ABoVE domain Globe

	Download Data	Period Mean (original grids) [g m-2 d-1]		Bias [g m-2 d-1]	RMSE [g m-2 d-1]	Phase Shift [months]		Bias Score [1]	RMSE Score [1]	Seasonal Cycle Score [1]	Overall Score [1]
Benchmark	[-]	1.70
ACCESS-ESM1-5	[-]	1.57		-0.135	1.20	0.258		0.748	0.537	0.985
BCC-CSM2-MR	[-]	1.91		0.202	1.40	0.258		0.735	0.496	0.985
CanESM2	[-]	1.08		-0.701	1.45	0.344		0.590	0.520	0.982
CanESM5	[-]	1.77		0.0653	1.04	0.323		0.762	0.593	0.980
CESM2	[-]	2.14		0.438	1.70	0.641		0.636	0.461	0.954	0.628
CNRM-ESM2-1	[-]	1.41		-0.293	1.29	0.322		0.708	0.520	0.983
EnsembleCMIP5	[-]	1.86		0.137	1.08	0.344		0.769	0.581	0.982
EnsembleCMIP6	[-]	1.84		0.134	1.04	0.258		0.775	0.592	0.985
GFDL_ESM4	[-]	1.51		-0.192	1.26	0.258		0.681	0.541	0.985
IPSL-CM5A-LR	[-]	3.04		1.30	2.17	0.344		0.538	0.417	0.982	0.589
IPSL-CM6A-LR	[-]	1.26		-0.447	1.40	0.258		0.635	0.530	0.985
MIROC-ES2L	[-]	3.24		1.53	2.18	0.635		0.435	0.454	0.962	0.576
MPI-ESM-LR	[-]	2.37		0.689	1.37	0.344		0.618	0.557	0.982	0.679
MPI-ESM1-2-LR	[-]	2.78		1.08	1.99	0.194		0.588	0.388	0.988
NorESM1-M	[-]	1.14		-0.551	1.49	0.344		0.652	0.473	0.982	0.645
NorESM2-LM	[-]	2.06		0.357	1.64	0.386		0.635	0.465	0.979	0.636
UKESM1-0-LL	[-]	1.85		0.141	1.32	0.194		0.678	0.512	0.988

	Download Data	Period Mean (original grids) [g m-2 d-1]		Bias [g m-2 d-1]	RMSE [g m-2 d-1]	Phase Shift [months]		Bias Score [1]	RMSE Score [1]	Seasonal Cycle Score [1]	Overall Score [1]
Benchmark	[-]	2.70
ACCESS-ESM1-5	[-]	2.18		-0.519	1.89	1.00		0.529	0.455	0.847
BCC-CSM2-MR	[-]	2.55		-0.141	1.84	1.12		0.581	0.430	0.856
CanESM2	[-]	2.24		-0.625	1.91	0.977		0.528	0.477	0.859
CanESM5	[-]	3.29		0.588	2.03	0.905		0.532	0.458	0.898
CESM2	[-]	2.31		-0.384	2.01	0.834		0.542	0.411	0.911	0.569
CNRM-ESM2-1	[-]	2.43		-0.268	1.81	1.14		0.559	0.420	0.845
EnsembleCMIP5	[-]	2.75		0.0473	1.74	0.749		0.580	0.492	0.897
EnsembleCMIP6	[-]	2.50		-0.201	1.53	0.735		0.604	0.488	0.904
GFDL_ESM4	[-]	1.48		-1.22	2.04	1.08		0.424	0.431	0.844
IPSL-CM5A-LR	[-]	3.50		0.946	2.45	0.828		0.454	0.416	0.878	0.541
IPSL-CM6A-LR	[-]	2.18		-0.545	1.88	0.907		0.512	0.459	0.864
MIROC-ES2L	[-]	2.62		-0.0734	2.04	1.42		0.518	0.439	0.796	0.548
MPI-ESM-LR	[-]	3.52		0.649	2.12	0.683		0.512	0.463	0.916	0.589
MPI-ESM1-2-LR	[-]	3.08		0.403	2.40	0.735		0.509	0.368	0.914
NorESM1-M	[-]	2.59		-0.121	1.99	1.03		0.556	0.424	0.848	0.563
NorESM2-LM	[-]	2.31		-0.383	1.98	0.982		0.514	0.418	0.885	0.559
UKESM1-0-LL	[-]	2.43		-0.259	2.00	1.14		0.540	0.404	0.856

