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Research data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ 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.HighResMIP.MOHC.HadGEM3-GC31-MM.control-1950' 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 HadGEM3-GC3.1-N216ORCA025 climate model, released in 2016, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N216; 432 x 324 longitude/latitude; 85 levels; top level 85 km), land: JULES-HadGEM3-GL7.1, ocean: NEMO-HadGEM3-GO6.0 (eORCA025 tripolar primarily 0.25 deg; 1440 x 1205 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: CICE-HadGEM3-GSI8 (eORCA025 tripolar primarily 0.25 deg; 1440 x 1205 longitude/latitude). The model was run by the Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK (MOHC) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
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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:Neubauer, David;
Neubauer, David
Neubauer, David in OpenAIREFerrachat, Sylvaine;
Siegenthaler-Le Drian, Colombe; Stoll, Jens; +18 AuthorsFerrachat, Sylvaine
Ferrachat, Sylvaine in OpenAIRENeubauer, David;
Neubauer, David
Neubauer, David in OpenAIREFerrachat, Sylvaine;
Siegenthaler-Le Drian, Colombe; Stoll, Jens; Folini, Doris Sylvia;Ferrachat, Sylvaine
Ferrachat, Sylvaine in OpenAIRETegen, Ina;
Tegen, Ina
Tegen, Ina in OpenAIREWieners, Karl-Hermann;
Wieners, Karl-Hermann
Wieners, Karl-Hermann in OpenAIREMauritsen, Thorsten;
Stemmler, Irene; Barthel, Stefan; Bey, Isabelle;Mauritsen, Thorsten
Mauritsen, Thorsten in OpenAIREDaskalakis, Nikos;
Heinold, Bernd;Daskalakis, Nikos
Daskalakis, Nikos in OpenAIREKokkola, Harri;
Kokkola, Harri
Kokkola, Harri in OpenAIREPartridge, Daniel;
Rast, Sebastian; Schmidt, Hauke;Partridge, Daniel
Partridge, Daniel in OpenAIRESchutgens, Nick;
Stanelle, Tanja;Schutgens, Nick
Schutgens, Nick in OpenAIREStier, Philip;
Stier, Philip
Stier, Philip in OpenAIREWatson-Parris, Duncan;
Watson-Parris, Duncan
Watson-Parris, Duncan in OpenAIRELohmann, Ulrike;
Lohmann, Ulrike
Lohmann, Ulrike in OpenAIREProject: 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.HAMMOZ-Consortium.MPI-ESM-1-2-HAM.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-HAM climate model, released in 2017, includes the following components: aerosol: HAM2.3, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), atmosChem: sulfur chemistry (unnamed), land: JSBACH 3.20, 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 ETH Zurich, Switzerland; Max Planck Institut fur Meteorologie, Germany; Forschungszentrum Julich, Germany; University of Oxford, UK; Finnish Meteorological Institute, Finland; Leibniz Institute for Tropospheric Research, Germany; Center for Climate Systems Modeling (C2SM) at ETH Zurich, Switzerland (HAMMOZ-Consortium) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2012 United KingdomPublisher:Wiley Frank Rosell; Ruairidh D. Campbell; Ruairidh D. Campbell; Ruairidh D. Campbell; David W. Macdonald;Pierre Nouvellet;
Chris Newman;Pierre Nouvellet
Pierre Nouvellet in OpenAIREpmid: 24501052
AbstractEcologists are increasingly aware of the importance of environmental variability in natural systems. Climate change is affecting both the mean and the variability in weather and, in particular, the effect of changes in variability is poorly understood. Organisms are subject to selection imposed by both the mean and the range of environmental variation experienced by their ancestors. Changes in the variability in a critical environmental factor may therefore have consequences for vital rates and population dynamics. Here, we examine ≥90‐year trends in different components of climate (precipitation mean and coefficient of variation (CV); temperature mean, seasonal amplitude and residual variance) and consider the effects of these components on survival and recruitment in a population of Eurasian beavers (n = 242) over 13 recent years. Within climatic data, no trends in precipitation were detected, but trends in all components of temperature were observed, with mean and residual variance increasing and seasonal amplitude decreasing over time. A higher survival rate was linked (in order of influence based on Akaike weights) to lower precipitation CV (kits, juveniles and dominant adults), lower residual variance of temperature (dominant adults) and lower mean precipitation (kits and juveniles). No significant effects were found on the survival of nondominant adults, although the sample size for this category was low. Greater recruitment was linked (in order of influence) to higher seasonal amplitude of temperature, lower mean precipitation, lower residual variance in temperature and higher precipitation CV. Both climate means and variance, thus proved significant to population dynamics; although, overall, components describing variance were more influential than those describing mean values. That environmental variation proves significant to a generalist, wide‐ranging species, at the slow end of the slow‐fast continuum of life histories, has broad implications for population regulation and the evolution of life histories.
