC3 always select the correct CCC model form, resulting in RMSE=0 (not shown on the $\log_{10}$-scale y-axis.)