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

Journal Article · · Nature Communications
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  1. Univ. of Zurich (Switzerland); McGill Univ., Montreal, QC (Canada)
  2. Univ. of Zurich (Switzerland)
  3. Univ. of Zurich (Switzerland); City Univ. of Hong Kong, Kowloon (Hong Kong)
  4. Hamilton College, Clinton, NY (United States)
  5. Alfred-Wegener Inst. (Germany)
  6. Univ. of Montreal, QC (Canada)
  7. Univ. of Alaska, Fairbanks, AK (United States)
  8. Swiss Federal Institute of Technology in Zürich (Switzerland); Swiss Federal Institute for Forest, Snow and Landscape Research (Switzerland)
  9. National Institute of Geophysics and Volcanology (Italy)
  10. Univ. of Colorado, Boulder, CO (United States)
  11. Univ. of Bristol (United Kingdom)
  12. Energia Nucleare ed Energie Alternative (ENEA) (Italy)
  13. UK Centre for Ecology & Hydrology (UKCEH) (United Kingdom)
  14. Nicolaus Copernicus Univ., Torun (Poland)
  15. Univ. of Hamburg (Germany)
  16. Finnish Meteorological Institute (Finland)
  17. Alfred-Wegener Inst. (Germany); Humboldt Univ. of Berlin (Germany)
  18. Univ. of Wisconsin, Madison, WI (United States)
  19. Univ. of Western Australia, Crawley, WA (Australia)
  20. Greenland Institute of Natural Resources (Greenland); Aarhus Univ. (Denmark)
  21. Argonne National Lab. (ANL), Argonne, IL (United States). Environmental Science Division
  22. Univ. of Oslo (Norway); Lund Univ. (Sweden)
  23. Univ. of Nebraska, Lincoln, NE (United States)
  24. University of Oulu (Finland)
  25. Helmholtz-Zentrum Potsdam (HZP), (Germany). German Research Centre for GeoSciences
  26. Aarhus Univ. (Denmark); Lund Univ. (Sweden)
  27. Swiss Federal Institute for Forest, Snow and Landscape Research (Switzerland)
  28. Wilfrid Laurier University (Canada)
  29. University of North Dakota, Grand Forks, ND (United States)
  30. Geological Survey of Denmark and Greenland (GEUS), Copenhagen (Denmark)
  31. Aarhus Univ. (Denmark); University of Oulu (Finland)
  32. California Institute of Technology (CalTech), Pasadena, CA (United States)
  33. National Oceanic and Atmospheric Administration (NOAA), Boulder, CO (United States)
  34. Alaska Biological Research, Fairbanks, AK (United States)
  35. Swiss Federal Institute of Technology in Zürich (Switzerland)
  36. Copenhagen Univ. (Denmark)
  37. Université Laval (Canada)
  38. Utrecht University (Netherlands); Nelson Mandela University (South Africa)
  39. University of Notre Dame, Notre Dame, IN (United States)
  40. Polar Knowledge Canada (Canada)
  41. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  42. Univ. of Edinburgh, Scotland (United Kingdom)
  43. Univ. of Arkansas, Fayetteville, AR (United States)
  44. Shinshu Univ., Matsumoto, Nagano (Japan)
  45. Northern Arizona Univ., Flagstaff, AZ (United States)
  46. DEVCOM Army Research Laboratory, New Mexico, NM (United States)
  47. Univ. of California, Santa Barbara, CA (United States)
  48. Max Planck Institute for Biogeochemistry, Jena (Germany)
  49. Osaka Metropolitan University (Japan)
  50. Univ. of Oslo (Norway)
  51. National Oceanic and Atmospheric Administration (NOAA), Boulder, CO (United States); Univ. of Colorado, Boulder, CO (United States)
  52. Univ. of Helsinki (Finland)
  53. Umea Univ. (Sweden)
  54. Australian Nuclear Science and Technology Organisation (ANSTO), Lucas Heights, NSW (Australia)
+ Show Author Affiliations

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.

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Data Center; Argonne National Laboratory (ANL), Argonne, IL (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER); Swiss National Science Foundation (SNF); National Science Foundation (NSF)
Contributing Organization:
Pacific Northwest National Laboratory (PNNL); Brookhaven National Laboratory (BNL); Argonne National Laboratory (ANL); Oak Ridge National Laboratory (ORNL)
Grant/Contract Number:
AC02-06CH11357; 178753; 1503912; 1043681; 1559691; 1542736; 89233218CNA000001
OSTI ID:
1895323
Alternate ID(s):
OSTI ID: 1902642; OSTI ID: 1922032
Report Number(s):
LA-UR-22-31630
Journal Information:
Nature Communications, Vol. 13, Issue 1; ISSN 2041-1723
Publisher:
Nature Publishing GroupCopyright Statement
Country of Publication:
United States
Language:
English

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