ABoVE domain Globe

	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	[-]	124.
ACCESS-ESM1-5	[-]	125.		1.13	25.9	0.127		0.913	0.747	0.992	0.850
ACCESS1-3	[-]	126.		2.59	21.6	0.881		0.875	0.777	0.926	0.839
BCC-CSM2-MR	[-]	118.		-6.76	32.9	0.572		0.906	0.699	0.962	0.817
CanESM2	[-]	138.		15.3	32.7	0.949		0.741	0.725	0.922	0.778
CanESM5	[-]	129.		4.24	21.4	0.318		0.918	0.786	0.979	0.867
CESM2	[-]	120.		-3.92	26.9	1.08		0.895	0.748	0.899	0.823
CNRM-ESM2-1	[-]	141.		16.6	25.9	0.381		0.811	0.798	0.975	0.845
EnsembleCMIP5	[-]	134.		11.8	26.9	0.339		0.776	0.769	0.978	0.823
EnsembleCMIP6	[-]	128.		3.35	19.8	0.381		0.897	0.817	0.975	0.876
GFDL_ESM4	[-]	120.		-4.56	22.4	0.445		0.935	0.779	0.971	0.866
HadGEM2-CC	[-]	138.		12.3	25.1	0.474		0.787	0.782	0.969	0.830
IPSL-CM5A-LR	[-]	140.		16.7	40.5	0.339		0.754	0.649	0.978	0.757
IPSL-CM6A-LR	[-]	133.		8.46	28.8	0.445		0.887	0.732	0.971	0.830
MIROC-ES2L	[-]	122.		-2.20	21.5	0.445		0.928	0.783	0.971	0.866
MPI-ESM1-2-LR	[-]	128.		3.74	32.0	0.445		0.851	0.717	0.971	0.814
NorESM1-M	[-]	132.		10.8	26.3	0.407		0.775	0.769	0.973	0.822
NorESM2-LM	[-]	115.		-8.88	31.2	0.699		0.882	0.712	0.954	0.815
UKESM1-0-LL	[-]	131.		7.04	22.6	0.0635		0.886	0.782	0.996	0.861

	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	[-]	167.
ACCESS-ESM1-5	[-]	178.		11.1	34.7	0.427		0.786	0.661	0.968	0.769
ACCESS1-3	[-]	178.		11.7	32.1	0.457		0.511	0.680	0.968	0.710
BCC-CSM2-MR	[-]	167.		0.0481	33.2	0.536		0.848	0.646	0.962	0.775
CanESM2	[-]	183.		18.4	35.7	0.481		0.476	0.670	0.963	0.695
CanESM5	[-]	179.		12.0	31.4	0.472		0.820	0.676	0.964	0.784
CESM2	[-]	175.		8.30	29.6	0.457		0.836	0.687	0.961	0.793
CNRM-ESM2-1	[-]	179.		11.6	30.9	0.521		0.794	0.694	0.957	0.785
EnsembleCMIP5	[-]	177.		11.7	27.1	0.362		0.510	0.730	0.973	0.736
EnsembleCMIP6	[-]	174.		6.95	23.7	0.442		0.855	0.745	0.967	0.828
GFDL_ESM4	[-]	166.		-0.653	28.0	0.467		0.866	0.692	0.962	0.803
HadGEM2-CC	[-]	182.		14.8	31.8	0.612		0.496	0.687	0.950	0.705
IPSL-CM5A-LR	[-]	184.		16.0	42.0	0.630		0.471	0.615	0.955	0.664
IPSL-CM6A-LR	[-]	172.		5.31	33.1	0.536		0.825	0.655	0.958	0.773
MIROC-ES2L	[-]	173.		5.80	28.4	0.487		0.856	0.689	0.960	0.799
MPI-ESM1-2-LR	[-]	171.		3.66	31.5	0.437		0.828	0.666	0.967	0.782
NorESM1-M	[-]	170.		6.22	28.3	0.488		0.534	0.696	0.967	0.723
NorESM2-LM	[-]	174.		7.02	30.9	0.501		0.833	0.675	0.959	0.786
UKESM1-0-LL	[-]	179.		11.8	30.3	0.516		0.809	0.689	0.960	0.787

ABoVE domain Globe Temporally integrated period mean Temporally integrated period mean bias Temporally integrated period mean bias score Temporally integrated period mean max month Temporally integrated mean phase shift Temporally integrated mean phase shift score Temporally integrated period mean rmse Temporally integrated period mean rmse score Spatially integrated regional mean cycle Spatially integrated regional mean detrended cycle Spatially integrated regional mean

  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