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  • 1
    Publication Date: 2024-04-12
    Description: Despite the importance of surface energy budgets (SEBs) for land-climate interactions in the Arctic, uncertainties in their prediction persist. In-situ observational data of SEB components - useful for research and model validation - are collected at relatively few sites across the terrestrial Arctic, and not all available datasets are readily interoperable. Furthermore, the terrestrial Arctic consists of a diversity of vegetation types, which are generally not well represented in land surface schemes of current Earth system models. Therefore, we here provide four datasets comprising: 1. Harmonized, standardized and aggregated in situ observations of SEB components at 64 vegetated and glaciated sites north of 60° latitude, in the time period 1994-2021 2. A description of all study sites and associated environmental conditions, including the vegetation types, which correspond to the classification of the Circumpolar Arctic Vegetation Map (CAVM, Raynolds et al. 2019). 3. Data generated in a literature synthesis from 358 study sites on vegetation or glacier (〉=60°N latitude) covered by 148 publications. 4. Metadata, including data contributor information and measurement heights of variables associated with Oehri et al. 2022.
    Keywords: Arctic; ArcticTundraSEB; Arctic Tundra Surface Energy Budget; dry tundra; Eddy covariance; eddy heat flux; glacier; graminoids; ground heat flux and net radiation; harmonized data; high latitude; Land-Atmosphere; Land-cover; latent and sensible heat; latent heat flux; longwave radiation; meteorological data; observatory data; Peat bog; Radiation fluxes; Radiative energy budget; sensible heat flux; shortwave radiation; shrub tundra; surface energy balance; synthetic data; tundra vegetation; wetland
    Type: Dataset
    Format: application/zip, 4 datasets
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  • 2
    Publication Date: 2024-04-22
    Description: Despite the importance of surface energy budgets (SEBs) for land-climate interactions in the Arctic, uncertainties in their prediction persist. In situ observational data of SEB components - useful for research and model validation - are collected at relatively few sites across the terrestrial Arctic, and not all available datasets are readily interoperable. Furthermore, the terrestrial Arctic consists of a diversity of vegetation types, which are generally not well represented in land surface schemes of current Earth system models. This dataset describes the environmental conditions for 64 tundra and glacier sites (〉=60°N latitude) across the Arctic, for which in situ measurements of surface energy budget components were harmonized (see Oehri et al. 2022). These environmental conditions are (proxies of) potential drivers of SEB-components and could therefore be called SEB-drivers. The associated environmental conditions, include the vegetation types graminoid tundra, prostrate dwarf-shrub tundra, erect-shrub tundra, wetland complexes, barren complexes (≤ 40% horizontal plant cover), boreal peat bogs and glacier. These land surface types (apart from boreal peat bogs) correspond to the main classification units of the Circumpolar Arctic Vegetation Map (CAVM, Raynolds et al. 2019). For each site, additional climatic and biophysical variables are available, including cloud cover, snow cover duration, permafrost characteristics, climatic conditions and topographic conditions.
    Keywords: Arctic; Arctic_SEB_CA-SCB; Arctic_SEB_CP1; Arctic_SEB_Dye-2; Arctic_SEB_EGP; Arctic_SEB_FI-Lom; Arctic_SEB_GL-NuF; Arctic_SEB_GL-ZaF; Arctic_SEB_GL-ZaH; Arctic_SEB_KAN_B; Arctic_SEB_KAN_L; Arctic_SEB_KAN_M; Arctic_SEB_KAN_U; Arctic_SEB_KPC_L; Arctic_SEB_KPC_U; Arctic_SEB_MIT; Arctic_SEB_NASA-E; Arctic_SEB_NASA-SE; Arctic_SEB_NASA-U; Arctic_SEB_NUK_K; Arctic_SEB_NUK_L; Arctic_SEB_NUK_N; Arctic_SEB_NUK_U; Arctic_SEB_QAS_A; Arctic_SEB_QAS_L; Arctic_SEB_QAS_M; Arctic_SEB_QAS_U; Arctic_SEB_RU-Che; Arctic_SEB_RU-Cok; Arctic_SEB_RU-Sam; Arctic_SEB_RU-Tks; Arctic_SEB_RU-Vrk; Arctic_SEB_Saddle; Arctic_SEB_SCO_L; Arctic_SEB_SCO_U; Arctic_SEB_SE-St1; Arctic_SEB_SJ-Adv; Arctic_SEB_SJ-Blv; Arctic_SEB_SouthDome; Arctic_SEB_Summit; Arctic_SEB_TAS_A; Arctic_SEB_TAS_L; Arctic_SEB_TAS_U; Arctic_SEB_THU_L; Arctic_SEB_THU_U; Arctic_SEB_Tunu-N; Arctic_SEB_UPE_L; Arctic_SEB_UPE_U; Arctic_SEB_US-A03; Arctic_SEB_US-A10; Arctic_SEB_US-An1; Arctic_SEB_US-An2; Arctic_SEB_US-An3; Arctic_SEB_US-Atq; Arctic_SEB_US-Brw; Arctic_SEB_US-EML; Arctic_SEB_US-HVa; Arctic_SEB_US-ICh; Arctic_SEB_US-ICs; Arctic_SEB_US-ICt; Arctic_SEB_US-Ivo; Arctic_SEB_US-NGB; Arctic_SEB_US-Upa; Arctic_SEB_US-xHE; Arctic_SEB_US-xTL; ArcticTundraSEB; Arctic Tundra Surface Energy Budget; Aspect; Aspect, coefficient of variation; Calculated average/mean values; Cloud cover; Cloud cover, standard deviation; Cloud top pressure; Cloud top pressure, standard deviation; Cloud top temperature; Cloud top temperature, standard deviation; Conrad's continentality index; Daily maximum; Daily mean; Data source; Date/Time of event; dry tundra; Eddy covariance; eddy heat flux; ELEVATION; Elevation, standard deviation; Event label; Field observation; glacier; graminoids; ground heat flux and net radiation; harmonized data; high latitude; Humidity, relative; Land-Atmosphere; Land-cover; Land cover classes; Land cover type; latent and sensible heat; latent heat flux; LATITUDE; Location ID; LONGITUDE; longwave