## 3 Results
### Evaluation of MERRA-2 SSR
#### 3.1.2 At Station Level  
In the context of evaluating the MERRA-2 SSR, it is necessary to examine and compare the fluxes on an individual station basis to identify possible dependencies on latitude or regional patterns. The spatial distribution of correlation coefficient (R) computed using deseasonalized SSR anomalies, RMSE, RRMSE, bias and relative bias, computed using SSR fluxes, between MERRA-2 and GEBA/BSRN stations are shown in Figure 5. The average value of R for all GEBA stations is 0.54, ranging from \(-\)0.25 to 0.92, while for BSRN stations it is 0.54 and ranges from 0.15 to 0.89. For more than 75% of GEBA/BSRN stations the R values are larger than 0.49/0.51 (Figure S5). Satisfactory R values (orange and reddish colors) appear over Europe, East Asia, North America and Australia, along with mixed patterns over Africa and South America. On the contrary, worse R values exist mostly over the tropical and sub-tropical regions of the globe, including Africa, Southern America, India and Indonesia. A similar behavior is found for R computed with SSR fluxes (Figure S4). Given that clouds are one of the main factors driving the SSR [10], and that in the tropics there is constantly large cloud cover, it can be assumed that the small correlation of MERRA-2 in these areas may be due to uncertainties in MERRA-2 cloud cover and optical properties, as also suggested by other studies [39, 47]. This is not unexpected since it has been shown that clouds are associated with the greatest uncertainty in climate modeling (e.g., [97, 98]). Relatively weak correlation is also found in relatively cloud-free areas like the Middle East and Egypt possibly due to uncertainties in MERRA-2 aerosols. Weak correlation is found in polar regions as well, where the discrepancies could be due to problems with clouds, but also with snow and ice cover [44, 99]. Concerning the R computed using absolute SSR fluxes, it can be noted (Figure S4) that the R values are lower in southern than in northern China. Such a pattern has been also reported in other studies, and has been attributed to the heavy air pollution, as well as to uncertainties in clouds, aerosols and their interaction [39]. Nevertheless, this pattern is not apparent in the results of R computed using SSR anomalies, where weak correlation coefficients are scattered all around China, revealing that correlating reanalysis and station SSR fluxes and SSR anomalies is not driven by the same factors. Figure 4: Seasonal variation of hemispherical averages of correlation coefficient R (**i,ii-a**), bias in W/m\({}^{2}\) (**i,ii-b**), percent relative bias in % (**i,ii-c**), root mean squared error (RMSE in W/m\({}^{2}\)) (**i,ii-d**) and percent relative RMSE in % (**i,ii-e**) between MERRA-2 and GEBA (blue) and BSRN (red) stations, computed using SSR fluxes (except for R that is computed using deseasonalized SSR anomalies) for the North Hemisphere ((**i**)-left column) and South Hemisphere ((**ii**)-right column). Figure 5: _Cont._  
Large RMSE values are computed over tropical and sub-tropical regions, confirming the difficulties of MERRA-2 to appropriately reproduce SSR there. The average RMSE for all GEBA stations is 35.47 W/m\({}^{2}\), with values ranging from 5.72 to 143.31 W/m\({}^{2}\) and with 75% of GEBA stations having RMSE less than 40.3 W/m\({}^{2}\). Respectively, the average RMSE for all BSRN stations is 28.65 W/m\({}^{2}\), still lower than the corresponding value for GEBA, ranging from 11.98 to 59.38 W/m\({}^{2}\) and with 75% of BSRN stations having RMSE less than 31.9 W/m\({}^{2}\) (Figure S5). Similar geographical patterns appear for the relative RMSE, for which 75% of GEBA/BSRN stations have values smaller than 26.1%/20.3% and with high values found especially over Southern China, North Europe and some tropical and subtropical regions.