Closing the Water Cycle from Observations across Scales: Where Do We Stand?

Author(s):
Dorigo, Wouter; Dietrich, Stephan; Aires, Filipe; Brocca, Luca; Carter, Sarah; Cretaux, Jean-Francois; Dunkerley, David; Enomoto, Hiroyuki; Forsberg, Rene; Guntner, Andreas; Hegglin, Michaela, I; Hollmann, Rainer; Hurst, Dale F.; Johannessen, Johnny A.; Kummerow, Christian; Lee, Tong; Luojus, Kari; Looser, Ulrich; Miralles, Diego G.; Pellet, Victor; Recknagel, Thomas; Vargas, Claudia Ruz; Schneider, Udo; Schoeneich, Philippe; Schroeder, Marc; Tapper, Nigel; Vuglinsky, Valery; Wagner, Wolfgang; Yu, Lisan; Zappa, Luca; Zemp, Michael; Aich, Valentin
Publication title: BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY
2021
 | Volume: 102 | Issue: 10
2021
DOI: 10.1175/BAMS-D-19-0316.1
Abstract:
Life on Earth vitally depends on the availability of water. Human pressure on freshwater resources is increasing, as is human exposure to weather-rela… 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 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. more
Application CDR CM-SAF Climate Data Records H-SAF Land

Climatological analysis of solar and wind energy in Germany using the Grosswetterlagen classification

Author(s):
Drücke, J.
Publication title: Renewable Energy
2021
 | Volume: 164 | Issue: February
2021
DOI: 10.1016/j.renene.2020.10.102
Abstract:
Solar and wind energy play an important role in current and future energy supply in Germany and Europe. The production of renewable energy highly depe… Solar and wind energy play an important role in current and future energy supply in Germany and Europe. The production of renewable energy highly depends on weather conditions resulting in an increasing impact of meteorological fluctuations on energy production. Here, climatological data of solar radiation and wind speed are used to simulate hourly capacity factors for solar and wind energy for Germany from 1995 to 2015. Using renewable energy production data for 2015 these data are converted into time series of generated electrical power. Events with very low energy production, i.e., shortfall events, have been identified and related to large-scale weather regimes over Europe. In Germany, on average about twice as much electrical energy is generated from wind compared to solar radiation; in addition there is a distinct annual cycle with an equal share of generated energy during summer and a 70/30% wind/solar share in winter. There is an unambiguous dependency of wind and solar energy production on weather regimes. Shortfall events in Germany only occur in winter, often associated with a high pressure system over Central Europe. During this weather regime, the renewable energy potential in Northern and Southeastern Europe is above average, possibly allowing to balance shortfall events in Germany. more
Application Atmosphere CDR CM-SAF Climate Data Records

Climatological Drought Monitoring in Switzerland Using EUMETSAT SAF Satellite Data

