## 3 Results
### Evaluation of MERRA-2 SSR
#### 3.1.1 All Stations  
The metrics of the overall comparison of MERRA-2 SSR fluxes and anomalies with the corresponding measurement-based fluxes and anomalies from 1099 GEBA and 70 BSRN stations are given in Table 1, whereas the associated scatterplot comparisons are shown in Figure 3. It seems that MERRA-2 SSR anomalies correlate satisfactorily with the station measurements (Table 1), yielding R values equal to 0.61 and 0.62 for GEBA and BSRN, respectively (the corresponding R values using SSR fluxes are, as expected, much higher, equal to 0.95 and 0.97). Overall, MERRA-2 overestimates SSR with respect to GEBA and BSRN, having positive mean absolute biases equal to 24.25 and 10.31 Wm\({}^{-2}\), respectively, corresponding to respective relative biases equal to 15.3% and 5.9%. It should be noted that as reported in the literature [96] the Chinese stations from GEBA may not be so representative for 0.5\({}^{\circ}\) grid boxes due to strong urbanization effects. Thus, the evaluation of MERRA-2 SSR against GEBA stations was repeated excluding the Chinese stations, which, indeed, led to a reduction of the MERRA overestimation by 13.6%. The MERRA overestimation with respect to GEBA/BSRN may lead to the conclusion that the atmosphere of MERRA-2 is more transparent, which is in agreement with other studies [44], possibly caused by an underestimation either of cloud cover or aerosol loadings, i.e., aerosol optical depth (AOD), which are the main drivers of GDB [12; 22]. Apart from the mean bias error, another significant statistical metric, showing how far from the regression line the data points are, is the RMSE, which is equal to 36.07 Wm\({}^{-2}\) for GEBA and 26.12 Wm\({}^{-2}\) for BSRN (corresponding to 22.8% and 15.1% of the station networks' mean values). These results, along with the obtained biases, show that MERRA-2 deviates less from the values of BSRN and a little more than from values of GEBA. A slightly better fitting of the applied linear regression line is also obtained for BSRN than GEBA, as indicated by the respective slope values of 0.99 and 1.04 (the corresponding values for SSR anomalies are equal to 0.50 and 0.47). Note that in a similar evaluation of MERRA SSR, but over the significantly shorter period 2001-2009, [39] estimated R values equal to 0.96 (0.82) for GEBA stations and 0.98 (0.92) for BSRN (numbers in parentheses give the R values computed using deseasonalized anomalies), i.e., again higher for BSRN than GEBA. They also reported a better performance of MERRA against BSRN than GEBA stations in terms of RMSE values (equal to 23.99 Wm\({}^{-2}\) for BSRN and 32.27 Wm\({}^{-2}\) for GEBA) as well as for bias (13.80 Wm\({}^{-2}\) for BSRN and 22.17 Wm\({}^{-2}\) for GEBA). In another evaluation of MERRA-2 SSR against GEBA and China Meteorological Administration (CMA) stations, [38], computed R values equal to 0.94 and 0.95 for the 1990-1999 and 2000-2009 periods, respectively. Our computed statistical metrics are in general agreement with the findings from the few existing evaluations of MERRA-2 SSR but provide more confidence about the obtained conclusions regarding the performance of the Reanalysis against the two reference ground station networks, since they refer to a much longer time period (up to about 40 years for GEBA and 30 years for BSRN). Moreover, the present study more thoroughly and appropriately evaluates the covariation of MERRA-2 SSR with stations, since it also estimates the correlation coefficients using SSR deseasonalized anomalies, which is more meaningful and appropriate. Apart from assessing the performance of MERRA-2 SSR on an annual basis, this has also been done on a seasonal basis, since this may help to identify possible source of errors. It should be noted that, due to the substantial seasonal variation of SSR fluxes, not only the absolute, but also the relative biases were computed.