Vegetation type is an important predictor of the arctic summer land surface energy budget

Jacqueline Oehri; Gabriela Schaepman-Strub; Jin-Soo Kim; Raleigh Grysko; Heather Kropp; Inge Grünberg; Vitalii Zemlianskii; Oliver Sonnentag; Eugénie S. Euskirchen; Merin Reji Chacko; Giovanni Muscari; Peter D. Blanken; Joshua F. Dean; Alcide di Sarra; Richard J. Harding; Ireneusz Sobota; Lars Kutzbach; Elena Plekhanova; Aku Riihelä; Julia Boike; Nathaniel B. Miller; Jason Beringer; Efrén López-Blanco; Paul C. Stoy; Ryan C. Sullivan; Marek Kejna; Frans-Jan W. Parmentier; John A. Gamon; Mikhail Mastepanov; Christian Wille; Marcin Jackowicz-Korczynski; Dirk N. Karger; William L. Quinton; Jaakko Putkonen; Dirk van As; Torben R. Christensen; Maria Z. Hakuba; Robert S. Stone; Stefan Metzger; Baptiste Vandecrux; Gerald V. Frost; Martin Wild; Birger Hansen; Daniela Meloni; Florent Domine; Mariska te Beest; Torsten Sachs; Aram Kalhori; Adrian V. Rocha; Scott N. Williamson; Sara Morris; Adam L. Atchley; Richard Essery; Benjamin R. K. Runkle; David Holl; Laura D. Riihimaki; Hiroki Iwata; Edward A. G. Schuur; Christopher J. Cox; Andrey A. Grachev; Joseph P. McFadden; Robert S. Fausto; Mathias Göckede; Masahito Ueyama; Norbert Pirk; Gijs de Boer; M. Syndonia Bret-Harte; Matti Leppäranta; Konrad Steffen; Thomas Friborg; Atsumu Ohmura; Colin W. Edgar; Johan Olofsson; Scott D. Chambers

doi:10.1038/s41467-022-34049-3

Vegetation type is an important predictor of the arctic summer land surface energy budget

Jacqueline Oehri, Gabriela Schaepman-Strub, Jin-Soo Kim, Raleigh Grysko, Heather Kropp, Inge Grünberg, Vitalii Zemlianskii, Oliver Sonnentag, Eugénie S. Euskirchen, Merin Reji Chacko, Giovanni Muscari, Peter D. Blanken, Joshua F. Dean, Alcide di Sarra, Richard J. Harding, Ireneusz Sobota, Lars Kutzbach, Elena Plekhanova, Aku Riihelä, Julia BoikeNathaniel B. Miller, Jason Beringer, Efrén López-Blanco, Paul C. Stoy, Ryan C. Sullivan, Marek Kejna, Frans-Jan W. Parmentier, John A. Gamon, Mikhail Mastepanov, Christian Wille, Marcin Jackowicz-Korczynski, Dirk N. Karger, William L. Quinton, Jaakko Putkonen, Dirk van As, Torben R. Christensen, Maria Z. Hakuba, Robert S. Stone, Stefan Metzger, Baptiste Vandecrux, Gerald V. Frost, Martin Wild, Birger Hansen, Daniela Meloni, Florent Domine, Mariska te Beest, Torsten Sachs, Aram Kalhori, Adrian V. Rocha, Scott N. Williamson, Sara Morris, Adam L. Atchley, Richard Essery, Benjamin R. K. Runkle, David Holl, Laura D. Riihimaki, Hiroki Iwata, Edward A. G. Schuur, Christopher J. Cox, Andrey A. Grachev, Joseph P. McFadden, Robert S. Fausto, Mathias Göckede, Masahito Ueyama, Norbert Pirk, Gijs de Boer, M. Syndonia Bret-Harte, Matti Leppäranta, Konrad Steffen, Thomas Friborg, Atsumu Ohmura, Colin W. Edgar, Johan Olofsson, Scott D. Chambers

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm−2) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.

