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Dataset containing all greenhouse gas emissions data submitted by countries under climate change convention (including CRF data) as published by the UNFCCC secretariat at 2024-07-05. Changes in this version compared to version 2024-07-04: No data changes. Provide the full dataset as a single parquet file instead of a collection of parquet files in a zip file. The dataset is also available via datalad. To obtain the dataset with datalad, see the instructions at https://github.com/mikapfl/unfccc_di_data .

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.MOHC.HadGEM3-GC31-MM.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 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.

During three cruises in the Mid Atlantic Ridge area in 2016 and 2017, we studied the biomass of mesopelagic fish down to a depth of 600 m and identified and quantified the species composition of the catches. The biomass density was estimated considering the volume of water filtered by the cross-sectional area of the trawl blinded with 16 mm meshes (130 m–2), the distanced covered by the trawl (m) at a towing speed (transformed to m s–1) the effective tow time (min) and the catch (kg).

Marine prey and predators will respond to future climate through physiological and behavioral adjustments. However, our understanding of how such direct effects may shift the outcome of predator–prey interactions is still limited. Here, we investigate the effects of ocean warming and acidification on foraging behavior and biomass of a common prey (shrimps, Palaemon spp.) tested in large mesocosms harboring natural resources and habitats. Acidification did not alter foraging behavior in prey. Under warming, however, prey showed riskier behavior by foraging more actively and for longer time periods, even in the presence of a live predator. No effects of longer-term exposure to climate stressors were detected on prey biomass. Our findings suggest that ocean warming may increase the availability of some prey to predators via a behavioral pathway (i.e., increased risk-taking by prey), likely by elevating metabolic demand of prey species. In order to allow full comparability with other ocean acidification data sets, the R package seacarb (Gattuso et al, 2020) was used to compute a complete and consistent set of carbonate system variables, as described by Nisumaa et al. (2010). In this dataset the original values were archived in addition with the recalculated parameters (see related PI). The date of carbonate chemistry calculation by seacarb is 2020-12-08.

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.PMIP.NCC.NorESM1-F' 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 NorESM1-F (a fast version of NorESM that is designed for paleo and multi-ensemble simulations) climate model, released in 2018, includes the following components: atmos: CAM4 (2 degree resolution; 144 x 96; 32 levels; top level 3 mb), land: CLM4, landIce: CISM, ocean: MICOM (1 degree resolution; 360 x 384; 70 levels; top grid cell minimum 0-2.5 m [native model uses hybrid density and generic upper-layer coordinate interpolated to z-level for contributed data]), ocnBgchem: HAMOCC5.1, seaIce: CICE4. The model was run by the NorESM Climate modeling Consortium consisting of CICERO (Center for International Climate and Environmental Research, Oslo 0349), MET-Norway (Norwegian Meteorological Institute, Oslo 0313), NERSC (Nansen Environmental and Remote Sensing Center, Bergen 5006), NILU (Norwegian Institute for Air Research, Kjeller 2027), UiB (University of Bergen, Bergen 5007), UiO (University of Oslo, Oslo 0313) and UNI (Uni Research, Bergen 5008), Norway. Mailing address: NCC, c/o MET-Norway, Henrik Mohns plass 1, Oslo 0313, Norway (NCC) in native nominal resolutions: atmos: 250 km, land: 250 km, landIce: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

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.

This is the dataset used to analyse biomass of fauna collected in farmed and wild kelp at the West coast of Norway (Søre Sunnmøre) in April 2019. Coordinates are given in the fil. 

Cities are progressively heightening their climate aspirations to curtail urban carbon emis- sions and establish a future where economies and communities can flourish within the Earth’s eco- logical limits. Consequently, numerous climate initiatives are being launched to control urban car- bon emissions, targeting various sectors, including transport, residential, agricultural, and energy. However, recent scientific literature underscores the disproportionate distribution of climate poli- cies. While cities in the Global North have witnessed several initiatives to combat climate change, cities in the Global South remain uncovered and highly vulnerable to climate hazards. To address this disparity, we employed the Balanced Iterative Reducing and Clustering using the Hierarchies (BRICH) algorithm to cluster cities from diverse geographical areas that exhibit comparable socio- economic profiles. This clustering strives to foster enhanced cooperation and collaboration among cities globally, with the goal of addressing climate change in a comprehensive manner. In summary, we identified similarities, pa􏰀erns, and clusters among peer cities, enabling mutual and generaliza- ble learning among worldwide peer-cities regarding urban climate policy exchange. This exchange occurs through three approaches: (i) inner-mutual learning, (ii) cross-mutual learning, and (iii) outer-mutual learning. Our findings mark a pivotal stride towards a􏰀aining worldwide climate ob- jectives through a shared responsibility approach. Furthermore, they provide preliminary insights into the implementation of “urban climate policy exchange” among peer cities on a global scale.