Precipitation regime classification based on cloud-top temperature time series for spatially-varied parameterization of precipitation models

Author(s):
Lu, S.; ten Veldhuis, M.-C.; van de Giesen, N.; Heemink, A.; Verlaan, M.
Publication title: Remote Sensing
2020
 | Volume: 12 | Issue: 2
2020
DOI: 10.3390/rs12020289
Abstract:
Satellite and reanalysis precipitation products perform poorly over regions with low-density ground observation networks. In order to improve space-de… Satellite and reanalysis precipitation products perform poorly over regions with low-density ground observation networks. In order to improve space-dependent parameterization of precipitation estimation models in data-scarce environments, the delineation boundaries of precipitation regimes should be accurately identified. Existing approaches to characterize precipitation regimes by seasonal or other climatological properties do not account for small scale spatial-temporal variability. Precipitation time series can be used to account for this small-scale variability in regime classification. Unfortunately, precipitation products with global coverage perform poorly at small time scales over data scarce regions. A methodology of using satellite-based cloud-top temperature (CTT) time series as a proxy of precipitation time series for precipitation regime classification was developed, and its potential and uncertainty were analyzed. A precipitation regime in this study was defined on the basis of characteristic small-scale temporal distribution and variability of precipitation at a given place. Dynamic time warping was used to calculate the distance between two time series. Criteria to select the optimal temporal scale of time series for clustering and the number of clusters were also developed. The method was validated over Germany and applied to Tanzania, characterized by complex climatology and low density ground observations. This approach was evaluated against precipitation regime classification based on a satellite precipitation product. Results show that CTT outcompetes satellite-based precipitation for classification of precipitation regime classification. The CTT-based classification can be used as precursor to spatially adapted precipitation estimation algorithms where parameters are calibrated by gauge data or other ground-based precipitation observations, and parameterization can be used for satellite-precipitation estimates, precipitation forecasts in numerical or stochastic weather models, etc. © 2020 by the authors. more
Application Atmosphere CM-SAF Climate Data Records

Predicted sea-ice loss will terminate Iceland's driftwood supply by 2060 CE

Author(s):
Kolar, Tomas; Rybnicek, Michal; Eggertsson, Olafur; Kirdyanov, Alexander; Cejka, Tomas; Cermak, Petr; Zid, Tomas; Vavrcik, Hanus; Buentgen, Ulf
Publication title: GLOBAL AND PLANETARY CHANGE
2022
 | Volume: 213
2022
DOI: 10.1016/j.gloplacha.2022.103834
Abstract:
Driftwood supply was a pivotal factor for the Norse expansion in medieval times and still exhibits an essential resource for Arctic settlements. The p… Driftwood supply was a pivotal factor for the Norse expansion in medieval times and still exhibits an essential resource for Arctic settlements. The physical causes and societal consequences of long-term changes in the distribution of Arctic driftwood are, however, poorly understood. Here, we use dendrochronology to reconstruct the age and origin of 289 driftwood samples that were collected at remote shorelines in northeast Iceland. Based on 240 reference tree-ring width chronologies from the boreal forest zone, and an overall provenance success of 73%, we show that most of the driftwood is pine and larch from the Yenisei catchment in central Siberia. Our study reveals an abrupt decline in the amount of driftwood reaching Iceland since the 1980s, which is corroborated by the experience of local farmers and fishers. Despite the direct and indirect effects of changes in both, logging activity across Siberia as well as Arctic Ocean currents, the predicted amount of sea-ice loss under anthropogenic global warming is likely to terminate Iceland's driftwood supply by 2060 CE. more
Application Climate Data Records Cryosphere OSI-SAF