Author(s):
Rassl, Annkatrin; Michel, Dominik; Hirschi, Martin; Duguay-Tetzlaff, Anke; Seneviratne, Sonia I.
Publication title: Remote Sensing
2022
 | Volume: 14 | Issue: 23
2022
DOI: 10.3390/rs14235961
Abstract:
Climatological drought monitoring in Switzerland relies heavily on station-based precipitation and temperature data. Due to the high spatial variabili… Climatological drought monitoring in Switzerland relies heavily on station-based precipitation and temperature data. Due to the high spatial variability and complexity of droughts, it is important to complement station-based drought indices with gridded information and to couple multiple drought indicators within the monitoring system. Here, long-term satellite-based drought parameters from the EUMETSAT SAF network are analyzed in terms of dry anomalies within their climatology’s, namely ASCAT soil water index (SWI), CM SAF land surface temperature (LST), complemented with NOAA vegetation data, and LSA SAF Meteosat evapotranspiration data. The upcoming EUMETSAT SAF climate data records on land surface temperature and evapotranspiration will cover for the first time the WMO climatological 30-year reference period. This study is the first study investigating the potential of those long-term data records for climate monitoring of droughts in Europe. The satellite datasets are compared with the standardized precipitation index (SPI), soil moisture observations from the SwissSMEX measurement network, with a modelled soil moisture index (SMI) based on observations, and with evapotranspiration measurements, focusing on the temporal dynamics of the anomalies. For vegetation and surface temperature, the dry years of 2003, 2015, and 2018 are clearly visible in the satellite data. CM SAF LSTs show strong anomalies at the beginning of the drought period. The comparison of in situ and modelled soil moisture and evapotranspiration measurements with the satellite parameters shows strong agreement in terms of anomalies. The SWI indicates high anomaly correlations of 0.56 to 0.83 with measurements and 0.63 to 0.76 with the SMI at grassland sites. The Meteosat evapotranspiration data strongly agree with the measurements, with anomaly correlations of 0.63 and 0.67 for potential and actual evapotranspiration, respectively. Due to the prevailing humid climate conditions at the considered sites, evapotranspiration anomalies during the investigated dry periods were mostly positive and thus not water limited, but were also a driver for soil moisture drought. The results indicate that EUMETSAT SAF satellite data can well complement the station-based drought monitoring in Switzerland with spatial information. more
Application CDR CM-SAF Climate Data Records H-SAF LSA-SAF Land

Cloud Climatologies from Global Climate Models-A Comparison of CMIP5 and CMIP6 Models with Satellite Data

Author(s):
Lauer, Axel; Bock, Lisa; Hassler, Birgit; Schröder, Marc; Stengel, Martin
Publication title: Journal of Climate
2023
 | Volume: 36 | Issue: 2
2023
DOI: 10.1175/JCLI-D-22-0181.1
Abstract:
Simulating clouds with global climate models is challenging as the relevant physics involves many nonlinear processes covering a wide range of spatial… 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 climate 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 reduced 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 reduced in the CMIP6 multimodel mean. While many known biases such as an underestimation in cloud cover in stratocumulus regions remain in CMIP6, we find that the CMIP5 problem of too few but too reflective clouds over the Southern Ocean is significantly improved. © 2022 American Meteorological Society. more
Atmosphere CDR CM-SAF Climate Data Records

Cloud Cover over the Arabian Peninsula from Global Remote Sensing and Reanalysis Products

