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. 2022 Oct 31;13(1):6379.
doi: 10.1038/s41467-022-34049-3.

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

Jacqueline Oehri  1   2 Gabriela Schaepman-Strub  3 Jin-Soo Kim #  4   5 Raleigh Grysko #  4 Heather Kropp #  6 Inge Grünberg #  7 Vitalii Zemlianskii #  4 Oliver Sonnentag #  8 Eugénie S Euskirchen #  9 Merin Reji Chacko #  4   10   11 Giovanni Muscari  12 Peter D Blanken  13 Joshua F Dean  14 Alcide di Sarra  15 Richard J Harding  16 Ireneusz Sobota  17 Lars Kutzbach  18 Elena Plekhanova  4 Aku Riihelä  19 Julia Boike  7   20 Nathaniel B Miller  21 Jason Beringer  22 Efrén López-Blanco  23   24 Paul C Stoy  21 Ryan C Sullivan  25 Marek Kejna  26 Frans-Jan W Parmentier  27   28 John A Gamon  29 Mikhail Mastepanov  24   30 Christian Wille  31 Marcin Jackowicz-Korczynski  24   28 Dirk N Karger  32 William L Quinton  33 Jaakko Putkonen  34 Dirk van As  35 Torben R Christensen  24   30 Maria Z Hakuba  36 Robert S Stone  37 Stefan Metzger  38   39 Baptiste Vandecrux  35 Gerald V Frost  40 Martin Wild  41 Birger Hansen  42 Daniela Meloni  43 Florent Domine  44   45 Mariska Te Beest  46   47 Torsten Sachs  31 Aram Kalhori  31 Adrian V Rocha  48 Scott N Williamson  49 Sara Morris  50 Adam L Atchley  51 Richard Essery  52 Benjamin R K Runkle  53 David Holl  18 Laura D Riihimaki  37   54 Hiroki Iwata  55 Edward A G Schuur  56 Christopher J Cox  50 Andrey A Grachev  57 Joseph P McFadden  58 Robert S Fausto  35 Mathias Göckede  59 Masahito Ueyama  60 Norbert Pirk  61 Gijs de Boer  50   54   62 M Syndonia Bret-Harte  9 Matti Leppäranta  63 Konrad Steffen  32 Thomas Friborg  42 Atsumu Ohmura  41 Colin W Edgar  9 Johan Olofsson  64 Scott D Chambers  65
Affiliations

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

Jacqueline Oehri et al. Nat Commun. .

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.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Relative importance of 15 drivers of the surface energy budget (SEB) for average summer surface energy flux magnitudes at non-glacier sites.
Bars show the mean (bar height) and range (lines for each bar) of explained variance (%) averaged across all possible models with 2 predictors for each SEB-driver and corresponding summer magnitudes of surface energy fluxes (Wm−2): (a) Rnet: net radiation, (b) H: sensible heat flux, (c) LE: latent heat flux, (d) G: ground heat flux. SEB-drivers: Vegetation type (dark green): local-scale, in situ vegetation type; CAVM type (green): landscape-scale, dominant vegetation type (surrounding area with radius of 500 m); CAVM subzone (light green): bioclimatic subzone; Permafrost extent (light blue): permafrost spatial extent; Permafrost ice content (grey): permafrost ground ice content; Temperature (red): mean annual air temperature; Summer warmth (dark orange): summer warmth index; Continentality (orange): Conrad’s continentality index; Precipitation (light purple): mean annual precipitation; Snow amount (dark purple): mean annual snow water equivalent; Snow duration (purple): median annual snow cover duration; Cloud cover (light orange): mean cloud cover; Cloud temperature (yellow): mean cloud-top temperature; Latitude (dark blue): latitude (WGS84); Altitude (light blue): mean altitude (surrounding area with radius of 500 m; see Methods and Supplementary Table 1). n: average nr. of site years with average nr. of sites in parentheses. Results are based on the vegetation subset of our data (excluding glacier sites): nr. of sites: 31, nr. of site years: 234, period: 1994–2021. We repeated the analysis with additional surface energy fluxes, including normalized fluxes expressed as percentage of maximum potential incoming shortwave radiation (indicated with “n.”-prefix in Supplementary Fig. 5). Source data are provided as a Source Data file.
Fig. 2
Fig. 2. Seasonalities of radiation and non-radiation fluxes of the surface energy budget (SEB).
Surface energy flux values (Wm−2) were averaged for each study site for each day of year (DOY) across all years available and then averaged (mean ± s.e.) for each DOY and smoothed (15-day moving average) for each vegetation type. Average number of site years (n) across all DOY’s and surface energy fluxes, and the number of study sites (in parentheses) are indicated in the top left corner of each figure. The area within the vertical gray lines represents the median snow-free period across the years 2000–2020 (MODIS), averaged across sites for each vegetation type. Radiation surface energy fluxes: Rnet (dark blue): net radiation; SWnet (purple): net shortwave radiation; LWnet (green): net longwave radiation. Non radiation surface energy fluxes: H (dark red): sensible heat flux; LE (light blue): latent heat flux; G (yellow green): ground heat flux. Results are based on the data subset of the period 2000–2021 and excluding barren vegetation type because of missing Rnet data: nr. sites = 61, nr. site years = 617. See Supplementary Fig. 6 for seasonality analyses with additional components of the surface energy budget. Note: flux direction convention is positive away from the surface for heat fluxes (i.e. H, LE and G). Source data are provided as a Source Data file.
Fig. 3
Fig. 3. Timing of the summer-regime relative to the snow-free period for selected fluxes and components of the surface energy budget (SEB).
Number of days difference between the start of the summer-regime period and the spring snow-free date (left panel, x-axis) as well as between the end of the summer-regime period and the autumn snow-onset date (right panel, x-axis). The summer-regime timings are derived from the smoothed seasonalities (mean ± s.e., see Fig. 2) of selected surface energy fluxes (y-axis), for different vegetation types (n = 5 per surface energy flux), colored dots: prostrate-shrub tundra (light red), graminoid tundra (yellow green), wetland complex (light blue), erect shrub tundra (green), boreal peat bog (blue). We excluded latent heat fluxes since they are >0 Wm−2 all year in most cases. Summer-regime is defined as the time when surface energy fluxes: >0 Wm−2, when surface temperature >0 °C, or when albedo <mean of annual minimum and maximum value, respectively (Methods). Results are based on the vegetation subset of the data for the period 2000–2021 and excluding barren vegetation type because of missing net radiation data; nr. sites = 28, nr. site years = 217. Note: flux direction convention is positive away from the surface for heat fluxes (i.e. H, LE and G). Source data are provided as a Source Data file.

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