Global - Land
bcc-csm1-1
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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 [-] 37.9
bcc-csm1-1 [-] 45.7 7.10 28.8 1.32 0.330 0.111 0.754 0.326
BCC-CSM2-MR [-] 49.3 11.1 28.7 1.36 0.299 0.141 0.771 0.338
CanESM2 [-] 38.9 0.973 28.3 1.34 0.416 0.112 0.768 0.352
CanESM5 [-] 51.2 12.3 32.3 1.34 0.250 0.108 0.761 0.307
CESM1-BGC [-] 54.9 15.6 31.9 1.04 0.259 0.161 0.835 0.354
CESM2 [-] 45.4 6.36 26.0 1.13 0.377 0.192 0.827 0.397
GFDL-ESM2G [-] 53.6 15.3 31.7 0.993 0.257 0.123 0.826 0.332
GFDL-ESM4 [-] 54.9 17.7 32.9 1.05 0.251 0.114 0.811 0.322
IPSL-CM5A-LR [-] 44.9 8.53 27.1 1.18 0.330 0.148 0.804 0.357
IPSL-CM6A-LR [-] 52.6 13.0 31.1 1.14 0.282 0.150 0.832 0.354
MeanCMIP5 [-] 50.6 12.0 27.0 1.04 0.319 0.208 0.797 0.383
MeanCMIP6 [-] 51.6 13.6 27.7 1.06 0.297 0.204 0.815 0.380
MIROC-ESM [-] 55.8 15.4 31.8 1.31 0.277 0.142 0.791 0.338
MIROC-ESM2L [-] 63.0 24.8 36.9 1.23 0.142 0.131 0.811 0.304
MPI-ESM-LR [-] 55.3 16.4 35.5 1.21 0.223 0.103 0.765 0.299
MPI-ESM1.2-HR [-] 51.2 16.0 36.4 1.09 0.230 0.0700 0.827 0.299
NorESM1-ME [-] 54.3 14.7 31.0 1.07 0.237 0.164 0.819 0.346
NorESM2-LM [-] 46.6 7.06 27.2 1.18 0.373 0.180 0.810 0.386
UK-HadGEM2-ES [-] 50.5 14.0 33.3 1.27 0.232 0.120 0.768 0.310
UKESM1-0-LL [-] 48.6 11.1 29.2 1.09 0.301 0.149 0.809 0.352

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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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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LatentHeat / FLUXNET2015 / 1991-2015 / 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
Data not available
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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: LE_F_MDS

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