Predicting September Arctic Sea Ice: A Multimodel Seasonal Skill Comparison

Author(s):
Bushuk, M.; Ali, S.; Bailey, D.A.; Bao, Q.; Batté, L.; Bhatt, U.S.; Blanchard-Wrigglesworth, E.; Blockley, E.; Cawley, G.; Chi, J.; Counillon, F.; Coulombe, P.G.; Cullather, R.I.; Diebold, F.X.; Dirkson, A.; Exarchou, E.; Göbel, M.; Gregory, W.; Guemas, V.; Hamilton, L.; He, B.; Horvath, S.; Ionita, M.; Kay, J.E.; Kim, E.; Kimura, N.; Kondrashov, D.; Labe, Z.M.; Lee, W.; Lee, Y.J.; Li, C.; Li, X.; Lin, Y.; Liu, Y.; Maslowski, W.; Massonnet, F.; Meier, W.N.; Merryfield, W.J.; Myint, H.; Acosta Navarro, J.C.; Petty, A.; Qiao, F.; Schröder, D.; Schweiger, A.; Shu, Q.; Sigmond, M.; Steele, M.; Stroeve, J.; Sun, N.; Tietsche, S.; Tsamados, M.; Wang, K.; Wang, J.; Wang, W.; Wang, Y.; Wang, Y.; Williams, J.; Yang, Q.; Yuan, X.; Zhang, J.; Zhang, Y.
Publication title: Bulletin of the American Meteorological Society
2024
 | Volume: 105 | Issue: 7
2024
DOI: 10.1175/BAMS-D-23-0163.1
Abstract:
This study quantifies the state of the art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multimodel dataset of retrospec… This study quantifies the state of the art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multimodel dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting of community contributions from 17 statistical models and 17 dynamical models. Prediction skill is compared over the period 2001–20 for predictions of pan-Arctic sea ice extent (SIE), regional SIE, and local sea ice concentration (SIC) initialized on 1 June, 1 July, 1 August, and 1 September. This diverse set of statistical and dynamical models can individually predict linearly detrended pan-Arctic SIE anomalies with skill, and a multimodel median prediction has correlation coefficients of 0.79, 0.86, 0.92, and 0.99 at these respective initialization times. Regional SIE predictions have similar skill to pan-Arctic predictions in the Alaskan and Siberian regions, whereas regional skill is lower in the Canadian, Atlantic, and central Arctic sectors. The skill of dynamical and statistical models is generally comparable for pan-Arctic SIE, whereas dynamical models outperform their statistical counterparts for regional and local predictions. The prediction systems are found to provide the most value added relative to basic reference forecasts in the extreme SIE years of 1996, 2007, and 2012. SIE prediction errors do not show clear trends over time, suggesting that there has been minimal change in inherent sea ice predictability over the satellite era. Overall, this study demonstrates that there are bright prospects for skillful operational predictions of September sea ice at least 3 months in advance. SIGNIFICANCE STATEMENT: The observed decline of Arctic sea ice extent has created an emerging need for predictions of sea ice on seasonal time scales. This study provides a comparison of September Arctic sea ice seasonal prediction skill across a diverse set of dynamical and statistical prediction models, quantifying the state of the art in the rapidly growing sea ice prediction research community. We find that both dynamical and statistical models can skillfully predict September Arctic sea ice 0–3 months in advance on pan-Arctic, regional, and local spatial scales. Our results demonstrate that there are bright prospects for skillful operational seasonal predictions of Arctic sea ice and highlight a number of crucial prediction system design aspects to guide future improvements. © 2024 American Meteorological Society. more
Application Climate Data Records Cryosphere OSI-SAF

Prescriptive analytics for low and high water management in German waterways

Author(s):
Brum, M.; Meißner, D.; Klein, B.; Hohenrainer, J.; Schwanenberg, D.; Patzke, S.
Publication title: At-Automatisierungstechnik
2024
 | Volume: 72 | Issue: 6
2024
DOI: 10.1515/auto-2024-0013
Abstract:
The real-time management of multi-purpose storage reservoirs aims at an efficient operation of existing hydraulic infrastructure. This management proc… The real-time management of multi-purpose storage reservoirs aims at an efficient operation of existing hydraulic infrastructure. This management process can be structured as a prescriptive analytics setup that considers both current and predicted system states to recommend actions and outline potential implications. In application to a reservoir and river system, it combines hydrological modelling components for the system schematization, observations and data assimilation for the identification of the current system state, meteorological and hydrological predictions as well as optimization-based techniques to support decision-making regarding reservoir operations. In this paper, we present the application of such a framework to the short-term management of the Eder and Diemel storage reservoirs. These reservoirs are operated by the German Federal Waterways and Shipping Administration (WSV) with the primary goal to support navigation in the River Weser during low flow periods. In addition, partially conflicting objectives such as flood protection, energy generation and recreation are considered. The implementation includes an explicit consideration of forecast uncertainty and its impact on the decision-making by using probabilistic forecasts in combination with a multi-stage stochastic optimization approach. We demonstrate the applicability of the approach based on low and high water use cases. Special attention is paid on the benefits of the probabilistic forecast in combination with the multi-stage stochastic optimization versus a deterministic setup. It provides an explicit translation of the forecast uncertainty in the decision variables, in this case the reservoir releases helping the operators to better anticipate the range of future release decisions. Furthermore, the stochastic approach is expected to provide more stable decisions in an operational setting, based on more stable forecasts by considering various possible realizations of the future instead of picking a single one, which gets random after 4-5 days. © 2024 Walter de Gruyter GmbH, Berlin/Boston. more
Application Atmosphere CM-SAF Climate Data Records