Original language	English
Article number	6379
Number of pages	12
Journal	Nature Communications
Volume	13
Issue number	1
DOIs	https://doi.org/10.1038/s41467-022-34049-3
Publication status	Published - Dec 2022

Keywords

Atmospheric dynamics
Climate and Earth system modelling
cryosperic science
ecosystem ecology
Phenology

Access to Document

10.1038/s41467-022-34049-3Licence: CC BY

s41467-022-34049-3 (1)Final published version, 2.03 MBLicence: CC BY

Cite this

Oehri, J., Schaepman-Strub, G., Kim, J.-S., Grysko, R., Kropp, H., Grünberg, I., Zemlianskii, V., Sonnentag, O., Euskirchen, E. S., Reji Chacko, M., Muscari, G., Blanken, P. D., Dean, J. F., di Sarra, A., Harding, R. J., Sobota, I., Kutzbach, L., Plekhanova, E., Riihelä, A., ... Chambers, S. D. (2022). Vegetation type is an important predictor of the arctic summer land surface energy budget. Nature Communications, 13(1), Article 6379. https://doi.org/10.1038/s41467-022-34049-3

@article{3ee9b93530ea49b4a46f9297cce1bf7e,

title = "Vegetation type is an important predictor of the arctic summer land surface energy budget",

abstract = "Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm−2) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.",

keywords = "Atmospheric dynamics, Climate and Earth system modelling, cryosperic science, ecosystem ecology, Phenology",

author = "Jacqueline Oehri and Gabriela Schaepman-Strub and Jin-Soo Kim and Raleigh Grysko and Heather Kropp and Inge Gr{\"u}nberg and Vitalii Zemlianskii and Oliver Sonnentag and Euskirchen, {Eug{\'e}nie S.} and {Reji Chacko}, Merin and Giovanni Muscari and Blanken, {Peter D.} and Dean, {Joshua F.} and {di Sarra}, Alcide and Harding, {Richard J.} and Ireneusz Sobota and Lars Kutzbach and Elena Plekhanova and Aku Riihel{\"a} and Julia Boike and Miller, {Nathaniel B.} and Jason Beringer and Efr{\'e}n L{\'o}pez-Blanco and Stoy, {Paul C.} and Sullivan, {Ryan C.} and Marek Kejna and Parmentier, {Frans-Jan W.} and Gamon, {John A.} and Mikhail Mastepanov and Christian Wille and Marcin Jackowicz-Korczynski and Karger, {Dirk N.} and Quinton, {William L.} and Jaakko Putkonen and {van As}, Dirk and Christensen, {Torben R.} and Hakuba, {Maria Z.} and Stone, {Robert S.} and Stefan Metzger and Baptiste Vandecrux and Frost, {Gerald V.} and Martin Wild and Birger Hansen and Daniela Meloni and Florent Domine and {te Beest}, Mariska and Torsten Sachs and Aram Kalhori and Rocha, {Adrian V.} and Williamson, {Scott N.} and Sara Morris and Atchley, {Adam L.} and Richard Essery and Runkle, {Benjamin R. K.} and David Holl and Riihimaki, {Laura D.} and Hiroki Iwata and Schuur, {Edward A. G.} and Cox, {Christopher J.} and Grachev, {Andrey A.} and McFadden, {Joseph P.} and Fausto, {Robert S.} and Mathias G{\"o}ckede and Masahito Ueyama and Norbert Pirk and {de Boer}, Gijs and Bret-Harte, {M. Syndonia} and Matti Lepp{\"a}ranta and Konrad Steffen and Thomas Friborg and Atsumu Ohmura and Edgar, {Colin W.} and Johan Olofsson and Chambers, {Scott D.}",