Global Change Biolog... arrow_drop_down Global Change BiologyArticle . 2012 . Peer-reviewedLicense: Wiley Online Library User AgreementData sources: Crossrefadd 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.euAccess RoutesGreen bronze 57 citations 57 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert Global Change Biolog... arrow_drop_down Global Change BiologyArticle . 2012 . Peer-reviewedLicense: Wiley Online Library User AgreementData sources: Crossrefadd 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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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2017 United KingdomPublisher:British Editorial Society of Bone & Joint Surgery Authors: Patrick, Sheila;McDowell, Andrew;
Lee, Andrew;McDowell, Andrew
McDowell, Andrew in OpenAIREFrau, Alessandra;
+4 AuthorsFrau, Alessandra
Frau, Alessandra in OpenAIREPatrick, Sheila;McDowell, Andrew;
Lee, Andrew;McDowell, Andrew
McDowell, Andrew in OpenAIREFrau, Alessandra;
Martin, Una; Gardner, Evelyn; McLorinan, G.C.; Eames, N;Frau, Alessandra
Frau, Alessandra in OpenAIREpmid: 28963158
Aims The aim of this study was to determine whether the sequential application of povidone iodine-alcohol (PVI) followed by chlorhexidine gluconate-alcohol (CHG) would reduce surgical wound contamination to a greater extent than PVI applied twice in patients undergoing spinal surgery. Patients and Methods A single-centre, interventional, two arm, parallel group randomised controlled trial was undertaken, involving 407 patients who underwent elective spinal surgery. For 203 patients, the skin was disinfected before surgery using PVI (10% [w/w (1% w/w available iodine)] in 95% industrial denatured alcohol, povidone iodine; Videne Alcoholic Tincture) twice, and for 204 patients using PVI once followed by CHG (2% [w/v] chlorhexidine gluconate in 70% [v/v] isopropyl alcohol; Chloraprep with tint). The primary outcome measure was contamination of the wound determined by aerobic and anaerobic bacterial growth from samples taken after disinfection. Results The detection of viable bacteria in any one of the samples taken after disinfection (culture-positive) was significantly lower in the group treated with both PVI and CHG than in the group treated with PVI alone (59 (29.1%) versus 85 (41.7%), p = 0.009; odds ratio 0.574; 95% confidence interval, 0.380 to 0.866). Conclusions Antisepsis of the skin with the sequential application of PVI and CHG more effectively reduces the contamination of a surgical wound than PVI alone. Cite this article: Bone Joint J 2017;99-B:1354–65.
The Bone & Joint Jou... arrow_drop_down Queen's University Belfast Research PortalArticle . 2017Data 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.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen bronze 19 citations 19 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert The Bone & Joint Jou... arrow_drop_down Queen's University Belfast Research PortalArticle . 2017Data 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.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2022Publisher:MDPI AG Authors: Tingting Liu;Xiaoxian Zhu;
Xiaoxian Zhu
Xiaoxian Zhu in OpenAIREMengqiu Cao;
Mengqiu Cao
Mengqiu Cao in OpenAIREdoi: 10.3390/su142114112
Although the United Nations’ Sustainable Development Goals (SDGs) advocate, through SDG 4 and SDG 10, equitable quality education and the reduction of inequalities within and between countries, respectively, few studies have examined how inequalities in regional sustainability influence higher education. Therefore, this study aims to examine the relationship between regional sustainability and higher education in China using fixed-effects panel modelling. A systematic force framework showing how regional sustainability drives higher education was constructed from economic, social, and environmental perspectives, and the endogeneity in the process of how regional sustainability affects higher education was explored by introducing one-year lagged values as instrumental variables. Our results show that regional sustainability has a significant impact on higher educational attainment in China, with differing effects in the eastern, central, and western regions, respectively. In central China, economic sustainability plays a significant positive role in higher educational attainment; in the western region, economic and social sustainability have stronger positive effects, while environmental sustainability has significantly negative effects. In terms of policy implications, our findings can be used to support regional development policies to promote regional higher education.
