The Hamburg Ocean-Atmosphere Parameters and Fluxes from Satellite Data (HOAPS) data set provides a long-term, consistent suite of global ocean-atmosphere climate variables over ice-free oceans derived from satellite passive microwave observations. Designed to support climate monitoring, air-sea interaction studies, and model evaluation, HOAPS offers more than three decades of key parameters such as vertically integrated water vapor, evaporation, near surface specific humidity, near surface wind speed, freshwater flux, latent heat flux, and, recently implemented, liquid water path.A defining feature of HOAPS is its inter-sensor calibration and physically consistent algorithms, ensuring that all products are produced using a uniform retrieval framework, minimizing artificial trends and discontinuities.In this presentation, we will focus on the upcoming HOAPS release (HOAPS v5.0), outline recent methodological updates such as newly implemented data sources, updates of the retrieval and radiative transfer model including a new bias correction scheme, improved uncertainty propagation, and more. Additionally, we will demonstrate the usefulness of the dataset through selected examples that highlight variability in the global water cycle. We will also discuss the implementation of a continuous extension of the dataset. HOAPS sustains to serve as a robust satellite-based reference for climate studies of air-sea fluxes and ocean-atmosphere coupling and more.

Traditional air temperature-based climate heat indices can be of high uncertainty in regions where ground observations are scarce. In this study, we calculate the Summer Days and Tropical Nights heat indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) for Switzerland and Europe, based on long-term Land Surface Temperature (LST) satellite climate data from EUMETSAT’s Satellite Application Facility on Climate Monitoring (CM SAF). We define relative indices that account for the intermittency of clear-sky LST satellite observations. Furthermore, we propose a novel “Extremely Hot Days index”, tailored to satellite LST data. We find that these LST-based indices are highly correlated with station-based air temperature indices in Switzerland, with coefficients of determination R2 of 0.86, 0.84, and 0.81. Results show a strong increase in LST-based heat indices of up to 12 days/decade since 1991 in parts of Europe, including the Po Valley and the Mediterranean coast. These new LST heat indices can capture changes in heatwave patterns and trends for clear-sky conditions in Europe with unprecedented spatial resolution. They complement traditional air temperature heat indices and enable future climate change studies, also in regions with sparse ground observations.

The US Department of Defense (DoD) Meteorological Satellite Program (DMSP) has provided a long-term record of passive microwave observations from the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Imager/Sounder (SSMIS). These observations, available from 1987 to the present, provide the backbone of data used for global precipitation measurements. The SSM/I and SSMIS instruments have similar lower frequency channels (19.35-85.0 GHz vs 19.35-91.655 GHz), with the SSMIS having higher frequency channels at 150 GHz and three around 183.31 GHz. The Precipitation Retrieval and Profiling Scheme (PRPS) is a retrieval scheme designed to be efficient and avoid the use of any external dynamic data sets, such as model information. This is particularly important for a truly independent data product that can be used for evaluating model performance. The PRPS was originally designed for use with cross track sounding instruments but has been adapted to other passive microwave sensors: here it has been adapted to utilise the SSMI and SSMIS Fundamental Climate Data Records generated by the EUMETSAT CM SAF. The PRPS-SSMIS relies upon an observational a priori database derived for each sensor paired with a database index file to provide a computationally efficient retrieval scheme. This poster will present an outline of the PRPS-SSMIS scheme together with the validation and intercomparison of the resulting precipitation products. At present the databases for the retrieval scheme are based upon 7 years of observations (2016-2022) from SSMIS sensors on the F16, F17, F18 DMSP satellites, matched to co-incident and co-temporal measurements of precipitation from the Global Precipitation Measurement (GPM) mission’s Dual frequency Precipitation Radar (DPR). Comparisons are made at a number of scales: ‘climate’ scale comparisons are made against the GPCP v3.2 global precipitation product, through to instantaneous precipitation retrievals which are compared with surface radar over the US and Europe. In addition, comparisons are made with the Ferraro and GPROF precipitation products to assess consistency with other estimates. Overall, the PRPS-SSMIS retrievals tend to underestimate the precipitation, primarily due to the internal assumptions in the retrieval scheme as a result of the skewed distribution of precipitation occurrence and may easily be corrected. Correlations between the PRPS-SSMIS products and the GPCP are similar to those of the GPROF-SSMIS products, particularly when a comparable spatial resolution is used. Both the GPROF and PRPS scheme outperform the Ferraro precipitation product in terms of bias and correlation and are more consistent over time.

