Global - Land
CLM-CRUJRA
Download Data
Period Mean (original grids) [1]
Model Period Mean (intersection) [1]
Benchmark Period Mean (intersection) [1]
Model Period Mean (complement) [1]
Benchmark Period Mean (complement) [1]
Bias [1]
Phase Shift [months]
Bias Score [1]
Seasonal Cycle Score [1]
Spatial Distribution Score [1]
Overall Score [1]
Benchmark [-] 0.521
CLM-CRUJRA [-] 0.459 0.492 0.521 0.321 0.554 -0.0286 1.49 0.517 0.775 0.973 0.755
CLM-GSWP3 [-] 0.478 0.520 0.521 0.309 0.574 -0.000427 1.42 0.556 0.782 0.924 0.754
CLM-Princeton [-] 0.447 0.480 0.520 0.312 0.584 -0.0399 1.64 0.540 0.741 0.979 0.753
ISBA-CTRIP-CRUJRA [-] 0.473 0.511 0.521 0.336 0.572 -0.00934 1.48 0.494 0.775 0.891 0.720
ISBA-CTRIP-GSWP3 [-] 0.472 0.519 0.520 0.310 0.611 -0.00225 1.57 0.485 0.756 0.849 0.697
ISBA-CTRIP-Princeton [-] 0.429 0.463 0.521 0.311 0.558 -0.0572 1.82 0.530 0.695 0.942 0.722
JSBACH-CRUJRA [-] 0.508 0.550 0.516 0.266 0.590 0.0337 2.17 0.422 0.634 0.787 0.614
JSBACH-GSWP3 [-] 0.500 0.546 0.516 0.235 0.592 0.0301 1.76 0.423 0.708 0.773 0.635
JSBACH-Princeton [-] 0.489 0.533 0.516 0.259 0.594 0.0168 2.10 0.423 0.649 0.748 0.607
Mean-CRUJRA [-] 0.492 0.530 0.521 0.360 0.512 0.00949 1.52 0.493 0.764 0.894 0.717
Mean-GSWP3 [-] 0.494 0.540 0.521 0.337 0.540 0.0197 1.46 0.485 0.770 0.847 0.701
Mean-Princeton [-] 0.465 0.503 0.521 0.340 0.545 -0.0177 1.72 0.573 0.717 0.907 0.732

Temporally integrated period mean click to collapse contents

BENCHMARK MEAN
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MODEL MEAN
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BIAS
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BIAS 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 click to collapse contents

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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EvaporativeFraction / FLUXCOM / 1980-2015 / global / MNAME

Benchmark
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CLM-CRUJRA
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CLM-GSWP3
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CLM-Princeton
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ISBA-CTRIP-CRUJRA
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ISBA-CTRIP-GSWP3
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ISBA-CTRIP-Princeton
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JSBACH-CRUJRA
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JSBACH-GSWP3
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JSBACH-Princeton
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Mean-CRUJRA
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Mean-GSWP3
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Mean-Princeton
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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-sh-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

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