Global - Land
bcc-csm1-1
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Period Mean (original grids) [Pg yr-1]
Model Period Mean (intersection) [Pg yr-1]
Benchmark Period Mean (intersection) [Pg yr-1]
Model Period Mean (complement) [Pg yr-1]
Benchmark Period Mean (complement) [Pg yr-1]
Bias [g m-2 d-1]
RMSE [g m-2 d-1]
Phase Shift [months]
Bias Score [1]
RMSE Score [1]
Seasonal Cycle Score [1]
Spatial Distribution Score [1]
Overall Score [1]
Benchmark [-] 114.
bcc-csm1-1 [-] 123. 112. 114. 8.79 0.0945 0.238 1.51 1.01 0.424 0.559 0.869 0.955 0.673
BCC-CSM2-MR [-] 114. 107. 113. 5.88 0.671 -0.0233 1.52 1.11 0.457 0.545 0.857 0.941 0.669
CanESM2 [-] 129. 117. 114. 9.54 0.0601 2.31 2.00 0.375 0.459 0.704 0.836 0.567
CanESM5 [-] 141. 128. 114. 10.1 0.730 1.87 1.60 0.392 0.438 0.786 0.948 0.600
CESM1-BGC [-] 129. 123. 113. 5.55 0.660 0.379 1.66 1.20 0.429 0.550 0.844 0.889 0.653
CESM2 [-] 110. 104. 113. 5.57 0.642 -0.0542 1.62 1.32 0.412 0.493 0.821 0.933 0.630
GFDL-ESM2G [-] 167. 152. 114. 12.4 1.26 2.78 1.38 0.263 0.251 0.818 0.897 0.496
GFDL-ESM4 [-] 129. 106. 114. 22.2 -0.177 1.59 1.49 0.458 0.470 0.786 0.939 0.625
IPSL-CM5A-LR [-] 165. 150. 113. 11.7 0.515 1.18 2.68 1.20 0.227 0.319 0.837 0.896 0.520
IPSL-CM6A-LR [-] 115. 109. 113. 5.27 0.708 0.111 1.39 1.14 0.491 0.534 0.852 0.961 0.674
MeanCMIP5 [-] 165. 141. 113. 24.1 0.266 0.637 1.42 0.987 0.401 0.610 0.875 0.967 0.693
MeanCMIP6 [-] 118. 112. 114. 5.93 0.167 1.18 0.903 0.526 0.625 0.893 0.958 0.725
MIROC-ESM [-] 129. 118. 102. 9.04 11.4 0.396 1.90 1.27 0.338 0.429 0.836 0.920 0.590
MIROC-ESM2L [-] 116. 104. 113. 9.90 0.119 -0.0111 1.95 1.99 0.358 0.422 0.692 0.920 0.563
MPI-ESM-LR [-] 169. 159. 104. 8.91 9.81 1.36 2.36 1.29 0.269 0.373 0.819 0.930 0.553
MPI-ESM1.2-HR [-] 133. 129. 108. 3.41 5.80 0.517 1.85 1.16 0.400 0.430 0.847 0.944 0.610
NorESM1-ME [-] 129. 120. 114. 7.82 0.386 1.86 1.25 0.381 0.537 0.832 0.856 0.629
NorESM2-LM [-] 107. 97.5 114. 7.59 -0.0828 1.63 1.31 0.398 0.502 0.824 0.938 0.633
UK-HadGEM2-ES [-] 137. 130. 113. 6.93 0.848 0.602 2.01 1.10 0.392 0.454 0.863 0.855 0.603
UKESM1-0-LL [-] 126. 119. 113. 7.06 0.825 0.387 1.77 1.16 0.412 0.491 0.847 0.924 0.633

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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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 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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GrossPrimaryProductivity / FLUXCOM / 1980-2014 / global / bcc-csm1-1