We present a new precipitation climate data record (CDR) GIRAFE (Global Interpolated Rainfall Estimation), which has recently been released by EUMETSATs Satellite Application Facility on Climate Monitoring (CM SAF). For now, it covers a time period of 21 years (2002 – 2022) with global coverage and 1° x 1° spatial resolution. GIRAFE is a completely satellite-based dataset obtained by merging infrared (IR) data from geostationary satellites and passive microwave radiometers (PMW) onboard polar-orbiting satellites. Additional to daily sum and monthly mean precipitation rate, a sampling uncertainty on daily scale within the range of geostationary satellites (55°S-55°N) is provided. The implementation of a continuous extension of GIRAFE via a so-called Interim CDR service started and associated data will become available. For retrieving instantaneous rain rates from PMW observations, three different retrievals for microwave imagers (HOAPS) and sounders (PNPR-CLIM and PRPS) were used. Quantile mapping is applied to the instantaneous rain rates of the 19 different PMW sensors to achieve stability in GIRAFE over time. The IR observations undergo a dedicated quality control procedure. The uncertainty estimation is based on decorrelation ranges from variograms in spatial and temporal dimensions. The merging of PMW and IR data as well as the technique for uncertainty estimation in GIRAFE is based on the Tropical Amount of Precipitation with an Estimate of ERrors (TAPEER) approach. Here, we present details on the GIRAFE algorithm and uncertainty estimation as well as results of the CM SAF quality assessment activity comprised of comparisons against other established global, regional and local precipitation products.

The Special Sensor Microwave Imager/Sounder (SSMIS) of the US Defense Meteorological Satellite Program (DMSP) has been the mainstay of observations used for precipitation retrievals over the last 20 years. The sensor, building upon the heritage of the DMSP Special Sensor Microwave/Imager (SSMI) series that operated between 1987 and 2020, provides precipitation-capable frequencies from 18-183 GHz at resolutions up to 15x13 km. The longevity of the SSMIS and the SSMI satellite series makes these sensors extremely important for the retrieval of precipitation at the climate-scale. The adaptation of the Precipitation Retrieval and Profiling Scheme (PRPS), originally developed for passive microwave sounders, to the SSMIS aims to provide model-free precipitation retrievals that can be incorporated into the Global Interpolated Rainfall Estimation (GIRAFE) product developed by EUMETSATs Satellite Application Facility on Climate Monitoring (CM SAF). Fundamental to the PRPS is the avoidance of external dynamic data sets, such as model information, to ensure that the retrieval scheme is purely a satellite-based observational product. The scheme relies upon the generation of observational databases, based upon co-temporal and co-located observations made by the satellite sensor(s) and observations of precipitation made by either satellite-based precipitation radar or surface radars. For the PRPS-SSMIS, the databases have been generated using observations from SSMIS sensors on the F16, F17, F18 satellites matched against the precipitation estimates provided by the NASA/JAXA Dual frequency Precipitation Radar (DPR) on the NASA/JAXA Global Precipitation Measurement mission (GPM) core observatory. The orbits of the SSMIS and GPM provide about 20,000 crossing points per satellite between 2016 and 2022, and generate about 30M co-located (