Author(s):
Yousef, Latifa A.; Temimi, Marouane; Molini, Annalisa; Weston, Michael; Wehbe, Youssef; Mandous, Abdulla Al
Publication title: Atmospheric Research
2020
 | Volume: 238
2020
DOI: 10.1016/j.atmosres.2020.104866
Abstract:
Clouds – their coverage, nature, and interactions with the energy budget of the planet – represent one of the primary sources of uncertainty in future… Clouds – their coverage, nature, and interactions with the energy budget of the planet – represent one of the primary sources of uncertainty in future climate prediction. Despite the crucial role of desert clouds in the distribution of water and energy budgets, their climatology is still largely incomplete. With arid regions projected to become dryer under global warming conditions, understanding the characteristics of their cloud cover can provide critical insights. In this work, cloud coverage was investigated over one of the Earth's most arid regions – the Arabian Peninsula. Four total cloud cover (TCC) products, namely the International Satellite Cloud Climatology Project H (ISCCP), the CM SAF Cloud, Albedo and Surface Radiation AVHRR 2 (CLARA) satellite datasets, the National Centers for Environmental Prediction – National Center for Atmospheric Research (NCEP–NCAR) Reanalysis (R-2) (NCEP) and the ECMWF Interim Re-Analysis (ERA) reanalyses, were used to construct a climatology of desert clouds over the peninsula between 1984 and 2009, accounting for the different products' uncertainties and limitations. Satellite retrievals and reanalysis fields were first validated against ground observations from the United Arab Emirates, for which homogeneity assessments were conducted. The validation was done using statistical indicators, including Normalized Root Mean Square Errors (nRMSE), relative biases (rBIAS), and correlation coefficients, on monthly and seasonal scales. The ISCCP dataset resulted in the highest correlations with the ground observations (overall 0.38) and the lowest nRMSE (overall 0.54), while CLARA had the lowest rBIAS (overall 0.02). All products showed discrepancies when compared to the ground observations, both annually and on a seasonal basis. When extended to the entire Arabian Peninsula, the satellite and reanalysis products showed decreasing spring and increasing summer (except for ISCCP) TCC evolution across the region. At inter-annual scales, the TCC over the peninsula showed a significant discontinuity in 1998. This shift could be linked to the forcing of the El Niño Southern Oscillation on water vapor transport over the region, as well as to documented artifacts in satellite retrievals and model outputs. These discrepancies are indicative of a need for detailed assessments to be made of the uncertainties existing in TCC data for the Arabian Peninsula. Plain Language Summary Clouds represent a primary source of uncertainty in future climate predictions. This is particularly pronounced in dryland clouds, given their sporadic and intermittent nature. With global warming predicted to enhance aridification trends in terrestrial arid zones, historical cloudiness trends over arid and hyper arid regions are very important – yet their climatology to date is still largely incomplete. The quality of observations and/or model outputs are an important consideration, making validation and intercomparison assessments imperative. This work investigated cloud cover over the Arabian Peninsula, one of the most arid regions on the Earth. Four cloud cover datasets were used, derived from satellite measurements and atmospheric reanalysis model outputs. The selected study period was between 1984 and 2009. The four datasets were initially compared with observations taken at ground stations in the United Arab Emirates. The four products were then studied over the entire Arabian Peninsula. Results showed that cloud cover displayed marked seasonal characteristics and large inter-annual temporal evolution. An abrupt change in regional cloud cover time series was found to occur in 1998. This could be attributed to the forcing of the El Niño Southern Oscillation, although significant documented uncertainties in satellite products over this time span call for deeper investigations of this causal relation. more
Atmosphere CDR CM-SAF Climate Data Records Validation

Cloud Detection with Historical Geostationary Satellite Sensors for Climate Applications

Author(s):
Stöckli, Reto; Bojanowski, Jędrzej S.; John, Viju O.; Duguay-Tetzlaff, Anke; Bourgeois, Quentin; Schulz, Jörg; Hollmann, Rainer
Publication title: Remote Sensing
2019
 | Volume: 11 | Issue: 9
2019
DOI: 10.3390/rs11091052
Abstract:
Can we build stable Climate Data Records (CDRs) spanning several satellite generations? This study outlines how the ClOud Fractional Cover dataset fro… Can we build stable Climate Data Records (CDRs) spanning several satellite generations? This study outlines how the ClOud Fractional Cover dataset from METeosat First and Second Generation (COMET) of the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) was created for the 25-year period 1991–2015. Modern multi-spectral cloud detection algorithms cannot be used for historical Geostationary (GEO) sensors due to their limited spectral resolution. We document the innovation needed to create a retrieval algorithm from scratch to provide the required accuracy and stability over several decades. It builds on inter-calibrated radiances now available for historical GEO sensors. It uses spatio-temporal information and a robust clear-sky retrieval. The real strength of GEO observations—the diurnal cycle of reflectance and brightness temperature—is fully exploited instead of just accounting for single “imagery”. The commonly-used naive Bayesian classifier is extended with covariance information of cloud state and variability. The resulting cloud fractional cover CDR has a bias of 1% Mean Bias Error (MBE), a precision of 7% bias-corrected Root-Mean-Squared-Error (bcRMSE) for monthly means, and a decadal stability of 1%. Our experience can serve as motivation for CDR developers to explore novel concepts to exploit historical sensor data. more
Application Atmosphere CDR CM-SAF Climate Data Records FCDR Method Validation

