This dataset represents future climate change projections for Durham Region developed as part of the Guide to Conducting a Climate Change Analysis: Lessons Learned in Durham Region (2020). The dataset includes summary information for 52 climate parameters under the RCP8.5 (business-as-usual or high emissions) and RCP4.5 (moderate) emissions scenario for the short (2011-2040), medium (2041-2070) and long-term (2071-2100) future using an ensemble of climate models. For more information, visit https://www.durham.ca/en/living-here/climateenergyandresilience.aspx?_mid_=32210. A copy of the Guide can be made available upon request.

This dataset represents future climate change projections for Durham Region developed as part of the Guide to Conducting a Climate Change Analysis: Lessons Learned in Durham Region (2020). The dataset includes summary information for 52 climate parameters under the RCP8.5 (business-as-usual or high emissions) and RCP4.5 (moderate) emissions scenario for the short (2011-2040), medium (2041-2070) and long-term (2071-2100) future using an ensemble of climate models. For more information, visit https://www.durham.ca/en/living-here/climateenergyandresilience.aspx?_mid_=32210. A copy of the Guide can be made available upon request.

This dataset represents future climate change projections for Durham Region developed as part of the Guide to Conducting a Climate Change Analysis: Lessons Learned in Durham Region (2020). The dataset includes summary information for 52 climate parameters under the RCP8.5 (business-as-usual or high emissions) and RCP4.5 (moderate) emissions scenario for the short (2011-2040), medium (2041-2070) and long-term (2071-2100) future using an ensemble of climate models. For more information, visit https://www.durham.ca/en/living-here/climateenergyandresilience.aspx?_mid_=32210. A copy of the Guide can be made available upon request.

This dataset represents future climate change projections for Durham Region developed as part of the Guide to Conducting a Climate Change Analysis: Lessons Learned in Durham Region (2020). The dataset includes summary information for 52 climate parameters under the RCP8.5 (business-as-usual or high emissions) and RCP4.5 (moderate) emissions scenario for the short (2011-2040), medium (2041-2070) and long-term (2071-2100) future using an ensemble of climate models. For more information, visit https://www.durham.ca/en/living-here/climateenergyandresilience.aspx?_mid_=32210. A copy of the Guide can be made available upon request.

Monthly averages of precipitation (mm/day) for 2050-2079 from UKCP18 regional projections (12km grid), using the RCP8.5 pathway.This data contains a field for each month’s average over the period. They are named 'pr' (precipitation), the month, and 'upper' 'median' or 'lower' as per the description below. E.g. 'pr July Median'.UKCP: https://www.metoffice.gov.uk/research/approach/collaboration/ukcp/indexWhat is the data?The data is from the UKCP18 regional projections using the RCP8.5 scenario. RCP8.5 is the highest of the plausible future emissions scenarios used by the IPCC, sometimes referred to as 'business as usual'.What do the 'median', 'upper', and 'lower' values mean?This scenario is run as 12 separate ensemble members. To select which ensemble members to use, a single value for the mean UK precipitation for the period 2050-2079 was taken from each ensemble member. They were then ranked in order from lowest precipitation to highest. The 'lower' fields are this data is the second lowest ranked ensemble member. The 'higher' fields are the second highest ranked ensemble member. The 'median' fields are the central (7th) ranked ensemble member.This gives a median value, and a spread of the ensemble members indicating the level of uncertainty in the projections.Recommendations for use of this data:1. We don't recommend using this data at the resolution of a single cell.The higher resolution of this data improves representation of topography, coasts, etc. but at the same time increases some of the uncertainty for individual grid cells. And so it is recommended to work with multiple grid cells, or an average of grid cells around a point to improve certainty.2. Consider whether the lower, median, or upper projections, or a combination, are most suitable for your use case.As described above, the spread of the ensemble members shown by the lower, median, and upper values indicates the level of uncertainty in the projections.Data source:pr_rcp85_land-rcm_uk_12km_12_mon-30y_200912-207911.nc (median)pr_rcp85_land-rcm_uk_12km_05_mon-30y_200912-207911.nc (lower)pr_rcp85_land-rcm_uk_12km_04_mon-30y_200912-207911.nc (upper)UKCP18 v20190731 (downloaded 04/11/2021)This dataset forms part of the Met Office’s Climate Data Portal service. This service is currently in Beta. We would like your help to further develop our service, please send us feedback via the site - https://climate-themetoffice.hub.arcgis.com/

