Life on Earth vitally depends on the availability of water. Human pressure on freshwater resources is increasing, as is human exposure to weather-related extremes (droughts, storms, floods) caused by climate change. Understanding these changes is pivotal for developing mitigation and adaptation strategies. The Global Climate Observing System (GCOS) defines a suite of Essential Climate Variables (ECVs), many related to the water cycle, required to systematically monitor the Earth's climate system. Since long-term observations of these ECVs are derived from different observation techniques, platforms, instruments, and retrieval algorithms, they often lack the accuracy, completeness, resolution, to consistently to characterize water cycle variability at multiple spatial and temporal scales.

Here, we review the capability of ground-based and remotely sensed observations of water cycle ECVs to consistently observe the hydrological cycle, published in October 2021 (https://doi.org/10.1175/BAMS-D-19-0316.1).
We evaluate the relevant land, atmosphere, and ocean water storages and the fluxes between them, including anthropogenic water use. Particularly, we assess how well they close on multiple temporal and spatial scales. On this basis, we discuss gaps in observation systems and formulate guidelines for future water cycle observation strategies. We conclude that, while long-term water-cycle monitoring has greatly advanced in the past, many observational gaps still need to be overcome to close the water budget and enable a comprehensive and consistent assessment across scales. Trends in water cycle components can only be observed with great uncertainty, mainly due to insufficient length and homogeneity.

An advanced closure of the water cycle requires improved model-data synthesis capabilities, particularly at regional to local scales.

At the level of a watershed, the water balance equation dictates that the net moisture transport through the atmospheric boundaries is
balanced by the river discharge and an accumulation/depletion in terrestrial sources such as the soil, surface waters and groundwater.

There are considerable uncertainties connected with modelled water balance components, especially since most models only simulate part of the system, either the atmosphere, the surface or the subsurface. Uncertainties in boundary conditions can therefore propagate as
biases in the simulated results. For example, not accounting for anthropogenic groundwater extraction can potentially introduce biases
in arid regions, where groundwater is a non-negligible source of moisture for the atmosphere.

The use of observations is therefore an important aid to evaluate model performances and to detect possible biases in water balance
components.

In this contribution, we compare total water storage changes derived from the Gravity Recovery Climate Experiment (GRACE) and its follow-on mission, with modelled components of the water balance. We use ERA5 reanalysis data to compute (net) atmospheric transports, and river discharge from GloFAS (Global Flood Awareness System). Furthermore, we use precipitation estimates together with evapotranspiration from the Surface Energy Balance System (SEBS). We finally perform an accounting of the water balance components for the world’s largest watersheds and show to what extent we can find agreements, inconsistencies and biases in the data and models.

Year-to-year variations of atmospheric carbon dioxide growth rate (CGR) are dominated by terrestrial ecosystems, particularly from the tropics, offering a natural experiment to explore terrestrial carbon-climate relationships. Recently, the launch of twin satellites of the Gravity Recovery and Climate Experiment (GRACE) enables the direct measurement of terrestrial water storage variability, and a subsequent analysis showed that it is tightly coupled to CGR, in addition to the well-documented temperature effects. Here we show that the strength of the interannual relationship between tropical terrestrial water availability and CGR has substantially increased from 1960 to 2018, based on reconstructed long-term terrestrial water storage variability and lagged precipitation observations. This might be related to declining spatial compensations of tropical water availability anomalies driven by changes in El Niño-Southern Oscillation (ENSO) teleconnections: Central Pacific ENSO, rather than “conventional” Eastern Pacific ENSO, controls interannual variations of tropical water storage anomaly in the last three decades (1989-2018). In addition, tropical aboveground carbon dynamics retrieved from microwave satellite observations of vegetation optical depth (VOD) reveal that water-sensitive semiarid ecosystems contribute largely to the interannual variability of CGR during 2011-2017. VOD measures the water content in plants and is thus a reasonable proxy of biomass. We employ the longest available VOD records starting from 1988 in the Ku-Band and find that interannual correlations between tropical semiarid VOD/VOD-derived AGB and CGR also significantly increased in the recent two decades (1997-2016) relative to the first two decades (1988-2007). This suggests that water-sensitive semiarid ecosystems might have become more relevant for CGR IAV and possibly contributed to the enhancing tropical water-carbon coupling. We also demonstrate that most state-of-the-art coupled Earth System Models and offline Land Surface Models do not reproduce this intensifying water-carbon coupling, which calls for improvements on water-carbon interactions to better constrain projections. Our results suggest that tropical water availability could increasingly control the interannual variability of the terrestrial carbon cycle and dominate tropical terrestrial carbon-climate feedbacks in the future.

Atmospheric moisture is a key factor for the redistribution of heat in the atmosphere and there is strong coupling between atmospheric circulation and moisture pathways which is responsible for most climate feedback mechanisms.

