## 2 Materials and Methods
### Fluixnet 2015  
FLUXNET 2015 is a collection of eddy covariance flux data from multiple regional networks [63] that is an extension of the FLUXNET La Thuile dataset (2007). It includes improvements to the data quality control protocols and the data processing pipeline and the use of reanalysis data to fill gaps in micrometeorological variable records. Figure 1 shows maps of the flux tower sites used in this study that include only Tier 1 data open and free for scientific use. A listing of each site used in this study is provided in the Appendix B.  
Figure 1: Locations and vegetation types of the FLUXNET 2015 flux tower sites used in this study. DBF: deciduous broadleaf forest; MF: mixed forest; ENF: evergreen needleleaf Forest; EBF: evergreen broadleaf forest; CRO: cropland; OSH: open shrubland; SAV: savannah; CSH: closed shrubland; GRA: grassland; WET: wetland; WSA: woody savannah; SNO: snow-covered.  
There are two main ways to estimate GPP (in units of g C m\({}^{-2}\) d\({}^{-1}\)) by partitioning of the NEE computed with the variable Ustar (u\({}^{*}\)) threshold (VUT): (1) night-time method (GPP_NT_VUT_REF) [5]; and (2) day-time method (GPP_DT_VUT_REF)[4]. All results shown here use an average of night- and day-time partitioning methods. We repeated all analyses with each method separately and found that the conclusions did not differ significantly depending on which method was used. We use daily (DD, calculated from half-hourly data) and monthly (MM, average from daily data) files. We also made use of the short-wave (SW) incoming (IN) flux (SW_IN_F) and potential (top of atmosphere, SW_IN_POT), both in W m\({}^{-2}\), provided in the dataset.  
We apply quality control similar to that in previous works [10]. We use only daily or monthly averages where 20% or less have come from gap-filled data. We also use only data where the NEE uncertainties provided in the FLUXNET 2015 dataset are \(<\)3 g C m\({}^{-2}\) d\({}^{-1}\). Finally, we remove data points where the day-time and night-time partitioning results in GPP estimates that differ by more than 3 g C m\({}^{-2}\) d\({}^{-1}\). Data processing methods are described at [[http://fluxnet.fluxdata.org/data/fluxnet2015-dataset/data-processing/](http://fluxnet.fluxdata.org/data/fluxnet2015-dataset/data-processing/)]([http://fluxnet.fluxdata.org/data/fluxnet2015-dataset/data-processing/](http://fluxnet.fluxdata.org/data/fluxnet2015-dataset/data-processing/)).  
Total uncertainty in derived GPP includes gap-filling, partitioning uncertainty, random uncertainty and threshold friction velocity uncertainty [64]. Following [PERSON] et al. [33], we compute uncertainties in 8-day GPP flux tower-derived data (\(\sigma_{\rm GPPtot}\)) according to:  
\[\sigma_{\rm GPPtot}=0.5085\,\,\,e^{0.1088\,\rm{+GPP_{8 day}}}. \tag{8}\]  
This formulation is based on a general fitting to data from the AmeriFlux network of flux measurements spanning the Americas. For the sample of 8-day data that meet all the criteria, this produces a mean uncertainty estimate of \(\sim\)0.86 g C m\({}^{-2}\) d\({}^{-1}\). Assuming that random uncertainties can be reduced by averaging and that random uncertainty represents 50% of the total uncertainty consistent with [PERSON] et al. [64], the uncertainties for monthly and daily data will be \(\sim\)0.68 and 1.8 g C m\({}^{-2}\) d\({}^{-1}\), respectively.