[en] Recently, climate extremes have been grabbing attention as important drivers of environmental change. Here, we assemble an observational inventory of energy and mass fluxes to quantify the ice loss from glaciers on the Russian High Arctic archipelago of Novaya Zemlya. Satellite altimetry reveals that 70 ± 19% of the 149 ± 29 Gt mass loss between 2011 and 2022 occurred in just four high-melt years. We find that 71 ± 3% of the melt, including the top melt cases, are driven by extreme energy imports from atmospheric rivers. The majority of ice loss occurs on leeward slopes due to foehn winds. 45 of the 54 high-melt days (>1 Gt d-1) in 1990 to 2022 show a combination of atmospheric rivers and foehn winds. Therefore, the frequency and intensity of atmospheric rivers demand accurate representation for reliable future glacier melt projections for the Russian High Arctic.
Research Center/Unit :
SPHERES - ULiège
Disciplines :
Earth sciences & physical geography
Author, co-author :
Haacker, J ; Department of Geoscience and Remote Sensing, University of Technology Delft, Delft, the Netherlands. j.m.haacker@tudelft.nl
Wouters, B ; Department of Geoscience and Remote Sensing, University of Technology Delft, Delft, the Netherlands. bert.wouters@tudelft.nl
Fettweis, Xavier ; Université de Liège - ULiège > Département de géographie > Climatologie et Topoclimatologie
Glissenaar, I A; Institute for Marine and Atmospheric Research, Utrecht University, Utrecht, the Netherlands ; Bristol Glaciology Centre, School of Geographical Sciences, University of Bristol, Bristol, UK
Box, J E ; Department of Glaciology and Climate, Geological Survey of Denmark and Greenland, Copenhagen, Denmark
Language :
English
Title :
Atmospheric-river-induced foehn events drain glaciers on Novaya Zemlya.
Publication date :
15 August 2024
Journal title :
Nature Communications
eISSN :
2041-1723
Publisher :
Springer Science and Business Media LLC, England
Volume :
15
Issue :
1
Pages :
7021
Peer reviewed :
Peer Reviewed verified by ORBi
Tags :
CÉCI : Consortium des Équipements de Calcul Intensif Tier-1 supercomputer
R. Hugonnet et al. Accelerated global glacier mass loss in the early twenty-first century Nature 2021 592 726 731 2021Natur.592.726H 1:CAS:528:DC%2BB3MXhtVSrurrP 33911269
Intergovernmental Panel On Climate Change (IPCC). The Ocean and Cryosphere in a Changing Climate: Special Report of the Intergovernmental Panel on Climate Change 1 edn (Cambridge University Press, 2022).
D.R. Rounce et al. Global glacier change in the 21st century: every increase in temperature matters Science 2023 379 78 83 2023Sci..379..78R 1:CAS:528:DC%2BB3sXmtlaqtw%3D%3D 36603094
Oppenheimer, M. et al. Sea level rise and implications for low-lying islands, coasts and communities. In The Ocean and Cryosphere in a Changing Climate: Special Report of the Intergovernmental Panel on Climate Change, (eds Pörtner, H.-O. et al.) 321–445 (Cambridge University Press, Cambridge, 2019).
B. Marzeion et al. Partitioning the uncertainty of ensemble projections of global glacier mass change Earth’s. Future 2020 8 e2019EF001470 2020EaFut..801470M
L. Jakob N. Gourmelen Glacier mass loss between 2010 and 2020 dominated by atmospheric forcing Geophys. Res. Lett. 2023 50 e2023GL102954 2023GeoRL.5002954J
R. Millan J. Mouginot A. Rabatel M. Morlighem Ice velocity and thickness of the world’s glaciers Nat. Geosci. 2022 15 124 129 2022NatGe.15.124M 1:CAS:528:DC%2BB38Xjt1elsL4%3D
RGI Consortium. Randolph Glacier Inventory - A Dataset of Global Glacier Outlines, Version 6.0. NSIDC: National Snow and Ice Data Center, https://doi.org/10.7265/N5-RGI-60 (2017).
J. Zeeberg S.L. Forman Changes in glacier extent on north Novaya Zemlya in the twentieth century Holocene 2001 11 161 175 2001Holoc.11.161Z
D. Maure C. Kittel C. Lambin A. Delhasse X. Fettweis Spatially heterogeneous effect of climate warming on the arctic land ice Cryosphere 2023 17 4645 4659 2023TCry..17.4645M
W. Kochtitzky et al. The unquantified mass loss of Northern Hemisphere marine-terminating glaciers from 2000–2020 Nat. Commun. 2022 13 1 10
Moholdt, G., Wouters, B. & Gardner, A. S. Recent mass changes of glaciers in the Russian High Arctic. Geophys. Res. Lett. 39, L10502 (2012).