radiation; Mean values; Median values; meteorological data; Number of vegetation types; observatory data; Peat bog; Permafrost, type; Permafrost extent; Permafrost ice content, description; Precipitation; Precipitation, coefficient of variation; Precipitation, daily, maximum; Precipitation, snow; Precipitation, sum; Pressure, atmospheric; p-value; Radiation fluxes; Radiative energy budget; Reference/source; sensible heat flux; Shannon Diversity Index; Shannon Diversity Index, maximum; shortwave radiation; shrub tundra; Site; Slope; Slope, coefficient of variation; Slope, mathematical; Snow, onset, day of the year; Snow cover, number of days; Snowfall, coefficient of variation; Snow-free days; Snow type; Soil water content, volumetric; Species present; Summer warmth index; surface energy balance; synthetic data; Temperature, air, annual mean; Temperature, air, coefficient of variation; Temperature, annual mean range; tundra vegetation; Type of study; Uniform resource locator/link to reference; Vapour pressure deficit; Vegetation type; wetland; Wind speed; Zone
    Type: Dataset
    Format: text/tab-separated-values, 4705 data points
    Location Call Number Expected Availability
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  • 3
    Publication Date: 2024-04-12
    Description: Despite the importance of surface energy budgets (SEBs) for land-climate interactions in the Arctic, uncertainties in their prediction persist. In situ observational data of SEB components - useful for research and model validation - are collected at relatively few sites across the terrestrial Arctic, and not all available datasets are readily interoperable. Furthermore, the terrestrial Arctic consists of a diversity of vegetation types, which are generally not well represented in land surface schemes of current Earth system models. This dataset contains metadata information about surface energy budget components measured at 64 tundra and glacier sites 〉60° N across the Arctic. This information was taken from the open-access repositories FLUXNET, Ameriflux, AON, GC-Net and PROMICE. The contained datasets are associated with the publication vegetation type as an important predictor of the Arctic Summer Land Surface Energy Budget by Oehri et al. 2022, and intended to support research of surface energy budgets and their relationship with environmental conditions, in particular vegetation characteristics across the terrestrial Arctic.
    Keywords: Aggregation type; Arctic; Arctic_SEB_CA-SCB; Arctic_SEB_CP1; Arctic_SEB_Dye-2; Arctic_SEB_EGP; Arctic_SEB_FI-Lom; Arctic_SEB_GL-NuF; Arctic_SEB_GL-ZaF; Arctic_SEB_GL-ZaH; Arctic_SEB_KAN_B; Arctic_SEB_KAN_L; Arctic_SEB_KAN_M; Arctic_SEB_KAN_U; Arctic_SEB_KPC_L; Arctic_SEB_KPC_U; Arctic_SEB_MIT; Arctic_SEB_NASA-E; Arctic_SEB_NASA-SE; Arctic_SEB_NASA-U; Arctic_SEB_NUK_K; Arctic_SEB_NUK_L; Arctic_SEB_NUK_N; Arctic_SEB_NUK_U; Arctic_SEB_QAS_A; Arctic_SEB_QAS_L; Arctic_SEB_QAS_M; Arctic_SEB_QAS_U; Arctic_SEB_RU-Che; Arctic_SEB_RU-Cok; Arctic_SEB_RU-Sam; Arctic_SEB_RU-Tks; Arctic_SEB_RU-Vrk; Arctic_SEB_Saddle; Arctic_SEB_SCO_L; Arctic_SEB_SCO_U; Arctic_SEB_SE-St1; Arctic_SEB_SJ-Adv; Arctic_SEB_SJ-Blv; Arctic_SEB_SouthDome; Arctic_SEB_Summit; Arctic_SEB_TAS_A; Arctic_SEB_TAS_L; Arctic_SEB_TAS_U; Arctic_SEB_THU_L; Arctic_SEB_THU_U; Arctic_SEB_Tunu-N; Arctic_SEB_UPE_L; Arctic_SEB_UPE_U; Arctic_SEB_US-A03; Arctic_SEB_US-A10; Arctic_SEB_US-An1; Arctic_SEB_US-An2; Arctic_SEB_US-An3; Arctic_SEB_US-Atq; Arctic_SEB_US-Brw; Arctic_SEB_US-EML; Arctic_SEB_US-HVa; Arctic_SEB_US-ICh; Arctic_SEB_US-ICs; Arctic_SEB_US-ICt; Arctic_SEB_US-Ivo; Arctic_SEB_US-NGB; Arctic_SEB_US-Upa; Arctic_SEB_US-xHE; Arctic_SEB_US-xTL; ArcticTundraSEB; Arctic Tundra Surface Energy Budget; Author(s); Data source; Date/Time of event; Day of the year; Description; dry tundra; Eddy covariance; eddy heat flux; Event label; Field observation; First year of observation; glacier; graminoids; ground heat flux and net radiation; harmonized data; high latitude; Institution; Instrument; Land-Atmosphere; Land-cover; Last year of observation; latent and sensible heat; latent heat flux; LATITUDE; Location ID; LONGITUDE; longwave radiation; meteorological data; Method comment; observatory data; Peat bog; Radiation fluxes; Radiative energy budget; Sample height; sensible heat flux; shortwave radiation; shrub tundra; surface energy balance; synthetic data; tundra vegetation; Type of study; Unit; Variable; wetland
    Type: Dataset
    Format: text/tab-separated-values, 20562 data points
    Location Call Number Expected Availability
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  • 4
    Publication Date: 2024-04-12
    Description: Despite the importance of surface energy budgets (SEBs) for land-climate interactions in the Arctic, uncertainties in their prediction persist. In situ observational data of SEB components - useful for research and model validation - are collected at relatively few sites across the terrestrial Arctic, and not all available datasets are readily interoperable. Furthermore, the terrestrial Arctic consists of a diversity of vegetation types, which are generally not well represented in land surface schemes of current Earth system models. This dataset describes the data generated in a literature synthesis, covering 358 study sites on vegetation or glacier (〉=60°N latitude), which contained surface energy budget observations. The literature synthesis comprised 148 publications searched on the ISI Web of Science Core Collection.
