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Research data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Authors: Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; +47 AuthorsSchupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Früh, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, Jörg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; Müller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich;Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.DKRZ.MPI-ESM1-2-HR.ssp126' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-HR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Deutsches Klimarechenzentrum, Hamburg 20146, Germany (DKRZ) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, landIce: none, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Müller, Wolfgang; Ilyina, Tatiana; Li, Hongmei; Timmreck, Claudia; Gayler, Veronika; Wieners, Karl-Hermann; Botzet, Michael; Brovkin, Victor; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, Jörg; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich;Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.DAMIP.MPI-M.MPI-ESM1-2-LR' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-LR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Max Planck Institute for Meteorology, Hamburg 20146, Germany (MPI-M) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, landIce: none, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Authors: Steger, Christian; Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; +47 AuthorsSteger, Christian; Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Bittner, Matthias; Jungclaus, Johann; Früh, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, Jörg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; Müller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich;Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.DWD.MPI-ESM1-2-HR' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-HR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Deutscher Wetterdienst, Offenbach am Main 63067, Germany (DWD) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, landIce: none, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Authors: Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; +44 AuthorsJungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, Jörg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; Müller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich;Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.MPI-M.MPI-ESM1-2-LR.historical' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-LR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Max Planck Institute for Meteorology, Hamburg 20146, Germany (MPI-M) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, landIce: none, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Authors: Steger, Christian; Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; +47 AuthorsSteger, Christian; Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Bittner, Matthias; Jungclaus, Johann; Früh, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, Jörg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; Müller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich;Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.DWD.MPI-ESM1-2-HR.ssp585' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-HR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Deutscher Wetterdienst, Offenbach am Main 63067, Germany (DWD) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, landIce: none, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2010 United Kingdom, GermanyPublisher:Springer Science and Business Media LLC Wolf, Benjamin; Zheng, Xunhua; Bruggemann, Nicolas; Chen, Weiwei; Dannenmann, Michael; Han, Xingguo; Sutton, Mark A.; Wu, Honghui; Yao, Zhisheng; Butterbach-Bahl, Klaus;doi: 10.1038/nature08931
Atmospheric concentrations of the greenhouse gas nitrous oxide (N(2)O) have increased significantly since pre-industrial times owing to anthropogenic perturbation of the global nitrogen cycle, with animal production being one of the main contributors. Grasslands cover about 20 per cent of the temperate land surface of the Earth and are widely used as pasture. It has been suggested that high animal stocking rates and the resulting elevated nitrogen input increase N(2)O emissions. Internationally agreed methods to upscale the effect of increased livestock numbers on N(2)O emissions are based directly on per capita nitrogen inputs. However, measurements of grassland N(2)O fluxes are often performed over short time periods, with low time resolution and mostly during the growing season. In consequence, our understanding of the daily and seasonal dynamics of grassland N(2)O fluxes remains limited. Here we report year-round N(2)O flux measurements with high and low temporal resolution at ten steppe grassland sites in Inner Mongolia, China. We show that short-lived pulses of N(2)O emission during spring thaw dominate the annual N(2)O budget at our study sites. The N(2)O emission pulses are highest in ungrazed steppe and decrease with increasing stocking rate, suggesting that grazing decreases rather than increases N(2)O emissions. Our results show that the stimulatory effect of higher stocking rates on nitrogen cycling and, hence, on N(2)O emission is more than offset by the effects of a parallel reduction in microbial biomass, inorganic nitrogen production and wintertime water retention. By neglecting these freeze-thaw interactions, existing approaches may have systematically overestimated N(2)O emissions over the last century for semi-arid, cool temperate grasslands by up to 72 per cent.
