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 [-] 321.
BCC-CSM2-MR [-] 319. -2.75 20.9 0.00 0.847 0.689 1.00 0.806
CanESM5 [-] 328. 6.72 21.2 0.00 0.828 0.708 1.00 0.811
CESM2 [-] 332. 10.7 22.0 0.310 0.801 0.713 0.979 0.802
GFDL-ESM4 [-] 321. 0.0539 16.1 0.00 0.934 0.734 1.00 0.850
IPSL-CM6A-LR [-] 323. 1.25 21.8 0.103 0.907 0.655 0.993 0.803
MeanCMIP6 [-] 327. 5.32 16.4 0.00 0.863 0.762 1.00 0.847
MIROC-ESM2L [-] 336. 14.6 26.0 0.00 0.738 0.719 1.00 0.794
MPI-ESM1.2-HR [-] 326. 5.10 18.3 0.00 0.896 0.710 1.00 0.829
NorESM2-LM [-] 331. 10.2 21.9 0.308 0.815 0.718 0.979 0.808
UKESM1-0-LL [-] 319. -2.66 23.0 0.00 0.855 0.659 1.00 0.793
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 [-] 379. 2.48 22.5 0.422 0.723 0.612 0.967 0.729
CanESM5 [-] 383. 6.91 23.8 0.446 0.709 0.610 0.967 0.724
CESM2 [-] 391. 14.6 26.6 0.806 0.637 0.623 0.937 0.705
GFDL-ESM4 [-] 377. 1.08 22.2 0.349 0.718 0.606 0.970 0.725
IPSL-CM6A-LR [-] 379. 2.83 23.8 0.472 0.703 0.611 0.962 0.722
MeanCMIP6 [-] 382. 5.54 19.4 0.447 0.724 0.674 0.966 0.760
MIROC-ESM2L [-] 384. 7.48 22.7 0.307 0.722 0.633 0.974 0.740
MPI-ESM1.2-HR [-] 378. 1.41 20.7 0.569 0.746 0.622 0.952 0.735
NorESM2-LM [-] 391. 14.6 27.2 0.765 0.637 0.610 0.942 0.700
UKESM1-0-LL [-] 378. 2.03 23.2 0.455 0.720 0.593 0.964 0.717
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 [-] 427.
BCC-CSM2-MR [-] 429. 2.49 18.1 0.164 0.797 0.623 0.989 0.758
CanESM5 [-] 428. 1.37 19.6 0.508 0.851 0.566 0.966 0.737
CESM2 [-] 434. 7.57 20.2 0.00 0.737 0.611 1.00 0.740
GFDL-ESM4 [-] 440. 13.3 26.4 0.344 0.658 0.564 0.977 0.691
IPSL-CM6A-LR [-] 427. 0.0677 21.5 0.328 0.822 0.546 0.961 0.719
MeanCMIP6 [-] 429. 2.63 16.5 0.164 0.817 0.638 0.989 0.771
MIROC-ESM2L [-] 434. 7.50 18.1 0.672 0.785 0.658 0.956 0.764
MPI-ESM1.2-HR [-] 432. 5.14 21.7 0.672 0.793 0.564 0.937 0.715
NorESM2-LM [-] 437. 10.4 23.0 0.672 0.728 0.571 0.956 0.706
UKESM1-0-LL [-] 426. -0.759 19.6 0.844 0.785 0.572 0.944 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 [-] 420.
BCC-CSM2-MR [-] 415. -4.56 19.2 0.847 0.849 0.682 0.944 0.789
CanESM5 [-] 420. 0.0697 21.9 0.847 0.846 0.644 0.944 0.769
CESM2 [-] 437. 17.2 27.2 1.01 0.702 0.676 0.933 0.747
GFDL-ESM4 [-] 411. -8.48 19.1 0.847 0.836 0.692 0.944 0.791
IPSL-CM6A-LR [-] 394. -26.0 30.4 0.847 0.570 0.664 0.944 0.710
MeanCMIP6 [-] 417. -2.97 15.5 0.847 0.906 0.727 0.944 0.826
MIROC-ESM2L [-] 424. 4.49 21.2 0.847 0.873 0.650 0.944 0.779
MPI-ESM1.2-HR [-] 410. -9.98 21.2 0.847 0.772 0.671 0.944 0.764
NorESM2-LM [-] 436. 16.8 28.7 1.01 0.657 0.658 0.933 0.726
UKESM1-0-LL [-] 428. 8.31 22.2 0.847 0.757 0.688 0.944 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 [-] 357.
