Mean State

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Period Mean (original grids) [W m-2]
Bias [W m-2]
RMSE [W m-2]
Phase Shift [months]
Bias Score [1]
RMSE Score [1]
Seasonal Cycle Score [1]
Overall Score [1]
Benchmark [-] 109.
BCC-CSM2-MR [-] 98.7 -9.97 32.2 0.803 0.852 0.644 0.928 0.767
CanESM5 [-] 107. -1.80 23.2 0.535 0.914 0.719 0.965 0.829
CESM2 [-] 107. -1.73 24.7 0.642 0.897 0.711 0.945 0.816
GFDL-ESM4 [-] 97.5 -11.2 27.2 0.589 0.821 0.720 0.961 0.806
IPSL-CM6A-LR [-] 115. 6.06 33.5 0.482 0.846 0.656 0.968 0.781
MeanCMIP6 [-] 107. -2.01 20.3 0.535 0.931 0.753 0.965 0.850
MIROC-ESM2L [-] 108. -0.223 22.4 0.535 0.904 0.732 0.965 0.833
MPI-ESM1.2-HR [-] 112. 3.55 27.3 0.535 0.876 0.701 0.965 0.811
NorESM2-LM [-] 105. -4.17 26.3 0.589 0.894 0.693 0.955 0.808
UKESM1-0-LL [-] 106. -2.83 23.8 0.482 0.924 0.713 0.962 0.828
Download Data
Period Mean (original grids) [W m-2]
Bias [W m-2]
RMSE [W m-2]
Phase Shift [months]
Bias Score [1]
RMSE Score [1]
Seasonal Cycle Score [1]
Overall Score [1]
Benchmark [-] 140.
BCC-CSM2-MR [-] 139. -0.874 31.8 0.662 0.810 0.604 0.944 0.741
CanESM5 [-] 145. 5.65 29.3 0.584 0.807 0.640 0.952 0.760
CESM2 [-] 145. 5.39 28.5 0.527 0.806 0.652 0.950 0.765
GFDL-ESM4 [-] 131. -8.42 27.4 0.591 0.803 0.655 0.951 0.766
IPSL-CM6A-LR [-] 143. 3.53 32.7 0.619 0.801 0.609 0.946 0.741
MeanCMIP6 [-] 142. 1.78 23.2 0.569 0.847 0.695 0.951 0.797
MIROC-ESM2L [-] 145. 5.14 27.8 0.519 0.818 0.650 0.953 0.768
MPI-ESM1.2-HR [-] 143. 3.65 27.6 0.669 0.822 0.647 0.942 0.765
NorESM2-LM [-] 144. 4.40 28.8 0.569 0.807 0.640 0.948 0.759
UKESM1-0-LL [-] 140. 0.425 27.0 0.662 0.820 0.655 0.947 0.769
Download Data
Period Mean (original grids) [W m-2]
Bias [W m-2]
RMSE [W m-2]
Phase Shift [months]
Bias Score [1]
RMSE Score [1]
Seasonal Cycle Score [1]
Overall Score [1]
Benchmark [-] 200.
BCC-CSM2-MR [-] 203. 3.50 30.2 0.738 0.716 0.576 0.950 0.705
CanESM5 [-] 202. 2.08 29.7 0.883 0.693 0.602 0.941 0.709
CESM2 [-] 197. -3.14 25.1 0.443 0.699 0.602 0.970 0.718
GFDL-ESM4 [-] 200. 0.515 26.9 0.738 0.752 0.622 0.950 0.737
IPSL-CM6A-LR [-] 196. -3.71 30.2 0.588 0.716 0.546 0.961 0.692
MeanCMIP6 [-] 199. -1.02 23.7 0.738 0.708 0.655 0.950 0.742
MIROC-ESM2L [-] 197. -3.05 29.6 0.586 0.646 0.587 0.961 0.695
MPI-ESM1.2-HR [-] 200. 0.0499 22.7 0.883 0.762 0.633 0.941 0.742
NorESM2-LM [-] 199. -0.991 29.4 0.590 0.666 0.582 0.960 0.697
UKESM1-0-LL [-] 195. -4.69 24.7 0.736 0.699 0.637 0.951 0.731
Download Data
Period Mean (original grids) [W m-2]
Bias [W m-2]
RMSE [W m-2]
Phase Shift [months]
Bias Score [1]
RMSE Score [1]
Seasonal Cycle Score [1]
Overall Score [1]
Benchmark [-] 196.
BCC-CSM2-MR [-] 192. -3.67 25.1 0.511 0.906 0.653 0.966 0.795
CanESM5 [-] 197. 1.07 26.7 1.02 0.910 0.622 0.932 0.772
CESM2 [-] 204. 7.94 22.0 0.511 0.793 0.731 0.966 0.805
GFDL-ESM4 [-] 191. -4.84 21.4 1.02 0.888 0.718 0.932 0.814
IPSL-CM6A-LR [-] 192. -4.15 32.5 0.847 0.904 0.586 0.944 0.755
MeanCMIP6 [-] 196. 0.226 18.5 1.02 0.929 0.730 0.932 0.830
MIROC-ESM2L [-] 204. 8.36 26.0 1.02 0.845 0.653 0.932 0.771
MPI-ESM1.2-HR [-] 193. -3.04 23.3 1.02 0.910 0.666 0.932 0.794
NorESM2-LM [-] 200. 4.34 23.3 0.847 0.809 0.687 0.944 0.782
