Climate services in support of climate change impact analyses for the German inland transportation system

Author(s):
Haensel, Stephanie; Brendel, Christoph; Haller, Michael; Kraehenmann, Stefan; Razafimaharo, Christene S.; Stanley, Kelly; Brienen, Susanne; Deutschlaender, Thomas; Rauthe, Monika; Walter, Andreas
Publication title: METEOROLOGISCHE ZEITSCHRIFT
2022
 | Volume: 31 | Issue: 3
2022
DOI: 10.1127/metz/2022/1117
Abstract:
Climate change and extreme weather events are an increasing challenge for society and the economy, including the transport sector. A sustainable and r… Climate change and extreme weather events are an increasing challenge for society and the economy, including the transport sector. A sustainable and resilient transportation system therefore requires information on the temporal and spatial pattern of risks induced by climate change and the assessment of resulting vulnerabilities. Such analyses in the past were usually made separately for each mode of transport based on different observational and climate model datasets and using different methodological approaches to analyse climatic changes and their impacts on the transport infrastructure. Within the research network “BMDV Network of Experts” an intermodal perspective is taken on transportation. Common observational and climate model datasets as well as a standardized analysis framework were coordinated and agreed upon to form the basis for comparable climate impact assessments for roads, railways and inland waterways. This manuscript introduces the climatological datasets and methodological approaches for the climate change and climate impact analysis used for the transportation sector and beyond. Selected results on the projected increases of extreme temperature and heavy precipitation are exemplarily presented in order to illustrate the need for developing climate change adaptation measures for the German inland transport system. more
Application Atmosphere CM-SAF Climate Data Records

Climate-induced decline in the quality and quantity of European hops calls for immediate adaptation measures

Author(s):
Mozny, M.; Trnka, M.; Vlach, V.; Zalud, Z.; Cejka, T.; Hajkova, L.; Potopova, V.; Semenov, M.A.; Semeradova, D.; Büntgen, U.
Publication title: Nature Communications
2023
 | Volume: 14 | Issue: 1
2023
DOI: 10.1038/s41467-023-41474-5
Abstract:
A recent rise in the global brewery sector has increased the demand for high-quality, late summer hops. The effects of ongoing and predicted climate c… A recent rise in the global brewery sector has increased the demand for high-quality, late summer hops. The effects of ongoing and predicted climate change on the yield and aroma of hops, however, remain largely unknown. Here, we combine meteorological measurements and model projections to assess the climate sensitivity of the yield, alpha content and cone development of European hops between 1970 and 2050 CE, when temperature increases by 1.4 °C and precipitation decreases by 24 mm. Accounting for almost 90% of all hop-growing regions, our results from Germany, the Czech Republic and Slovenia show that hop ripening started approximately 20 days earlier, production declined by almost 0.2 t/ha/year, and the alpha content decreased by circa 0.6% when comparing data before and after 1994 CE. A predicted decline in hop yield and alpha content of 4–18% and 20–31% by 2050 CE, respectively, calls for immediate adaptation measures to stabilize an ever-growing global sector. © 2023, Springer Nature Limited. more
Application Atmosphere CM-SAF Climate Data Records

Climatic precipitation efficiency and its dependence on environmental factors over the Sichuan Basin and adjacent regions, Southwest China

Author(s):
Mo, Shuying; Zhao, Pengguo; Zhao, Chuanfeng; Xiao, Hui; Wang, Yuting; Zhang, Peiwen; Wen, Xiaohang; Qiu, Shuang
Publication title: Theoretical and Applied Climatology
2024
 | Volume: 155 | Issue: 5
2024
DOI: 10.1007/s00704-024-04835-8
Abstract:
Based on satellite observation and reanalysis data, basic features of cloud water and precipitation and the dependence of precipitation efficiency (PE… Based on satellite observation and reanalysis data, basic features of cloud water and precipitation and the dependence of precipitation efficiency (PE) on environmental factors over the Sichuan Basin and adjacent regions are investigated. Results found that the spatiotemporal distribution characteristics of precipitation and cloud water over the Sichuan Basin and adjacent regions are consistent. The liquid water path (LWP) and ice water path (IWP) in the Sichuan Basin (SCB) are richer than the West Sichuan Plateau (WSP) and Yunnan-Guizhou Plateau (YGP), and the contribution of IWP to precipitation in Sichuan Basin and adjacent regions is greater than that of LWP. Furthermore, the results indicate that PE has the most significant dependence on the low-tropospheric relative humidity (RH) and the convective available potential energy (CAPE) over the Sichuan Basin and adjacent regions. Higher RH and CAPE contribute to a larger PE in the Sichuan Basin. The CAPE has a positive effect on the PE, which indicates that PE is directly affected by precipitation convection, mainly due to the special topography of the Sichuan Basin and adjacent regions, leading to frequent convective activities. The ratio of LWP to IWP (RLI) affects PE. The RLI decreases with the increase of IWP, leading to an increase in PE. RLI is negatively correlated with PE, which further indicates that ice water clouds have a more significant impact on PE over the Sichuan Basin and adjacent regions. Through this study, we can enhance our understanding of the formation processes, spatio-temporal structures, and evolutionary mechanisms of cloud precipitation in the Sichuan Basin and its adjacent areas. This is crucial for unraveling the dynamics of atmospheric water cycle, climate change processes, and optimizing the utilization efficiency of cloud water resources. more
Application Atmosphere CM-SAF Climate Data Records

Climatology of Tropical Cyclone Precipitation in the S2S Models

Author(s):
García-Franco, J.L.; Lee, C.-Y.; Camargo, S.J.; Tippett, M.K.; Kim, D.; Molod, A.; Lim, Y.-K.
Publication title: Weather and Forecasting
2023
 | Volume: 38 | Issue: 9
2023
DOI: 10.1175/WAF-D-23-0029.1
Abstract:
This study evaluates the representation of tropical cyclone precipitation (TCP) in reforecasts from the Subseasonal to Seasonal (S2S) Prediction Proje… This study evaluates the representation of tropical cyclone precipitation (TCP) in reforecasts from the Subseasonal to Seasonal (S2S) Prediction Project. The global distribution of precipitation in S2S models shows relevant biases in the multimodel mean ensemble that are characterized by wet biases in total precipitation and TCP, except for the Atlantic. The TCP biases can contribute more than 50% of the total precipitation biases in basins such as the southern Indian Ocean and South Pacific. The magnitude and spatial pattern of these biases exhibit little variation with lead time. The origins of TCP biases can be attributed to biases in the frequency of tropical cyclone occurrence. The S2S models sim-ulate too few TCs in the Atlantic and western North Pacific and too many TCs in the Southern Hemisphere and eastern North Pacific. At the storm scale, the average peak precipitation near the storm center is lower in the models than observations due to a too high proportion of weak TCs. However, this bias is offset in some models by higher than observed precipitation rates at larger radii (300–500 km). An analysis of the mean TCP for each TC at each grid point reveals an overestimation of TCP rates, particularly in the near-equatorial Indian and western Pacific Oceans. These findings suggest that the simulation of TC occurrence and the storm-scale precipitation require better representation in order to reduce TCP biases and enhance the subseasonal prediction skill of mean and extreme total precipitation. © 2023 American Meteorological Society. more
Application Atmosphere CM-SAF Climate Data Records

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 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 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 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 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 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 CM-SAF Climate Data Records Method Validation
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