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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.euAccess Routesgold 4 citations 4 popularity Top 10% 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=10.3390/su142114112&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2021 United KingdomPublisher:Elsevier BV Funded by:UKRI | Energy Saving Innovations...UKRI| Energy Saving Innovations and Economy-Wide Rebound EffectsThe increasing depletion of natural resources, combined with a wider set of pressures on the environment, has, in recent years, highlighted the need for a more efficient use of energy and a development process that involves alternative energy sources. Energy efficiency has received much attention as a solution, implying both monetary and emissions savings. However, the latter may be partially offset by the income and demand effects of the former, both in more efficient sectors and in spreading to the wider economy. This is the problem of rebound effects. Taking Spain as a case study, and introducing an energy-related CGE model that develops the inclusion of renewables, this paper evaluates a combination of efficiency initiatives to deliver both reduced energy use by households and a more sustainable supply of energy. Our findings suggest that a package aimed at improving efficiency in household electricity and petroleum use, combined with a more competitive supply of energy from renewable sources, may be the only way to get reductions in all energy use, and thus benefit the economy. Specifically, we consider how this package may lead to positive economic impacts and associated rebound effects, where the latter are focused on a greener energy supply.
CORE arrow_drop_down StrathprintsArticle . 2021License: CC BY NC NDData 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.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 6 citations 6 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
visibility 7visibility views 7 download downloads 7 Powered bymore_vert CORE arrow_drop_down StrathprintsArticle . 2021License: CC BY NC NDData 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=10.1016/j.energy.2021.121335&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:NSF Arctic Data Center Atwood, Trisha; Beard, Karen; Waring, Bonnie; Adkins, Jaron;Saunders, Taylor;
Saunders, Taylor
Saunders, Taylor in OpenAIREdoi: 10.18739/a2cc0tv7w
Global change drivers that modify the quality and quantity of litter inputs to soil affect greenhouse gas fluxes, and thereby constitute a feedback to climate change. Carbon cycling in the Yukon-Kuskokwim (Y-K) River Delta, a subarctic wetland system, is influenced by landscape variations in litter quality and quantity generated by herbivores (migratory birds) that create ‘grazing lawns’ of short stature, nitrogen-rich vegetation. To identify the mechanisms by which these changes in litter inputs affect soil carbon balance, we independently manipulated qualities and quantities of litter representative of levels found in the Y-K Delta in a fully factorial microcosm experiment. We measured carbon dioxide (CO2) fluxes from these microcosms weekly. To help us identify how litter inputs influenced greenhouse gas fluxes, we sequenced soil fungal and bacterial communities, and measured soil microbial biomass carbon, dissolved carbon, inorganic nitrogen, and enzyme activity. We found that positive correlations between litter input quantity and CO2 flux were dependent upon litter type, due to differences in litter stoichiometry and changes to the structure of decomposer communities, especially the soil fungi. These community shifts were particularly pronounced when litter was added in the form of herbivore feces, and in litter input treatments that induced nitrogen limitation (i.e., senesced litter). The sensitivity of carbon cycling to litter quality and quantity in this system demonstrates that herbivores can strongly impact greenhouse gas fluxes through their influence on plant growth and tissue chemistry.
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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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: Liddicoat, Spencer; Wiltshire, Andy; Robertson, Eddy;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.C4MIP.MOHC.UKESM1-0-LL' 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 UKESM1.0-N96ORCA1 climate model, released in 2018, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), atmosChem: UKCA-StratTrop, land: JULES-ES-1.0, ocean: NEMO-HadGEM3-GO6.0 (eORCA1 tripolar primarily 1 deg with meridional refinement down to 1/3 degree in the tropics; 360 x 330 longitude/latitude; 75 levels; top grid cell 0-1 m), ocnBgchem: MEDUSA2, seaIce: CICE-HadGEM3-GSI8 (eORCA1 tripolar primarily 1 deg; 360 x 330 longitude/latitude). The model was run by the Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK (MOHC) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 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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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
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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:Ziehn, Tilo;
Ziehn, Tilo
Ziehn, Tilo in OpenAIREDix, Martin;
Dix, Martin
Dix, Martin in OpenAIREMackallah, Chloe;
Mackallah, Chloe
Mackallah, Chloe in OpenAIREChamberlain, Matthew;
+4 AuthorsChamberlain, Matthew
Chamberlain, Matthew in OpenAIREZiehn, Tilo;
Ziehn, Tilo
Ziehn, Tilo in OpenAIREDix, Martin;
Dix, Martin
Dix, Martin in OpenAIREMackallah, Chloe;
Mackallah, Chloe
Mackallah, Chloe in OpenAIREChamberlain, Matthew;
Chamberlain, Matthew
Chamberlain, Matthew in OpenAIRELenton, Andrew;
Lenton, Andrew