note = "Funding Information: This work was supported in part by the Research Network for Geosciences in Berlin and Potsdam (SO 087 GeoX to I.G.), the U.S. Department of Energy (DE-SC0013306 to G.d.B., DE‐AC02‐06CH11357 to R.C.S.), the National Science Foundation (#1936752 to M.S.B.-H., E.S.E., C.W.E., #1556772 to A.V.R.), the Swiss National Science Foundation (20BD21_193907 and 20BD21_184131 to D.N.K.), the EU-BASIS programme (ENV4-CT97-0637 to R.J.H.), the German Research Foundation (EXC 2037 {\textquoteleft}CLICCS{\textquoteright} 390683824 to D.H. and L.K.), the National Science Centre, Poland (2017/25/B/ST10/00540 to I.S.), the Research council of Norway (no. 274711 to F.-J.W.P. and no. 301552 to N.P.), the Swedish Research Council (no. 2017-05268 to F.-J.W.P.), Greenland Research Council (no. 80.35 to E.L.-B.), the Arctic Challenge for Sustainability II (JPMXD1420318865 to M.U.), the National Research Foundation of Korea (NRF-2020R1A6A3A03038242 to J.-S.K.), the NOAA Physical Sciences Laboratory, ARM (arm.gov) and ArcticNet (arcticnet.ulaval.ca). Funding Information: We acknowledge the funding and the support of the Swiss National Science Foundation SNF Grant Nr. 178753 (http://p3.snf.ch/Project-178753) to G.S.S., the University of Zurich Research Priority Program Global Change and Biodiversity (URPP GCB, https://www.gcb.uzh.ch/en.html) and the EU-CHARTER project (European Commission RIA #869471, http://www.charter-arctic.org/). We thank Colin R. Lloyd, Bruce C. Forbes and John C. Moore for their support and stimulating discussions. Funding for the AmeriFlux data portal was provided by the U.S. Department of Energy Office of Science. Data from the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) and the Greenland Analogue Project (GAP) were provided by the Geological Survey of Denmark and Greenland (GEUS) at http://www.promice.dk. AON datasets were provided by the Institute of Arctic Biology, UAF, based upon work supported by the National Science Foundation under grant #1503912. The National Ecological Observatory Network is a program sponsored by the National Science Foundation and operated under cooperative agreement by Battelle. This material is based in part upon work supported by the National Science Foundation through the NEON Program. ArcticDEM data is provided by the Polar Geospatial Center under NSF-OPP awards 1043681, 1559691, and 1542736. The ISCCP H-series cloud data is provided by NOAA/NCEI. MODIS data is provided by the NASA National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC). Ameriflux and FLUXNET site ID{\textquoteright}s and corresponding doi{\textquoteright}s: CA-SCB (https://doi.org/10.17190/AMF/1498754), FI-Lom (https://doi.org/10.18140/FLX/1440228), GL-NuF (https://doi.org/10.18140/FLX/1440222), GL-ZaF (https://doi.org/10.18140/FLX/1440223), GL-ZaH (https://doi.org/10.18140/FLX/1440224), RU-Che (https://doi.org/10.18140/FLX/1440181), RU-Cok (https://doi.org/10.18140/FLX/1440182), RU-Sam (https://doi.org/10.18140/FLX/1440185), RU-Tks (https://doi.org/10.18140/FLX/1440244), RU-Vrk (https://doi.org/10.18140/FLX/1440245), SE-St1 (https://doi.org/10.18140/FLX/1440187), SJ-Adv (https://doi.org/10.18140/FLX/1440241), SJ-Blv (https://doi.org/10.18140/FLX/1440242), US-A03 (https://doi.org/10.17190/AMF/1498752), US-A10 (https://doi.org/10.17190/AMF/1498753), US-An1 (https://doi.org/10.17190/AMF/1246142), US-An2 (https://doi.org/10.17190/AMF/1246143), US-An3 (https://doi.org/10.17190/AMF/1246144), US-Atq (https://doi.org/10.17190/AMF/1246029), US-Brw (https://doi.org/10.17190/AMF/1246041), US-EML (https://doi.org/10.17190/AMF/1418678), US-HVa (https://doi.org/10.17190/AMF/1246064), US-ICh (https://doi.org/10.17190/AMF/1246133), US-ICs (https://doi.org/10.17190/AMF/1246130), US-ICt (https://doi.org/10.17190/AMF/1246131), US-Ivo (https://doi.org/10.17190/AMF/1246067), US-NGB (https://doi.org/10.17190/AMF/1436326), US-Upa (https://doi.org/10.17190/AMF/1246108), US-xHE (https://doi.org/10.17190/AMF/1617729), US-xTL (https://doi.org/10.17190/AMF/1617739). This work was supported in part by the Research Network for Geosciences in Berlin and Potsdam (SO 087 GeoX to I.G.), the U.S. Department of Energy (DE-SC0013306 to G.d.B., DE‐AC02‐06CH11357 to R.C.S.), the National Science Foundation (#1936752 to M.S.B.-H., E.S.E., C.W.E., #1556772 to A.V.R.), the Swiss National Science Foundation (20BD21_193907 and 20BD21_184131 to D.N.K.), the EU-BASIS programme (ENV4-CT97-0637 to R.J.H.), the German Research Foundation (EXC 2037 {\textquoteleft}CLICCS{\textquoteright} 390683824 to D.H. and L.K.), the National Science Centre, Poland (2017/25/B/ST10/00540 to I.S.), the Research council of Norway (no. 274711 to F.-J.W.P. and no. 301552 to N.P.), the Swedish Research Council (no. 2017-05268 to F.-J.W.P.), Greenland Research Council (no. 80.35 to E.L.-B.), the Arctic Challenge for Sustainability II (JPMXD1420318865 to M.U.), the National Research Foundation of Korea (NRF-2020R1A6A3A03038242 to J.-S.K.), the NOAA Physical Sciences Laboratory, ARM (arm.gov) and ArcticNet (arcticnet.ulaval.ca). This study is dedicated to Koni Steffen who tragically lost his life on 8 August 2020, while maintaining energy budget measurement instrumentation at Swiss camp on the rapidly melting ice sheet of Greenland. Funding Information: We acknowledge the funding and the support of the Swiss National Science Foundation SNF Grant Nr. 178753 ( http://p3.snf.ch/Project-178753 ) to G.S.S., the University of Zurich Research Priority Program Global Change and Biodiversity (URPP GCB, https://www.gcb.uzh.ch/en.html ) and the EU-CHARTER project (European Commission RIA #869471, http://www.charter-arctic.org/ ). We thank Colin R. Lloyd, Bruce C. Forbes and John C. Moore for their support and stimulating discussions. Funding for the AmeriFlux data portal was provided by the U.S. Department of Energy Office of Science. Data from the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) and the Greenland Analogue Project (GAP) were provided by the Geological Survey of Denmark and Greenland (GEUS) at http://www.promice.dk . AON datasets were provided by the Institute of Arctic Biology, UAF, based upon work supported by the National Science Foundation under grant #1503912. The National Ecological Observatory Network is a program sponsored by the National Science Foundation and operated under cooperative agreement by Battelle. This material is based in part upon work supported by the National Science Foundation through the NEON Program. ArcticDEM data is provided by the Polar Geospatial Center under NSF-OPP awards 1043681, 1559691, and 1542736. The ISCCP H-series cloud data is provided by NOAA/NCEI. MODIS data is provided by the NASA National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC). Publisher Copyright: {\textcopyright} 2022, The Author(s).",