Accurate diagnosis of regional atmospheric and surface energy budgets is critical for understanding the spatial distribution of heat uptake associated with the Earth’s energy imbalance (EEI). This contribution discusses frameworks and methods for consistent evaluation of key quantities of those budgets using observationally constrained data sets. It thereby touches upon assumptions made in data products which have implications for these evaluations. We evaluate 2001–2020 average regional total (TE) and dry static energy (DSE) budgets using satellite-based and reanalysis data. For the first time, a consistent framework is applied to the ensemble of the 5th generation European Reanalysis (ERA5), version 2 of modern-era retrospective analysis for research and applications (MERRA-2), and the Japanese 55-year Reanalysis (JRA55). Uncertainties of the computed budgets are assessed through inter-product spread and evaluation of physical constraints. Furthermore, we use the TE budget to infer fields of net surface energy flux. Results indicate biases < 1 W/m2 on the global, < 5 W/m2 on the continental, and 15 W/m2 on the regional scale. Inferred net surface energy fluxes exhibit reduced large-scale biases compared to surface flux data based on remote sensing and models. We use the DSE budget to infer atmospheric diabatic heating from condensational processes. Comparison to observation-based precipitation data indicates larger uncertainties (10–15 Wm−2 globally) in the DSE budget compared to the TE budget, which is reflected by increased spread in reanalysis-based fields. Continued validation efforts of atmospheric energy budgets are needed to document progress in new and upcoming observational products, and to understand their limitations when performing EEI research.

The amount of energy reaching Earth's surface from the Sun is a quantity of high importance for the climate system and for renewable energy applications. SARAH-3 (SurfAce Radiation DAtaset Heliosat, https://doi.org/10.5676/EUM_SAF_CM/SARAH/V003, Pfeifroth et al., 2023) is a new version of a satellite-based climate data record of surface solar radiation parameters, generated and distributed by the European Organisation of Meteorological Satellites (EUMETSAT) Climate Monitoring Satellite Application Facility (CM SAF). SARAH-3 provides data from 1983 onwards, i.e. more than 4 decades of data, and has a spatial resolution of 0.05 degrees x 0.05 degrees, a temporal resolution of 30 min and daily and monthly means for the region covered by the Meteosat field of view (65 degrees W to 65 degrees E and 65 degrees S to 65 degrees N). SARAH-3 consists of seven parameters: surface irradiance, direct irradiance, direct normal irradiance, sunshine duration, daylight, photosynthetically active radiation and effective cloud albedo. SARAH-3 data between 1983 and 2020 have been generated with stable input data (i.e. satellite and auxiliary data) to ensure a high temporal stability; these data are temporally extended by operational near-real-time processing - the so-called Interim Climate Data Record. The data record is suitable for various applications, from climate monitoring to renewable energy. The validation of SARAH-3 shows good accuracy (deviations of similar to 5 W m(-2) from surface reference measurements for monthly surface irradiance), stability of the data record and further improvements over its predecessor SARAH-2.1. One reason for this improved quality is the new treatment of snow-covered surfaces in the algorithm, reducing the misclassification of snow as clouds. The SARAH-3 data record reveals an increase in the surface irradiance (similar to +3 W m(-2) per decade) during recent decades in Europe, in line with surface observations.

Since 2011, the Global Energy and Water cycle Exchanges (GEWEX) Water Vapor Assessment (G-VAP) has provided performance analyses for state-of-the-art reanalysis and satellite water vapour products to the GEWEX Data and Analysis Panel (GDAP) and the user community in general. A significant component of the work undertaken by G-VAP is to characterise the quality and uncertainty of these water vapour records to (i) ensure full exploitation and (ii) avoid incorrect use or interpretation of results. This study presents results from the second phase of G-VAP, where we have extended and expanded our analysis of total column water vapour (TCWV) from phase 1, in conjunction with updating the G-VAP archive. For version 2 of the archive, we consider 28 freely available and mature satellite and reanalysis data products, remapped to a regular longitude–latitude grid of 2° × 2° and on monthly time steps between January 1979 and December 2019. We first analysed all records for a “common” short period of 5 years (2005–2009), focusing on variability (spatial and seasonal) and deviation from the ensemble mean. We observed that clear-sky daytime-only satellite products were generally drier than the ensemble mean, and seasonal variability/disparity in several regions up to 12 kg m−2 related to original spatial resolution and temporal sampling. For 11 of the 28 data records, further analysis was undertaken between 1988–2014. Within this “long period”, key results show (i) trends between −1.18 ± 0.68 to 3.82 ± 3.94 kg m−2 per decade and −0.39 ± 0.27 to 1.24 ± 0.85 kg m−2 per decade were found over ice-free global oceans and land surfaces, respectively, and (ii) regression coefficients of TCWV against surface temperatures of 6.17 ± 0.24 to 27.02 ± 0.51 % K−1 over oceans (using sea surface temperature) and 3.00 ± 0.17 to 7.77 ± 0.16 % K−1 over land (using surface air temperature). It is important to note that trends estimated within G-VAP are used to identify issues in the data records rather than analyse climate change. Additionally, breakpoints have been identified and characterised for both land and ocean surfaces within this period. Finally, we present a spatial analysis of correlations to six climate indices within the long period, highlighting regional areas of significant positive and negative correlation and the level of agreement among records.