    Keywords: Arctic; Arctic_SEB_1; Arctic_SEB_1951-2009_1; Arctic_SEB_1965-2000_1; Arctic_SEB_1965-2000_2; Arctic_SEB_1965-2000_3; Arctic_SEB_1965-2000_4; Arctic_SEB_1969-2013_1; Arctic_SEB_1970-1972_1; Arctic_SEB_1970-1979_1; Arctic_SEB_1972-2004_1; Arctic_SEB_1972-2004_10; Arctic_SEB_1972-2004_11; Arctic_SEB_1972-2004_2; Arctic_SEB_1972-2004_3; Arctic_SEB_1972-2004_4; Arctic_SEB_1972-2004_5; Arctic_SEB_1972-2004_6; Arctic_SEB_1972-2004_7; Arctic_SEB_1972-2004_8; Arctic_SEB_1972-2004_9; Arctic_SEB_1979-1995_1; Arctic_SEB_1979-1995_2; Arctic_SEB_1979-1995_3; Arctic_SEB_1979-1995_4; Arctic_SEB_1979-2005_1; Arctic_SEB_1980-1981_1; Arctic_SEB_1981-1997_1; Arctic_SEB_1981-1997_2; Arctic_SEB_1983-2005_1; Arctic_SEB_1983-2005_2; Arctic_SEB_1983-2005_3; Arctic_SEB_1984-1991_1; Arctic_SEB_1985-1989_1; Arctic_SEB_1985-2016_1; Arctic_SEB_1988-1988_1; Arctic_SEB_1988-1988_2; Arctic_SEB_1988-1988_3; Arctic_SEB_1988-1988_4; Arctic_SEB_1988-1988_5; Arctic_SEB_1989-1990_1; Arctic_SEB_1990-1991_1; Arctic_SEB_1991-1991_1; Arctic_SEB_1991-1999_1; Arctic_SEB_1991-1999_2; Arctic_SEB_1991-1999_3; Arctic_SEB_1992-1992_1; Arctic_SEB_1992-1997_1; Arctic_SEB_1994-1994_1; Arctic_SEB_1994-1994_2; Arctic_SEB_1994-1994_3; Arctic_SEB_1994-1994_4; Arctic_SEB_1994-1996_1; Arctic_SEB_1994-1996_10; Arctic_SEB_1994-1996_11; Arctic_SEB_1994-1996_12; Arctic_SEB_1994-1996_13; Arctic_SEB_1994-1996_14; Arctic_SEB_1994-1996_15; Arctic_SEB_1994-1996_16; Arctic_SEB_1994-1996_17; Arctic_SEB_1994-1996_2; Arctic_SEB_1994-1996_3; Arctic_SEB_1994-1996_4; Arctic_SEB_1994-1996_5; Arctic_SEB_1994-1996_6; Arctic_SEB_1994-1996_7; Arctic_SEB_1994-1996_8; Arctic_SEB_1994-1996_9; Arctic_SEB_1994-2008_1; Arctic_SEB_1994-2008_2; Arctic_SEB_1994-2009_1; Arctic_SEB_1994-2015_1; Arctic_SEB_1994-2015_2; Arctic_SEB_1994-2015_3; Arctic_SEB_1994-2015_4; Arctic_SEB_1994-2015_5; Arctic_SEB_1994-2015_6; Arctic_SEB_1995-1995_1; Arctic_SEB_1995-1995_2; Arctic_SEB_1995-1996_1; Arctic_SEB_1995-1997_1; Arctic_SEB_1995-1997_2; Arctic_SEB_1995-1997_3; Arctic_SEB_1995-1997_4; Arctic_SEB_1995-1998_1; Arctic_SEB_1995-1999_1; Arctic_SEB_1996-1997_1; Arctic_SEB_1996-1999_1; Arctic_SEB_1996-2005_1; Arctic_SEB_1996-2005_2; Arctic_SEB_1996-2005_3; Arctic_SEB_1997-1998_1; Arctic_SEB_1997-1999_1; Arctic_SEB_1997-2018_1; Arctic_SEB_1997-2018_10; Arctic_SEB_1997-2018_11; Arctic_SEB_1997-2018_12; Arctic_SEB_1997-2018_13; Arctic_SEB_1997-2018_14; Arctic_SEB_1997-2018_15; Arctic_SEB_1997-2018_16; Arctic_SEB_1997-2018_17; Arctic_SEB_1997-2018_18; Arctic_SEB_1997-2018_19; Arctic_SEB_1997-2018_2; Arctic_SEB_1997-2018_20; Arctic_SEB_1997-2018_21; Arctic_SEB_1997-2018_22; Arctic_SEB_1997-2018_23; Arctic_SEB_1997-2018_24; Arctic_SEB_1997-2018_25; Arctic_SEB_1997-2018_3; Arctic_SEB_1997-2018_4; Arctic_SEB_1997-2018_5; Arctic_SEB_1997-2018_6; Arctic_SEB_1997-2018_7; Arctic_SEB_1997-2018_8; Arctic_SEB_1997-2018_9; Arctic_SEB_1998-1998_1; Arctic_SEB_1998-1999_1; Arctic_SEB_1998-2000_1; Arctic_SEB_1998-2001_1; Arctic_SEB_1998-2005_1; Arctic_SEB_1998-2011_1; Arctic_SEB_1998-2011_2; Arctic_SEB_1998-2011_3; Arctic_SEB_1998-2013_1; Arctic_SEB_1999-1999_1; Arctic_SEB_1999-2000_1; Arctic_SEB_1999-2008_1; Arctic_SEB_1999-2008_2; Arctic_SEB_1999-2009_1; Arctic_SEB_1999-2014_1; Arctic_SEB_2000-2000_1; Arctic_SEB_2000-2000_2; Arctic_SEB_2000-2000_3; Arctic_SEB_2000-2000_4; Arctic_SEB_2000-2002_1; Arctic_SEB_2000-2002_2; Arctic_SEB_2000-2002_3; Arctic_SEB_2000-2003_1; Arctic_SEB_2000-2003_2; Arctic_SEB_2000-2003_3; Arctic_SEB_2000-2007_1; Arctic_SEB_2000-2007_2; Arctic_SEB_2000-2007_3; Arctic_SEB_2000-2007_4; Arctic_SEB_2000-2008_1; Arctic_SEB_2000-2010_1; Arctic_SEB_2000-2011_1; Arctic_SEB_2000-2011_10; Arctic_SEB_2000-2011_11; Arctic_SEB_2000-2011_2; Arctic_SEB_2000-2011_3; Arctic_SEB_2000-2011_4; Arctic_SEB_2000-2011_5; Arctic_SEB_2000-2011_6; Arctic_SEB_2000-2011_7; Arctic_SEB_2000-2011_8; Arctic_SEB_2000-2011_9; Arctic_SEB_2000-2014_1; Arctic_SEB_2001-2003_1; Arctic_SEB_2002-2002_1; Arctic_SEB_2002-2003_1; Arctic_SEB_2002-2003_2; Arctic_SEB_2002-2004_1; Arctic_SEB_2002-2010_1; Arctic_SEB_2002-2012_1; Arctic_SEB_2002-2012_2; Arctic_SEB_2002-2012_3; Arctic_SEB_2003-2003_1; Arctic_SEB_2003-2004_1; Arctic_SEB_2003-2007_1; Arctic_SEB_2003-2008_1; Arctic_SEB_2003-2008_2; Arctic_SEB_2003-2010_1; Arctic_SEB_2003-2010_2; Arctic_SEB_2003-2010_3; Arctic_SEB_2003-2011_1; Arctic_SEB_2004-2004_1; Arctic_SEB_2004-2006_1; Arctic_SEB_2004-2013_1; Arctic_SEB_2005-2005_1; Arctic_SEB_2006-2006_1; Arctic_SEB_2006-2006_2; Arctic_SEB_2006-2007_1; Arctic_SEB_2006-2007_10; Arctic_SEB_2006-2007_11; Arctic_SEB_2006-2007_12; Arctic_SEB_2006-2007_13; Arctic_SEB_2006-2007_14; Arctic_SEB_2006-2007_2; Arctic_SEB_2006-2007_3; Arctic_SEB_2006-2007_4; Arctic_SEB_2006-