Boreal forest Global - Land Subtropical desert Temperate grassland/desert Temperate seasonal forest Tropical seasonal forest/savanna Tundra Woodland/shrubland

	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	[-]	21.4
ACCESS-ESM1-5	[-]	15.7		-5.71	12.1	0.963		0.599	0.508	0.883	0.624
BCC-CSM2-MR	[-]	24.0		2.61	16.2	1.55		0.452	0.357	0.787	0.488
CanESM5	[-]	29.2		7.75	16.7	1.34		0.482	0.343	0.813	0.495
CESM2	[-]	21.6		0.212	11.1	1.18		0.612	0.471	0.840	0.599
GFDL-ESM4	[-]	28.7		7.27	18.8	1.39		0.387	0.357	0.813	0.479
IPSL-CM6A-LR	[-]	23.5		2.08	16.3	1.39		0.528	0.369	0.813	0.520
MeanCMIP6	[-]	25.5		4.06	13.2	1.23		0.502	0.422	0.830	0.544
MIROC-ESM2L	[-]	22.0		0.597	13.5	1.23		0.572	0.416	0.846	0.562
MPI-ESM1.2-HR	[-]	20.5		-0.923	13.3	1.12		0.587	0.440	0.860	0.582
NorESM2-LM	[-]	19.3		-2.13	14.4	1.39		0.574	0.385	0.792	0.534
UKESM1-0-LL	[-]	30.7		9.26	19.7	1.18		0.349	0.279	0.849	0.439

	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	[-]	29.1
ACCESS-ESM1-5	[-]	24.5		-4.63	14.4	1.26		0.518	0.471	0.834	0.573
BCC-CSM2-MR	[-]	26.4		-2.71	15.3	1.56		0.516	0.432	0.785	0.541
CanESM5	[-]	31.3		2.17	14.7	1.37		0.539	0.424	0.813	0.550
CESM2	[-]	27.9		-1.26	13.4	1.16		0.572	0.449	0.841	0.578
GFDL-ESM4	[-]	33.5		4.32	15.7	1.15		0.502	0.414	0.860	0.548
IPSL-CM6A-LR	[-]	26.2		-2.92	14.8	1.36		0.531	0.435	0.813	0.554
MeanCMIP6	[-]	29.4		0.229	12.5	1.24		0.573	0.489	0.825	0.594
MIROC-ESM2L	[-]	25.2		-3.97	13.5	1.36		0.532	0.464	0.814	0.568
MPI-ESM1.2-HR	[-]	28.4		-0.718	15.2	1.24		0.501	0.436	0.837	0.552
NorESM2-LM	[-]	26.8		-2.33	14.1	1.34		0.557	0.440	0.806	0.561
UKESM1-0-LL	[-]	37.0		7.87	17.4	1.17		0.432	0.391	0.848	0.515

	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	[-]	43.0
ACCESS-ESM1-5	[-]	48.7		5.70	12.3	1.02		0.514	0.454	0.899	0.580
BCC-CSM2-MR	[-]	40.0		-2.97	12.6	1.02		0.654	0.432	0.915	0.608
CanESM5	[-]	45.2		2.17	14.2	1.02		0.434	0.440	0.899	0.553
CESM2	[-]	40.5		-2.51	8.98	0.583		0.658	0.495	0.961	0.652
GFDL-ESM4	[-]	40.6		-2.37	8.44	0.586		0.678	0.540	0.961	0.680
IPSL-CM6A-LR	[-]	37.7		-5.30	10.9	0.876		0.581	0.445	0.908	0.595
MeanCMIP6	[-]	41.1		-1.89	11.3	0.876		0.591	0.501	0.908	0.625
MIROC-ESM2L	[-]	39.9		-3.12	16.7	1.31		0.286	0.462	0.853	0.516
MPI-ESM1.2-HR	[-]	46.3		3.32	15.0	1.02		0.362	0.464	0.899	0.547
NorESM2-LM	[-]	38.2		-4.78	10.8	0.729		0.633	0.442	0.918	0.609
UKESM1-0-LL	[-]	53.0		10.0	19.0	0.874		0.252	0.406	0.909	0.493

	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	[-]	45.3
ACCESS-ESM1-5	[-]	38.1		-7.15	10.5	0.681		0.638	0.591	0.955	0.694
BCC-CSM2-MR	[-]	41.1		-4.12	11.2	0.681		0.671	0.516	0.955	0.664
CanESM5	[-]	45.1		-0.199	11.3	0.681		0.791	0.477	0.955	0.675
CESM2	[-]	37.7		-7.59	11.6	0.508		0.612	0.546	0.966	0.667
GFDL-ESM4	[-]	41.5		-3.74	10.2	0.681		0.693	0.582	0.955	0.703
IPSL-CM6A-LR	[-]	34.6		-10.7	13.6	0.508		0.487	0.551	0.966	0.639
MeanCMIP6	[-]	39.9		-5.37	9.53	0.508		0.706	0.600	0.966	0.718
MIROC-ESM2L	[-]	37.3		-7.93	11.3	0.511		0.599	0.574	0.966	0.678
MPI-ESM1.2-HR	[-]	43.1		-2.17	10.0	0.681		0.693	0.568	0.955	0.696
NorESM2-LM	[-]	34.7		-10.6	14.6	0.508		0.489	0.520	0.966	0.624
UKESM1-0-LL	[-]	45.5		0.263	8.63	0.681		0.765	0.595	0.955	0.728

