Mean State

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Period Mean (original grids) [watt/m2]
Model Period Mean (intersection) [watt/m2]
Benchmark Period Mean (intersection) [watt/m2]
Model Period Mean (complement) [watt/m2]
Benchmark Period Mean (complement) [watt/m2]
Bias [watt/m2]
RMSE [watt/m2]
Phase Shift [months]
Bias Score [1]
RMSE Score [1]
Seasonal Cycle Score [1]
Spatial Distribution Score [1]
Overall Score [1]
Benchmark [-] 48.9
CRUJRA [-] 32.0 45.1 48.9 -11.2 67.6 -3.20 14.1 0.920 0.679 0.598 0.834 0.972 0.736
PPE_GSWP3V1 [-] 34.7 42.7 48.9 8.38 67.6 -5.62 15.0 0.894 0.656 0.595 0.851 0.990 0.737
R20_GSWP3V1 [-] 34.0 41.8 48.9 7.92 56.9 -6.57 14.9 0.893 0.632 0.604 0.852 0.987 0.736

Temporally integrated period mean

BENCHMARK MEAN
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MODEL MEAN
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BIAS
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BIAS SCORE
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RMSE
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RMSE SCORE
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BENCHMARK MAX MONTH
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MODEL MAX MONTH
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DIFFERENCE IN MAX MONTH
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SEASONAL CYCLE SCORE
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SPATIAL TAYLOR DIAGRAM
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MODEL COLORS
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Spatially integrated regional mean

MODEL COLORS
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REGIONAL MEAN
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ANNUAL CYCLE
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MONTHLY ANOMALY
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ANNUAL CYCLE
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All Models

Benchmark
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CRUJRA
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PPE_GSWP3V1
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R20_GSWP3V1
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Data Information

  Title:
FLUXCOM (RS+METEO) Global Land Energy Fluxes using GSWP3 climate data

  Version:
1

  Institutions:
Department Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Germany

  Source:
Data generated by machine learning to merge energy flux measurements from FLUXNET eddy covariance towers with MODIS remote sensing and GSWP3v1 meteorological data (RS+METEO)

  History:
2019-05-07: downloaded source from doi:10.17871/FLUXCOM_EnergyFluxes_v1
2019-06-28: converted to netCDF with https://github.com/mmu2019/Datasets/blob/master/read-le-fluxcom.py

  References:
Jung, M., S. Koirala, U. Weber, K. Ichii, F. Gans, G. Camps-Valls, D. Papale, C. Schwalm, G. Tramontana, M. Reichstein (2019), The FLUXCOM ensemble of global land-atmosphere energy fluxes, Scientific Data, submitted, 1-12, https://arxiv.org/abs/1812.04951

Tramontana, G., M. Jung, C.R. Schwalm, K. Ichii, G. Camps-Valls, B. Raduly, M. Reichstein, M.A. Arain, A. Cescatti, G. Kiely, L. Merbold, P. Serrano-Ortiz, S. Sickert, S. Wolf, and D. Papale (2016), Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms, Biogeosciences, 13, 4291-4313, doi:10.5194/bg-13-4291-2016

  Comments:
time_period: 1980-01 through 2014-12
original_temporal_resolution: monthly
original_spatial_resolution: 0.5 degree
original_units: MJ/m2/day
final_temporal_resolution: monthly
final_spatial_resolution: 0.5 degree
final_units: watt/m2

  Convention:
CF-1.7