Climate Distance Mapper is an interactive web mapping application designed to facilitate informed seed sourcing decisions and to aid in directing regional seed collections. Implemented as a shiny web application (Chang et al. 2017), Climate Distance Mapper is hosted on the web at: https://usgs-werc-shinytools.shinyapps.io/Climate_Distance_Mapper/. The application is designed to guide restoration seed sourcing in the desert southwest by allowing users to interactively match seed sources with restoration sites climatic differences – in the form of multivariate climate distance values – between restoration sites and the surrounding landscape. Climatic distances are based on a combination of variables likely to influence patterns of local adaptation among plant populations, including: mean annual temperature, summer maximum temperature, winter minimum temperature, temperature seasonality, annual temperature range, mean annual precipitation, winter precipitation, summer precipitation, precipitation seasonality, long-term winter precipitation variability, and long-term summer precipitation variability. The climate variables are first transformed into principal components (PCA analysis), which standardizes the variables and accounts for collinearity while emphasizing the most important climate gradients. All climate data is obtained through ClimateNA (Wang et al. 2016), an application for dynamically downscaling PRISM climate data (Daly et al. 2008). The fundamental unit of measure in Climate Distance Mapper is the multivariate climate distance, which is defined as the multivariate Euclidean distance between climate-based principal components at input points and those at other grid cells throughout the chosen spatial extent. All distance calculations incorporate 5 principal components derived from an original set of 12 climate variables. The conversion to principal components standardizes and accounts for collinearity in climate variables, while ensuring that the most important climate gradients are given the most weight (i.e., because principal components are ordered in terms of the variability they express). Conceptually, multivariate Euclidean distance with principal components may be viewed as an approximation of the multivariate Mahalanobis distance calculated on the original climate variables. Mahalanobis distance is the distance between groups weighted by the within-group dispersion. Our procedure for calculating climate distance is thereby meant to emphasize natural gradients that distinguish climatic regimes across landscapes without giving any variable undue weight due to differences in units or scale. All multivariate climate distance values are relativized to the 95th percentile of the maximum possible climate distance in a given region (regions may be set dynamically by the user), such that values roughly correspond to a percentage of the total climate variability (using the 95th percentile of the maximum climate distance reduces the influence of outlier grid cells). This means that a climate distance of 0.2 is roughly analogous to 20% of the total climate variability in the selected region. Within Climate Distance Mapper, users can also constrain results to a specific level of climate similarity (e.g., areas with 90% similar climates). Climate Distance Mapper also supports projections into future climate – either by comparing the current climate at input points with the future climate across the landscape (forward projection, from current climate forward to future climate), or by comparing the future climate at input points with the current climate across the landscape (backward projection, from future climate back to current climate). Future climate is defined as the predicted 30-year average for the 2040-2070 period using an ensemble average of three models from the Coupled Model Intercomparison Project phase 5 (CMIP5) database corresponding to the 5th IPCC Assessment Report for future projections (IPCC 2014). We selected the RCP 4.5 (moderate emission) and RCP8.5 (high emission) scenarios for projections. The future climate models include CCSM4 (Community Climate System Model, version 4.0), GFDL-CM3 (Geophysical Fluid Dynamics Laboratory Climate Model, version 3), and HadGEM2-ES (Hadley Centre Global Environmental Model, version 2 (Earth System). All future climate data were generated using ClimateNA (Wang et al. 2016). References: Chang, W., Cheng, J., Allaire, JJ., Xie, Y., McPherson, J. 2017. shiny: Web Application Framework for R. R package version 1.0.0. https://CRAN.R-project.org/package=shiny. Daly, C., M. Halbleib, J. J. Smith, W. P. Gibson, M. K. Doggett, G. H. Taylor, J. Curtis, and P. A. Pasteris. 2008. Physiographically-sensitive mapping of temperature and precipitation across the conterminous United States. International Journal of Climatology 28:2031–2064. IPCC. 2014. Climate Change 2014: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press. Wang, T., A. Hamann, D. Spittlehouse, and C. Carroll. 2016. Locally downscaled and spatially customizable climate data for historical and future periods for North America. PLoS ONE 11: e0156720. https://doi.org/10.1371/journal.pone.0156720

Summary: Ongoing greenhouse gas emissions are simultaneously shifting many elements of Earth’s climate beyond thresholds that can impact humanity. By affecting the balance between incoming solar radiation and outgoing infrared radiation, man-made greenhouse gases are increasing the Earth’s energy budget ultimately leading to warming . Given interconnected physics, warming can affect other aspects of the Earth’s climate system. For instance, by enhancing water evaporation and increasing the air’s capacity to hold moisture, warming can lead to i) extreme precipitation, also increasing risk of floods, in commonly wet places or ii) drought in commonly dry places, also increasing risk of wildfires, and heatwaves when heat transfer from water evaporation ceases. In the oceans, CO2 interacts with water to produce carbonic acid leading to ocean acidification whereas warming of water molecules increases the volume they occupy adding to the sea-level rise from melting land ice. Ocean warming can also supply moisture increasing the strength of storms. In an extensive literature review, we found traceable evidence for 467 pathways in which human health, water, food, economy, infrastructure and security have been recently impacted by climate hazards such as warming, heatwaves, precipitation, drought, floods, fires, storms, sea level rise, and changes in natural land cover and ocean chemistry. By 2100, on average, the world’s population will be exposed concurrently to the equivalent of the largest magnitude in one of these hazards if greenhouse gasses are aggressively reduced or three if they are not; some tropical coastal areas will be exposed to the largest changes in up to six hazards concurrently. These findings highlight that greenhouse gas emissions pose a broad threat to humanity by simultaneously intensifying many hazards, which humanity is vulnerable to.Map description: This web app shows the cumulative index of 11 climate hazards: warming, drought, heatwaves, fires, precipitation, floods, storms, water scarcity, sea level rise, and changes in natural land cover and ocean chemistry. All climate hazards were scaled between zero and the 95th percentile change projected in the given hazard globally by 2095 under RCP 8.5 (worse case scenario); In other words, a pixel with a value of zero in a given hazards suggests that that hazard will not change in that pixel. In turn, a pixel with a value of 1 suggests that the most extreme increase of that hazard anywhere in the world will occur in that pixel. This standardization allowed for the summation of changes in all hazards at a given pixel to generate a cumulative index of climate change shown in this web app globally under three alternative scenarios.Journal: Nature Climate ChangeAuthors: Camilo Mora, Daniele Spirandelli , Erik Franklin , Michael Kantar, John Lynham , Wendy Miles , Charlotte Smith , Kelle Freel , Jade Moy , Leo Louis , Evan Barba , Keith Bettinger , Abby Frazier , John Colburn IX , Naota Hanasaki , Ed Hawkins , Yukiko Hirabayashi , Wolfgang Knorr , Christopher Little , Kerry Emanuel , Justin Sheffield , Jonathan Patz , Cynthia Hunter.For more information please review Cumulative Exposure to Climate Change by Mora et al from the University of Hawai'i's Department of Geography.