UKESM1-0-LL [-] 195. -0.924 20.1 1.02 0.873 0.725 0.932 0.814
Download Data
Period Mean (original grids) [W m-2]
Bias [W m-2]
RMSE [W m-2]
Phase Shift [months]
Bias Score [1]
RMSE Score [1]
Seasonal Cycle Score [1]
Overall Score [1]
Benchmark [-] 123.
BCC-CSM2-MR [-] 117. -5.13 33.4 0.486 0.861 0.612 0.965 0.762
CanESM5 [-] 128. 5.84 30.9 0.442 0.806 0.668 0.968 0.777
CESM2 [-] 129. 6.50 30.3 0.575 0.823 0.670 0.945 0.777
GFDL-ESM4 [-] 108. -15.1 29.2 0.420 0.798 0.674 0.967 0.778
IPSL-CM6A-LR [-] 125. 2.24 34.9 0.531 0.804 0.627 0.956 0.754
MeanCMIP6 [-] 123. 0.164 25.1 0.420 0.856 0.708 0.963 0.809
MIROC-ESM2L [-] 128. 5.58 30.2 0.509 0.811 0.670 0.957 0.777
MPI-ESM1.2-HR [-] 123. 0.654 28.7 0.420 0.859 0.663 0.970 0.789
NorESM2-LM [-] 129. 6.02 30.0 0.464 0.828 0.661 0.960 0.778
UKESM1-0-LL [-] 121. -1.73 29.3 0.464 0.838 0.666 0.967 0.784
Download Data
Period Mean (original grids) [W m-2]
Bias [W m-2]
RMSE [W m-2]
Phase Shift [months]
Bias Score [1]
RMSE Score [1]
Seasonal Cycle Score [1]
Overall Score [1]
Benchmark [-] 193.
BCC-CSM2-MR [-] 215. 21.8 37.0 1.01 0.477 0.336 0.873 0.505
CanESM5 [-] 213. 19.7 36.2 1.08 0.568 0.343 0.857 0.528
CESM2 [-] 205. 12.2 32.1 0.944 0.660 0.359 0.865 0.561
GFDL-ESM4 [-] 202. 8.57 29.4 1.01 0.736 0.372 0.861 0.585
IPSL-CM6A-LR [-] 204. 11.5 34.0 1.01 0.629 0.335 0.861 0.540
MeanCMIP6 [-] 207. 14.0 27.5 1.01 0.644 0.442 0.861 0.597
MIROC-ESM2L [-] 205. 12.1 31.2 1.21 0.651 0.388 0.840 0.567
MPI-ESM1.2-HR [-] 216. 22.7 36.9 1.62 0.464 0.344 0.793 0.486
NorESM2-LM [-] 205. 12.1 34.8 0.944 0.616 0.322 0.858 0.529
UKESM1-0-LL [-] 208. 15.2 32.1 1.42 0.578 0.382 0.846 0.547
Download Data
Period Mean (original grids) [W m-2]
Bias [W m-2]
RMSE [W m-2]
Phase Shift [months]
Bias Score [1]
RMSE Score [1]
Seasonal Cycle Score [1]
Overall Score [1]
Benchmark [-] 56.1
BCC-CSM2-MR [-] 32.4 -23.7 39.0 0.874 0.686 0.547 0.899 0.670
CanESM5 [-] 46.5 -9.63 29.8 0.729 0.832 0.577 0.935 0.730
CESM2 [-] 44.4 -11.7 30.3 0.438 0.816 0.601 0.954 0.743
GFDL-ESM4 [-] 59.9 3.75 28.5 0.438 0.774 0.568 0.954 0.716
IPSL-CM6A-LR [-] 51.4 -4.72 30.9 0.729 0.834 0.578 0.908 0.725
MeanCMIP6 [-] 48.4 -7.66 25.0 0.438 0.810 0.618 0.954 0.750
MIROC-ESM2L [-] 58.4 2.28 22.8 0.438 0.782 0.625 0.954 0.746
MPI-ESM1.2-HR [-] 43.8 -12.3 31.0 0.874 0.805 0.573 0.899 0.712
NorESM2-LM [-] 39.7 -16.4 32.3 0.874 0.797 0.582 0.899 0.715
UKESM1-0-LL [-] 50.3 -5.84 22.4 0.583 0.812 0.618 0.944 0.748
Download Data
Period Mean (original grids) [W m-2]
Bias [W m-2]
RMSE [W m-2]
Phase Shift [months]
Bias Score [1]
RMSE Score [1]
Seasonal Cycle Score [1]
Overall Score [1]
Benchmark [-] 147.
BCC-CSM2-MR [-] 150. 2.89 28.1 0.649 0.874 0.677 0.957 0.796
CanESM5 [-] 154. 7.35 28.2 0.464 0.849 0.695 0.969 0.802
CESM2 [-] 156. 8.64 28.2 0.325 0.820 0.713 0.978 0.806
GFDL-ESM4 [-] 137. -9.48 25.4 0.580 0.827 0.715 0.961 0.805
IPSL-CM6A-LR [-] 152. 4.57 30.2 0.601 0.834 0.679 0.960 0.788
MeanCMIP6 [-] 150. 3.27 21.2 0.533 0.885 0.753 0.965 0.839
MIROC-ESM2L [-] 153. 6.30 26.8 0.231 0.871 0.697 0.985 0.812
MPI-ESM1.2-HR [-] 151. 4.09 24.3 0.556 0.882 0.721 0.963 0.822
NorESM2-LM [-] 155. 7.80 26.6 0.462 0.840 0.715 0.969 0.810
UKESM1-0-LL [-] 148. 1.20 26.0 0.626 0.853 0.711 0.958 0.808