Present and future aerosol impacts on Arctic climate change in the GISS-E2.1 Earth system model

Author(s):
Im, U.; Tsigaridis, K.; Faluvegi, G.; Langen, P.L.; French, J.P.; Mahmood, R.; Thomas, M.A.; Von Salzen, K.; Thomas, D.C.; Whaley, C.H.; Klimont, Z.; Skov, H.; Brandt, Jø.
Publication title: Atmospheric Chemistry and Physics
2021
 | Volume: 21 | Issue: 13
2021
DOI: 10.5194/acp-21-10413-2021
Abstract:
The Arctic is warming 2 to 3 times faster than the global average, partly due to changes in short-lived climate forcers (SLCFs) including aerosols. In… The Arctic is warming 2 to 3 times faster than the global average, partly due to changes in short-lived climate forcers (SLCFs) including aerosols. In order to study the effects of atmospheric aerosols in this warming, recent past (1990-2014) and future (2015-2050) simulations have been carried out using the GISS-E2.1 Earth system model to study the aerosol burdens and their radiative and climate impacts over the Arctic (>60 N), using anthropogenic emissions from the Eclipse V6b and the Coupled Model Intercomparison Project Phase 6 (CMIP6) databases, while global annual mean greenhouse gas concentrations were prescribed and kept fixed in all simulations. Results showed that the simulations have underestimated observed surface aerosol levels, in particular black carbon (BC) and sulfate (SO42-), by more than 50%, with the smallest biases calculated for the atmosphere-only simulations, where winds are nudged to reanalysis data. CMIP6 simulations performed slightly better in reproducing the observed surface aerosol concentrations and climate parameters, compared to the Eclipse simulations. In addition, simulations where atmosphere and ocean are fully coupled had slightly smaller biases in aerosol levels compared to atmosphere-only simulations without nudging. Arctic BC, organic aerosol (OA), and SO42- burdens decrease significantly in all simulations by 10%-60% following the reductions of 7%-78% in emission projections, with the Eclipse ensemble showing larger reductions in Arctic aerosol burdens compared to the CMIP6 ensemble. For the 2030-2050 period, the Eclipse ensemble simulated a radiative forcing due to aerosol-radiation interactions (RFARI) of -0.39±0.01Wm-2, which is -0.08Wm-2 larger than the 1990-2010 mean forcing (-0.32Wm-2), of which -0.24±0.01Wm-2 was attributed to the anthropogenic aerosols. The CMIP6 ensemble simulated a RFARI of -0.35 to -0.40Wm-2 for the same period, which is -0.01 to -0.06Wm-2 larger than the 1990-2010 mean forcing of -0.35Wm-2. The scenarios with little to no mitigation (worst-case scenarios) led to very small changes in the RFARI, while scenarios with medium to large emission mitigations led to increases in the negative RFARI, mainly due to the decrease in the positive BC forcing and the decrease in the negative SO42- forcing. The anthropogenic aerosols accounted for -0.24 to -0.26Wm-2 of the net RFARI in 2030-2050 period, in Eclipse and CMIP6 ensembles, respectively. Finally, all simulations showed an increase in the Arctic surface air temperatures throughout the simulation period. By 2050, surface air temperatures are projected to increase by 2.4 to 2.6C in the Eclipse ensemble and 1.9 to 2.6C in the CMIP6 ensemble, compared to the 1990-2010 mean. Overall, results show that even the scenarios with largest emission reductions leads to similar impact on the future Arctic surface air temperatures and sea-ice extent compared to scenarios with smaller emission reductions, implying reductions of greenhouse emissions are still necessary to mitigate climate change. © Copyright: more
Atmosphere CM-SAF Climate Data Records Validation

Probabilistic cloud masking for the generation of CM SAF cloud climate data records from AVHRR and SEVIRI sensors

