A collection of high-resolution climate grid surfaces for land areas in Tasmania based on CMIP6-era Regional Climate Projection Modelling (via the Australian Climate Service). There are 304 climate products available that delineate temperature and rainfall parameters specific to crop growing requirements that form part of the enterprise suitability mapping program (refer here: https://dpipwe.tas.gov.au/agriculture/investing-in-irrigation/enterprise-suitability-toolkit/enterprise-suitability-maps). Broadly speaking these products include climate risk parameters including frost risk, heat risk and extreme rainfall risk as well as crop related indices including growing degree days and chill hours. Furthermore, mean monthly climate variables including mean monthly maximum/minimum air temperature and rainfall products are also produced. Refer here for dataset inventory: https://nrmdatalibrary.dpipwe.tas.gov.au/FactSheets/WfW/ListMapUserNotes/Inventory_DCM_Tas.pdf CMIP6-era Regional Climate projections were incorporated into the modelling framework to simulate projected climate (according to SSP3-7.0) for years 2030, 2050, 2070 and 2100. These projections were downscaled, and bias corrected to a spatial grid resolution of 30m. Also, note that these outputs relate to the baseline climate maps defined here: https://www.thelist.tas.gov.au/app/content/data/geo-meta-data-record?detailRecordUID=ba62f124-5906-4471-a01c-9b57b6142055 All products can be accessed via Web Map Service: https://spatial.dpipwe.tas.gov.au/naturalassets/Climate/wms Or viewed in the following Web Map application: https://arcg.is/vaHDG

This project downscaled seven Regional Climate Models (RCMs) from the Australian Climate Service (https://geonetwork.nci.org.au/geonetwork/srv/eng/catalog.search#/metadata/f2764_6935_0449_3524) to a 30m spatial resolution for application in local crop suitability mapping across Tasmania, Australia. The resulting datasets complement the existing baseline climate map series (https://www.thelist.tas.gov.au/app/content/data/geo-meta-data-record?detailRecordUID=ba62f124-5906-4471-a01c-9b57b6142055). The RCMs represent the Shared Socioeconomic Pathway 3 scenario with a 7.0 W/m3 radiative forcing (SSP3-7.0), a medium-to-high emissions trajectory assuming limited additional climate policy under the SSP3 socioeconomic narrative. For further information on greenhouse gas scenarios, see the Climate Change in Australia overview (https://www.climatechangeinaustralia.gov.au/en/changing-climate/future-climate-scenarios/greenhouse-gas-scenarios/). Downscaling was performed using regression trees and spatial covariates to disaggregate each RCM to over 500 baseline sites. Bias adjustment was then applied using a non parametric quantile mapping method with empirical quantile transformation, following Gudmundsson (2016), based on historical observations (NRE Tasmania and Bureau of Meteorology data, 1998-2017). From the bias adjusted data, key climatic variables were derived including frost risk, heat risk, growing degree days (GDD), growing season temperature (GST), rainfall, and chill hours for four projection periods: 2030, 2050, 2070, and 2100. Variable specific parameters followed definitions outlined in the [Inventory DCM Tas documentation](https://nrmdatalibrary.dpipwe.tas.gov.au/FactSheets/WfW/ListMapUserNotes/Inventory_DCM_Tas.pdf). Spatial interpolation of site level projections was conducted using regression trees and thin plate splines to generate continuous raster surfaces for each variable. Each variable was modelled separately for all seven RCMs, then combined into a seven member ensemble to produce mean climate surfaces representing the final projections. All outputs are available for GIS integration via the following Web Map Service (WMS): https://spatial.dpipwe.tas.gov.au/naturalassets/Climate/wms **References** Gudmundsson, L. (2016). R package 'qmap': Statistical Transformations for Post Processing Climate Model Output. Version 1.0-4. [https://cran.r-project.org/web/packages/qmap/qmap.pdf]

Position Accuracy: 30m. Attribute Accuracy: Preliminary assessment of the downscaling method revealed that the climatic variables inherited the general spatial trends of their equivalent coarse scale counterpart provided by the raw CFT data. This revealed that the correlation coefficient (R2) between the downscaled estimates and their corresponding raw CFT grid cell were always above >0.8, signifying strong agreement between the raw coarse data and downscaled values. From this, it is assumed that resulting spatial outputs are within acceptable predictions ranges emanating from the downscaling and bias correction processes employed.