Nature arrow_drop_down KITopen (Karlsruhe Institute of Technologie)Article . 2010Data sources: Bielefeld Academic Search Engine (BASE)Natural Environment Research Council: NERC Open Research ArchiveArticle . 2010Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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more_vert Nature arrow_drop_down KITopen (Karlsruhe Institute of Technologie)Article . 2010Data sources: Bielefeld Academic Search Engine (BASE)Natural Environment Research Council: NERC Open Research ArchiveArticle . 2010Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2019 GermanyPublisher:MDPI AG Kristian Näschen; Bernd Diekkrüger; Constanze Leemhuis; Larisa Seregina; Roderick van der Linden;This article illustrates the impact of potential future climate scenarios on water quantity in time and space for an East African floodplain catchment surrounded by mountainous areas. In East Africa, agricultural intensification is shifting from upland cultivation into the wetlands due to year-round water availability and fertile soils. These advantageous agricultural conditions might be hampered through climate change impacts. Additionally, water-related risks, like droughts and flooding events, are likely to increase. Hence, this study investigates future climate patterns and their impact on water resources in one production cluster in Tanzania. To account for these changes, a regional climate model ensemble of the Coordinated Regional Downscaling Experiment (CORDEX) Africa project was analyzed to investigate changes in climatic patterns until 2060, according to the RCP4.5 (representative concentration pathways) and RCP8.5 scenarios. The semi-distributed Soil and Water Assessment Tool (SWAT) was utilized to analyze the impacts on water resources according to all scenarios. Modeling results indicate increasing temperatures, especially in the hot dry season, intensifying the distinctive features of the dry and rainy season. This consequently aggravates hydrological extremes, such as more-pronounced flooding and decreasing low flows. Overall, annual averages of water yield and surface runoff increase up to 61.6% and 67.8%, respectively, within the bias-corrected scenario simulations, compared to the historical simulations. However, changes in precipitation among the analyzed scenarios vary between −8.3% and +22.5% of the annual averages. Hydrological modeling results also show heterogeneous spatial patterns inside the catchment. These spatio-temporal patterns indicate the possibility of an aggravation for severe floods in wet seasons, as well as an increasing drought risk in dry seasons across the scenario simulations. Apart from that, the discharge peak, which is crucial for the flood recession agriculture in the floodplain, is likely to shift from April to May from the 2020s onwards.
KITopen (Karlsruhe I... arrow_drop_down KITopen (Karlsruhe Institute of Technologie)Article . 2019License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/w11040859&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 36 citations 36 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert KITopen (Karlsruhe I... arrow_drop_down KITopen (Karlsruhe Institute of Technologie)Article . 2019License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/w11040859&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2022 GermanyPublisher:Springer Science and Business Media LLC Behling, R.; Roessner, S.; Förster, S.; Saemian, P.; Tourian, M.; Portele, T.; Lorenz, C.;AbstractIran has experienced a drastic increase in water scarcity in the last decades. The main driver has been the substantial unsustainable water consumption of the agricultural sector. This study quantifies the spatiotemporal dynamics of Iran’s hydrometeorological water availability, land cover, and vegetation growth and evaluates their interrelations with a special focus on agricultural vegetation developments. It analyzes globally available reanalysis climate data and satellite time series data and products, allowing a country-wide investigation of recent 20+ years at detailed spatial and temporal scales. The results reveal a wide-spread agricultural expansion (27,000 km$$^2$$ 2 ) and a significant cultivation intensification (48,000 km$$^2$$ 2 ). At the same time, we observe a substantial decline in total water storage that is not represented by a decrease of meteorological water input, confirming an unsustainable use of groundwater mainly for agricultural irrigation. As consequence of water scarcity, we identify agricultural areas with a loss or reduction of vegetation growth (10,000 km$$^2$$ 2 ), especially in irrigated agricultural areas under (hyper-)arid conditions. In Iran’s natural biomes, the results show declining trends in vegetation growth and land cover degradation from sparse vegetation to barren land in 40,000 km$$^2$$ 2 , mainly along the western plains and foothills of the Zagros Mountains, and at the same time wide-spread greening trends, particularly in regions of higher altitudes. Overall, the findings provide detailed insights in vegetation-related causes and consequences of Iran’s anthropogenic drought and can support sustainable management plans for Iran or other semi-arid regions worldwide, often facing similar conditions.