Cloudiness information services for solar energy management in West Africa

Author(s):
Danso, D.K.; Anquetin, S.; Diedhiou, A.; Adamou, R.
Publication title: Atmosphere
2020
 | Volume: 11 | Issue: 8
2020
DOI: https://doi.org/10.3390/atmos11080857
Abstract:
In West Africa (WA), interest in solar energy development has risen in recent years with many planned and ongoing projects currently in the region. Ho… In West Africa (WA), interest in solar energy development has risen in recent years with many planned and ongoing projects currently in the region. However, a major drawback to this development in the region is the intense cloud cover that reduces the incoming solar radiation when present and causes fluctuations in solar power production. Therefore, understanding the occurrence of clouds and their link to the surface solar radiation in the region is important for making plans to manage future solar energy production. In this study, we use the state-of-the-art European Centre for Medium-range Weather Forecasts ReAnalysis (ERA5) dataset to examine the occurrence and persistence of cloudy and clear-sky conditions in the region. Then, we investigate the effects of cloud cover on the quantity and variability of the incoming solar radiation. The cloud shortwave radiation attenuation (CRA↓SW) is used to quantify the amount of incoming solar radiation that is lost due to clouds. The results showed that the attenuation of incoming solar radiation is stronger in all months over the southern part of WA near the Guinea Coast. Across the whole region, the maximum attenuation occurs in August, with a mean CRA↓SW of about 55% over southern WA and between 20% and 35% in the Sahelian region. Southern WA is characterized by a higher occurrence of persistent cloudy conditions, while the Sahel region and northern WA are associated with frequent clear-sky conditions. Nonetheless, continuous periods with extremely low surface solar radiation were found to be few over the whole region. The analysis also showed that the surface solar radiation received from November to April only varies marginally from one year to the other. However, there is a higher uncertainty during the core of the monsoon season (June to October) with regard to the quantity of incoming solar radiation. The results obtained show the need for robust management plans to ensure the long-term success of solar energy projects in the region more
Atmosphere CDR CM-SAF Climate Data Records Validation

Clouds dissipate quickly during solar eclipses as the land surface cools

Author(s):
Trees, Victor J. H.; de Roode, Stephan R.; Wiltink, Job I.; Meirink, Jan Fokke; Wang, Ping; Stammes, Piet; Siebesma, A. Pier
Publication title: Communications Earth & Environment
2024
 | Volume: 5 | Issue: 1
2024
DOI: 10.1038/s43247-024-01213-0
Abstract:
Clouds affected by solar eclipses could influence the reflection of sunlight back into space and might change local precipitation patterns. Satellite … Clouds affected by solar eclipses could influence the reflection of sunlight back into space and might change local precipitation patterns. Satellite cloud retrievals have so far not taken into account the lunar shadow, hindering a reliable spaceborne assessment of the eclipse-induced cloud evolution. Here we use satellite cloud measurements during three solar eclipses between 2005 and 2016 that have been corrected for the partial lunar shadow together with large-eddy simulations to analyze the eclipse-induced cloud evolution. Our corrected data reveal that, over cooling land surfaces, shallow cumulus clouds start to disappear at very small solar obscurations (~15%). Our simulations explain that the cloud response was delayed and was initiated at even smaller solar obscurations. We demonstrate that neglecting the disappearance of clouds during a solar eclipse could lead to a considerable overestimation of the eclipse-related reduction of net incoming solar radiation. These findings should spur cloud model simulations of the direct consequences of sunlight-intercepting geoengineering proposals, for which our results serve as a unique benchmark. more
Application Atmosphere CDR Climate Data Records LSA-SAF NWC-SAF OSI-SAF Ocean Terrestrial

Cloud property retrievals for climate monitoring: Implications of differences between Spinning Enhanced Visible and Infrared Imager (SEVIRI) on METEOSAT-8 and Advanced Very High Resolution Radiometer (AVHRR) on NOAA-17