year = "2022",

month = dec,

doi = "10.1038/s41467-022-34049-3",

language = "English",

volume = "13",

journal = "Nature Communications",

issn = "2041-1723",

publisher = "Nature Research",

number = "1",

}

Oehri, J, Schaepman-Strub, G, Kim, J-S, Grysko, R, Kropp, H, Grünberg, I, Zemlianskii, V, Sonnentag, O, Euskirchen, ES, Reji Chacko, M, Muscari, G, Blanken, PD, Dean, JF, di Sarra, A, Harding, RJ, Sobota, I, Kutzbach, L, Plekhanova, E, Riihelä, A, Boike, J, Miller, NB, Beringer, J, López-Blanco, E, Stoy, PC, Sullivan, RC, Kejna, M, Parmentier, F-JW, Gamon, JA, Mastepanov, M, Wille, C, Jackowicz-Korczynski, M, Karger, DN, Quinton, WL, Putkonen, J, van As, D, Christensen, TR, Hakuba, MZ, Stone, RS, Metzger, S, Vandecrux, B, Frost, GV, Wild, M, Hansen, B, Meloni, D, Domine, F, te Beest, M, Sachs, T, Kalhori, A, Rocha, AV, Williamson, SN, Morris, S, Atchley, AL, Essery, R, Runkle, BRK, Holl, D, Riihimaki, LD, Iwata, H, Schuur, EAG, Cox, CJ, Grachev, AA, McFadden, JP, Fausto, RS, Göckede, M, Ueyama, M, Pirk, N, de Boer, G, Bret-Harte, MS, Leppäranta, M, Steffen, K, Friborg, T, Ohmura, A, Edgar, CW, Olofsson, J & Chambers, SD 2022, 'Vegetation type is an important predictor of the arctic summer land surface energy budget', Nature Communications, vol. 13, no. 1, 6379. https://doi.org/10.1038/s41467-022-34049-3

TY - JOUR

T1 - Vegetation type is an important predictor of the arctic summer land surface energy budget

AU - Oehri, Jacqueline

AU - Schaepman-Strub, Gabriela

AU - Kim, Jin-Soo

AU - Grysko, Raleigh

AU - Kropp, Heather

AU - Grünberg, Inge

AU - Zemlianskii, Vitalii

AU - Sonnentag, Oliver

AU - Euskirchen, Eugénie S.

AU - Reji Chacko, Merin

AU - Muscari, Giovanni

AU - Blanken, Peter D.

AU - Dean, Joshua F.

AU - di Sarra, Alcide

AU - Harding, Richard J.

AU - Sobota, Ireneusz

AU - Kutzbach, Lars

AU - Plekhanova, Elena

AU - Riihelä, Aku

AU - Boike, Julia

AU - Miller, Nathaniel B.

AU - Beringer, Jason

AU - López-Blanco, Efrén

AU - Stoy, Paul C.

AU - Sullivan, Ryan C.

AU - Kejna, Marek

AU - Parmentier, Frans-Jan W.

AU - Gamon, John A.

AU - Mastepanov, Mikhail

AU - Wille, Christian

AU - Jackowicz-Korczynski, Marcin

AU - Karger, Dirk N.

AU - Quinton, William L.

AU - Putkonen, Jaakko

AU - van As, Dirk

AU - Christensen, Torben R.

AU - Hakuba, Maria Z.

AU - Stone, Robert S.

AU - Metzger, Stefan

AU - Vandecrux, Baptiste

AU - Frost, Gerald V.

AU - Wild, Martin

AU - Hansen, Birger

AU - Meloni, Daniela

AU - Domine, Florent

AU - te Beest, Mariska

AU - Sachs, Torsten

AU - Kalhori, Aram

AU - Rocha, Adrian V.

AU - Williamson, Scott N.

AU - Morris, Sara

AU - Atchley, Adam L.

AU - Essery, Richard

AU - Runkle, Benjamin R. K.

AU - Holl, David

AU - Riihimaki, Laura D.

AU - Iwata, Hiroki

AU - Schuur, Edward A. G.

AU - Cox, Christopher J.

AU - Grachev, Andrey A.

AU - McFadden, Joseph P.

AU - Fausto, Robert S.

AU - Göckede, Mathias

AU - Ueyama, Masahito

AU - Pirk, Norbert

AU - de Boer, Gijs

AU - Bret-Harte, M. Syndonia

AU - Leppäranta, Matti

AU - Steffen, Konrad

AU - Friborg, Thomas

AU - Ohmura, Atsumu

AU - Edgar, Colin W.

AU - Olofsson, Johan

AU - Chambers, Scott D.