KITopen (Karlsruhe I... arrow_drop_down KITopen (Karlsruhe Institute of Technologie)Article . 2022License: CC BYData sources: Bielefeld Academic Search Engine (BASE)GFZpublic (German Research Centre for Geosciences, Helmholtz-Zentrum Potsdam)Article . 2022License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1038/s41598-022-24712-6&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 7 citations 7 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert KITopen (Karlsruhe I... arrow_drop_down KITopen (Karlsruhe Institute of Technologie)Article . 2022License: CC BYData sources: Bielefeld Academic Search Engine (BASE)GFZpublic (German Research Centre for Geosciences, Helmholtz-Zentrum Potsdam)Article . 2022License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1038/s41598-022-24712-6&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2020 France, Germany, FrancePublisher:MDPI AG Björn Ole Sander; Pia Schneider; Ryan Romasanta; Kristine Samoy-Pascual; Evangeline B. Sibayan; Constancio A. Asis; Reiner Wassmann;handle: 10568/109955
Reducing methane (CH4) emission from paddy rice production is an important target for many Asian countries in order to comply with their climate policy commitments. National greenhouse gas (GHG) inventory approaches like the Tier-2 approach of the Intergovernmental Panel on Climate Change (IPCC) are useful to assess country-scale emissions from the agricultural sector. In paddy rice, alternate wetting and drying (AWD) is a promising and well-studied water management technique which, as shown in experimental studies, can effectively reduce CH4 emissions. However, so far little is known about GHG emission rates under AWD when the technique is fully controlled by farmers. This study assesses CH4 and nitrous oxide (N2O) fluxes under continuous flooded (CF) and AWD treatments for seven subsequent seasons on farmers’ fields in a pumped irrigation system in Central Luzon, Philippines. Under AWD management, CH4 emissions were substantially reduced (73% in dry season (DS), 21% in wet season (WS)). In all treatments, CH4 is the major contributor to the total GHG emission and is, thus, identified as the driving force to the global warming potential (GWP). The contribution of N2O emissions to the GWP was higher in CF than in AWD, however, these only offset 15% of the decrease in CH4 emission and, therefore, did not jeopardize the strong reduction in the GWP. The study proves the feasibility of AWD under farmers’ management as well as the intended mitigation effect. Resulting from this study, it is recommended to incentivize dissemination strategies in order to improve the effectiveness of mitigation initiatives. A comparison of single CH4 emissions to calculated emissions with the IPCC Tier-2 inventory approach identified that, although averaged values showed a sufficient degree of accuracy, fluctuations for single measurement points have high variation which limit the use of the method for field-level assessments.
KITopen (Karlsruhe I... arrow_drop_down KITopen (Karlsruhe Institute of Technologie)Article . 2020License: CC BYData sources: Bielefeld Academic Search Engine (BASE)CGIAR CGSpace (Consultative Group on International Agricultural Research)Article . 2020License: CC BYFull-Text: https://hdl.handle.net/10568/109955Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/agriculture10080350&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 21 citations 21 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert KITopen (Karlsruhe I... arrow_drop_down KITopen (Karlsruhe Institute of Technologie)Article . 2020License: CC BYData sources: Bielefeld Academic Search Engine (BASE)CGIAR CGSpace (Consultative Group on International Agricultural Research)Article . 2020License: CC BYFull-Text: https://hdl.handle.net/10568/109955Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/agriculture10080350&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2020 GermanyPublisher:Proceedings of the National Academy of Sciences Funded by:ANR | BASC, EC | FutureMARESANR| BASC ,EC| FutureMARESOsamu Saito; Yunne-Jai Shin; Elena Bukvareva; Guy F. Midgley; Carlo Rondinini; Ignacio Palomo; Ignacio Palomo; Melanie Kolb; Almut Arneth; Thierry Oberdorff; Paul Leadley;Recent assessment reports by the Intergovernmental Panel on Climate Change (IPCC) and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) have highlighted the risks to humanity arising from the unsustainable use of natural resources. Thus far, land, freshwater, and ocean exploitation have been the chief causes of biodiversity loss. Climate change is projected to be a rapidly increasing additional driver for biodiversity loss. Since climate change and biodiversity loss impact human societies everywhere, bold solutions are required that integrate environmental and societal objectives. As yet, most existing international biodiversity targets have overlooked climate change impacts. At the same time, climate change mitigation measures themselves may harm biodiversity directly. The Convention on Biological Diversity’s post-2020 framework offers the important opportunity to address the interactions between climate change and biodiversity and revise biodiversity targets accordingly by better aligning these with the United Nations Framework Convention on Climate Change Paris Agreement and the Sustainable Development Goals. We identify the considerable number of existing and proposed post-2020 biodiversity targets that risk being severely compromised due to climate change, even if other barriers to their achievement were removed. Our analysis suggests that the next set of biodiversity targets explicitly addresses climate change-related risks since many aspirational goals will not be feasible under even lower-end projections of future warming. Adopting more flexible and dynamic approaches to conservation, rather than static goals, would allow us to respond flexibly to changes in habitats, genetic resources, species composition, and ecosystem functioning and leverage biodiversity’s capacity to contribute to climate change mitigation and adaptation.