2007_5; Arctic_SEB_2006-2007_6; Arctic_SEB_2006-2007_7; Arctic_SEB_2006-2007_8; Arctic_SEB_2006-2007_9; Arctic_SEB_2006-2008_1; Arctic_SEB_2006-2008_2; Arctic_SEB_2006-2009_1; Arctic_SEB_2007-2007_1; Arctic_SEB_2007-2008_1; Arctic_SEB_2007-2009_1; Arctic_SEB_2007-2009_2; Arctic_SEB_2007-2010_1; Arctic_SEB_2007-2014_1; Arctic_SEB_2007-2015_1; Arctic_SEB_2007-2015_2; Arctic_SEB_2008-2008_1; Arctic_SEB_2008-2008_2; Arctic_SEB_2008-2008_3; Arctic_SEB_2008-2009_1; Arctic_SEB_2008-2010_1; Arctic_SEB_2008-2010_2; Arctic_SEB_2008-2010_3; Arctic_SEB_2008-2011_1; Arctic_SEB_2008-2012_1; Arctic_SEB_2008-2012_2; Arctic_SEB_2008-2012_3; Arctic_SEB_2009-2012_1; Arctic_SEB_2009-2012_2; Arctic_SEB_2009-2012_3; Arctic_SEB_2009-2012_4; Arctic_SEB_2009-2012_5; Arctic_SEB_2009-2014_1; Arctic_SEB_2009-2014_2; Arctic_SEB_2010-2014_1; Arctic_SEB_2010-2014_2; Arctic_SEB_2010-2014_3; Arctic_SEB_2010-2014_4; Arctic_SEB_2010-2014_5; Arctic_SEB_2011-2011_1; Arctic_SEB_2011-2013_1; Arctic_SEB_2011-2014_1; Arctic_SEB_2012-2012_1; Arctic_SEB_2012-2013_1; Arctic_SEB_2012-2013_2; Arctic_SEB_2012-2013_3; Arctic_SEB_2012-2013_4; Arctic_SEB_2012-2014_1; Arctic_SEB_2012-2015_1; Arctic_SEB_2012-2015_2; Arctic_SEB_2012-2015_3; Arctic_SEB_2012-2015_4; Arctic_SEB_2012-2015_5; Arctic_SEB_2013-2013_1; Arctic_SEB_2013-2014_1; Arctic_SEB_2013-2015_1; Arctic_SEB_2013-2015_2; Arctic_SEB_2013-2015_3; Arctic_SEB_2014-2014_1; Arctic_SEB_2014-2015_1; Arctic_SEB_2014-2016_1; Arctic_SEB_2015-2015_1; Arctic_SEB_2015-2015_2; Arctic_SEB_2015-2015_3; ArcticTundraSEB; Arctic Tundra Surface Energy Budget; Author(s); Classification; Comment; Data collection methodology; Data type; Date/Time of event; dry tundra; Eddy covariance; eddy heat flux; ELEVATION; Energy budget, description; Event label; Field observation; First year of observation; glacier; glaciers; graminoids; ground heat flux and net radiation; harmonized data; high latitude; Identification; Journal/report title; Land-Atmosphere; Land-cover; Last year of observation; latent and sensible heat; latent heat flux; LATITUDE; Location; LONGITUDE; longwave radiation; meteorological data; observatory data; Peat bog; Persistent Identifier; Publication type; Radiation fluxes; Radiative energy budget; Resolution; Season; sensible heat flux; shortwave radiation; shrub tundra; Spatial coverage; surface energy balance; synthetic data; Title; tundra vegetation; Type of study; Variable; Vegetation type; wetland; wetlands; Year of publication
    Type: Dataset
    Format: text/tab-separated-values, 8650 data points
    Location Call Number Expected Availability
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  • 5
    Publication Date: 2024-04-12
    Description: Despite the importance of surface energy budgets (SEBs) for land-climate interactions in the Arctic, uncertainties in their prediction persist. In situ observational data of SEB components - useful for research and model validation - are collected at relatively few sites across the terrestrial Arctic, and not all available datasets are readily interoperable. Furthermore, the terrestrial Arctic consists of a diversity of vegetation types, which are generally not well represented in land surface schemes of current Earth system models. This dataset comprises harmonized, standardized and aggregated in-situ observations of surface energy budget components measured at 64 sites on vegetated and glaciated sites north of 60° latitude, in the time period from 1994 till 2021. The surface energy budget components include net radiation, sensible heat flux, latent heat flux, ground heat flux, net shortwave radiation, net longwave radiation, surface temperature and albedo, which were aggregated to daily mean, minimum and maximum values from hourly and half-hourly measurements. Data were retrieved from the monitoring networks FLUXNET, AmeriFlux, AON, GC-Net and PROMICE.
    Keywords: Albedo; Albedo, maximum; Albedo, minimum; Arctic; Arctic_SEB_CA-SCB; Arctic_SEB_CP1; Arctic_SEB_Dye-2; Arctic_SEB_EGP; Arctic_SEB_FI-Lom; Arctic_SEB_GL-NuF; Arctic_SEB_GL-ZaF; Arctic_SEB_GL-ZaH; Arctic_SEB_KAN_B; Arctic_SEB_KAN_L; Arctic_SEB_KAN_M; Arctic_SEB_KAN_U; Arctic_SEB_KPC_L; Arctic_SEB_KPC_U; Arctic_SEB_MIT; Arctic_SEB_NASA-E; Arctic_SEB_NASA-SE; Arctic_SEB_NASA-U; Arctic_SEB_NUK_K; Arctic_SEB_NUK_L; Arctic_SEB_NUK_N; Arctic_SEB_NUK_U; Arctic_SEB_QAS_A; Arctic_SEB_QAS_L; Arctic_SEB_QAS_M; Arctic_SEB_QAS_U; Arctic_SEB_RU-Che; Arctic_SEB_RU-Cok; Arctic_SEB_RU-Sam; Arctic_SEB_RU-Tks; Arctic_SEB_RU-Vrk; Arctic_SEB_Saddle; Arctic_SEB_SCO_L; Arctic_SEB_SCO_U; Arctic_SEB_SE-St1; Arctic_SEB_SJ-Adv; Arctic_SEB_SJ-Blv; Arctic_SEB_SouthDome; Arctic_SEB_Summit; Arctic_SEB_TAS_A; Arctic_SEB_TAS_L; Arctic_SEB_TAS_U; Arctic_SEB_THU_L; Arctic_SEB_THU_U; Arctic_SEB_Tunu-N; Arctic_SEB_UPE_L; Arctic_SEB_UPE_U; Arctic_SEB_US-A03; Arctic_SEB_US-A10; Arctic_SEB_US-An1; Arctic_SEB_US-An2; Arctic_SEB_US-An3; Arctic_SEB_US-Atq; Arctic_SEB_US-Brw; Arctic_SEB_US-EML; Arctic_SEB_US-HVa; Arctic_SEB_US-ICh; Arctic_SEB_US-ICs; Arctic_SEB_US-ICt; Arctic_SEB_US-Ivo; Arctic_SEB_US-NGB; Arctic_SEB_US-Upa; Arctic_SEB_US-xHE; Arctic_SEB_US-xTL; ArcticTundraSEB; Arctic