P. Tepes P. Nienow N. Gourmelen Accelerating ice mass loss across arctic Russia in response to atmospheric warming, sea ice decline, and atlantification of the Eurasian arctic shelf seas J. Geophys. Res. Earth Surf. 2021 126 e2021JF006068 2021JGRF.12606068T
J.R. Carr H. Bell R. Killick T. Holt Exceptional retreat of Novaya Zemlya’s marine-terminating outlet glaciers between 2000 and 2013 Cryosphere 2017 11 2149 2174 2017TCry..11.2149C
A.K. Melkonian M.J. Willis M.E. Pritchard A.J. Stewart Recent changes in glacier velocities and thinning at Novaya Zemlya Remote Sens. Environ. 2016 174 244 257 2016RSEnv.174.244M
R.S. Fausto D. van As J.E. Box W. Colgan P.L. Langen Quantifying the surface energy fluxes in south Greenland during the 2012 high melt episodes using in-situ observations Front. Earth Sci. 2016 4 82 2016FrEaS..4..82F
K.S. Mattingly et al. Increasing extreme melt in northeast Greenland linked to foehn winds and atmospheric rivers Nat. Commun. 2023 14 2023NatCo.14.1743M 1:CAS:528:DC%2BB3sXmsFSntrc%3D 36990994 10060376
J.E. Box et al. Greenland ice sheet rainfall climatology, extremes and atmospheric river rapids Meteorol. Appl. 2023 30 e2134 2023MeApp.30.2134B
J.D. Wille et al. Intense atmospheric rivers can weaken ice shelf stability at the Antarctic Peninsula Commun. Earth Environ. 2022 3 1 14
W. Ma et al. The role of interdecadal climate oscillations in driving arctic atmospheric river trends Nat. Commun. 2024 15 2024NatCo.15.2135M 1:CAS:528:DC%2BB2cXlsFGqsb0%3D 38459001 10923828
B. Wouters A.S. Gardner G. Moholdt Global glacier mass loss during the GRACE satellite mission (2002-2016) Front. Earth Sci. 2019 7 96 2019FrEaS..7..96W
C. Sommer T. Seehaus A. Glazovsky M.H. Braun Brief communication: increased glacier mass loss in the Russian High Arctic (2010–2017) Cryosphere 2022 16 35 42 2022TCry..16..35S
R.S. Fausto et al. The implication of nonradiative energy fluxes dominating Greenland ice sheet exceptional ablation area surface melt in 2012 Geophys. Res. Lett. 2016 43 2649 2658 2016GeoRL.43.2649F
X. Zou et al. Strong warming over the Antarctic Peninsula during combined atmospheric river and foehn events: contribution of shortwave radiation and turbulence J. Geophys. Res. Atmos. 2023 128 e2022JD038138 2023JGRD.12838138Z
Barry, R. G. Mountain Weather and Climate 3 edn, p. 159 (Cambridge University Press, 2008).
W.J. Wiscombe S.G. Warren A model for the spectral albedo of snow. I: pure snow J. Atmos. Sci. 1980 37 2712 2733 1980JAtS..37.2712W
Z. Li Q. Ding M. Steele A. Schweiger Recent upper Arctic Ocean warming expedited by summertime atmospheric processes Nat. Commun. 2022 13 2022NatCo.13.362L 1:CAS:528:DC%2BB38XhsVWrs7Y%3D 35042876 8766491
R. Bintanja O. Andry Towards a rain-dominated Arctic Nat. Clim. Change 2017 7 263 267 2017NatCC..7.263B
E. Ciraci I. Velicogna T.C. Sutterley Mass balance of Novaya Zemlya archipelago, Russian High Arctic, using time-variable gravity from GRACE and altimetry data from ICESat and CryoSat-2 Remote Sens. 2018 10 1817 2018RemS..10.1817C
A. Damseaux X. Fettweis M. Lambert Y. Cornet Representation of the rain shadow effect in Patagonia using an orographic-derived regional climate model Int. J. Climatol. 2020 40 1769 1783
Delhasse, A., Beckmann, J., Kittel, C. & Fettweis, X. Coupling the regional climate MAR model with the ice sheet model PISM mitigates the melt-elevation positive feedback. The Cryosphere Discussions 1–21 (2023).
Hersbach, H. Complete ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service (C3S) Data Store (CDS), https://doi.org/10.24381/cds.143582cf (2017).