KITopen (Karlsruhe I... arrow_drop_down KITopen (Karlsruhe Institute of Technologie)Article . 2021License: CC BY NC NDData sources: Bielefeld Academic Search Engine (BASE)Proceedings of the National Academy of SciencesArticle . 2020 . Peer-reviewedLicense: CC BY NC NDData sources: CrossrefRecolector de Ciencia Abierta, RECOLECTAArticle . 2020License: CC BY NC SAData sources: Recolector de Ciencia Abierta, RECOLECTAadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1073/pnas.2009584117&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 193 citations 193 popularity Top 0.1% influence Top 10% impulse Top 0.1% Powered by BIP!
more_vert KITopen (Karlsruhe I... arrow_drop_down KITopen (Karlsruhe Institute of Technologie)Article . 2021License: CC BY NC NDData sources: Bielefeld Academic Search Engine (BASE)Proceedings of the National Academy of SciencesArticle . 2020 . Peer-reviewedLicense: CC BY NC NDData sources: CrossrefRecolector de Ciencia Abierta, RECOLECTAArticle . 2020License: CC BY NC SAData sources: Recolector de Ciencia Abierta, RECOLECTAadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1073/pnas.2009584117&type=result"></script>'); --> </script>
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Research data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Authors: Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; +47 AuthorsSchupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Früh, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, Jörg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; Müller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich;Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.DKRZ.MPI-ESM1-2-HR.ssp126' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-HR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Deutsches Klimarechenzentrum, Hamburg 20146, Germany (DKRZ) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, landIce: none, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.26050/wdcc/ar6.c6spdkme2s126&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.26050/wdcc/ar6.c6spdkme2s126&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Müller, Wolfgang; Ilyina, Tatiana; Li, Hongmei; Timmreck, Claudia; Gayler, Veronika; Wieners, Karl-Hermann; Botzet, Michael; Brovkin, Victor; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, Jörg; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich;Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.DAMIP.MPI-M.MPI-ESM1-2-LR' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-LR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Max Planck Institute for Meteorology, Hamburg 20146, Germany (MPI-M) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, landIce: none, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.26050/wdcc/ar6.c6damxml2&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Authors: Steger, Christian; Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; +47 AuthorsSteger, Christian; Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Bittner, Matthias; Jungclaus, Johann; Früh, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, Jörg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; Müller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich;Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.DWD.MPI-ESM1-2-HR' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-HR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Deutscher Wetterdienst, Offenbach am Main 63067, Germany (DWD) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, landIce: none, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Authors: Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; +44 AuthorsJungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, Jörg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; Müller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich;Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.MPI-M.MPI-ESM1-2-LR.historical' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-LR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Max Planck Institute for Meteorology, Hamburg 20146, Germany (MPI-M) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, landIce: none, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Authors: Steger, Christian; Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; +47 AuthorsSteger, Christian; Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Bittner, Matthias; Jungclaus, Johann; Früh, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, Jörg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; Müller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich;Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.DWD.MPI-ESM1-2-HR.ssp585' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-HR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Deutscher Wetterdienst, Offenbach am Main 63067, Germany (DWD) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, landIce: none, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.26050/wdcc/ar6.c6spdwme2s585&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2010 United Kingdom, GermanyPublisher:Springer Science and Business Media LLC Wolf, Benjamin; Zheng, Xunhua; Bruggemann, Nicolas; Chen, Weiwei; Dannenmann, Michael; Han, Xingguo; Sutton, Mark A.; Wu, Honghui; Yao, Zhisheng; Butterbach-Bahl, Klaus;doi: 10.1038/nature08931