Temporally integrated period mean

BENCHMARK MEAN
Data not available
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MODEL MEAN
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BIAS
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BIAS SCORE
Data not available
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RMSE
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RMSE SCORE
Data not available
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BENCHMARK MAX MONTH
Data not available
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MODEL MAX MONTH
Data not available
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DIFFERENCE IN MAX MONTH
Data not available
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SEASONAL CYCLE SCORE
Data not available
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Spatially integrated regional mean

MODEL COLORS
Data not available
REGIONAL MEAN
Data not available
ANNUAL CYCLE
Data not available
MONTHLY ANOMALY
Data not available
ANNUAL CYCLE
Data not available

All Models

Benchmark
Data not available
Data not available
BCC-CSM2-MR
Data not available
Data not available
CanESM5
Data not available
Data not available
CESM2
Data not available
Data not available
GFDL-ESM4
Data not available
Data not available
IPSL-CM6A-LR
Data not available
Data not available
MeanCMIP6
Data not available
Data not available
MIROC-ESM2L
Data not available
Data not available
MPI-ESM1.2-HR
Data not available
Data not available
NorESM2-LM
Data not available
Data not available
UKESM1-0-LL
Data not available
Data not available

Data Information

  Title:
FluxNet Tower eddy covariance measurements (Tier 1)

  Version:
2015

  Institutions:
FluxNet, AmeriFlux, AfriFlux, AsiaFlux, ChinaFlux, Fluxnet-Canada, KoFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, GreenGrass, OzFlux-TERN, LBA, NECC, ICOS, TCOS-Siberia, and USCCC

  References:
Reichstein, M., D. Papale, R. Valentini, M. Aubinet, C. Bernhofer, A. Knohl, T. Laurila, A. Lindroth, E. Moors, K. Pilegaard, and G. Seufert (2007), Determinants of terrestrialecosystem carbon balance inferred from European eddy covarianceflux sites, Geophys. Res. Lett., 34, L01402, doi:10.1029/2006GL027880

Lasslop, G., M. Reichstein, D. Papale, A.D. Richardson, A. Arneth, A. Barr, P. Stoy, and G. Wohlfahrt (2010), Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: critical issues and global evaluation, Global Change Biology, 16, 187-208, doi:10.1111/j.1365-2486.2009.02041.x

Knauer, J., S. Zaehle, B.E. Medlyn, M. Reichstein, C.A. Williams, M. Migliavacca, M.G. De Kauwe, C. Werner, C. Keitel, P. Kolari, J.-M. Limousin, and M.-L. Linderson (2018), Towards physiologically meaningful water use efficiency estimates from eddy covariance data, Global Change Biology, 24(2), 694-710, doi:10.1111/gcb.13893

  Comment:
Fluxnet variable(s) used: SW_IN_F-SW_OUT