# Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate
## 2 The Water Cycle
### Modeling the Processes  
It is essential to obtain robust and physically sound figures of the response of the water cycle to atmospheric warming. Accurate modeling is fundamental to disentangling the effects of radiative forcing by greenhouse gases on the water cycle components on the regional and global levels [37]. In particular, attention is to be paid to local changes in precipitation and its extremes that depend upon small changes in large-scale atmospheric circulation, as well as regional feedback mechanisms. Allan et al. [72] argue that climate modeling may have insufficient predictive capabilities to determine such responses at the scale required by impact models. This calls for the determination of robust, large-scale responses in the hydrological cycle across models [73], which is still not satisfactory, especially regarding the representation of clouds and microphysical processes, rainfall variability, and the influence of land-atmosphere coupling on rainfall patterns and their variability [74; 75; 76]. An increase in model spatial resolution, encouraged by enhanced computer power, would seem to be a reasonable way forward, but a recent study [77] showed that a straightforward resolution increase in global hydrological models is most likely not the best solution to improve discharge predictions, thus emphasizing the role of a better representation of processes and of improved parameterizations.  
An acceleration of the water cycle is generally predicted by all models, but not enough is known of the physical mechanisms behind the phenomenon [78]. Precipitation does not tell the whole story of hydrological changes; in particular, it may obscure the fact that in a warmer world, more precipitation will lead to regions getting more rain, but others not getting enough to keep pace with the growing evaporative demand [79]. The use of drought indices has recently produced contradictory results concerning model outputs predicting that dry regions will become drier and wet regions wetter (the DDWW paradigm), showing that DDWW theory is more useful when only precipitation is considered, while proving less conclusive when also evapotranspiration and soil conditions are integrated [80].  
Soil moisture memory seems to be large enough so that Diermeyer et al. [81] suggest that the real-time monitoring and accurate model initialization of land surfaces in forecast models could help improving medium-range weather and sub-seasonal climate forecasts. At the same time, surface ocean salinity observations have shown that the water cycle has amplified at less than the Clausius-Clapeyron (CC) rate following recent global warming, thus adding confidence to projections of the total water cycle change under greenhouse gases emission scenarios [82].  
In this perspective, high-resolution estimates of the terrestrial water and energy storages are necessary to overcome the lack of reliable land-surface fields available globally and in near-real time. This requires the integration of data from advanced observing systems [83] and modeling community efforts, as in the case of the Global Land Data Assimilation System (GLDAS) [84], in order to make sense of the large amount of observation data which is available nowadays [85; 86].  
All authors point out several key limitations in the quantitative appraisal due to unrealistic model results and incomplete and unsatisfactory global observational datasets. However, the combination of ground-based and remote sensing data is considered a way to reduce sampling issues, both in space and, progressively, in time [1]. Note also that significant uncertainties are associated with state-of-the-art climate datasets when examining so-called \"macroweather\" (from a few months to a few decades) precipitation variability [(87)].