Atmospheric concentrations of the greenhouse gas nitrous oxide (N(2)O) have increased significantly since pre-industrial times owing to anthropogenic perturbation of the global nitrogen cycle, with animal production being one of the main contributors. Grasslands cover about 20 per cent of the temperate land surface of the Earth and are widely used as pasture. It has been suggested that high animal stocking rates and the resulting elevated nitrogen input increase N(2)O emissions. Internationally agreed methods to upscale the effect of increased livestock numbers on N(2)O emissions are based directly on per capita nitrogen inputs. However, measurements of grassland N(2)O fluxes are often performed over short time periods, with low time resolution and mostly during the growing season. In consequence, our understanding of the daily and seasonal dynamics of grassland N(2)O fluxes remains limited. Here we report year-round N(2)O flux measurements with high and low temporal resolution at ten steppe grassland sites in Inner Mongolia, China. We show that short-lived pulses of N(2)O emission during spring thaw dominate the annual N(2)O budget at our study sites. The N(2)O emission pulses are highest in ungrazed steppe and decrease with increasing stocking rate, suggesting that grazing decreases rather than increases N(2)O emissions. Our results show that the stimulatory effect of higher stocking rates on nitrogen cycling and, hence, on N(2)O emission is more than offset by the effects of a parallel reduction in microbial biomass, inorganic nitrogen production and wintertime water retention. By neglecting these freeze-thaw interactions, existing approaches may have systematically overestimated N(2)O emissions over the last century for semi-arid, cool temperate grasslands by up to 72 per cent.
Nature arrow_drop_down KITopen (Karlsruhe Institute of Technologie)Article . 2010Data sources: Bielefeld Academic Search Engine (BASE)Natural Environment Research Council: NERC Open Research ArchiveArticle . 2010Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1038/nature08931&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesbronze 262 citations 262 popularity Top 1% influence Top 1% impulse Top 1% Powered by BIP!
more_vert Nature arrow_drop_down KITopen (Karlsruhe Institute of Technologie)Article . 2010Data sources: Bielefeld Academic Search Engine (BASE)Natural Environment Research Council: NERC Open Research ArchiveArticle . 2010Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1038/nature08931&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2019 GermanyPublisher:MDPI AG Kristian Näschen; Bernd Diekkrüger; Constanze Leemhuis; Larisa Seregina; Roderick van der Linden;This article illustrates the impact of potential future climate scenarios on water quantity in time and space for an East African floodplain catchment surrounded by mountainous areas. In East Africa, agricultural intensification is shifting from upland cultivation into the wetlands due to year-round water availability and fertile soils. These advantageous agricultural conditions might be hampered through climate change impacts. Additionally, water-related risks, like droughts and flooding events, are likely to increase. Hence, this study investigates future climate patterns and their impact on water resources in one production cluster in Tanzania. To account for these changes, a regional climate model ensemble of the Coordinated Regional Downscaling Experiment (CORDEX) Africa project was analyzed to investigate changes in climatic patterns until 2060, according to the RCP4.5 (representative concentration pathways) and RCP8.5 scenarios. The semi-distributed Soil and Water Assessment Tool (SWAT) was utilized to analyze the impacts on water resources according to all scenarios. Modeling results indicate increasing temperatures, especially in the hot dry season, intensifying the distinctive features of the dry and rainy season. This consequently aggravates hydrological extremes, such as more-pronounced flooding and decreasing low flows. Overall, annual averages of water yield and surface runoff increase up to 61.6% and 67.8%, respectively, within the bias-corrected scenario simulations, compared to the historical simulations. However, changes in precipitation among the analyzed scenarios vary between −8.3% and +22.5% of the annual averages. Hydrological modeling results also show heterogeneous spatial patterns inside the catchment. These spatio-temporal patterns indicate the possibility of an aggravation for severe floods in wet seasons, as well as an increasing drought risk in dry seasons across the scenario simulations. Apart from that, the discharge peak, which is crucial for the flood recession agriculture in the floodplain, is likely to shift from April to May from the 2020s onwards.
