Brief communication: CESM2 climate forcing (1950–2014) yields realistic Greenland ice sheet surface mass balance

Abstract

Abstract. We present a reconstruction of historical (1950–2014) surface mass balance (SMB) of the Greenland ice sheet (GrIS) using a high-resolution regional climate model (RACMO2; ∼11 km) to dynamically downscale the climate of the Community Earth System Model version 2 (CESM2; ∼111 km). After further statistical downscaling to 1 km spatial resolution, evaluation using in situ SMB measurements and remotely sensed GrIS mass change shows good agreement. Comparison with an ensemble of previously conducted RACMO2 simulations forced by climate reanalysis demonstrates that the current product realistically represents the long-term average and variability of individual SMB components and captures the recent increase in meltwater runoff that accelerated GrIS mass loss. This means that, for the first time, climate forcing from an Earth system model (CESM2), which assimilates no observations, can be used without additional corrections to reconstruct the historical GrIS SMB and its recent decline that initiated mass loss in the 1990s. This paves the way for attribution studies of future GrIS mass loss projections and contribution to sea level rise.

A 21st Century Warming Threshold for Sustained Greenland Ice Sheet Mass Loss

“…allowing us to determine a regional warming threshold for which GrIS SMB becomes negative: 4.5 ± 0.3°C (vertical red line in Figure 1b). The uncertainty of 0.3°C is based on the model SMB uncertainty of 48 Gt yr −1 derived from a comparison between SMB from in situ measurements and from the historical CESM2-forced RACMO2 simulation at 1 km (Noël et al, 2020) (see supporting information). The statistical uncertainty of the quadratic fit is comparatively small (<0.1°C calculated from a 68% confidence band) and therefore neglected.…”

“…Here we present a novel physically-based approach, using explicitly resolved surface energy balance from a state-of-the-art regional climate model (RACMO2.3p2) to dynamically downscale a CMIP6 projection from the Community Earth System Model (CESM2) under a high-end climate warming, further statistically downscaled to 1 km resolution. Uniquely, without any bias correction in the forcing, this physically-based model setup reproduces a realistic present-day GrIS SMB (Noël et al, 2020), and shows high correlation with the native CESM2 forcing, enabling us to project meaningful GrIS SMB under various CMIP6 warming scenarios after applying a small linear correction. Since GrIS geometrical changes and the associated (elevation-temperature) feedback are expected to remain small during the 21 st century (Edwards et al, 2014;Le clec'h et al, 2019;Aschwanden et al, 2019), first-order inferences about the SMB = 0 threshold can be made assuming constant ice sheet geometry.…”

The ERA5-Land soil temperature bias in permafrost regions

“…ac.cn/zh-hans/data/ef949bb0-26d4-4cb6-acc2-3385413b91ee/; Su and Yang, 2019). The Nordicana D data are available from http://www.cen.ulaval.ca/nordicanad/en_index.aspx (Nordicana D, 2020), GI-UAF is available from the Permafrost Laboratory of the University of Alaska (https://permafrost.gi.alaska.edu/content/ data-and-maps; Permafrost Laboratory, 2020), and the datasets from Julia Boike are available from https://doi.pangaea.de/10.1594/ PANGAEA.880120 (Boike et al, 2017) and https://doi.pangaea. de/10.1594/PANGAEA.905236 (Boike et al, 2019b).…”

First Application of Artificial Neural Networks to Estimate 21st Century Greenland Ice Sheet Surface Melt

“…The current mass loss of the GrIS is the result of increased ice discharge and decreased surface mass balance (SMB), with the latter being the dominant contributor (Fettweis et al., 2017 ; van den Broeke et al., 2016 ), and the cause of mass loss acceleration (Enderlin et al., 2014 ; Shepherd et al., 2020 ). The main contributor to this contemporary GrIS surface mass loss is increased surface melt (Fettweis et al., 2017 ; Noël et al., 2020 ). Projections of future GrIS surface melt are scarce, as most global climate models do not feature a (realistic) melt calculation (Cullather et al., 2014 ; Lenaerts et al., 2019 ; Vizcaino et al., 2014 ).…”