Tundra Surface Energy Budget; Bowen ratio; Calculated from Ground heat, flux / Net radiation; Calculated from Heat, flux, latent / Net radiation; Calculated from Heat, flux, sensible / Heat, flux, latent; Calculated from Heat, flux, sensible / Net radiation; Calculated from Heat, flux, sensible + Heat, flux, latent + Ground heat, flux; Calculated from Long-wave downward radiation, maximum - Long-wave upward radiation, maximum; Calculated from Long-wave downward radiation, minimum - Long-wave upward radiation, minimum; Calculated from Long-wave downward radiation - Long-wave upward radiation; Calculated from Long-wave net radiation / Net radiation; Calculated from Short-wave downward (GLOBAL) radiation, maximum - Short-wave upward (REFLEX) radiation, maximum; Calculated from Short-wave downward (GLOBAL) radiation, minimum - Short-wave upward (REFLEX) radiation, minimum; Calculated from Short-wave downward (GLOBAL) radiation - Short-wave upward (REFLEX) radiation; Calculated from Short-wave net radiation, maximum + Long-wave net radiation, maximum; Calculated from Short-wave net radiation, minimum + Long-wave net radiation, minimum; Calculated from Short-wave net radiation / Net radiation; Calculated from Short-wave net radiation + Long-wave net radiation; Calculated from Short-wave upward (REFLEX) radiation / Short-wave downward (GLOBAL) radiation; Calculated from Surface temperature, maximum - Temperature, air, maximum; Calculated from Surface temperature, minimum - Temperature, air, minimum; Calculated from Surface temperature - Temperature, air; Cloud coverage; Cloud coverage, maximum; Cloud coverage, minimum; Daily maximum; Daily mean; Daily minimum; Data source; DATE/TIME; Day of the year; dry tundra; Eddy covariance; eddy heat flux; ELEVATION; Event label; Field observation; glacier; graminoids; Ground heat, flux; Ground heat, flux, maximum; Ground heat, flux, minimum; Ground heat, flux/Net radiation ratio; ground heat flux and net radiation; harmonized data; Heat, flux, latent; Heat, flux, latent, maximum; Heat, flux, latent, minimum; Heat, flux, latent/Net radiation ratio; Heat, flux, sensible; Heat, flux, sensible, maximum; Heat, flux, sensible, minimum; Heat flux, sensible/Net radiation ratio; high latitude; Humidity, relative; Humidity, relative, maximum; Humidity, relative, minimum; Land-Atmosphere; Land-cover; latent and sensible heat; latent heat flux; LATITUDE; Location ID; LONGITUDE; Long-wave downward radiation; Long-wave downward radiation, maximum; Long-wave downward radiation, minimum; Long-wave net radiation; Long-wave net radiation, maximum; Long-wave net radiation, minimum; Long-wave net radiation/Net radiation ratio; longwave radiation; Long-wave upward radiation; Long-wave upward radiation, maximum; Long-wave upward radiation, minimum; meteorological data; Month; Net radiation; Net radiation, maximum; Net radiation, minimum; Normalized by X / Potential incoming solar radiation, maximum * 100; observatory data; Original variable; Peat bog; Potential incoming solar radiation; Potential incoming solar radiation, maximum; Potential incoming solar radiation, minimum; Precipitation; Precipitation, daily, maximum; Precipitation, daily, minimum; Pressure, atmospheric; Pressure, atmospheric, maximum; Pressure, atmospheric, minimum; Radiation fluxes; Radiative energy budget; sensible heat flux; Short-wave downward (GLOBAL) radiation; Short-wave downward (GLOBAL) radiation, maximum; Short-wave downward (GLOBAL) radiation, minimum; Short-wave net radiation; Short-wave net radiation, maximum; Short-wave net radiation, minimum; Short-wave net radiation/Net radiation ratio; shortwave radiation; Short-wave upward (REFLEX) radiation; Short-wave upward (REFLEX) radiation, maximum; Short-wave upward (REFLEX) radiation, minimum; shrub tundra; Soil water content, volumetric; Soil water content, volumetric, maximum; Soil water content, volumetric, minimum; surface energy balance; Surface temperature; Surface temperature, maximum; Surface temperature, minimum; synthetic data; Temperature, air; Temperature, air, maximum; Temperature, air, minimum; Temperature, soil; Temperature, soil, maximum; Temperature, soil, minimum; Temperature gradient, 0-2m above surface; Temperature gradient, 0-2m above surface, maximum; Temperature gradient, 0-2m above surface, minimum; tundra vegetation; Type of study; Vapour pressure deficit; Vapour pressure deficit, maximum; Vapour pressure deficit, minimum; wetland; Wind direction; Wind speed; Wind speed, maximum; Wind speed, minimum; Year of observation
