## 5 Conclusions  
We illustrated the difference between the climate normal and climatology using a satellite-based climate data record globally. Since the latest climate normal period (1991-2020) falls entirely in the satellite era, it opens up new opportunities to compute the climate normals of many different essential climate variables, going well beyond the normals usually computed using in situ measurements for a limited number of variables such as temperature and precipitation. Furthermore, the recent advances in data rescue, better calibration, improved retrieval algorithms, and data completeness mean that the satellite-based climate data records are becoming increasingly suitable for computing climate normals at a global scale. In this demonstrative study, we applied the WMO guidelines on data completeness while computing the climate normals of various cloud properties and incoming surface solar radiation provided in the CLARA-A3 climate data record. We have shown that for certain variables, such as total cloud fraction and surface solar radiation, the climate normals and climatologies are practically identical during the most recent 30-year period (1991-2020). This shows the benefits of data completeness in the recent satellite era. However, for other variables, such as daytime and nighttime cloud fractions, that are defined using a particular threshold on solar zenith angles, the guidelines on data completeness while computing the monthly mean parameter could be difficult to fulfil in the mid- to higher-latitude regions. This is simply due to the fact that the daily changes in the solar zenith angles over these regions are stronger than those in the equatorial regions. In these cases, the climate normals based only on monthly data are the same as climatologies, and both of them differ significantly from the true climate normals. This means that no significant gains are obtained by computing only monthly based climate normals. It is to be noted that these conclusions could also apply to other variables such as cloud microphysical properties (cloud optical depth, effective radius, water path, etc.) that are also retrieved under certain solar illumination conditions. Figure 11: Same as in Figure 1, but for the incoming solar radiation at the surface (in W/m\({}^{2}\)). The situation is even more complex for some of the other variables such as cloud top properties that are retrieved only in the presence of clouds. There are many regions on Earth where clouds can be absent for more than 5 days in a row or for more than 11 days in total in a given month. Over these regions, which are located mainly in the tropical dry belt and Antarctica, the D5 and/or D11 conditions are not fulfilled, thus leading to gaps in climate normals. It is clear that a wider discussion about the suitability of WMO guidelines in the satellite era is needed to extend them in future to account for different physical conditions pertaining to different climate variables derived from satellite-based observations. For example, similar difficulties could also arise when computing climate normals for aerosol properties, trace gases, surface temperatures, etc., where limited clear-sky conditions and cloud detection sensitivity could influence the fulfilment of the D5 and/or D11 conditions. The impact of clear-sky biases on the climate normals of these variables also needs to be understood globally. In spite of some local data gaps, the satellite-based climate data records show good promise for computing climate normals globally. Having both the increasingly detailed and the holistic perspectives from the satellite community could help enormously to better formulate future WMO guidelines on climate normals. In future, it would be worthwhile to investigate whether the conclusions derived based on the CLARA-A3 CDR here also hold when other CDRs are used for computing climate normals. It would also be interesting to intercompare the different realizations of climate normals based on different CDRs for the same ECVs.