08 Nov 2021
08 Nov 2021
Status: a revised version of this preprint is currently under review for the journal HESS.

High-resolution modeling of glacier mass balance and surface runoff in western Norway driven by bias-corrected climate forcing

Yongmei Gong and Irina Rogozhina Yongmei Gong and Irina Rogozhina
  • Department of Geography, Norwegian University of Science and Technology, Trondheim, 7049, Norway

Abstract. Western Norway hosts many glacierized drainage basins with complex terrain and local climate. These drainage basins face challenges related to long-term planning of hydropower production and flood risk mitigation under global warming. To enable forward vision of such efforts, bias-corrected outputs from state-of-the-art regional climate models and reanalysis provide climatic forcing for impact simulations. We utilize a distributed, process-based snow evolution model with a daily temporal and 100 m × 100 m spatial resolution to investigate the applicability of different bias-corrected climate forcing data for multidecadal reconstructions of glacier surface mass balance and surface runoff regimes in western Norway. These simulations are driven by climatic forcing from the bias-corrected NORA10 hindcast in 2000–2014, which has been produced specifically for western Norway and treated as a benchmark dataset, as well as ten bias-corrected and uncorrected CORDEX outputs under different Representative Concentration Pathway scenarios in 2000–2020. Downscaled drainage basin-wide air temperature, precipitation and glacier-wide surface mass balance are then validated against observations.

The variables mentioned above produced by the benchmark simulation match available observations well. The mean annual surface mass balance of glaciers in most glacierized basins is negative in 2001–2014, and its evolution is mainly correlated with trends in annual snowfall. There is a general negative west to east gradient in seasonal and annual unit area runoff, which peaks between 2005 and 2008 in most drainage basins. Snow meltwater is the largest contributor to both seasonal and annual runoff in all drainage basins except for two of the westernmost ones. Drainage basins with denser glacier coverage turn out to have a later peak runoff discharge date. The correction applied to the CORDEX forcing reversed the cold bias in the original datasets, while the agreement between bias-corrected and observed precipitation rates varies strongly from basin to basin. As a result, simulations driven by bias-corrected CORDEX datasets produce lower annual surface mass balance in the most and least glacierized drainage basins, i.e., Basin 1 and 17, respectively. They all produce more unit area runoff in Basin 1 and less in Basin 17 both seasonally and annually, with only a few exceptions. We conclude that the identified errors will likely be inherited by the results of the future projections, casting doubts on the applicability of bias-corrected CORDEX forcing to directly drive local scale projections and the modeled outputs in developing climate change adaptation strategies.

Yongmei Gong and Irina Rogozhina

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-500', Anonymous Referee #1, 27 Jan 2022
    • AC1: 'Reply on RC1', Yongmei Gong, 31 Mar 2022
  • RC2: 'Comment on hess-2021-500', Anonymous Referee #2, 05 Feb 2022
    • AC2: 'Reply on RC2', Yongmei Gong, 31 Mar 2022
  • RC3: 'Comment on hess-2021-500', Anonymous Referee #3, 08 Feb 2022
    • AC3: 'Reply on RC3', Yongmei Gong, 01 Apr 2022

Yongmei Gong and Irina Rogozhina

Yongmei Gong and Irina Rogozhina


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Short summary
The results from our snow evolution modeling of glacierized drainage basins in western Norway forced by bias-corrected, IPCC class regional climate model experiment CORDEX outputs reveal that the applicability of such forcing to directly drive local scale projections is not satisfactory. It is necessary to correct the original CORDEX datasets for bias against reference data that represent the current climate conditions of a specific area of interest for future projections.