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Reconstruction of 1870-2018 ocean heat content (OHC_GF_1870_2018.nc) and thermosteric sea level (ThSL_GF_1870_2018.nc) using Green's functions. The method is described in Zanna et al., PNAS, 2019 This dataset is an update from the original published version with changes described here.

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.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.

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.

This package generates all of the model runs and plotting code for "Current infrastructure does not yet commit us to 1.5°C warming". See enclosed README file for dependencies and how to run.

Nearly three billion people in low- and middle-income countries (LMICs) rely on polluting fuels, resulting in millions of avoidable deaths annually. Polluting fuels also emit short-lived climate forcers and greenhouse gases (GHGs). Liquefied petroleum gas (LPG) and grid-based electricity are scalable alternatives to polluting fuels but have raised climate and health concerns. Here, we compare emissions and climate impacts of a business-as-usual household cooking fuel trajectory to four large-scale transitions to gas and/or grid electricity in 77 LMICs. We account for upstream and end-use emissions from gas and electric cooking, assuming electrical grids evolve according to the 2022 World Energy Outlook’s “Stated Policies” Scenario. We input the emissions into a reduced-complexity climate model to estimate radiative forcing and temperature changes associated with each scenario. We find full transitions to LPG and/or electricity decrease emissions from both well-mixed GHG and short-lived climate forcers, resulting in a roughly 5 millikelvin global temperature reduction by 2040. Transitions to LPG and/or electricity also reduce annual emissions of PM2.5 by over 6 Mt (99%) by 2040, which would substantially lower health risks from Household Air Pollution. Primary input data was collected from the following sources: Baseline household fuel choices - WHO household energy database (https://www.nature.com/articles/s41467-021-26036-x) End-use emissions - US EPA lifecycle assessment of household fuels (https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=339679&Lab=NRMRL&simplesearch=0&showcriteria=2&sortby=pubDate&timstype=Published+Report&datebeginpublishedpresented) Upstream emissions - Argonne National Labs GREET Model (https://greet.es.anl.gov/index.php) Current and future population estimates - UNECA (http://data.un.org/Explorer.aspx?d=EDATA) Input data was processed by defining household fuel choice scenarios, estimating national household fuel consumption based on these scenarios, and applying fuel-specific emission factors to create country-specific emission pathways. These emission pathways were input into the FaIR model (https://zenodo.org/record/5513022#.Yt_jfHbMLb0) which generated additional data for each scenario including time series of pollution concentrations, radiative forcing, and temperature changes. All data is provided in CSV format. Nothing proprietary is required. 

This dataset contains multi-thousand-year ensemble simulations of wintertime (December-February) precipitation total and average zonal winds at 250 hPa and 850 hPa. It includes data in a present-day scenario (2006-2015) and two future scenarios within which the world would be 1.5°C and 2.0°C warmer than pre-industrial conditions in 1850-1900. The simulations were run through the global model of the atmosphere and land surface HadAM4 (Williams et al., 2003) with a horizontal resolution of 5/6°x5/9° (approximately 60km in middle latitudes) and 38 vertical levels and a large ensemble. Following the HAPPI experiment design described by Mitchell et al. (2017), simulations were driven by prescribed fields of sea ice concentration, sea surface temperature, and atmospheric gas concentrations. The prescribed fields are observations for the present-day scenario. For future simulations, the prescribed fields were modified based on changes derived from CMIP5 multi-model means. The different realisations of the large ensemble were obtained through perturbing the initial conditions of each ensemble member on November 1st. For more details, see the description in Watson et al. (2020), who present the dataset, and in Bevacqua et al. (2021) where the dataset was used to study the spatial footprint of wintertime precipitation extremes. IMPORTANT: Note that a small fraction of the ensemble members is repeated in the dataset. Duplicates should be identified (for example, via the function duplicated() in the R software) and removed prior to any analysis. {"references": ["Bevacqua, E., Shepherd, T.G., Watson, P.A.G., Sparrow, S., Wallom, D., and Mitchell, D.: Larger spatial footprint of wintertime total precipitation extremes in a warmer climate, In review, 2021.", "Mitchell, D., AchutaRao, K., Allen, M., Bethke, I., Beyerle, U., Ciavarella, A., Forster, P. M., Fuglestvedt, J., Gillett, N., Haustein, K., Ingram, W., Iversen, T., Kharin, V., Klingaman, N., Massey, N., Fischer, E., Schleussner, C.-F., Scinocca, J., Seland, \u00d8., Shiogama, H., Shuckburgh, E., Sparrow, S., Stone, D., Uhe, P., Wallom, D., Wehner, M., and Zaaboul, R.: Half a degree additional warming, prognosis and projected impacts (HAPPI): background and experimental design, Geosci. Model Dev., 10, 571\u2013583, https://doi.org/10.5194/gmd-10-571-2017, 2017.", "Watson, P., Sparrow, S., Ingram, W., Wilson, S., Marie, D., Zappa, G., Jones, R., Mitchell, D., Woollings, T., and Allen, M.: Multi-thousand member ensemble atmospheric simulations with global 60km resolution using climateprediction.net, EGU General Assembly 2020, Online, 4\u20138 May 2020, EGU2020-10895, https://doi.org/10.5194/egusphere-egu2020-10895, 2020.", "Williams, K., Ringer, M., & Senior, C.: Evaluating the cloud response to climate change and current climate variability. Climate Dynamics, 20 (7-8), 705-721, 2003."]} We thank JASMIN and CEDA for providing the facilities required to work with climateprediction.net. We thank the volunteers who have donated their computing time to climateprediction.net.

The underlying dataset for the Net Zero Tracker's Net Zero Stocktake Report 2024.