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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.

This data corresponds to the manuscript titled: Sustainable sugarcane vinasse biorefinement toward biobased chemicals and bioenergy generation

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.

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.

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.MRI.MRI-ESM2-0.hist-bgc' 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 MRI-ESM2.0 climate model, released in 2017, includes the following components: aerosol: MASINGAR mk2r4 (TL95; 192 x 96 longitude/latitude; 80 levels; top level 0.01 hPa), atmos: MRI-AGCM3.5 (TL159; 320 x 160 longitude/latitude; 80 levels; top level 0.01 hPa), atmosChem: MRI-CCM2.1 (T42; 128 x 64 longitude/latitude; 80 levels; top level 0.01 hPa), land: HAL 1.0, ocean: MRI.COM4.4 (tripolar primarily 0.5 deg latitude/1 deg longitude with meridional refinement down to 0.3 deg within 10 degrees north and south of the equator; 360 x 364 longitude/latitude; 61 levels; top grid cell 0-2 m), ocnBgchem: MRI.COM4.4, seaIce: MRI.COM4.4. The model was run by the Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, Japan (MRI) in native nominal resolutions: aerosol: 250 km, atmos: 100 km, atmosChem: 250 km, land: 100 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

The ecological impacts of multiple stressors are hard to predict but important to understand. When multiple stressors influence foundation species, the effects can cascade throughout the ecosystem. Gulf of Mexico seagrass ecosystems are currently experiencing a suite of novel stressors, including warmer water temperatures and increased herbivory due to tropicalization and conservation efforts. We investigated the impact of warming temperatures and grazing history on plant performance, morphology, and palatability by integrating a mesocosm study using the seagrass Thalassia testudinum with feeding trials using the sea urchin Lytechinus variegatus. Warming temperatures negatively impacted T. testudinum tolerance traits, reducing belowground biomass by 34%, productivity by 74%, shoot density by 10%, and the number of leaves per plant by 24%, and negatively impacted resistance traits through 13% lower toughness of young leaves and a trend for reduced leaf carbon:nitrogen. Lytechinus variegatus individuals preferred to consume plants grown under heated conditions, which supports findings of enhanced palatability. Simulated turtle grazing impacted more plant traits than grazing by other herbivores, potentially diminishing plant resilience to future disturbances through reduced rhizome non-structural carbohydrate concentrations and increasing palatability through reduced fiber content and 23% lower leaf carbon:phosphorus. Simulated turtle, simulated parrotfish, and urchin grazing reduced leaf carbon:nitrogen by 11%, also potentially increasing nutritive value. Interactions between warming temperatures and grazers on plant traits were additive for 16 out of 19 response variables. However, the stressors non-additively impacted the number of leaves per plant, fiber content, and epiphyte load. We suggest that the impacts of grazers on leaf turnover rate and leaf age may vary based on water temperature, potentially driving these interactions. Overall, increased temperatures and grazing pressure will likely reduce seagrass resilience, structure, and biomass, potentially impacting feedback systems and producing negative consequences for seagrass cover, associated species, and ecosystem services.

Climate trends during maize growing period and their impacts on spring maize yield in North China was investigated. This dataset contains: 1) information of stations in cultivation region for spring maize in North China; 2) Trend in temperature and its effect on yield in cultivation region for spring maize in North China; 3) Trend in radiation and its effect on yield in cultivation region for spring maize in North China; 4) Trend in precipitation and its effect on yield in cultivation region for spring maize in North China. Climate trends during maize growing period and their impacts on spring maize yield in North China was investigated. This dataset contains: 1) information of stations in cultivation region for spring maize in North China; 2) Trend in temperature and its effect on yield in cultivation region for spring maize in North China; 3) Trend in radiation and its effect on yield in cultivation region for spring maize in North China; 4) Trend in precipitation and its effect on yield in cultivation region for spring maize in North China.