This paper presents the third edition of The Satellite Application Facility on Climate Monitoring's (CM SAF) cloud, albedo, and surface radiation dataset from advanced very-high-resolution radiometer (AVHRR) data, CLARA-A3. The content of earlier CLARA editions, namely cloud, surface albedo, and surface radiation products, has been extended with two additional surface albedo products (blue- and white-sky albedo), three additional surface radiation products (net shortwave and longwave radiation, and surface radiation budget), and two top of atmosphere radiation budget products (reflected solar flux and outgoing longwave radiation). The record length is extended to 42 years (1979–2020) by also incorporating results from the first version of the advanced very high resolution radiometer imager (AVHRR/1). A continuous extension of the climate data record (CDR) has also been implemented by processing an interim climate data record (ICDR) based on the same set of algorithms but with slightly changed ancillary input data. All products are briefly described together with validation results and intercomparisons with currently existing similar CDRs. The extension of the product portfolio and the temporal coverage of the data record, together with product improvements, is expected to enlarge the potential of using CLARA-A3 for climate change studies and, in particular, studies of potential feedback effects between clouds, surface albedo, and radiation. The CLARA-A3 data record is hosted by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) CM SAF and is freely available at https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V003 (Karlsson et al., 2023b).

Simulating clouds with global climate models is challenging as the relevant physics involves many nonlinear processes covering a wide range of spatial and temporal scales. As key components of the hydrological cycle and the cli-mate system, an evaluation of clouds from models used for climate projections is an important prerequisite for assessing the confidence in the results from these models. Here, we compare output from models contributing to phase 6 of the Coupled Model Intercomparison Project (CMIP6) with satellite data and with results from their predecessors (CMIP5). We use multiproduct reference datasets to estimate the observational uncertainties associated with different sensors and with internal variability on a per-pixel basis. Selected cloud properties are also analyzed by region and by dynamical regime and thermodynamic conditions. Our results show that for parameters such as total cloud cover, cloud water path, and cloud radiative effect, the CMIP6 multimodel mean performs slightly better than the CMIP5 ensemble mean in terms of mean bias, pattern correlation, and relative root-mean square deviation. The intermodel spread in CMIP6, however, is not re-duced compared to CMIP5. Compared with CALIPSO-ICECLOUD data, the CMIP5/6 models overestimate cloud ice, particularly in the lower and middle troposphere, partly due to too high ice fractions for given temperatures. This bias is re-duced in the CMIP6 multimodel mean. While many known biases such as an underestimation in cloud cover in stratocumu-lus regions remain in CMIP6, we find that the CMIP5 problem of too few but too reflective clouds over the Southern Ocean is significantly improved.

