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

Abstract

Future hydroclimate projections of global climate models for East-Central Europe diverge to a great extent, thus, constrain adaptation strategies. To reach a more comprehensive understanding of this regional spread in model projections, we make use of the CMIP5 multi-model ensemble and six single-model initial condition large ensemble (SMILE) simulations to separate the effects of model structural differences and internal variability, respectively, on future hydroclimate projection uncertainty. To account for model uncertainty, we rank 32 CMIP5 models based on their predictive skill in reproducing multidecadal past hydroclimate variability. Specifically, we compare historical model simulations to long instrumental and reanalysis surface temperature and precipitation records. The top 3–ranked models—that best reproduce regional past multidecadal temperature and precipitation variability—show reduced spread in their projected future precipitation variability indicating less dry summer and wetter winter conditions in part at odds with previous expectations for Central Europe. Furthermore, not only does the regionally best performing CMIP5 models belong to the previously identified group of models with more realistic land-atmosphere interactions, their future summer precipitation projections also emerge from the range of six SMILEs’ future simulations. This suggests an important role for land-atmosphere coupling in regulating hydroclimate uncertainty on top of internal variability in the upcoming decades. Our results help refine the relative contribution of structural differences between models in affecting future hydroclimate uncertainty in the presence of irreducible internal variability in East-Central Europe.

Record summer rains in 2019 led to massive loss of surface and cave ice in SE Europe

“…The rapid melting of these glacierets was accelerated by the warm and moist conditions resulting from turbulent heat fluxes near the surface of the ice (Marks et al, 1998;Pomeroy et al, 2016) in addition to the heat delivered directly by rainwater. The relative area loss of Snezhnika glacieret was ∼ 0.94 % yr −1 between 1959 and 2008, similar to the average global value of 1 % yr −1 (Vaughan et al, 2013), but increased to 1.86 % yr −1 between 2008 and 2018 and 19.3 % yr −1 in 2019. For Banski Suhodol glacieret, the relative area loss between 2018 and 2019 was close to that of Snezhnika glacieret at 17.2 % yr −1 .…”

Large ensemble climate model simulations: introduction, overview, and future prospects for utilising multiple types of large ensemble

“…This sampling additionally allows for future projections of events with long return periods to be made (e.g. van der Wiel et al, 2019). Different applications require different types of SMILEs.…”

“…Some examples include the investigation of the role of internal variability and model differences in affecting future projections (Maher et al, 2020;Lehner et al, 2020;Maher et al, 2021), trends in sea surface temperature patterns (Olonscheck et al, 2020) and South American summer rainfall (Díaz et al, 2021), the decadal modulation of global warming (Liguori et al, 2020), the time of emergence of ocean biogeochemical trends (Schlunegger et al, 2020), and Arctic extremes (Landrum and Holland, 2020). These SMILEs have also been investigated for use in adaption decision making (Mankin et al, 2020), hydroclimate uncertainty in east-central Europe (Topál et al, 2020), and uncertainty in projections of global land monsoon precipitation (Zhou et al, 2020).…”

Depth‐Duration‐Frequency of Extreme Precipitation Events Under Internal Climate Variability: Indian Summer Monsoon

“…The ISM's extreme events affect about one‐sixth of the world's population, and its variability has increased significantly since the 1950s (Ghosh et al., 2012, 2016; Goswami & Chakravorty, 2017; Roxy & Chaithra, 2018). While, substantial progress has been made on quantifying sources of uncertainty in climate projections since the inception of the Coupled Model Inter‐comparison Project (Hawkins & Sutton, 2011; Tebaldi & Knutti, 2007; Topál et al., 2020), the role of internal variability for climate risk assessment, adaptation management, and decision making are yet to be fully realized (Deser, 2020).…”

“…The three significant sources of uncertainty in future climate change projection are forcing uncertainty, model uncertainty, and internal variability (Deser, Phillips, et al., 2012, Hawkins & Sutton, 2011; Topál et al., 2020). Forcing uncertainty, also termed as scenario uncertainty, arises from incomplete knowledge of external factors influencing the climate system, such as future trajectories of anthropogenic emissions of greenhouse gases, stratospheric ozone concentrations, and land‐use change.…”