    Type: Dataset
    Format: text/tab-separated-values, 17112737 data points
    Location Call Number Expected Availability
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  • 6
    Publication Date: 2020-11-15
    Description: Soils are warming as air temperatures rise across the Arctic and Boreal region concurrent with the expansion of tall-statured shrubs and trees in the tundra. Changes in vegetation structure and function are expected to alter soil thermal regimes, thereby modifying climate feedbacks related to permafrost thaw and carbon cycling. However, current understanding of vegetation impacts on soil temperature is limited to local or regional scales and lacks the generality necessary to predict soil warming and permafrost stability on a pan-Arctic scale. Here we synthesize shallow soil and air temperature observations with broad spatial and temporal coverage collected across 106 sites representing nine different vegetation types in the permafrost region. We showed ecosystems with tall-statured shrubs and trees (〉 40 cm) have warmer shallow soils than those with short-statured tundra vegetation when normalized to a constant air temperature. In tree and tall shrub vegetation types, cooler temperatures in the warm season do not lead to cooler mean annual soil temperature indicating that ground thermal regimes in the cold-season rather than the warm-season are most critical for predicting soil warming in ecosystems underlain by permafrost. Our results suggest that the expansion of tall shrubs and trees into tundra regions can amplify shallow soil warming, and could increase the potential for increased seasonal thaw depth and increase soil carbon cycling rates and lead to increased carbon dioxide loss and further permafrost thaw.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 7
    Publication Date: 2020-04-22
    Description: Current analyses and predictions of spatially‐explicit patterns and processes in ecology most often rely on climate data interpolated from standardized weather stations. This interpolated climate data represents long‐term average thermal conditions at coarse spatial resolutions only. Hence, many climate‐forcing factors that operate at fine spatiotemporal resolutions are overlooked. This is particularly important in relation to effects of observation height (e.g. vegetation, snow and soil characteristics) and in habitats varying in their exposure to radiation, moisture and wind (e.g. topography, radiative forcing, or cold‐air pooling). Since organisms living close to the ground relate more strongly to these microclimatic conditions than to free‐air temperatures, microclimatic ground and near‐surface data are needed to provide realistic forecasts of the fate of such organisms under anthropogenic climate change, as well as of the functioning of the ecosystems they live in. To fill this critical gap, we highlight a call for temperature time series submissions to SoilTemp, a geospatial database initiative compiling soil and near‐surface temperature data from all over the world. Currently this database contains time series from 7538 temperature sensors from 51 countries across all key biomes. The database will pave the way towards an improved global understanding of microclimate and bridge the gap between the available climate data and the climate at fine spatiotemporal resolutions relevant to most organisms and ecosystem processes.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Miscellaneous , notRev
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  • 8
    Publication Date: 2022-10-27
    Description: © The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Kropp, H., Loranty, M. M., Natali, S. M., Kholodov, A. L., Rocha, A., V., Myers-Smith, I., Abbot, B. W., Abermann, J., Blanc-Betes, E., Blok, D., Blume-Werry, G., Boike, J., Breen, A. L., Cahoon, S. M. P., Christiansen, C. T., Douglas, T. A., Epstein, H. E., Frost, G., V., Goeckede, M., Hoye, T. T., Mamet, S. D., O'Donnell, J. A., Olefeldt, D., Phoenix, G. K., Salmon, V. G., Sannel, A. B. K., Smith, S. L., Sonnentag, O., Vaughn, L. S., Williams, M., Elberling, B., Gough, L., Hjort, J., Lafleur, P. M., Euskirchen, E. S., Heijmans, M. M. P. D., Humphreys, E. R., Iwata, H., Jones, B. M., Jorgenson, M. T., Gruenberg, I., Kim, Y., Laundre, J., Mauritz, M., Michelsen, A., Schaepman-Strub, G., Tape, K. D., Ueyama, M., Lee, B., Langley, K., & Lund, M. Shallow soils are warmer under trees and tall shrubs across arctic and boreal ecosystems. Environmental Research Letters, 16(1), (2021): 015001. doi:10.1088/1748-9326/abc994.
