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  • 1
    Electronic Resource
    Electronic Resource
    A note on the analogy between momentum transfer across a rough solid surface and the air-sea interface (1995)
    Springer
    Boundary layer meteorology 76 (1995), S. 181-197 
    Details
    ISSN: 1573-1472
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Physics
    Notes: Abstract The aerodynamic classification of the resistance laws above solid surfaces is based on the use of a so-called Reynolds roughness number ReΘ s =h s u */υ, whereh s is the effective roughness height, ν-viscosity,u *-friction velocity. The recent experimental studies reported by Toba and Ebuchi (1991), demonstrated that the observed “variability” of the sea roughness cannot be explained only on the basis of the classification of aerodynamic conditions of the sea surface proposed by Kitaigorodskii and Volkov (1965) and Kitaigorodskii (1968) even though the latter approach gains some support from recent experimental studies (see for example Geernaertet al. 1986). In this paper, an attempt is made to explain some of the recently observed features of the “variability” of surface roughness (Toba and Ebuchi, 1991; Donelanet al., 1993). The “fluctuating” regime of the sea surface roughness is also described. It is shown that the contribution from the dissipation subrange to the variability of the sea surface can be very important and by itself can explain Charnock's (1955) regime.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00710896
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    Paper (German National Licenses)
    Fulltext
  • 2
    Electronic Resource
    Electronic Resource
    Free convection frequency spectra of atmospheric turbulence over the sea (1994)
    Springer
    Boundary layer meteorology 69 (1994), S. 27-42 
    Details
    ISSN: 1573-1472
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Physics
    Notes: Abstract Frequency spectra of atmospheric turbulenceS α (f) in the inertial subrange are considered in the free convection regime over the sea surface in a case of motionless instrument measurements (Eulerian frequency spectra). The frequency spectra formulaef * S α(f)/σ α 2 =c α(f */f)5/3 for wind velocity (α=1–3), temperature (α=t) and humidity (α=e) fluctuations are derived on the basis of similarity theory and the “−5/3 law”. These relations also can be derived from a consideration of convective large-scale advection of small eddies. The frequency scalef * = (N 1 β 2/∈)1/2 ≈ (βH/z 2)1/3 is the lower bound of the inertial subrange and it is of order 10−2 Hz. The spectra formulae are compared with direct measurements of atmospheric turbulence from the fixed research tower in the coastal zone of the Black Sea in calm weather. It is shown that these formulae are realized at least over two to three decades of the frequency range (approximately from 10−2 to 10 Hz) and values of the numerical coefficients are found. The derived formulae can be used for calculations of sensible and latent heat fluxes by measuring the high-frequency range of spectra at a fixed point at low wind speeds when the conventional inertial dissipation method is not applicable.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00713293
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    Paper (German National Licenses)
    Fulltext
  • 3
    facet.materialart.
    Unknown
    Harmonized in-situ observations of surface energy fluxes and environmental drivers at 64 Arctic vegetation and glacier sites (2022)
    PANGAEA
    Details
    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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    DATA PANGAEA
  • 4
    facet.materialart.
    Unknown
    Harmonized in-situ observations of surface energy fluxes and environmental drivers at 64 Arctic vegetation and glacier sites - Metadata (2022)
    PANGAEA
    Details
    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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    DATA PANGAEA
  • 5
    facet.materialart.
    Unknown
    Harmonized in-situ observations of surface energy fluxes and environmental drivers at 64 Arctic vegetation and glacier sites - Surface energy budget componenent data (2022)
    PANGAEA
    Details
    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
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    DATA PANGAEA
  • 6
    facet.materialart.
    Unknown
    Literature synthesis data of surface energy fluxes and environmental drivers from Arctic vegetation and glacier sites (2022)
    PANGAEA
    Details
    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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    DATA PANGAEA
  • 7
    facet.materialart.
    Unknown
    Harmonized in-situ observations of surface energy fluxes and environmental drivers at 64 Arctic vegetation and glacier sites - Environmental conditions (2022)
    PANGAEA
    Details
    Publication Date: 2024-05-05
    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
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    SHEBA flux–profile relationships in the stable atmospheric boundary layer (2007)
    Springer
    Details
    Publication Date: 2007-04-17
    Print ISSN: 0006-8314
    Electronic ISSN: 1573-1472
    Topics: Geosciences , Physics
    Published by Springer
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    International Arctic Systems for Observing the Atmosphere: An International Polar Year Legacy Consortium (2016)
    American Meteorological Society
    Details
    Publication Date: 2016-06-01
    Description: International Arctic Systems for Observing the Atmosphere (IASOA) activities and partnerships were initiated as a part of the 2007–09 International Polar Year (IPY) and are expected to continue for many decades as a legacy program. The IASOA focus is on coordinating intensive measurements of the Arctic atmosphere collected in the United States, Canada, Russia, Norway, Finland, and Greenland to create synthesis science that leads to an understanding of why and not just how the Arctic atmosphere is evolving. The IASOA premise is that there are limitations with Arctic modeling and satellite observations that can only be addressed with boots-on-the-ground, in situ observations and that the potential of combining individual station and network measurements into an integrated observing system is tremendous. The IASOA vision is that by further integrating with other network observing programs focusing on hydrology, glaciology, oceanography, terrestrial, and biological systems it will be possible to understand the mechanisms of the entire Arctic system, perhaps well enough for humans to mitigate undesirable variations and adapt to inevitable change.
    Print ISSN: 0003-0007
    Electronic ISSN: 1520-0477
    Topics: Geography , Physics
    Published by American Meteorological Society
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    CASPER: Coupled Air–Sea Processes and Electromagnetic Ducting Research (2018)
    American Meteorological Society
    Details
    Publication Date: 2018-07-01
    Description: The Coupled Air–Sea Processes and Electromagnetic Ducting Research (CASPER) project aims to better quantify atmospheric effects on the propagation of radar and communication signals in the marine environment. Such effects are associated with vertical gradients of temperature and water vapor in the marine atmospheric surface layer (MASL) and in the capping inversion of the marine atmospheric boundary layer (MABL), as well as the horizontal variations of these vertical gradients. CASPER field measurements emphasized simultaneous characterization of electromagnetic (EM) wave propagation, the propagation environment, and the physical processes that gave rise to the measured refractivity conditions. CASPER modeling efforts utilized state-of-the-art large-eddy simulations (LESs) with a dynamically coupled MASL and phase-resolved ocean surface waves. CASPER-East was the first of two planned field campaigns, conducted in October and November 2015 offshore of Duck, North Carolina. This article highlights the scientific motivations and objectives of CASPER and provides an overview of the CASPER-East field campaign. The CASPER-East sampling strategy enabled us to obtain EM wave propagation loss as well as concurrent environmental refractive conditions along the propagation path. This article highlights the initial results from this sampling strategy showing the range-dependent propagation loss, the atmospheric and upper-oceanic variability along the propagation range, and the MASL thermodynamic profiles measured during CASPER-East.
    Print ISSN: 0003-0007
    Electronic ISSN: 1520-0477
    Topics: Geography , Physics
    Published by American Meteorological Society
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