KITopen (Karlsruhe I... arrow_drop_down KITopen (Karlsruhe Institute of Technologie)Article . 2019License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/w11040859&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 36 citations 36 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert KITopen (Karlsruhe I... arrow_drop_down KITopen (Karlsruhe Institute of Technologie)Article . 2019License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/w11040859&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2022 GermanyPublisher:Springer Science and Business Media LLC Behling, R.; Roessner, S.; Förster, S.; Saemian, P.; Tourian, M.; Portele, T.; Lorenz, C.;AbstractIran has experienced a drastic increase in water scarcity in the last decades. The main driver has been the substantial unsustainable water consumption of the agricultural sector. This study quantifies the spatiotemporal dynamics of Iran’s hydrometeorological water availability, land cover, and vegetation growth and evaluates their interrelations with a special focus on agricultural vegetation developments. It analyzes globally available reanalysis climate data and satellite time series data and products, allowing a country-wide investigation of recent 20+ years at detailed spatial and temporal scales. The results reveal a wide-spread agricultural expansion (27,000 km$$^2$$ 2 ) and a significant cultivation intensification (48,000 km$$^2$$ 2 ). At the same time, we observe a substantial decline in total water storage that is not represented by a decrease of meteorological water input, confirming an unsustainable use of groundwater mainly for agricultural irrigation. As consequence of water scarcity, we identify agricultural areas with a loss or reduction of vegetation growth (10,000 km$$^2$$ 2 ), especially in irrigated agricultural areas under (hyper-)arid conditions. In Iran’s natural biomes, the results show declining trends in vegetation growth and land cover degradation from sparse vegetation to barren land in 40,000 km$$^2$$ 2 , mainly along the western plains and foothills of the Zagros Mountains, and at the same time wide-spread greening trends, particularly in regions of higher altitudes. Overall, the findings provide detailed insights in vegetation-related causes and consequences of Iran’s anthropogenic drought and can support sustainable management plans for Iran or other semi-arid regions worldwide, often facing similar conditions.
KITopen (Karlsruhe I... arrow_drop_down KITopen (Karlsruhe Institute of Technologie)Article . 2022License: CC BYData sources: Bielefeld Academic Search Engine (BASE)GFZpublic (German Research Centre for Geosciences, Helmholtz-Zentrum Potsdam)Article . 2022License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1038/s41598-022-24712-6&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 7 citations 7 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert KITopen (Karlsruhe I... arrow_drop_down KITopen (Karlsruhe Institute of Technologie)Article . 2022License: CC BYData sources: Bielefeld Academic Search Engine (BASE)GFZpublic (German Research Centre for Geosciences, Helmholtz-Zentrum Potsdam)Article . 2022License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1038/s41598-022-24712-6&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2020 France, Germany, FrancePublisher:MDPI AG Björn Ole Sander; Pia Schneider; Ryan Romasanta; Kristine Samoy-Pascual; Evangeline B. Sibayan; Constancio A. Asis; Reiner Wassmann;handle: 10568/109955
Reducing methane (CH4) emission from paddy rice production is an important target for many Asian countries in order to comply with their climate policy commitments. National greenhouse gas (GHG) inventory approaches like the Tier-2 approach of the Intergovernmental Panel on Climate Change (IPCC) are useful to assess country-scale emissions from the agricultural sector. In paddy rice, alternate wetting and drying (AWD) is a promising and well-studied water management technique which, as shown in experimental studies, can effectively reduce CH4 emissions. However, so far little is known about GHG emission rates under AWD when the technique is fully controlled by farmers. This study assesses CH4 and nitrous oxide (N2O) fluxes under continuous flooded (CF) and AWD treatments for seven subsequent seasons on farmers’ fields in a pumped irrigation system in Central Luzon, Philippines. Under AWD management, CH4 emissions were substantially reduced (73% in dry season (DS), 21% in wet season (WS)). In all treatments, CH4 is the major contributor to the total GHG emission and is, thus, identified as the driving force to the global warming potential (GWP). The contribution of N2O emissions to the GWP was higher in CF than in AWD, however, these only offset 15% of the decrease in CH4 emission and, therefore, did not jeopardize the strong reduction in the GWP. The study proves the feasibility of AWD under farmers’ management as well as the intended mitigation effect. Resulting from this study, it is recommended to incentivize dissemination strategies in order to improve the effectiveness of mitigation initiatives. A comparison of single CH4 emissions to calculated emissions with the IPCC Tier-2 inventory approach identified that, although averaged values showed a sufficient degree of accuracy, fluctuations for single measurement points have high variation which limit the use of the method for field-level assessments.