	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	[-]	25.5
ACCESS-ESM1-5	[-]	18.0		-7.54	14.7	1.52		0.522	0.479	0.778	0.564
BCC-CSM2-MR	[-]	19.7		-5.79	15.1	1.88		0.546	0.431	0.717	0.531
CanESM5	[-]	23.6		-1.92	14.6	1.88		0.538	0.429	0.714	0.527
CESM2	[-]	23.0		-2.47	15.2	1.68		0.544	0.425	0.732	0.532
GFDL-ESM4	[-]	30.8		5.28	16.0	1.35		0.538	0.414	0.824	0.547
IPSL-CM6A-LR	[-]	22.4		-3.12	15.7	1.88		0.525	0.405	0.714	0.512
MeanCMIP6	[-]	23.8		-1.72	13.6	1.72		0.554	0.482	0.727	0.561
MIROC-ESM2L	[-]	20.8		-4.71	14.7	1.67		0.535	0.454	0.748	0.548
MPI-ESM1.2-HR	[-]	20.4		-5.12	16.1	1.35		0.521	0.432	0.806	0.548
NorESM2-LM	[-]	22.0		-3.56	15.1	2.07		0.541	0.427	0.663	0.515
UKESM1-0-LL	[-]	33.8		8.32	19.6	1.61		0.412	0.349	0.752	0.466

	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	[-]	32.9
ACCESS-ESM1-5	[-]	35.7		2.81	9.23	1.96		0.572	0.341	0.732	0.496
BCC-CSM2-MR	[-]	33.9		0.986	9.25	1.55		0.425	0.366	0.799	0.489
CanESM5	[-]	34.4		1.49	8.47	1.48		0.513	0.364	0.827	0.517
CESM2	[-]	30.5		-2.36	8.69	1.48		0.508	0.360	0.819	0.512
GFDL-ESM4	[-]	39.5		6.63	12.0	1.42		0.320	0.294	0.831	0.435
IPSL-CM6A-LR	[-]	28.8		-4.09	9.63	1.35		0.452	0.380	0.843	0.514
MeanCMIP6	[-]	35.8		2.94	7.42	1.55		0.544	0.428	0.822	0.555
MIROC-ESM2L	[-]	29.1		-3.75	6.91	1.62		0.576	0.407	0.806	0.549
MPI-ESM1.2-HR	[-]	43.5		10.6	14.1	1.49		0.227	0.335	0.818	0.429
NorESM2-LM	[-]	28.9		-3.94	9.17	1.42		0.472	0.405	0.823	0.526
UKESM1-0-LL	[-]	43.0		10.1	15.0	1.42		0.226	0.360	0.818	0.441

	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	[-]	40.5
ACCESS-ESM1-5	[-]	44.3		3.77	43.1	0.874		0.552	0.453	0.899	0.589
BCC-CSM2-MR	[-]	56.3		15.8	48.1	1.02		0.570	0.421	0.889	0.576
CanESM5	[-]	52.8		12.2	32.8	1.02		0.692	0.476	0.889	0.633
CESM2	[-]	43.0		2.45	34.0	0.729		0.645	0.490	0.882	0.627
GFDL-ESM4	[-]	27.8		-12.7	36.2	0.583		0.611	0.429	0.892	0.590
IPSL-CM6A-LR	[-]	45.6		5.05	41.2	0.874		0.643	0.431	0.873	0.594
MeanCMIP6	[-]	42.4		1.88	32.5	0.583		0.675	0.490	0.892	0.637
MIROC-ESM2L	[-]	32.5		-8.07	25.9	0.729		0.626	0.522	0.882	0.638
MPI-ESM1.2-HR	[-]	50.0		9.52	40.1	0.874		0.536	0.460	0.873	0.582
NorESM2-LM	[-]	41.8		1.27	38.5	0.729		0.636	0.471	0.882	0.615
UKESM1-0-LL	[-]	48.6		8.10	24.8	0.729		0.671	0.549	0.882	0.663

	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	[-]	28.8
ACCESS-ESM1-5	[-]	22.1		-6.75	13.8	1.02		0.443	0.479	0.872	0.568
BCC-CSM2-MR	[-]	23.6		-5.26	12.8	1.53		0.495	0.483	0.793	0.563
CanESM5	[-]	32.1		3.23	14.3	1.02		0.539	0.460	0.870	0.582
CESM2	[-]	28.9		0.0730	12.2	0.714		0.582	0.471	0.923	0.612
GFDL-ESM4	[-]	34.6		5.76	14.3	0.970		0.514	0.437	0.898	0.572
IPSL-CM6A-LR	[-]	24.8		-4.06	11.6	1.06		0.554	0.501	0.866	0.605
MeanCMIP6	[-]	29.2		0.356	10.4	0.877		0.601	0.530	0.886	0.637
MIROC-ESM2L	[-]	24.4		-4.44	12.8	1.20		0.514	0.493	0.840	0.585
MPI-ESM1.2-HR	[-]	27.6		-1.22	12.7	1.22		0.528	0.446	0.839	0.565
NorESM2-LM	[-]	29.0		0.162	11.7	0.788		0.585	0.475	0.907	0.610
UKESM1-0-LL	[-]	35.7		6.90	14.9	0.785		0.512	0.441	0.932	0.581

Boreal forest Global - Land Subtropical desert Temperate grassland/desert Temperate rain forest Temperate seasonal forest Tropical rain forest Tropical seasonal forest/savanna Tundra Woodland/shrubland 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_OUT