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 [-] 92.3
ACCESS-ESM1-5 [-] 160. 67.7 72.5 3.01 0.0614 0.393 0.504 0.338
BCC-CSM2-MR [-] 132. 39.4 43.5 1.00 0.273 0.442 0.935 0.523
BGCLND [-] 121. 29.2 35.8 0.508 0.416 0.491 0.966 0.591
BGCLNDATM_progCO2 [-] 125. 33.1 37.9 0.508 0.259 0.508 0.966 0.561
CanESM5 [-] 156. 63.8 67.9 3.52 0.0791 0.428 0.468 0.351
CNRM-ESM2-1 [-] 144. 52.0 57.5 0.492 0.138 0.403 0.968 0.478
EC-Earth3-CC [-] 136. 43.5 46.9 0.508 0.164 0.445 0.966 0.505
MeanCMIP6 [-] 139. 46.5 50.6 0.00 0.199 0.451 1.00 0.525
MIROC-ES2L [-] 157. 64.8 69.0 2.52 0.0836 0.403 0.536 0.356
MPI-ESM1-2-LR [-] 160. 68.2 69.9 0.492 0.0643 0.481 0.968 0.499
MRI-ESM2-0 [-] 147. 55.2 58.1 0.508 0.0994 0.392 0.966 0.462
NorESM2-LM [-] 134. 42.2 46.0 1.00 0.168 0.455 0.935 0.503
UKESM1-0-LL [-] 129. 37.1 46.5 0.508 0.273 0.345 0.966 0.482
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 [-] 144.
ACCESS-ESM1-5 [-] 153. 8.67 16.1 1.02 0.532 0.381 0.933 0.557
BCC-CSM2-MR [-] 146. 2.15 16.4 1.02 0.855 0.303 0.933 0.598
BGCLND [-] 130. -13.9 18.0 0.00 0.363 0.445 1.00 0.563
BGCLNDATM_progCO2 [-] 143. -1.63 21.7 1.02 0.888 0.203 0.933 0.557
CanESM5 [-] 136. -8.29 18.0 1.02 0.547 0.329 0.933 0.534
CNRM-ESM2-1 [-] 124. -20.5 23.9 1.02 0.224 0.353 0.933 0.466
EC-Earth3-CC [-] 143. -1.35 13.3 0.00 0.906 0.382 1.00 0.667
MeanCMIP6 [-] 136. -8.12 16.7 1.02 0.553 0.355 0.933 0.549
MIROC-ES2L [-] 146. 1.79 19.0 1.02 0.878 0.253 0.933 0.579
MPI-ESM1-2-LR [-] 152. 8.23 20.4 0.00 0.549 0.273 1.00 0.524
MRI-ESM2-0 [-] 115. -29.6 32.7 1.02 0.116 0.292 0.933 0.408
NorESM2-LM [-] 140. -3.93 18.0 1.02 0.751 0.284 0.933 0.563
UKESM1-0-LL [-] 127. -17.5 22.7 1.02 0.280 0.305 0.933 0.455
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 [-] 87.4
ACCESS-ESM1-5 [-] 89.7 2.35 24.8 0.655 0.768 0.658 0.936 0.755
BCC-CSM2-MR [-] 79.3 -8.02 25.0 0.712 0.748 0.653 0.934 0.747
BGCLND [-] 75.7 -12.1 23.3 0.537 0.736 0.700 0.950 0.772
BGCLNDATM_progCO2 [-] 81.4 -6.39 22.2 0.584 0.786 0.674 0.951 0.771
CanESM5 [-] 80.1 -7.29 24.3 0.635 0.770 0.661 0.939 0.758
CNRM-ESM2-1 [-] 82.3 -5.03 23.5 0.639 0.771 0.672 0.937 0.763
EC-Earth3-CC [-] 78.3 -9.02 23.8 0.655 0.758 0.674 0.935 0.760
MeanCMIP6 [-] 79.6 -7.75 21.0 0.552 0.774 0.698 0.950 0.780
MIROC-ES2L [-] 86.1 -1.28 24.8 0.712 0.780 0.649 0.922 0.750
MPI-ESM1-2-LR [-] 84.5 -2.85 24.1 0.598 0.765 0.668 0.940 0.760
MRI-ESM2-0 [-] 74.5 -12.9 26.3 0.599 0.694 0.674 0.942 0.746
NorESM2-LM [-] 80.3 -7.01 23.5 0.552 0.760 0.665 0.948 0.759
UKESM1-0-LL [-] 77.2 -10.1 23.3 0.614 0.745 0.683 0.941 0.763
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 [-] 54.7
ACCESS-ESM1-5 [-] 57.3 2.65 24.5 0.285 0.847 0.654 0.975 0.782
BCC-CSM2-MR [-] 43.2 -11.5 24.5 0.692 0.792 0.669 0.946 0.769
BGCLND [-] 47.0 -7.90 20.0 0.255 0.803 0.725 0.981 0.808
BGCLNDATM_progCO2 [-] 50.0 -4.94 19.1 0.403 0.844 0.706 0.968 0.806
CanESM5 [-] 48.6 -6.05 22.2 0.366 0.820 0.680 0.973 0.788
CNRM-ESM2-1 [-] 55.4 0.726 21.0 0.305 0.847 0.699 0.977 0.806
EC-Earth3-CC [-] 46.4 -8.29 21.7 0.427 0.810 0.701 0.969 0.795
MeanCMIP6 [-] 48.0 -6.68 19.1 0.265 0.829 0.716 0.980 0.810
MIROC-ES2L [-] 52.2 -2.44 24.0 0.387 0.829 0.655 0.972 0.778
MPI-ESM1-2-LR [-] 50.1 -4.58 19.4 0.326 0.841 0.706 0.976 0.807
MRI-ESM2-0 [-] 47.1 -7.53 19.9 0.326 0.813 0.708 0.976 0.801
NorESM2-LM [-] 46.9 -7.80 21.1 0.285 0.832 0.692 0.979 0.799
UKESM1-0-LL [-] 49.1 -5.54 19.7 0.305 0.831 0.711 0.977 0.808
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 [-] 133.