Author(s):
Roebeling, R. A.; Feijt, A. J.; Stammes, P.
Publication title: Journal of Geophysical Research
2006
 | Volume: 111 | Issue: D20
2006
DOI: 10.1029/2005JD006990
Abstract:
In the framework of the Satellite Application Facility on Climate Monitoring (CM-SAF) an algorithm was developed to retrieve Cloud Physical Properties… In the framework of the Satellite Application Facility on Climate Monitoring (CM-SAF) an algorithm was developed to retrieve Cloud Physical Properties (CPP) from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat Second Generation (METEOSAT−8) and the Advanced Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration (NOAA) satellites. This paper presents the CPP algorithm and determines if SEVIRI can be used together with AVHRR to build a consistent and accurate data set of cloud optical thickness (COT) and cloud liquid water path (CLWP) over Europe for climate research purposes. After quantifying the differences in 0.6 and 1.6 μm operational calibrated reflectances of SEVIRI and AVHRR, a recalibration procedure is proposed to normalize and absolutely calibrate these reflectances. The effects of recalibration, spatial resolution, and viewing geometry differences on the SEVIRI and AVHRR cloud property retrievals are evaluated. The intercomparison of 0.6 and 1.6 μm operationally calibrated reflectances indicates ∼6 and ∼26% higher reflectances for SEVIRI than for AVHRR. These discrepancies result in retrieval differences between AVHRR and SEVIRI of ∼8% for COT and ∼60% for CLWP. Owing to recalibration these differences reduce to ∼5%, while the magnitude of the median COT and CLWP values of AVHRR decrease ∼2 and ∼60% and the SEVIRI values increase ∼10 and ∼55%, respectively. The differences in spatial resolution and viewing geometry slightly influence the retrieval precision. Thus the CPP algorithm can be used to build a consistent and high-quality data set of SEVIRI and AVHRR retrieved cloud properties for climate research purposes, provided the instrument reflectances are recalibrated, preferably guided by the satellite operators. more
Atmosphere CDR CM-SAF Climate Data Records Method

Cloud-probability-based estimation of black-sky surface albedo from AVHRR data

Author(s):
Manninen, Terhikki; Jaaskelainen, Emmihenna; Siljamo, Niilo; Riihela, Aku; Karlsson, Karl-Goran
Publication title: ATMOSPHERIC MEASUREMENT TECHNIQUES
2022
 | Volume: 15 | Issue: 4
2022
DOI: 10.5194/amt-15-879-2022
Abstract:
This paper describes a new method for cloud-correcting observations of black-sky surface albedo derived using the Advanced Very High Resolution Radiom… This paper describes a new method for cloud-correcting observations of black-sky surface albedo derived using the Advanced Very High Resolution Radiometer (AVHRR). Cloud cover constitutes a major challenge for surface albedo estimation using AVHRR data for all possible conditions of cloud fraction and cloud type with any land cover type and solar zenith angle. This study shows how the new cloud probability (CP) data to be provided as part of edition A3 of the CLARA (CM SAF cLoud, Albedo and surface Radiation dataset from AVHRR data) record from the Satellite Application Facility on Climate Monitoring (CM SAF) project of EUMETSAT can be used instead of traditional binary cloud masking to derive cloud-free monthly mean surface albedo estimates. Cloudy broadband albedo distributions were simulated first for theoretical cloud distributions and then using global cloud probability (CP) data for 1 month. A weighted mean approach based on the CP values was shown to produce very-high-accuracy black-sky surface albedo estimates for simulated data. The 90 % quantile for the error was 1.1 % (in absolute albedo percentage) and that for the relative error was 2.2 %. AVHRR-based and in situ albedo distributions were in line with each other and the monthly mean values were also consistent. Comparison with binary cloud masking indicated that the developed method improves cloud contamination removal. more
Application CDR CM-SAF Climate Data Records Land
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