KITopen (Karlsruhe I... arrow_drop_down KITopen (Karlsruhe Institute of Technologie)Article . 2020License: CC BYData sources: Bielefeld Academic Search Engine (BASE)CGIAR CGSpace (Consultative Group on International Agricultural Research)Article . 2020License: CC BYFull-Text: https://hdl.handle.net/10568/109955Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/agriculture10080350&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 21 citations 21 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert KITopen (Karlsruhe I... arrow_drop_down KITopen (Karlsruhe Institute of Technologie)Article . 2020License: CC BYData sources: Bielefeld Academic Search Engine (BASE)CGIAR CGSpace (Consultative Group on International Agricultural Research)Article . 2020License: CC BYFull-Text: https://hdl.handle.net/10568/109955Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/agriculture10080350&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2020 GermanyPublisher:Proceedings of the National Academy of Sciences Funded by:ANR | BASC, EC | FutureMARESANR| BASC ,EC| FutureMARESOsamu Saito; Yunne-Jai Shin; Elena Bukvareva; Guy F. Midgley; Carlo Rondinini; Ignacio Palomo; Ignacio Palomo; Melanie Kolb; Almut Arneth; Thierry Oberdorff; Paul Leadley;Recent assessment reports by the Intergovernmental Panel on Climate Change (IPCC) and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) have highlighted the risks to humanity arising from the unsustainable use of natural resources. Thus far, land, freshwater, and ocean exploitation have been the chief causes of biodiversity loss. Climate change is projected to be a rapidly increasing additional driver for biodiversity loss. Since climate change and biodiversity loss impact human societies everywhere, bold solutions are required that integrate environmental and societal objectives. As yet, most existing international biodiversity targets have overlooked climate change impacts. At the same time, climate change mitigation measures themselves may harm biodiversity directly. The Convention on Biological Diversity’s post-2020 framework offers the important opportunity to address the interactions between climate change and biodiversity and revise biodiversity targets accordingly by better aligning these with the United Nations Framework Convention on Climate Change Paris Agreement and the Sustainable Development Goals. We identify the considerable number of existing and proposed post-2020 biodiversity targets that risk being severely compromised due to climate change, even if other barriers to their achievement were removed. Our analysis suggests that the next set of biodiversity targets explicitly addresses climate change-related risks since many aspirational goals will not be feasible under even lower-end projections of future warming. Adopting more flexible and dynamic approaches to conservation, rather than static goals, would allow us to respond flexibly to changes in habitats, genetic resources, species composition, and ecosystem functioning and leverage biodiversity’s capacity to contribute to climate change mitigation and adaptation.
KITopen (Karlsruhe I... arrow_drop_down KITopen (Karlsruhe Institute of Technologie)Article . 2021License: CC BY NC NDData sources: Bielefeld Academic Search Engine (BASE)Proceedings of the National Academy of SciencesArticle . 2020 . Peer-reviewedLicense: CC BY NC NDData sources: CrossrefRecolector de Ciencia Abierta, RECOLECTAArticle . 2020License: CC BY NC SAData sources: Recolector de Ciencia Abierta, RECOLECTAadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1073/pnas.2009584117&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 193 citations 193 popularity Top 0.1% influence Top 10% impulse Top 0.1% Powered by BIP!
more_vert KITopen (Karlsruhe I... arrow_drop_down KITopen (Karlsruhe Institute of Technologie)Article . 2021License: CC BY NC NDData sources: Bielefeld Academic Search Engine (BASE)Proceedings of the National Academy of SciencesArticle . 2020 . Peer-reviewedLicense: CC BY NC NDData sources: CrossrefRecolector de Ciencia Abierta, RECOLECTAArticle . 2020License: CC BY NC SAData sources: Recolector de Ciencia Abierta, RECOLECTAadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1073/pnas.2009584117&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu