Mean State

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 [-] 436.
BCC-CSM2-MR [-] 465. 28.9 28.9 1.02 0.0436 0.475 0.933 0.481
BGCv2LND.GSW [-] 468. 32.1 33.3 1.02 0.0308 0.479 0.933 0.481
BGCv2LNDATM [-] 464. 28.2 32.3 1.02 0.0471 0.374 0.933 0.432
CanESM5 [-] 468. 31.9 32.9 1.02 0.0316 0.396 0.933 0.439
GFDL-ESM4 [-] 463. 27.0 30.9 2.00 0.0538 0.264 0.756 0.334
MeanCMIP6 [-] 469. 33.0 33.3 1.02 0.0280 0.438 0.933 0.459
MIROC-ES2L [-] 469. 32.8 32.3 1.02 0.0284 0.405 0.933 0.443
UKESM1-0-LL [-] 480. 44.3 43.7 1.02 0.00818 0.331 0.933 0.400
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 [-] 452.
BCC-CSM2-MR [-] 467. 15.2 16.2 2.03 0.0225 0.232 0.749 0.309
BGCv2LND.GSW [-] 454. 2.14 4.46 1.02 0.585 0.397 0.933 0.578
BGCv2LNDATM [-] 451. -0.192 3.57 1.02 0.953 0.406 0.933 0.674
CanESM5 [-] 461. 8.94 11.2 1.02 0.107 0.344 0.933 0.432
GFDL-ESM4 [-] 467. 15.8 17.6 1.02 0.0191 0.180 0.933 0.328
MeanCMIP6 [-] 465. 12.9 14.1 0.00 0.0393 0.392 1.00 0.456
MIROC-ES2L [-] 453. 1.16 7.03 1.02 0.748 0.180 0.933 0.510
UKESM1-0-LL [-] 464. 12.4 14.3 0.00 0.0444 0.381 1.00 0.452
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 [-] 376.
BCC-CSM2-MR [-] 378. 1.20 21.8 0.300 0.721 0.615 0.976 0.732
BGCv2LND.GSW [-] 380. 3.42 19.9 0.253 0.733 0.657 0.980 0.757
BGCv2LNDATM [-] 383. 6.35 21.9 0.386 0.715 0.630 0.970 0.736
CanESM5 [-] 384. 7.20 24.2 0.414 0.707 0.608 0.970 0.723
GFDL-ESM4 [-] 380. 3.73 22.2 0.340 0.709 0.616 0.971 0.728
MeanCMIP6 [-] 380. 3.72 19.1 0.251 0.739 0.665 0.981 0.763
MIROC-ES2L [-] 383. 7.09 22.7 0.259 0.720 0.624 0.978 0.736
UKESM1-0-LL [-] 378. 1.92 23.7 0.365 0.718 0.592 0.971 0.718
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 [-] 339.
BCC-CSM2-MR [-] 332. -7.22 21.2 0.133 0.812 0.619 0.991 0.760
BGCv2LND.GSW [-] 335. -3.06 17.7 0.103 0.853 0.653 0.993 0.788
BGCv2LNDATM [-] 344. 5.12 18.8 0.0344 0.804 0.676 0.998 0.789
CanESM5 [-] 340. 0.918 18.8 0.0667 0.822 0.664 0.996 0.786
GFDL-ESM4 [-] 339. -0.628 17.0 0.0333 0.844 0.673 0.998 0.797
MeanCMIP6 [-] 337. -1.86 15.0 0.0333 0.841 0.704 0.998 0.812
MIROC-ES2L [-] 340. 0.901 19.4 0.0333 0.816 0.649 0.998 0.778
UKESM1-0-LL [-] 334. -5.29 20.1 0.233 0.842 0.623 0.984 0.768
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 [-] 450.
BCC-CSM2-MR [-] 465. 15.3 18.9 0.00 0.0143 0.204 1.00 0.355
BGCv2LND.GSW [-] 453. 2.66 5.77 0.00 0.508 0.328 1.00 0.541
BGCv2LNDATM [-] 449. -0.989 5.84 0.508 0.259 0.313 0.966 0.463
CanESM5 [-] 494. 43.5 48.3 0.00 0.000238 0.0138 1.00 0.257
GFDL-ESM4 [-] 469. 19.0 28.4 1.02 0.0481 0.0348 0.874 0.248
MeanCMIP6 [-] 468. 17.9 22.4 0.00 0.00781 0.154 1.00 0.329
MIROC-ES2L [-] 452. 2.33 6.35 1.02 0.523 0.205 0.874 0.452
UKESM1-0-LL [-] 468. 18.0 25.1 0.00 0.00909 0.114 1.00 0.309
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 [-] 360.
BCC-CSM2-MR [-] 360. -0.0179 21.3 0.225 0.783 0.633 0.984 0.758
BGCv2LND.GSW [-] 363. 2.74 19.2 0.143 0.799 0.679 0.989 0.787
BGCv2LNDATM [-] 368. 7.63 22.3 0.348 0.759 0.652 0.973 0.759
CanESM5 [-] 368. 7.49 23.6 0.320 0.760 0.643 0.976 0.756
GFDL-ESM4 [-] 362. 1.40 20.2 0.237 0.788 0.666 0.980 0.775
MeanCMIP6 [-] 364. 3.45 18.6 0.189 0.799 0.691 0.986 0.792
MIROC-ES2L [-] 368. 7.70 23.4 0.166 0.749 0.653 0.988 0.761
UKESM1-0-LL [-] 362. 2.08 23.6 0.272 0.776 0.614 0.982 0.747

Temporally integrated period mean

BENCHMARK MEAN
Data not available
Data not available
MODEL MEAN
Data not available
Data not available
BIAS
Data not available
Data not available
BIAS SCORE
Data not available
Data not available
RMSE
Data not available
Data not available
RMSE SCORE
Data not available
Data not available
BENCHMARK MAX MONTH
Data not available
Data not available
MODEL MAX MONTH
Data not available
Data not available
DIFFERENCE IN MAX MONTH
Data not available
Data not available
SEASONAL CYCLE SCORE
Data not available
Data not available

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
BGCv2LND.GSW
Data not available
Data not available
BGCv2LNDATM
Data not available
Data not available
CanESM5
Data not available
Data not available
GFDL-ESM4
Data not available
Data not available
MeanCMIP6
Data not available
Data not available
MIROC-ES2L
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: LW_OUT