H. Hersbach et al. The ERA5 global reanalysis Q. J. R. Meteorol. Soc. 2020 146 1999 2049 2020QJRMS.146.1999H
H. Gallée G. Schayes Development of a three-dimensional meso-γ primitive equation model: Katabatic winds simulation in the area of Terra Nova Bay, Antarctica Mon. Weather Rev. 1994 122 671 685 1994MWRv.122.671G
K. De Ridder G. Schayes The IAGL land surface model J. Appl. Meteorol. (1988-2005) 1997 36 167 182
E. Brun P. David M. Sudul G. Brunot A numerical model to simulate snow-cover stratigraphy for operational avalanche forecasting J. Glaciol. 1992 38 13 22 1992JGlac.38..13B
H. Gallée G. Guyomarc’h E. Brun Impact of snow drift on the Antarctic Ice Sheet surface mass balance: Possible sensitivity to snow-surface properties Bound. Layer. Meteorol. 2001 99 1 19 2001BoLMe.99..1G
X. Fettweis et al. GrSMBMIP: Intercomparison of the modelled 1980–2012 surface mass balance over the Greenland Ice Sheet Cryosphere 2020 14 3935 3958 2020TCry..14.3935F
C. Amory et al. Performance of MAR (v3.11) in simulating the drifting-snow climate and surface mass balance of Adélie Land, East Antarctica Geosci. Model Dev. 2021 14 3487 3510 2021GMD..14.3487A
R.J. Hogan A. Bozzo A flexible and efficient radiation scheme for the ECMWF model J. Adv. Model. Earth Syst. 2018 10 1990 2008 2018JAMES.10.1990H
J.-J. Morcrette Assessment of the ECMWF model cloudiness and surface radiation fields at the ARM SGP site Mon. Weather Rev. 2002 130 257 277 2002MWRv.130.257M
X. Fettweis M. Tedesco M. van den Broeke J. Ettema Melting trends over the Greenland ice sheet (1958–2009) from spaceborne microwave data and regional climate models Cryosphere 2011 5 359 375 2011TCry..5.359F
A. Delhasse et al. Brief communication: evaluation of the near-surface climate in ERA5 over the Greenland Ice Sheet Cryosphere 2020 14 957 965 2020TCry..14.957D
B.J. Wallis A.E. Hogg J.M. van Wessem B.J. Davison M.R. van den Broeke Widespread seasonal speed-up of west Antarctic Peninsula glaciers from 2014 to 2021 Nat. Geosci. 2023 16 231 237 2023NatGe.16.231W 1:CAS:528:DC%2BB3sXjvFOjsbw%3D
D.J. Wingham et al. CryoSat: a mission to determine the fluctuations in Earth’s land and marine ice fields Adv. Space Res. 2006 37 841 871 2006AdSpR.37.841W
J. Haacker B. Wouters C. Slobbe Systematic errors observed in CryoSat-2 elevation swaths on mountain glaciers and their implications IEEE Trans. Geosci. Remote Sens. 2023 61 1 12
Porter, C. et al. ArcticDEM, Version 3.0. Havard Dataverse, https://doi.org/10.7910/DVN/OHHUKH (2018).
Wilcox, R. R. Chapter 3 - Estimating Measures of Location and Scale. In Wilcox, R. R. (ed.) Introduction to Robust Estimation and Hypothesis Testing 5 edn, 45–106 (Academic Press, 2022).
R. McNabb C. Nuth A. Kääb L. Girod Sensitivity of glacier volume change estimation to DEM void interpolation Cryosphere 2019 13 895 910 2019TCry..13.895M
A. Morris G. Moholdt L. Gray T.V. Schuler T. Eiken CryoSat-2 interferometric mode calibration and validation: a case study from the Austfonna ice cap, Svalbard Remote Sens. Environ. 2022 269 112805
M. Huss Density assumptions for converting geodetic glacier volume change to mass change Cryosphere 2013 7 877 887 2013TCry..7.877H
T.A. O’Brien et al. Detection uncertainty matters for understanding atmospheric rivers Bull. Am. Meteorol. Soc. 2020 101 E790 E796
Schyberg, H. Arctic regional reanalysis on single levels from 1991 to present. Copernicus Climate Change Service (C3S) Data Store (CDS), https://doi.org/10.24381/CDS.713858F6 (2021).
L. Bengtsson et al. The HARMONIE–AROME model configuration in the ALADIN–HIRLAM NWP system Mon. Weather Rev. 2017 145 1919 1935 2017MWRv.145.1919B
Schaaf, C. & Wang, Z. MODIS/Terra+Aqua BRDF/Albedo Daily L3 Global - 500m V061. NASA EOSDIS Land Processes Distributed Active Archive Center, https://doi.org/10.5067/MODIS/MCD43A3.061 (2021).
