## 5 Comparison with SiF Retrieved from GOME-2A by [PERSON] et al. (2013)  
We now compare our global SiF time series with SiF NASA (v26). In Section 2 we discussed our retrieval setup and indicated where it differed from the implementation in SiF NASA (v26), the most current NASA version at this time. The main differences are the selection of the reference scenes for determination of the PCs (desert areas, yearly vs. mainly cloudy ocean, daily), the fit window (712-783 nm vs. 734-758 nm) and the number of PCs (35 vs. 12). Both time series are spatiotemporal composites of retrieved SiF values averaged per month and in 0.5\({}^{\circ}\) by 0.5\({}^{\circ}\) grid boxes. Figure 8 shows a scatter plot of the two SiF data sets where a single point represents a monthly grid box average from the global time series between January 2007 and June 2015. Only grid boxes that are over land and for which the monthly average in each time series is calculated from at least three successful SiF retrievals are taken into account. The correlation between the two datasets is 0.78 and the RMS difference is 0.35 mW/sr/m\({}^{2}\)/nm. Slightly different effective cloud fractions are used to filter the retrievals that go into the global SiF maps (0.4 in our implementation vs. 0.3 in SiF NASA, v26), but this has a negligible effect on the comparison. We find that filtering with cloud fractions other than 0.4 hardly changes the correlation. This is in agreement with [PERSON] et al. (2016),  
Figure 7: Observed Sun-induced Fluorescence (SiF) for the period January 2007 to June 2015 over Australian flux tower sites. SiF data are de-seasonalized. Positive anomalies are given in blue, negative anomalies in red. For some locations two sites with different biomes fall into the same GOME-2A SiF gridcell (Riggs + Whroo; Fogg Dam + Howard; Daly Uncleared + Pasture). who show that filtering with higher cloud fractions decreases SiF values but leaves temporal patterns mostly unaffected. A global map of correlations is depicted in Figure 9. The number of data points per grid box is high because of the long time series (up to 102 months per grid box). Firstly, we see that correlations of SiF retrieved over the ocean are quite small but slightly positive and do not exhibit any spatial patterns. This may be due to noise having similar effects on the retrieval solution for the two implementations (the fit windows partly overlap). The absence of spatial patterns in correlations over the oceans argues in favour of this explanation. Similarly, we see small but slightly positive correlations over land free of vegetation (e.g., the Saharan desert). Secondly, correlations between the two time series are in general substantial over vegetated areas. However, there is a clear dependency on the vegetation type. In particular, we see that correlations for tropical rainforests drop to the same background levels observed for non-vegetated areas (see, for example, the rainforests in Central and South America, West and Central Africa, and Indonesia). We have checked and excluded the possibility of a confounding effect of a reduced number of valid monthly SiF averages because of increased cloud contamination for these areas. Either the two retrieval implementations respond differently to SiF over tropical rainforests or the natural variability of SiF for these regions is too small (compared to the noise error) to reveal a correlation in the first place. Figure 8: Scatter plot of the global GOME-2A SiF time series presented in this paper (SiF KNMI) and SiF NASA (v26). Each data point represents a monthly 0.5\({}^{\circ}\) by 0.5\({}^{\circ}\) grid box average between January 2007 and June 2015. Only grid boxes over land are taken into account. Figure 9: Global map of correlations between the GOME-2A SiF time series presented in this paper (SiF KNMI) and SiF NASA (v26). The correlation for a particular grid box is calculated from 102 months at maximum. When less than three months in one of the two time series report valid SiF averages, a correlation is not calculated (purple).