Since 2007, the Meteorological Operational satellite (MetOp) series of platforms operated by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) has provided valuable observations of the Earth's surface and atmosphere for meteorological and climate applications. With 15 years of data already collected, the next generation of MetOp satellites will see this measurement record extend to and beyond 2045. Although a primary role is in operational meteorology, tropospheric temperature and water vapour profiles will be key data products produced using infrared and microwave sounding instruments on board. Considering the MetOp data record that will span 40 years, these profiles will form an essential climate data record (CDR) for studying long-term atmospheric changes. Therefore, the performance of these products must be characterized to support the robustness of any current or future analysis. In this study, we validate 9.5 years of profile data produced using the Infrared and Microwave Sounding (IMS) scheme with the European Space Agency (ESA) Water Vapour Climate Change Initiative (WV_cci) project against radiosondes from two different archives. The Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) and Analyzed RadioSoundings Archive (ARSA) data records were chosen for the validation exercise to provide the contrast between global observations (ARSA) with sparser characterized climate measurements (GRUAN). Results from this study show that IMS temperature and water vapour profile biases are within 0.5 K and 10 % of the reference for “global” scales. We further demonstrate the difference between diurnal sampling and cloud amount match-ups on observed biases and discuss the implications that sampling also plays on attributing these effects. Finally, we present the first look at the profile bias stability from the IMS product, where we observe global stabilities ranging from −0.32 ± 0.18 to 0.1 ± 0.27 K per decade and −1.76 ± 0.19 to 0.79 ± 0.83 % ppmv (parts per million by volume) per decade for temperature and water vapour profiles, respectively. We further break down the profile stability into diurnal and latitudinal values and relate all observed results to required climate performance. Overall, we find the results from this study demonstrate the real potential for tropospheric water vapour and temperature profile CDRs from the MetOp series of platforms.

CLAAS-3, the third edition of the Cloud property dAtAset using SEVIRI (Spinning Enhanced Visible and InfraRed Imager), was released in December 2022. It is based on observations from SEVIRI, on board geostationary satellites Meteosat-8, 9, 10 and 11, which are operated by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). CLAAS-3 was produced and released by the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF), which aims to provide high-quality satellite-based data records suitable for climate monitoring applications. Compared to previous CLAAS releases, CLAAS-3 is expanded in terms of both temporal extent and cloud properties included, and it is based on partly updated retrieval algorithms. The available data span the period from 2004 to present, covering Europe; Africa; the Atlantic Ocean; and parts of South America, the Middle East and the Indian Ocean. They include cloud fractional coverage, cloud-top height, phase (liquid or ice) and optical and microphysical properties (water path, optical thickness, effective radius and droplet number concentration), from instantaneous data (every 15 min) to monthly averages. In this study we present an extensive evaluation of CLAAS-3 cloud properties, based on independent reference data sets. These include satellite-based retrievals from active and passive sensors, ground-based observations and in situ measurements from flight campaigns. Overall results show very good agreement, with small biases attributable to different sensor characteristics, retrieval/sampling approaches and viewing/illumination conditions. These findings demonstrate the fitness of CLAAS-3 to support the intended applications, which include evaluation of climate models, cloud characterisation and process studies focusing especially on the diurnal cycle and cloud filtering for other applications. The CLAAS-3 data record is publicly available via the CM SAF website at https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V003 (Meirink et al., 2022).

Satellite based precipitation climate data records (CDRs) have recently emerged and provide new observational sources to characterize of the changing nature of global precipitation. These products rely on the use of passive microwave instruments. At the daily scale, these CDRs are prone to performance sensitivity resulting from the availability of microwave observations. As the configuration of the microwave sounders and imagers fleet evolves over time, adding new satellites and instruments or losing old platforms, the climate-oriented performances of the CDRs are likely impacted. In this study, this effect is quantified using a prototype constellation-based quasi-global precipitation product algorithm and data-denial experiments. The constellation change has a small impact of the long-term average climatology both in terms of mean and distribution recalling the resilience of the climatology of such a multi-platform product to the fluctuations of the amount of available input data. The interannual variability on the other hand is more impacted. More large rainfall amounts are relatively more perturbed than the lower rain daily accumulation with anomalies up to 30% for some configurations. The method to correct for the artefact is detailed and while some aspects of the computations are product-specific, the major outcome of this study should apply to various similar products as well.