Author(s):
Karlsson, K.-G.; Johansson, E.; Håkansson, N.; Sedlar, J.; Eliasson, S.
Publication title: Remote Sensing
2020
 | Volume: 12 | Issue: 4
2020
DOI: 10.3390/rs12040713
Abstract:
Cloud screening in satellite imagery is essential for enabling retrievals of atmospheric and surface properties. For climate data record (CDR) generat… Cloud screening in satellite imagery is essential for enabling retrievals of atmospheric and surface properties. For climate data record (CDR) generation, cloud screening must be balanced, so both false cloud-free and false cloudy retrievals are minimized. Many methods used in recent CDRs show signs of clear-conservative cloud screening leading to overestimated cloudiness. This study presents a new cloud screening approach for Advanced Very-High-Resolution Radiometer (AVHRR) and Spinning Enhanced Visible and Infrared Imager (SEVIRI) imagery based on the Bayesian discrimination theory. The method is trained on high-quality cloud observations from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. The method delivers results designed for optimally balanced cloud screening expressed as cloud probabilities together with information on for which clouds (minimum cloud optical thickness) the probabilities are valid. Cloud screening characteristics over 28 different Earth surface categories were estimated. Using independent CALIOP observations (including all observed clouds) in 2010 for validation, the total global hit rates for AVHRR data and the SEVIRI full disk were 82% and 85%, respectively. High-latitude oceans had the best performance, with a hit rate of approximately 93%. The results were compared to the CM SAF cLoud, Albedo, and surface RAdiation dataset from AVHRR data-second edition (CLARA-A2) CDR and showed general improvements over most global regions. Notably, the Kuipers' Skill Score improved, verifying a more balanced cloud screening. The new method will be used to prepare the new CLARA-A3 and CLAAS-3 (CLoud property dAtAset using SEVIRI, Edition 3) CDRs in the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) project. © 2020 by the author. more
Atmosphere CM-SAF Climate Data Records Method

Projected Changes in Solar PV and Wind Energy Potential over West Africa: An Analysis of CORDEX-CORE Simulations

Author(s):
Ndiaye, A; Moussa, MS; Dione, C; Sawadogo, W; Bliefernicht, J; Dungall, L; Kunstmann, H
Publication title: ENERGIES
2022
 | Volume: 15 | Issue: 24
2022
DOI: 10.3390/en15249602
Abstract:
Renewable energy development is growing fast and is expected to expand in the next decades in West Africa as a contribution to addressing the power de… Renewable energy development is growing fast and is expected to expand in the next decades in West Africa as a contribution to addressing the power demand and climate change mitigation. However, the future impacts of climate change on solar PV and the wind energy potential in the region are still unclear. This study investigates the expected future impacts of climate change on solar PV and wind energy potential over West Africa using an ensemble of three regional climate models (RCMs). Each RCM is driven by three global climate models (GCMs) from the new coordinated high-resolution output for regional evaluations (CORDEX-CORE) under the RCP8.5 scenario. Two projection periods were used: the near future (2021-2050) and the far future (2071-2100). For the model evaluation, reanalysis data from ERA5 and satellite-based climate data (SARAH-2) were used. The models and their ensemble mean (hereafter Mean) show acceptable performance for the simulations of the solar PV potential, the wind power density, and related variables with some biases. The Mean predicts a general decrease in the solar PV potential over the region of about -2% in the near future and -4% in the far future. The wind power density (WPD) is expected to increase by about 20% in the near future and 40% in the far future. The changes for solar PV potential seem to be consistent, although the intensity differs according to the RCM used. For the WPD, there are some discrepancies among the RCMs in terms of intensity and direction. This study can guide governments and policymakers in decision making for future solar and wind energy projects in the region. more
Application Atmosphere CM-SAF Climate Data Records