The beach topography and nearshore bathymetry data contained in this object where used to create a sediment budget model and produce the data figures in the paper titled “A Climatic Sand Management Model for Cardiff State Beach, CA” by Gopal et al., 2023. The data set includes annual mean beach width and nearshore mobile sediment volume estimates between 2000-2019 at South Torrey Pines State Beach and Cardiff State Beach, CA. MATLAB program code to read the data files, model the sediment budget equation in Gopal et al, and recreate the data figures in the paper is also included. Abstract: An empirically based sediment budget model is developed for Cardiff State Beach CA to assess management strategies to maintain beach width subject to mean sea level rise (MSLR) and potentially more frequent El Niño storms. Two decades (2000-2019) of surveys support the hypothesis that the rocky reefs bounding this beach retain sand added to the nearshore zone, except during strong El Niño years with more severe storm waves. The subaerial beach has widened by ~60 m during the last 20 years owing to nourishment (~17,000 cubic m/yr) of imported sand, and sand bypassed annually by dredging a lagoon inlet at the beach's updrift end. The observed widening yields 1 m/yr of mean beach width increase for each 6 cubic m/m-shoreline of added sand. A strong El Niño year is modeled with a permanent volume loss coupled with a shoreline retreat that recovers partially as the beach profile adjusts between El Niño years. Calibrated with observations from Cardiff and South Torrey Pines (a control beach), the model is used to project beach change through 2050. All modeled scenarios suggest that no bypassing or nourishment (no “management”) will result in tens of meters of beach width loss. However, continued bypassing would partially mitigate MSLR and El Niño beach width losses. An artificially built (living shoreline) dune that backs the beach, if completely undermined during strong El Niño storm waves, stores enough sand to balance one-third of the expected volume loss that year, and may make the beach more resilient and speed subsequent recovery. Model program scripts were written using MATLAB R2023a.

This dataset contains the data displayed in the figures or the article "High-resolution projections of ambient heat for major European cities using different heat metrics". The different files contain: Data_Fig1_DeltaTXx_EURO-CORDEX_1981-2010_to_3K-European-warming_RCP85.nc: Change of yearly maximum temperature in Europe between 1981-2010 and 3 °C European warming relative to 1981-2010. Data_Fig2_timeseries-GSAT-ESAT_EURO-CORDEX_CMIP5_CMIP6_1971-2100_RCP85_SSP585.xlsx: Time series of global mean surface air temperature (GSAT) for CMIP5 and CMIP6 models, and for European mean surface air temperature (ESAT) for EURO-CORDEX, CMIP5, and CMIP6 models for the period 1971-2100. Data_Fig3_TX-distribution_distance-from-city-centre_E-OBS_1981-2010.xlsx: Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for E-OBS for all investigated cities. Temperature data are indicated as a function of the distance to the city centre. Data_Fig3_TX-distribution_distance-from-city-centre_ERA5-Land_1981-2010.xlsx: Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for ERA5-Land for all investigated cities. Temperature data are indicated as a function of the distance to the city centre. Data_Fig3_TX-distribution_distance-from-city-centre_EURO-CORDEX_1981-2010.xlsx: Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for the EURO-CORDEX models for all investigated cities. Temperature data are indicated as a function of the distance to the city centre. Data_Fig3_TX-distribution_distance-from-city-centre_weather-stations_1981-2010.xlsx: Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for GSOD and ECA&D stations for all investigated cities. Temperature data are indicated as a function of the distance to the city centre. Data_Fig4_TX-ambient-heat_EURO-CORDEX_3K-European-warming.xlsx: Daytime heat metrics for the investigated cities: HWMId-TX at 3 °C European warming relative to 1981-2010, TX exceedances above 30 °C at 3 °C European warming relative to 1981-2010, and TXx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for EURO-CORDEX models. Data_Fig5_Contribution-of-explanatory-variables-to-total-explained-variance.xlsx: Contribution of different explanatory variables (climate and location factors) to the total explained variance of spatial patterns of heat metrics. Data_Fig6_TN-ambient-heat_EURO-CORDEX_3K-European-warming.xlsx: Nighttime heat metrics for the investigated cities: HWMId-TN at 3 °C European warming relative to 1981-2010, TN exceedances above 20 °C at 3 °C European warming relative to 1981-2010, and TNx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for EURO-CORDEX models. Data_Fig7_TX-ambient-heat_CMIP5_3K-European-warming.xlsx: Daytime heat metrics for the investigated cities: HWMId-TX at 3 °C European warming relative to 1981-2010, TX exceedances above 30 °C at 3 °C European warming relative to 1981-2010, and TXx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for CMIP5 models. Data_Fig7_TX-ambient-heat_CMIP6_3K-European-warming.xlsx: Daytime heat metrics for the investigated cities: HWMId-TX at 3 °C European warming relative to 1981-2010, TX exceedances above 30 °C at 3 °C European warming relative to 1981-2010, and TXx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for CMIP6 models. Data_Fig8_GCM-RCM-matrix_ambient-heat_3K-European-warming.xlsx: GCM-RCM matrices for the three heat metrics.