This study makes use of the new total column water vapour data record (CDR‐2 (v2)), developed by the European Space Agency (ESA) in coordination with the Satellite Application Facility on Climate Monitoring (CM SAF), to analyse the adequacy of the integrated vertical water vapour column (IWV) data provided by the European Centre for Medium‐Range Weather Forecasts (ECMWF) ERA5 and ERA‐Interim reanalyses in regions of critical interest for moisture transport mechanisms. This information is critical for the initialization of moisture transport models—both Eulerian and Lagrangian—used to study the main mechanisms and predict the future evolution of moisture transport events. In particular, almost 40,000 atmospheric river (AR) and nocturnal low‐level jet (NLLJ) events identified on a global scale between 2002 and 2017 have been used to study the variability between the cited reanalyses and CDR‐2, in terms of both bias in the observed values of IWV during each particular event and daily temporal correlation fields. Although some notable discrepancies are reported in the main tropical rainforest regions, it is observed that, in regions of high interest for both ARs and NLLJs, the degree of agreement between the reanalyses and CDR‐2 is high. The bias observed in the regions of interest is generally low, and the temporal correlation in the IWV fields is above 0.8 in most areas. ERA5 appears to show slightly better performance than ERA‐Interim when resolving the moisture column, and both show greater similarity to CDR‐2 in the midlatitudes compared with tropical regions. The probability density functions constructed on an event‐to‐event basis reinforce these ideas. We conclude that the evaluations presented here using CDR‐2 serve to strengthen avaliable evidence that the ECMWF reanalyses can safely be used in the initializations of Lagrangian dispersion models and Eulerian moisture tracer simulations—commonly used for the analysis of main advection mechanisms—in the vast majority of regions critical to the study of ARs and LLJs. They can also safely be used for the detection of moisture source–sink regions in the study of the global hydrological cycle in these regions.

o assess progress on the Paris Agreement goals, the Global Climate Observing System (GCOS) aims at sustained "provision of reliable physical, chemical, and biochemical observations and data records for the total climate system."Overall, systematic observations are indispensable for understanding and quantifying processes, budgets, and reservoirs within the global carbon, energy, and water cycles.Earth system studies require datasets that, for analysis, must be internally consistent-often across many variables.Satellite data are increasingly important in evaluating, initializing, and parameterizing processes in models.Over the past 10 years, space agencies (including

Water vapor is an important component in the water and energy cycle of the Arctic. Especially in the light of Arctic amplification, changes of water vapor are of high interest but are difficult to observe due to the data sparsity of the region. The ACLOUD/PASCAL campaign performed in May/June 2017 in the Arctic North Atlantic sector offers the opportunity to investigate the quality of various satellite and numerical model reanalysis products. For this purpose reference Integrated Water Vapor (IWV) measurements at R/V Polarstern frozen into the ice (around 82° N, 10° E) and at t Ny-Ålesund are used to investigate the quality of instantaneous satellite retrievals from AIRS, AMSR2, GOME2, IASI and MIRS. These products use different parts of the electromagnetic spectrum and have different uncertainty characteristics related to the presence of clouds and/or surface characteristics. Therefore, the analysis is expanded to all radiosonde stations within the region. Due to the strong spatio-temporal variability of IWV - in particular during atmospheric river events - sampling issues are important that arise due to the different satellite orbits as well the synoptic radiosonde launch times. Following up on this analysis the question arises whether the satellite data are suitable for a long-term monitoring and trend assessment of water vapor in the Arctic. For this purpose we will also present an analysis of monthly mean values for May and June 2017 - two months with strongly changing surface characteristics in the Arctic - and investigate their performance relative to various reanalyses.