    Description: Soils are warming as air temperatures rise across the Arctic and Boreal region concurrent with the expansion of tall-statured shrubs and trees in the tundra. Changes in vegetation structure and function are expected to alter soil thermal regimes, thereby modifying climate feedbacks related to permafrost thaw and carbon cycling. However, current understanding of vegetation impacts on soil temperature is limited to local or regional scales and lacks the generality necessary to predict soil warming and permafrost stability on a pan-Arctic scale. Here we synthesize shallow soil and air temperature observations with broad spatial and temporal coverage collected across 106 sites representing nine different vegetation types in the permafrost region. We showed ecosystems with tall-statured shrubs and trees (〉40 cm) have warmer shallow soils than those with short-statured tundra vegetation when normalized to a constant air temperature. In tree and tall shrub vegetation types, cooler temperatures in the warm season do not lead to cooler mean annual soil temperature indicating that ground thermal regimes in the cold-season rather than the warm-season are most critical for predicting soil warming in ecosystems underlain by permafrost. Our results suggest that the expansion of tall shrubs and trees into tundra regions can amplify shallow soil warming, and could increase the potential for increased seasonal thaw depth and increase soil carbon cycling rates and lead to increased carbon dioxide loss and further permafrost thaw.
    Description: We thank G Peter Kershaw, LeeAnn Fishback, Cathy Wilson, and Coleen Iversen for assistance in collection of data. We thank the Permafrost Carbon Network for support and organization of the data synthesis. We thank Vladimir Romanovsky for his feedback and contribution of publicly available data. This project was supported by the National Science Foundation (Grant No. 1417745 to M L, Grant No. 1417700 to S M N, Grant No. 1417908 to A K, Grant No. 1556772 to A R, Grant No. 1637459 to L G, Grant No. 1636476 and Grant No. 1503912 to E S E, Grant No. 1806213 to B M J, Grant No. 1833056 to K D T), UK Natural Environment Research Council (Grant No. NE/M016323/1 to I H M S, Grant No. NE/K00025X/1 to G K P, Grant No. NE/K000292/1 to M W), Natural Sciences and Engineering Research (to P L, I H M S, Grant No. RGPIN-2016-04688 to D O), Council of Canada, Canadian Graduate Scholarship to (I H M -S), Greenland Ecosystem Monitoring Programme: ClimateBasis (to J A and K A), The Next-Generation Ecosystem Experiments (NGEE Arctic) project is supported by the Office of Biological and Environmental Research in the DOE Office of Science (to A L B), Engineer Research and Development Center Army Direct (6.1) Research Program and the Strategic Environmental Research and Development Program (projects RC-2110 and 18-1170 to T A D), United States Geological Survey (to E E S), Arctic Challenge for Sustainability (ArCS; Grant No. JPMXD1300000000) and ArCS II (Grant No. JPMXD1420318865) (to M U and H I), the Danish National Research Foundation (Grant No. CENPERM DNRF100 to B E), the Academy of Finland (Grant No. 315519), the National Research Foundation of Korea (Grant Nos. NRF-2016M1A5A1901769; KOPRI-PN20081 to K Y and B Y L), Research Network for Geosciences in Berlin and Potsdam (to I G), the Swiss National Science Foundation (Grant No. 140631 to G S S), the URPP Global Change and Biodiversity, University of Zurich (to G S S), the University of Alberta Northern Research Awards (to D O), and the Northern Scientific Training Program (to D O), and UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE) Office of Science, Biological and Environmental Research (to V G S). S M has been supported by grants and/or in-kind from Natural Sciences and Engineering Research Council of Canada, AMAX Northwest Mining, Co. (North American Tungsten Corp., Ltd), Imperial Oil, Ltd, University of Alberta, Earthwatch International (EI), The Garfield Weston Foundation, Wapusk National Park, Churchill Northern Studies Centre, and the Northern Scientific Training Program. All code for this project are archived (DOI: 10.5281/zenodo.4041165). The data that support the findings of this study are openly available through the Arctic Data Center (Heather Kropp, Michael Loranty, Britta Sannel, Jonathan O'Donnell, Elena Blanc-Betes, et al 2020. Synthesis of soil-air temperature and vegetation measurements in the pan-Arctic. 1990-2016. Arctic Data Center. doi:10.18739/A2736M31X).