BCC-CSM2-MR [-] 356. -0.458 21.3 0.295 0.790 0.633 0.980 0.759
CanESM5 [-] 363. 6.55 22.5 0.418 0.753 0.655 0.969 0.758
CESM2 [-] 369. 12.5 24.9 0.885 0.716 0.658 0.938 0.742
GFDL-ESM4 [-] 353. -3.58 19.4 0.221 0.780 0.664 0.985 0.773
IPSL-CM6A-LR [-] 360. 3.06 23.7 0.393 0.772 0.625 0.974 0.749
MeanCMIP6 [-] 361. 4.09 17.9 0.418 0.799 0.705 0.972 0.795
MIROC-ESM2L [-] 365. 8.52 22.4 0.123 0.757 0.665 0.992 0.769
MPI-ESM1.2-HR [-] 354. -2.91 17.9 0.393 0.833 0.673 0.974 0.788
NorESM2-LM [-] 370. 13.4 25.4 0.860 0.696 0.652 0.939 0.735
UKESM1-0-LL [-] 356. -0.380 22.0 0.418 0.806 0.609 0.972 0.749
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 [-] 449.
BCC-CSM2-MR [-] 468. 19.1 25.1 1.02 0.331 0.452 0.901 0.534
CanESM5 [-] 470. 20.7 30.0 0.749 0.372 0.405 0.942 0.531
CESM2 [-] 472. 23.2 28.7 1.08 0.292 0.423 0.880 0.505
GFDL-ESM4 [-] 470. 21.2 29.7 0.813 0.298 0.316 0.923 0.463
IPSL-CM6A-LR [-] 461. 11.7 21.2 0.543 0.404 0.455 0.944 0.565
MeanCMIP6 [-] 467. 17.7 23.0 0.612 0.343 0.502 0.951 0.575
MIROC-ESM2L [-] 461. 12.3 21.1 0.680 0.528 0.436 0.927 0.581
MPI-ESM1.2-HR [-] 465. 16.1 25.2 1.15 0.378 0.371 0.876 0.499
NorESM2-LM [-] 471. 22.0 27.5 0.748 0.306 0.418 0.927 0.517
UKESM1-0-LL [-] 462. 13.1 23.6 0.680 0.442 0.421 0.919 0.551
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 [-] 292.
BCC-CSM2-MR [-] 268. -24.0 30.4 0.00 0.619 0.584 1.00 0.697
CanESM5 [-] 274. -17.8 22.4 0.207 0.692 0.571 0.986 0.705
CESM2 [-] 287. -5.03 28.3 0.617 0.637 0.588 0.959 0.693
GFDL-ESM4 [-] 284. -7.72 26.6 0.00 0.612 0.626 1.00 0.716
IPSL-CM6A-LR [-] 284. -8.35 27.9 0.207 0.662 0.628 0.986 0.726
MeanCMIP6 [-] 281. -11.0 21.2 0.207 0.623 0.642 0.986 0.723
MIROC-ESM2L [-] 285. -6.95 22.6 0.00 0.615 0.607 1.00 0.707
MPI-ESM1.2-HR [-] 275. -17.1 23.9 0.610 0.677 0.629 0.960 0.723
NorESM2-LM [-] 288. -3.94 27.8 0.617 0.612 0.610 0.959 0.698
UKESM1-0-LL [-] 268. -24.0 23.4 0.207 0.647 0.595 0.986 0.706
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 [-] 377.
BCC-CSM2-MR [-] 382. 4.41 23.5 0.453 0.749 0.619 0.967 0.738
CanESM5 [-] 384. 7.02 24.7 0.450 0.722 0.619 0.967 0.732
CESM2 [-] 395. 17.6 29.3 0.857 0.617 0.630 0.935 0.703
GFDL-ESM4 [-] 378. 0.308 23.3 0.357 0.751 0.611 0.965 0.735
IPSL-CM6A-LR [-] 383. 6.11 24.3 0.620 0.693 0.642 0.950 0.732
MeanCMIP6 [-] 384. 6.68 21.3 0.547 0.721 0.681 0.955 0.759
MIROC-ESM2L [-] 383. 5.63 23.6 0.332 0.731 0.643 0.973 0.747
MPI-ESM1.2-HR [-] 380. 2.62 21.9 0.597 0.754 0.637 0.949 0.744
NorESM2-LM [-] 394. 16.9 30.5 0.786 0.637 0.606 0.939 0.697
UKESM1-0-LL [-] 382. 4.32 24.9 0.451 0.699 0.608 0.965 0.720

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
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: LW_OUT