N1 - Funding Information: This work was supported in part by the Research Network for Geosciences in Berlin and Potsdam (SO 087 GeoX to I.G.), the U.S. Department of Energy (DE-SC0013306 to G.d.B., DE‐AC02‐06CH11357 to R.C.S.), the National Science Foundation (#1936752 to M.S.B.-H., E.S.E., C.W.E., #1556772 to A.V.R.), the Swiss National Science Foundation (20BD21_193907 and 20BD21_184131 to D.N.K.), the EU-BASIS programme (ENV4-CT97-0637 to R.J.H.), the German Research Foundation (EXC 2037 ‘CLICCS’ 390683824 to D.H. and L.K.), the National Science Centre, Poland (2017/25/B/ST10/00540 to I.S.), the Research council of Norway (no. 274711 to F.-J.W.P. and no. 301552 to N.P.), the Swedish Research Council (no. 2017-05268 to F.-J.W.P.), Greenland Research Council (no. 80.35 to E.L.-B.), the Arctic Challenge for Sustainability II (JPMXD1420318865 to M.U.), the National Research Foundation of Korea (NRF-2020R1A6A3A03038242 to J.-S.K.), the NOAA Physical Sciences Laboratory, ARM (arm.gov) and ArcticNet (arcticnet.ulaval.ca). Funding Information: We acknowledge the funding and the support of the Swiss National Science Foundation SNF Grant Nr. 178753 (http://p3.snf.ch/Project-178753) to G.S.S., the University of Zurich Research Priority Program Global Change and Biodiversity (URPP GCB, https://www.gcb.uzh.ch/en.html) and the EU-CHARTER project (European Commission RIA #869471, http://www.charter-arctic.org/). We thank Colin R. Lloyd, Bruce C. Forbes and John C. Moore for their support and stimulating discussions. Funding for the AmeriFlux data portal was provided by the U.S. Department of Energy Office of Science. Data from the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) and the Greenland Analogue Project (GAP) were provided by the Geological Survey of Denmark and Greenland (GEUS) at http://www.promice.dk. AON datasets were provided by the Institute of Arctic Biology, UAF, based upon work supported by the National Science Foundation under grant #1503912. The National Ecological Observatory Network is a program sponsored by the National Science Foundation and operated under cooperative agreement by Battelle. This material is based in part upon work supported by the National Science Foundation through the NEON Program. ArcticDEM data is provided by the Polar Geospatial Center under NSF-OPP awards 1043681, 1559691, and 1542736. The ISCCP H-series cloud data is provided by NOAA/NCEI. MODIS data is provided by the NASA National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC). Ameriflux and FLUXNET site ID’s and corresponding doi’s: CA-SCB (https://doi.org/10.17190/AMF/1498754), FI-Lom (https://doi.org/10.18140/FLX/1440228), GL-NuF (https://doi.org/10.18140/FLX/1440222), GL-ZaF (https://doi.org/10.18140/FLX/1440223), GL-ZaH (https://doi.org/10.18140/FLX/1440224), RU-Che (https://doi.org/10.18140/FLX/1440181), RU-Cok (https://doi.org/10.18140/FLX/1440182), RU-Sam (https://doi.org/10.18140/FLX/1440185), RU-Tks (https://doi.org/10.18140/FLX/1440244), RU-Vrk (https://doi.org/10.18140/FLX/1440245), SE-St1 (https://doi.org/10.18140/FLX/1440187), SJ-Adv (https://doi.org/10.18140/FLX/1440241), SJ-Blv (https://doi.org/10.18140/FLX/1440242), US-A03 (https://doi.org/10.17190/AMF/1498752), US-A10 (https://doi.org/10.17190/AMF/1498753), US-An1 (https://doi.org/10.17190/AMF/1246142), US-An2 (https://doi.org/10.17190/AMF/1246143), US-An3 (https://doi.org/10.17190/AMF/1246144), US-Atq (https://doi.org/10.17190/AMF/1246029), US-Brw (https://doi.org/10.17190/AMF/1246041), US-EML (https://doi.org/10.17190/AMF/1418678), US-HVa (https://doi.org/10.17190/AMF/1246064), US-ICh (https://doi.org/10.17190/AMF/1246133), US-ICs (https://doi.org/10.17190/AMF/1246130), US-ICt (https://doi.org/10.17190/AMF/1246131), US-Ivo (https://doi.org/10.17190/AMF/1246067), US-NGB (https://doi.org/10.17190/AMF/1436326), US-Upa (https://doi.org/10.17190/AMF/1246108), US-xHE (https://doi.org/10.17190/AMF/1617729), US-xTL (https://doi.org/10.17190/AMF/1617739). This work was supported in part by the Research Network for Geosciences in Berlin and Potsdam (SO 087 GeoX to I.G.), the U.S. Department of Energy (DE-SC0013306 to G.d.B., DE‐AC02‐06CH11357 to R.C.S.), the National Science Foundation (#1936752 to M.S.B.