ACCESS-ESM1-5 [-] 165. 31.7 39.7 3.72 0.209 0.355 0.356 0.319
BCC-CSM2-MR [-] 169. 35.6 42.0 1.02 0.213 0.379 0.894 0.466
BGCLND [-] 143. 9.38 16.5 0.339 0.629 0.490 0.978 0.646
BGCLNDATM_progCO2 [-] 141. 7.28 20.4 0.339 0.698 0.423 0.978 0.630
CanESM5 [-] 137. 4.09 26.6 3.40 0.841 0.302 0.413 0.464
CNRM-ESM2-1 [-] 136. 2.30 16.4 0.339 0.834 0.466 0.978 0.686
EC-Earth3-CC [-] 143. 9.77 24.9 3.72 0.629 0.347 0.356 0.420
MeanCMIP6 [-] 153. 19.7 25.8 0.339 0.382 0.459 0.978 0.569
MIROC-ES2L [-] 151. 17.3 26.8 3.72 0.444 0.377 0.356 0.388
MPI-ESM1-2-LR [-] 151. 17.4 30.1 3.72 0.423 0.289 0.354 0.339
MRI-ESM2-0 [-] 165. 31.8 39.3 0.339 0.218 0.393 0.978 0.495
NorESM2-LM [-] 154. 20.9 26.9 0.339 0.358 0.440 0.978 0.554
UKESM1-0-LL [-] 150. 16.6 27.8 3.72 0.447 0.347 0.354 0.374
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 [-] 81.8
ACCESS-ESM1-5 [-] 82.8 0.980 23.9 0.494 0.804 0.688 0.963 0.786
BCC-CSM2-MR [-] 70.2 -11.6 24.2 0.574 0.784 0.687 0.959 0.780
BGCLND [-] 68.7 -13.6 22.8 0.450 0.765 0.736 0.966 0.801
BGCLNDATM_progCO2 [-] 73.0 -9.27 21.7 0.486 0.806 0.708 0.964 0.796
CanESM5 [-] 72.4 -9.39 23.6 0.443 0.795 0.698 0.967 0.789
CNRM-ESM2-1 [-] 76.5 -5.34 22.3 0.509 0.808 0.711 0.961 0.798
EC-Earth3-CC [-] 70.8 -11.0 23.6 0.509 0.781 0.705 0.962 0.788
MeanCMIP6 [-] 72.3 -9.57 20.3 0.465 0.802 0.732 0.966 0.808
MIROC-ES2L [-] 78.4 -3.42 23.6 0.509 0.821 0.685 0.963 0.789
MPI-ESM1-2-LR [-] 76.5 -5.29 22.6 0.443 0.808 0.711 0.967 0.799
MRI-ESM2-0 [-] 66.1 -15.8 24.4 0.429 0.740 0.719 0.969 0.786
NorESM2-LM [-] 72.7 -9.15 22.7 0.400 0.795 0.701 0.971 0.792
UKESM1-0-LL [-] 69.5 -12.3 22.7 0.472 0.779 0.718 0.964 0.795

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
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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
Data not available
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DIFFERENCE IN MAX MONTH
Data not available
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SEASONAL CYCLE SCORE
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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
ACCESS-ESM1-5
Data not available
Data not available
BCC-CSM2-MR
Data not available
Data not available
BGCLND
Data not available
Data not available
BGCLNDATM_progCO2
Data not available
Data not available
CanESM5
Data not available
Data not available
CNRM-ESM2-1
Data not available
Data not available
EC-Earth3-CC
Data not available
Data not available
MeanCMIP6
Data not available
Data not available
MIROC-ES2L
Data not available
Data not available
MPI-ESM1-2-LR
Data not available
Data not available
MRI-ESM2-0
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: NETRAD