The development of algorithms for the retrieval of water cycle components from satellite data – such as total column water vapor content (TCWV), precipitation (P), latent heat flux, and evaporation (E) – has seen much progress in the past 3 decades. In the present study, we compare six recent satellite-based retrieval algorithms and ERA5 (the European Centre for Medium-Range Weather Forecasts' fifth reanalysis) freshwater flux (E−P) data regarding global and regional, seasonal and interannual variation to assess the degree of correspondence among them. The compared data sets are recent, freely available, and documented climate data records (CDRs), developed with a focus on stability and homogeneity of the time series, as opposed to instantaneous accuracy. One main finding of our study is the agreement of global ocean means of all E−P data sets within the uncertainty ranges of satellite-based data. Regionally, however, significant differences are found among the satellite data and with ERA5. Regression analyses of regional monthly means of E, P, and E−P against the statistical median of the satellite data ensemble (SEM) show that, despite substantial differences in global E patterns, deviations among E−P data are dominated by differences in P throughout the globe. E−P differences among data sets are spatially inhomogeneous. We observe that for ERA5 long-term global E−P is very close to 0 mm d−1 and that there is good agreement between land and ocean mean E−P, vertically integrated moisture flux divergence (VIMD), and global TCWV tendency. The fact that E and P are balanced globally provides an opportunity to investigate the consistency between E and P data sets. Over ocean, P (nearly) balances with E if the net transport of water vapor from ocean to land (approximated by over-ocean VIMD, i.e., ∇⋅(vq)ocean) is taken into account. On a monthly timescale, linear regression of Eocean-∇⋅(vq)ocean with Pocean yields R2=0.86 for ERA5, but smaller R2 values are found for satellite data sets. Global yearly climatological totals of water cycle components (E, P, E−P, and net transport from ocean to land and vice versa) calculated from the data sets used in this study are in agreement with previous studies, with ERA5 E and P occupying the upper part of the range. Over ocean, both the spread among satellite-based E and the difference between two satellite-based P data sets are greater than E−P, and these remain the largest sources of uncertainty within the observed global water budget. We conclude that, for a better understanding of the global water budget, the quality of E and P data sets needs to be improved, and the uncertainties more rigorously quantified.

ABSTRACTLife 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 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, and resolution, to consistently 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. 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.

Das CM SAF (EUMETSAT Satellite Application Facility on Climate Monitoring) produziert, archiviert und stellt unter https://www.cmsaf.eu langjährige satellitenbasierte Klimadatensätze von vielen GCOS Essential Climate Variables (ECVs, essentielle Klimavariablen) bereit, die inzwischen auch die komplette aktuelle WMO Klimareferenzperiode 1990-2020 abdecken und damit eine gute Grundlage für die Analyse von Klimavariabilität und Klimawandel liefern. Seit 1999 hat das CM SAF kontinuierlich eine nachhaltige Infrastruktur zur Erzeugung von Klimadatensätzen aufgebaut, mit der Zeitreihen in hoher Qualität in einer operationellen Umgebung erzeugt werden, die auch aktuelle wissenschaftliche Entwicklungen berücksichtigen. Der inhaltliche Fokus des CM SAF liegt auf ECVs, wie Wolken, Wasserdampf, Niederschlag, Landoberflächentemperatur oder der Strahlungskomponenten (langwellig/kurzwellig) am Erdboden und am Oberrand der Atmosphäre, die durch GCOS (Global Climate Observing System) definiert wurden und im Zusammenhang mit dem globalen Energie und Wasser Kreislauf stehen. Einerseits nutzt das CM SAF dazu polarumlaufende Satelliten mit einer globalen räumlichen Abdeckung. Andererseits werden vom CM SAF für Afrika und Europa, Klimadatensätze für Wolken und Strahlung basierend auf den zeitlich hochaufgelösten Messungen der METEOSAT-Instrumente erzeugt. Alle Daten des CM SAF werden kostenlos abgebeben, sind umfangreich dokumentiert und unabhängig extern begutachtet, um eine hohe Qualität zu gewährleisten. Dies wird unterstützt durch einen umfassenden Service für Kunden, indem beispielsweise Trainingsworkshops und andere Aktivitäten angeboten werden. Diese Präsentation wird einen Überblick über die aktuellen und geplanten Aktivitäten des CM SAF geben und soll interessierten Nutzern durch beispielhafte Anwendungen den Umgang mit CM SAF Produkten verdeutlichen. Zudem werden zukünftige mögliche Anwendungen der Datensätze aufgezeigt.