-H., E.S.E., C.W.E., #1556772 to A.V.R.), the Swiss National Science Foundation (20BD21_193907 and 20BD21_184131 to D.N.K.), the EU-BASIS programme (ENV4-CT97-0637 to R.J.H.), the German Research Foundation (EXC 2037 ‘CLICCS’ 390683824 to D.H. and L.K.), the National Science Centre, Poland (2017/25/B/ST10/00540 to I.S.), the Research council of Norway (no. 274711 to F.-J.W.P. and no. 301552 to N.P.), the Swedish Research Council (no. 2017-05268 to F.-J.W.P.), Greenland Research Council (no. 80.35 to E.L.-B.), the Arctic Challenge for Sustainability II (JPMXD1420318865 to M.U.), the National Research Foundation of Korea (NRF-2020R1A6A3A03038242 to J.-S.K.), the NOAA Physical Sciences Laboratory, ARM (arm.gov) and ArcticNet (arcticnet.ulaval.ca). This study is dedicated to Koni Steffen who tragically lost his life on 8 August 2020, while maintaining energy budget measurement instrumentation at Swiss camp on the rapidly melting ice sheet of Greenland. Funding Information: We acknowledge the funding and the support of the Swiss National Science Foundation SNF Grant Nr. 178753 ( http://p3.snf.ch/Project-178753 ) to G.S.S., the University of Zurich Research Priority Program Global Change and Biodiversity (URPP GCB, https://www.gcb.uzh.ch/en.html ) and the EU-CHARTER project (European Commission RIA #869471, http://www.charter-arctic.org/ ). We thank Colin R. Lloyd, Bruce C. Forbes and John C. Moore for their support and stimulating discussions. Funding for the AmeriFlux data portal was provided by the U.S. Department of Energy Office of Science. Data from the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) and the Greenland Analogue Project (GAP) were provided by the Geological Survey of Denmark and Greenland (GEUS) at http://www.promice.dk . AON datasets were provided by the Institute of Arctic Biology, UAF, based upon work supported by the National Science Foundation under grant #1503912. The National Ecological Observatory Network is a program sponsored by the National Science Foundation and operated under cooperative agreement by Battelle. This material is based in part upon work supported by the National Science Foundation through the NEON Program. ArcticDEM data is provided by the Polar Geospatial Center under NSF-OPP awards 1043681, 1559691, and 1542736. The ISCCP H-series cloud data is provided by NOAA/NCEI. MODIS data is provided by the NASA National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC). Publisher Copyright: © 2022, The Author(s).

PY - 2022/12

Y1 - 2022/12

N2 - Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm−2) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.

AB - Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm−2) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.

KW - Atmospheric dynamics

KW - Climate and Earth system modelling

KW - cryosperic science

KW - ecosystem ecology

KW - Phenology

UR - http://www.scopus.com/inward/record.url?scp=85140941186&partnerID=8YFLogxK

U2 - 10.1038/s41467-022-34049-3

DO - 10.1038/s41467-022-34049-3

M3 - Article

SN - 2041-1723

VL - 13

JO - Nature Communications

JF - Nature Communications

IS - 1

M1 - 6379

ER -

Vegetation type is an important predictor of the arctic summer land surface energy budget

Abstract

Keywords

Access to Document

Fingerprint

Cite this