    Keywords: Arctic ; Boreal forest ; Soil temperature ; Vegetation change ; Permafrost
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 9
    Publication Date: 2022-05-27
    Description: © The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Chu, H., Luo, X., Ouyang, Z., Chan, W. S., Dengel, S., Biraud, S. C., Torn, M. S., Metzger, S., Kumar, J., Arain, M. A., Arkebauer, T. J., Baldocchi, D., Bernacchi, C., Billesbach, D., Black, T. A., Blanken, P. D., Bohrer, G., Bracho, R., Brown, S., Brunsell, N. A., Chen, J., Chen, X., Clark, K., Desai, A. R., Duman, T., Durden, D., Fares, S., Forbrich, I., Gamon, J. A., Gough, C. M., Griffis, T., Helbig, M., Hollinger, D., Humphreys, E., Ikawa, H., Iwata, H., Ju, Y., Knowles, J. F., Knox, S. H., Kobayashi, H., Kolb, T., Law, B., Lee, X., Litvak, M., Liu, H., Munger, J. W., Noormets, A., Novick, K., Oberbauer, S. F., Oechel, W., Oikawa, P., Papuga, S. A., Pendall, E., Prajapati, P., Prueger, J., Quinton, W. L., Richardson, A. D., Russell, E. S., Scott, R. L., Starr, G., Staebler, R., Stoy, P. C., Stuart-Haentjens, E., Sonnentag, O., Sullivan, R. C., Suyker, A., Ueyama, M., Vargas, R., Wood, J. D., & Zona, D. Representativeness of eddy-covariance flux footprints for areas surrounding AmeriFlux sites. Agricultural and Forest Meteorology, 301, (2021): 108350, https://doi.org/10.1016/j.agrformet.2021.108350.
    Description: Large datasets of greenhouse gas and energy surface-atmosphere fluxes measured with the eddy-covariance technique (e.g., FLUXNET2015, AmeriFlux BASE) are widely used to benchmark models and remote-sensing products. This study addresses one of the major challenges facing model-data integration: To what spatial extent do flux measurements taken at individual eddy-covariance sites reflect model- or satellite-based grid cells? We evaluate flux footprints—the temporally dynamic source areas that contribute to measured fluxes—and the representativeness of these footprints for target areas (e.g., within 250–3000 m radii around flux towers) that are often used in flux-data synthesis and modeling studies. We examine the land-cover composition and vegetation characteristics, represented here by the Enhanced Vegetation Index (EVI), in the flux footprints and target areas across 214 AmeriFlux sites, and evaluate potential biases as a consequence of the footprint-to-target-area mismatch. Monthly 80% footprint climatologies vary across sites and through time ranging four orders of magnitude from 103 to 107 m2 due to the measurement heights, underlying vegetation- and ground-surface characteristics, wind directions, and turbulent state of the atmosphere. Few eddy-covariance sites are located in a truly homogeneous landscape. Thus, the common model-data integration approaches that use a fixed-extent target area across sites introduce biases on the order of 4%–20% for EVI and 6%–20% for the dominant land cover percentage. These biases are site-specific functions of measurement heights, target area extents, and land-surface characteristics. We advocate that flux datasets need to be used with footprint awareness, especially in research and applications that benchmark against models and data products with explicit spatial information. We propose a simple representativeness index based on our evaluations that can be used as a guide to identify site-periods suitable for specific applications and to provide general guidance for data use.
    Description: We thank the AmeriFlux site teams for sharing their data and metadata with the network. Funding for these flux sites is acknowledged in the site data DOI, shown in Table S1. This analysis was supported in part by funding provided to the AmeriFlux Management Project by the U.S. Department of Energy's Office of Science under Contract No. DE-AC02-05CH11231. All footprint climatologies, site-level representativeness indices, and monthly EVI and sensor location biases can be accessed via the Zenodo Data Repository (Datasets S1–S6, http://doi.org/10.5281/zenodo.4015350).
    Keywords: Flux footprint ; Spatial representativeness ; Landsat EVI ; Land cover ; Sensor location bias ; Model-data benchmarking
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 10
    Publication Date: 2022-11-02
    Description: 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.
    Description: Published
    Description: 6379
    Description: 5A. Ricerche polari e paleoclima
    Description: JCR Journal
    Keywords: Arctic climate ; vegetation type ; surface energy budget
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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