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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.

Building energy simulation is an analytical process to help building owners and designers evaluate the energy performance of the building. Uncertainty in the building energy modelling influences the building renovation from two perspectives: 1) calculating as-built energy consumption, 2) analysing the energy performance of renovation alternatives. Energy models can enhance by incorporating contextual and surrounding data. To this aim, we conducted a systematic study to investigate the effect of surrounding buildings in different distances, heights, and directions in studying the as-built energy consumption of an example building. The research also investigates the impact of a specific surrounding building on the energy performance of three different renovation alternatives, namely the modification of windows, external walls, and roofs. The results demonstrate that a higher height to distance ratio of the surrounding buildings often causes a decrease in energy consumption. In addition, a surrounding building located in the south direction causes more effect on the energy result than other directions when the building is in the northern hemisphere. For renovation scenarios, if there is a specific building in the south of the building under renovation, the window modification leads to less energy consumption than other renovation scenarios. The paper discusses that for renovation projects, an initial examination of surrounding buildings before selecting the renovation alternative is crucial; since different placements of surrounding buildings can affect the performance of renovation scenarios differently, which can cause a variation in the cost of renovation.

Over the last decade, the science of climate change has evolved rapidly. Nevertheless, there is considerable uncertainty about the evolution of climate over 50 or 100 year time-scales, and while confidence in global circulation models is growing there is greater appreciation of the uncertainties involved, especially in ‘downscaling' the global models to produce climate projections at the regional and local scales. Acknowledging this unavoidable uncertainty, we have developed a statistical approach to incorporate a wide range of possible climate scenarios in impacts models that uses median, and 90th and 10th percentile changes in temperature and rainfall from a number of general circulation models, from which future scenarios are developed. These scenarios were compiled by SANBI (Guy Midgley and Danni Guo). Highest temperature increases and largest rainfall decreases were combined to generate a ‘high-risk' or ‘worst case' scenario, and vice-versa for a ‘low-risk' or ‘best case' scenario. Likewise the median projected changes in rainfall and temperature were combined to generate an intermediate scenario. Based on outputs from 15 global circulation models that were spatially interpolated, we developed three climate scenarios for South Africa for approximately 2050 as follows:1 - Best case scenario: smallest predicted increases in temperature and changes in rainfall, - Intermediate scenario: middle of the range (median) predicted increases in temperature increases and changes in rainfall, - Worst case scenario: greatest predicted increases in temperature and changes in rainfall. This means that the results presented are not dependent on any particular global circulation model but hold under a very wide range of possible climate futures, enhancing the robustness of conclusions as climate changes and as climate science revises outputs and projections. Note that the study was based on medium term data (for 2050) as this represented a compromise between the uncertainty associated with the very long time horizon data (2100) and the very small changes predicted by the shorter duration data (e.g. 2020). This 50 year time horizon also represents a reasonable long term planning horizon as it is within the lifetimes of most people currently living. When this study is updated based on new climate data, if possible (we are dependent on work done by a third party), we will include both the medium term and long term predictions. In the interim, as climate change occurs gradually over time, it is useful to conceptualize the worst case scenarios for 2050 as being likely to represent the intermediate case scenarios in 2100. Available documentation: Driver A., Sink, K.J., Nel, J.N., Holness, S., Van Niekerk, L., Daniels, F., Madjiet, P.A., Jonas, Z. and Maze, K. 2012. National Biodiversity Assessment 2011: An assessment of South Africa's biodiversity and ecosystems. Synthesis Report. South African National Biodiversity Institute and Department of Environmental Affairs, Pretoria.