Water isotopologues can make a unique contribution for better understanding this coupling. In recent years, global observations of water vapour isotopologue data have become available from different satellite missions including thermal-infrared sensors which are mostly sensitive to the free troposphere and shortwave-infrared (SWIR) sensors provide column averaged values that are also sensitive to the boundary layer. The Sentinel 5P mission launched in 2017 allows to observe water isotopologue from its SWIR channels with much improved spatial and temporal coverage compared to previously available SWIR sensors thus representing a major advance for scientific and operational applications.

We have developed a retrieval of water isotopologues for S5P based on the University of Leicester Full Physics (UoL-FP) retrieval algorithm and used it to generate global water isotopologues datasets. The quality of the water isotopologues datasets has been assessed against fiducial reference data from MUSICA NDACC and TCCON. To assess the scientific value of the water isotopologues datasets from
Sentinel 5P, we have carried out model simulation with the Isotope-enabled models ICON-ART and COSMO.

In this presentation, we describe the water isotopologues datasets obtained from Sentinel 5P and its characterisation against ground-based reference data. Further, we will focus on two case studies for Central Europe and West Africa to discuss the scientific impact of this new water isotopologues dataset. We will conclude with an outlook outlining future development steps.

We analyze the Arctic water budget, including its atmospheric, terrestrial, and oceanic components. For this purpose, we use various state-of-the-art reanalyses, including the European Centre for Medium Range Weather Forecast's (ECMWF) latest products ERA5 and ERA5-Land as well as ocean data from the CMEMS Global Reanalysis Ensemble Product (GREP) and evaluate them against available satellite (e.g., GRACE) and in-situ river discharge observations. Seasonal cycles and trends of the major water components (i.a., precipitation, evaporation, water vapor flux divergence, river discharge and storage components) are examined for the major Arctic catchments and on a Pan-Arctic scale. Furthermore, we investigate the new Global Flood Awareness System (GloFAS) river discharge reanalysis version 3.1, which combines the hydrological model LISFLOOD with atmospheric forcing from ERA5, and find distinct improvements concerning trends and seasonal peaks in comparison to runoff estimates from other reanalyses.
To obtain a consistent estimate of all physical terms, we combine the most credible estimates of the individual budget terms and perform a variational optimization to obtain closed water budgets on annual and seasonal scales. With this procedure, any budget residuals are distributed across the budget terms according to their relative uncertainties. This provides us with up-to-date monthly and annual estimates of atmospheric, terrestrial and oceanic water fluxes and water storage components. Especially on the annual scale the variational adjustment yields reliable estimates, requiring only moderate adjustments of less than 3% for each individual term. On a seasonal scale however, for some calendar months the adjustment was not possible within the error bounds of the a priori estimates of the individual budget terms, caused primarily by seasonal offsets between surface water input to the ocean (through runoff and precipitation) and oceanic lateral transports in ocean reanalyses.
Furthermore, we use the observationally constrained estimates of the Arctic water cycle to validate seasonal cycles, long term trends, and variabilities of the individual Arctic water cycle terms as simulated by climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6).

Severe floods and droughts with devastating consequences on society and environments have been increasingly observed in recent years around the world. Even though their occurrences have been attributed to global warming and associated climate change and anthropogenic effects in many studies, systematic examination of hydrological extremes and their changes has been lacking due to lack of temporally and spatially consistent hydrological observations with global coverage. The Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) satellites have been mapping Earth’s gravity field nearly continuously since 2002. Temporal variability of observed gravity can be used to infer terrestrial water storage (TWS) changes in equivalent water heights (cm). In addition to their global coverage and nearly two decades of record, GRACE/GRACE-FO derived TWS anomalies represent total water storage changes in the entire soil profile and therefore, can better capture the extent and the depth of hydrological extremes. To this end, we identified more than 500 wet and dry events each globally using GRACE and GRACE-FO data and a spatial and temporal clustering algorithm to examine hydrological extremes and their changes. Clustering helps to properly examine characteristics of extreme events without being restricted to pre-defined geographic boundaries. Missing data were filled using simulated TWS from our global GRACE/GRACE-FO data assimilation into the Catchment land surface model (CLSM) simulation. We identified the most intense extreme events at continents and globally, using an intensity metric proposed in a previous study. These most intense events reflect impacts of large-scale climate variability such as strong El Nino and La Nina episodes, extreme precipitation, and warming induced earlier snowmelt and permafrost degradation. Intensity of extreme events may also be exacerbated by global warming. In this presentation, we present some of these most intense events and explore their hydroclimatic causes. We also discuss our efforts on using GRACE/GRACE-FO data for monitoring droughts and pluvials globally in conjunction with a data assimilation CLSM.