Download Data
Evapotranspiration|GLEAMv3.3a Hellinger Distance [1]
Precipitation|GPCPv2.3 Hellinger Distance [1]
SurfaceDownwardSWRadiation|CERESed4.1 Hellinger Distance [1]
SurfaceNetSWRadiation|CERESed4.1 Hellinger Distance [1]
SurfaceAirTemperature|CRU4.02 Hellinger Distance [1]
Evapotranspiration|GLEAMv3.3a Score [1]
Precipitation|GPCPv2.3 Score [1]
SurfaceDownwardSWRadiation|CERESed4.1 Score [1]
SurfaceNetSWRadiation|CERESed4.1 Score [1]
SurfaceAirTemperature|CRU4.02 Score [1]
Overall Score [1]
bcc-csm1-1 [-] 0.521 0.655 0.595 0.656 0.704 0.801 0.881 0.752 0.718 0.886 0.808
BCC-CSM2-MR [-] 0.528 0.640 0.537 0.616 0.686 0.798 0.833 0.731 0.770 0.751 0.777
CanESM2 [-] 0.528 0.595 0.563 0.706 0.706 0.546 0.637 0.651 0.739 0.752 0.665
CanESM5 [-] 0.551 0.585 0.552 0.635 0.711 0.919 0.863 0.591 0.603 0.754 0.746
CESM1-BGC [-] 0.477 0.588 0.556 0.715 0.683 0.832 0.742 0.690 0.846 0.785 0.779
CESM2 [-] 0.514 0.581 0.530 0.640 0.673 0.779 0.859 0.753 0.741 0.774 0.781
GFDL-ESM2G [-] 0.493 0.564 0.508 0.551 0.584 0.815 0.707 0.559 0.536 0.629 0.649
GFDL-ESM4 [-] 0.504 0.626 0.535 0.640 0.664 0.777 0.851 0.724 0.760 0.730 0.768
IPSL-CM5A-LR [-] 0.475 0.487 0.609 0.535 0.551 0.767 0.650 0.503 0.489 0.565 0.595
IPSL-CM6A-LR [-] 0.711 0.774 0.734 0.726 0.776 0.759 0.829 0.728 0.662 0.884 0.772
MeanCMIP5 [-] 0.527 0.626 0.558 0.613 0.681 0.836 0.833 0.696 0.734 0.822 0.784
MeanCMIP6 [-] 0.555 0.654 0.558 0.657 0.721 0.741 0.890 0.727 0.745 0.809 0.783
MIROC-ESM [-] 0.480 0.626 0.540 0.512 0.663 0.854 0.838 0.651 0.652 0.791 0.757
MIROC-ESM2L [-] 0.510 0.610 0.593 0.561 0.647 0.709 0.785 0.762 0.710 0.728 0.739
MPI-ESM-LR [-] 0.556 0.529 0.549 0.564 0.622 0.783 0.759 0.578 0.595 0.577 0.659
MPI-ESM1.2-HR [-] 0.526 0.589 0.482 0.638 0.670 0.897 0.789 0.656 0.680 0.680 0.741
NorESM1-ME [-] 0.457 0.613 0.561 0.696 0.644 0.839 0.676 0.637 0.808 0.761 0.744
NorESM2-LM [-] 0.509 0.608 0.526 0.631 0.684 0.788 0.903 0.754 0.740 0.804 0.798
UK-HadGEM2-ES [-] 0.517 0.587 0.536 0.564 0.608 0.829 0.656 0.635 0.764 0.783 0.733
UKESM1-0-LL [-] 0.531 0.593 0.526 0.582 0.592 0.925 0.806 0.607 0.699 0.760 0.759

Evapotranspiration/GLEAMv3.3a

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Precipitation/GPCPv2.3

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SurfaceDownwardSWRadiation/CERESed4.1

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SurfaceNetSWRadiation/CERESed4.1

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SurfaceAirTemperature/CRU4.02

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GrossPrimaryProductivity / FLUXCOM / 1980-2014 / global / MNAME

Benchmark
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bcc-csm1-1
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BCC-CSM2-MR
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CanESM2
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CanESM5
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CESM1-BGC
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CESM2
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GFDL-ESM2G
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GFDL-ESM4
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IPSL-CM5A-LR
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IPSL-CM6A-LR
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MeanCMIP5
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MeanCMIP6
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MIROC-ESM
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MIROC-ESM2L
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MPI-ESM-LR
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MPI-ESM1.2-HR
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NorESM1-ME
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NorESM2-LM
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UK-HadGEM2-ES
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UKESM1-0-LL
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Data Information

  Title:
FLUXCOM (RS+METEO) Global Land Carbon Fluxes using CRUNCEP climate data

  Version:
1

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

  Source:
Data generated by Artificial Neural Networks and forced with CRUNCEPv6 meteorological data and MODIS (RS+METEO)

  History:
2020-08-25: downloaded source from ftp://ftp.bgc-jena.mpg.de/pub/outgoing/FluxCom/CarbonFluxes_v1_2017/RS+METEO/CRUNCEPv6/raw/monthly/
2020-08-25: converted to netCDF, additionally we apply a mask where |var|<1e-15 for all time.

  References:
Jung, M., M. Reichstein, C.R. Schwalm, C. Huntingford, S. Sitch, A. Ahlstrom, A. Arneth, G. Camps-Valls, P. Ciais, P. Friedlingstein, F. Gans, K. Ichii, A.K. Jain, E. Kato, D. Papale, B. Poulter, B. Raduly, C. Rodenbeck, G. Tramontana, N. Viovy, Y.P. Wang, U. Weber, S. Zaehle and N. Zeng (2017), Compensatory water effects link yearly global land CO2 sink changes to temperature, Nature, 541, 516-520, doi:10.1038/nature20780

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 2013-12
temporal_resolution: monthly
spatial_resolution: 0.5 degree
units: g m-2 day-1

  Convention:
CF-1.8

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