Lenton, Andrew in OpenAIRELaw, Rachel;
Druken, Kelsey; Ridzwan, Syazwan Mohamed;Law, Rachel
Law, Rachel in OpenAIREProject: 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.CSIRO.ACCESS-ESM1-5.hist-nat' 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 Australian Community Climate and Earth System Simulator Earth System Model Version 1.5 climate model, released in 2019, includes the following components: aerosol: CLASSIC (v1.0), atmos: HadGAM2 (r1.1, N96; 192 x 145 longitude/latitude; 38 levels; top level 39255 m), land: CABLE2.4, ocean: ACCESS-OM2 (MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m), ocnBgchem: WOMBAT (same grid as ocean), seaIce: CICE4.1 (same grid as ocean). The model was run by the Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia (CSIRO) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Embargo end date: 28 Apr 2022Publisher:Dryad This spreadsheet contains nine tabs to present the data used in the article 'Microclimate-driven trends in spring-emergence phenology in a temperate reptile (Vipera berus): Evidence for a potential ‘climate trap’?' (Turner & Maclean, 2022; Ecology and Evolution). The first tab, labelled 'Metadata_README', contains metadata for the dataset, including identification and affliliations of the authors, a description of the tabs in the spreadsheet, and descriptions of data labels used in the spreadsheet tabs. The second tab, labelled 'adder_sightings', comprises of records of Vipera berus (adder) sightings in Cornwall, United Kingdom, sourced from the Environmental Records Centre for Cornwall and the Isles of Scilly (www.erccis.org.uk), the Record Pool (www.recordpool.org.uk) and the Cornish Biodiversity Network (www.cornishbiodiversitynetwork.org). Due to data sensitivities and issues associated with the General Data Protection Regulation, information pertaining to the locations and dates of adder sightings in some instances in the dataset have only be provided at reduced spatial and temporal resolutions. A unique identification code for locations has been attributed to records. For full-resolution access, contact the data custodian and corresponding author. The raw datasets of adder sightings were filtered prior to inclusion in the analysis in Turner and Maclean. See the main text for all filtering procedures and microclimate modelling. The remaining tabs contain data relating to each adder sighting location in 'adder_sightings' for each year 1983 - 2017 computed from microclimate models using the microclima R package (Maclean et al., 2019). The third tab, labelled 'total_spring_frost', contains annual rates of spring ground frost. The fourth, fifth and sixt tabs, labelled 'Cue1(i)', "Cue1(ii)', and 'Cue1(iii)', each contain predicted annual adder emergence timing and computed rates of post-emergence spring ground frost using the 5th, 2.5th and 10th percentile thresholds, respectively, of an accumulated (degree-hours) temperature cue for adder emergence. The seventh tab, labelled 'Cue2', contains predicted annual adder emergence timing and computed rates of post-emergence spring ground frost using a sharp rise in accumulated (degree-hours) temperature cue for adder emergence. The eighth tab, labelled 'Cue3', contains predicted annual adder emergence timing and computed rates of post-emergence spring ground frost using a below-ground temperature gradient collapse cue for adder emergence. Lastly, the ninth tab, labelled 'Cue4', contains predicted annual adder emergence timing and computed rates of post-emergence spring ground frost using a critical air temperature (10°C) cue for adder emergence. The main text presents the analysis of adder emergence and spring ground frost data from the 'Cue1(i)'. Analysis of data from 'Cue1(ii)', 'Cue1(iii)', 'Cue2', 'Cue3', and 'Cue4' are presented in the Supplementary Information for Turner and Maclean. Climate change will increase the exposure of organisms to higher temperatures, but can also drive phenological shifts that alter their susceptibility to conditions at the onset of breeding cycles. Organisms rely on climatic cues to time annual life-cycle events, but the extent to which climate change has altered cue reliability remains unclear. Here, we examine the risk of a ‘climate trap’ – a climatically-driven desynchronisation of the cues that determine life-cycle events and fitness later in the season in a temperate reptile, the European adder (Vipera berus). During the winter, adders hibernate underground, buffered against sub-zero temperatures, and re-emerge in the spring to reproduce. We derived annual spring-emergence trends between 1983 and 2017 from historical observations in Cornwall, United Kingdom, and related these trends to the microclimatic conditions that adders experienced. Using a mechanistic microclimate model, estimates of below- and near-ground temperatures were used to derive accumulated degree-hour and absolute temperature thresholds that predicted annual spring-emergence timing. Trends in annual emergence timing and subsequent exposure to ground frost were then quantified. We found that adders have advanced their phenology towards earlier emergence. Earlier emergence was associated with increased exposure to ground frost and, contradicting the expected effects of macroclimate warming, increased post-emergence exposure to ground frost at some locations. The susceptibility of adders to this ‘climate trap’ was related to the rate at which frost risk diminishes relative to advancement in phenology, which depends on the seasonality of climate. We emphasise the need to consider exposure to changing microclimatic conditions when forecasting biological impacts of climate change.
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