Present-Day Greenland Ice Sheet Climate and Surface Mass Balance in CESM2

Author(s):
van Kampenhout, L.; Lenaerts, J.T.M.; Lipscomb, W.H.; Lhermitte, S.; Noël, B.; Vizcaíno, M.; Sacks, W.J.; van den Broeke, M.R.
Publication title: Journal of Geophysical Research: Earth Surface
2020
 | Volume: 125 | Issue: 2
2020
DOI: 10.1029/2019JF005318
Abstract:
The response of the Greenland Ice Sheet (GrIS) to a warmer climate is uncertain on long time scales. Climate models, such as those participating in th… The response of the Greenland Ice Sheet (GrIS) to a warmer climate is uncertain on long time scales. Climate models, such as those participating in the Coupled Model Intercomparison Project phase 6 (CMIP6), are used to assess this uncertainty. The Community Earth System Model version 2.1 (CESM2) is a CMIP6 model capable of running climate simulations with either one-way coupling (fixed ice sheet geometry) or two-way coupling (dynamic geometry) to the GrIS. The model features prognostic snow albedo, online downscaling using elevation classes, and a firn pack to refreeze percolating melt water. Here we evaluate the representation of the GrIS surface energy balance and surface mass balance in CESM2 at 1° resolution with fixed GrIS geometry. CESM2 agrees closely with ERA-Interim reanalysis data for key controls on GrIS SMB: surface pressure, sea ice extent, 500 hPa geopotential height, wind speed, and 700 hPa air temperature. Cloudsat-CALIPSO data show that supercooled liquid-containing clouds are adequately represented, whereas comparisons to Moderate Resolution Imaging Spectroradiometer and CM SAF Cloud, Albedo, and Surface Radiation data set from Advanced Very High Resolution Radiometer data second edition data suggest that CESM2 underestimates surface albedo. The seasonal cycle and spatial patterns of surface energy balance and surface mass balance components in CESM2 agree well with regional climate model RACMO2.3p2, with GrIS-integrated melt, refreezing, and runoff bracketed by RACMO2 counterparts at 11 and 1 km. Time series of melt, runoff, and SMB show a break point around 1990, similar to RACMO2. These results suggest that GrIS SMB is realistic in CESM2, which adds confidence to coupled ice sheet-climate experiments that aim to assess the GrIS contribution to future sea level rise. ©2020. The Authors. more
Application CM-SAF Climate Data Records Cryosphere Validation

Projected loss of brown macroalgae and seagrasses with global environmental change

Author(s):
Manca, Federica; Benedetti-Cecchi, Lisandro; Bradshaw, Corey J. A.; Cabeza, Mar; Gustafsson, Camilla; Norkko, Alf M.; Roslin, Tomas V.; Thomas, David N.; White, Lydia; Strona, Giovanni
Publication title: Nature Communications
2024
 | Volume: 15 | Issue: 1
2024
DOI: 10.1038/s41467-024-48273-6
Abstract:
Although many studies predict extensive future biodiversity loss and redistribution in the terrestrial realm, future changes in marine biodiversity re… Although many studies predict extensive future biodiversity loss and redistribution in the terrestrial realm, future changes in marine biodiversity remain relatively unexplored. In this work, we model global shifts in one of the most important marine functional groups—ecosystem-structuring macrophytes—and predict substantial end-of-century change. By modelling the future distribution of 207 brown macroalgae and seagrass species at high temporal and spatial resolution under different climate-change projections, we estimate that by 2100, local macrophyte diversity will decline by 3–4% on average, with 17 to 22% of localities losing at least 10% of their macrophyte species. The current range of macrophytes will be eroded by 5–6%, and highly suitable macrophyte habitat will be substantially reduced globally (78–96%). Global macrophyte habitat will shift among marine regions, with a high potential for expansion in polar regions. more
Application Atmosphere CM-SAF Climate Data Records Ocean

pvlib iotools—Open-source Python functions for seamless access to solar irradiance data

Author(s):
Jensen, Adam R.; Anderson, Kevin S.; Holmgren, William F.; Mikofski, Mark A.; Hansen, Clifford W.; Boeman, Leland J.; Loonen, Roel
Publication title: Solar Energy
2023
 | Volume: 266
2023
DOI: 10.1016/j.solener.2023.112092
Abstract:
Access to accurate solar resource data is critical for numerous applications, including estimating the yield of solar energy systems, developing radia… Access to accurate solar resource data is critical for numerous applications, including estimating the yield of solar energy systems, developing radiation models, and validating irradiance datasets. However, lack of standardization in data formats and access interfaces across providers constitutes a major barrier to entry for new users. pvlib python’s iotools subpackage aims to solve this issue by providing standardized Python functions for reading local files and retrieving data from external providers. All functions follow a uniform pattern and return convenient data outputs, allowing users to seamlessly switch between data providers and explore alternative datasets. The pvlib package is community-developed on GitHub: https://github.com/pvlib/pvlib-python. As of pvlib python version 0.9.5, the iotools subpackage supports 12 different datasets, including ground measurement, reanalysis, and satellite-derived irradiance data. The supported ground measurement networks include the Baseline Surface Radiation Network (BSRN), NREL MIDC, SRML, SOLRAD, SURFRAD, and the US Climate Reference Network (CRN). Additionally, satellite-derived and reanalysis irradiance data from the following sources are supported: PVGIS (SARAH & ERA5), NSRDB PSM3, and CAMS Radiation Service (including McClear clear-sky irradiance). more
Atmosphere CM-SAF Climate Data Records Software for CDR development
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