ABoVE domain Globe

	Download Data	Precipitation|GPCPv2.3 Hellinger Distance [1]	SurfaceDownwardSWRadiation|CERESed4.1 Hellinger Distance [1]	SurfaceAirTemperature|CRU4.02 Hellinger Distance [1]		Precipitation|GPCPv2.3 Score [1]	SurfaceDownwardSWRadiation|CERESed4.1 Score [1]	SurfaceAirTemperature|CRU4.02 Score [1]	Overall Score [1]
ACCESS-ESM1-5	[-]	1.00				0.432
BCC-CSM2-MR	[-]	0.846				0.793
CanESM2	[-]
CanESM5	[-]	1.00				0.464
CESM2	[-]		0.823	0.823			0.412	0.488	0.450
CNRM-ESM2-1	[-]	0.871				0.613
EnsembleCMIP5	[-]	0.737	0.823			0.947	0.724
EnsembleCMIP6	[-]	0.790				0.841
GFDL_ESM4	[-]
IPSL-CM5A-LR	[-]	1.00	1.00	1.00		0.525	0.155	0.381	0.354
IPSL-CM6A-LR	[-]	0.896				0.864
MIROC-ES2L	[-]	0.927	1.00	1.00		0.616	0.579	0.579	0.591
MPI-ESM-LR	[-]								0.00
MPI-ESM1-2-LR	[-]	0.927	1.00			0.550	0.619
NorESM1-M	[-]	0.781	0.781	0.781		0.575	0.686	0.710	0.657
NorESM2-LM	[-]	0.677	0.958	0.927		0.686	0.917	0.613	0.738
UKESM1-0-LL	[-]

	Download Data	Precipitation|GPCPv2.3 Hellinger Distance [1]	SurfaceDownwardSWRadiation|CERESed4.1 Hellinger Distance [1]	SurfaceAirTemperature|CRU4.02 Hellinger Distance [1]		Precipitation|GPCPv2.3 Score [1]	SurfaceDownwardSWRadiation|CERESed4.1 Score [1]	SurfaceAirTemperature|CRU4.02 Score [1]	Overall Score [1]
ACCESS-ESM1-5	[-]	0.724	0.808			0.707	0.641
BCC-CSM2-MR	[-]	0.747	0.814			0.753	0.609
CanESM2	[-]	0.746				0.680
CanESM5	[-]	0.746	0.729			0.574	0.534
CESM2	[-]		0.746	0.794			0.604	0.573	0.589
CNRM-ESM2-1	[-]	0.711	0.857			0.622	0.595
EnsembleCMIP5	[-]	0.668	0.699	0.735		0.801	0.724	0.855
EnsembleCMIP6	[-]	0.726	0.723			0.749	0.613
GFDL_ESM4	[-]	0.747				0.601
IPSL-CM5A-LR	[-]	0.661	0.774	0.805		0.507	0.532	0.555	0.531
IPSL-CM6A-LR	[-]	0.714	0.774			0.612	0.607
MIROC-ES2L	[-]	0.719	0.774	0.817		0.693	0.576	0.746	0.671
MPI-ESM-LR	[-]								0.00
MPI-ESM1-2-LR	[-]	0.742	0.795	0.706		0.659	0.658	0.568
NorESM1-M	[-]	0.659	0.765	0.774		0.599	0.610	0.625	0.611
NorESM2-LM	[-]	0.701	0.757	0.766		0.667	0.639	0.695	0.667
UKESM1-0-LL	[-]	0.698				0.729

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 Relationship with Precipitation Relationship with func_Precipitation Relationship with SurfaceDownwardSWRadiation Relationship with func_SurfaceDownwardSWRadiation Relationship with SurfaceAirTemperature Relationship with func_SurfaceAirTemperature

  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: RECO_DT_VUT_REF