The Ontario Climate Data Portal, a user-friendly portal of Ontario-specific climate projections

An easily accessible climate data portal, http://yorku.ca/ocdp , was developed and officially launched in 2018 to disseminate a super ensemble of high-resolution regional climate change projections for the province of Ontario, Canada. The spatial resolution is ~10 km × ~10 km and temporal resolution is one day, UTC. The data covers 120 years from 1981 to 2100. This user-friendly portal provides users with thousands of static and interactive maps, decadal variation trend lines, summary tables, reports and terabytes of bias-corrected downscaled data. The data portal was generated with an emphasis on interactive visualization of climate change information for researchers and the public to understand to what extent climate could change locally under different emission scenarios in the future. This paper presents an introduction to the portal structure and functions, the large extent of the datasets available and the data development methodology.

[1]  Julien Boé,et al.  Statistical and dynamical downscaling of the Seine basin climate for hydro‐meteorological studies , 2007 .

[2]  Erika Coppola,et al.  Enhanced summer convective rainfall at Alpine high elevations in response to climate warming , 2016 .

[3]  C. Frei,et al.  Downscaling from GCM precipitation: a benchmark for dynamical and statistical downscaling methods , 2006 .

[4]  Rolf Müller,et al.  From ERA-Interim to ERA5: considerable impact of ECMWF's next-generation reanalysis on Lagrangian transport simulations , 2018, Atmospheric Chemistry and Physics.

[5]  Robert A. Norheim,et al.  An Overview of the Columbia Basin Climate Change Scenarios Project: Approach, Methods, and Summary of Key Results , 2013 .

[6]  D. Maraun Reply to “Comment on ‘Bias Correction, Quantile Mapping, and Downscaling: Revisiting the Inflation Issue’” , 2013 .

[7]  P. Mahadevan,et al.  An overview , 2007, Journal of Biosciences.

[8]  Francis W. Zwiers,et al.  Avoiding Inhomogeneity in Percentile-Based Indices of Temperature Extremes , 2005 .

[9]  N. Altman An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression , 1992 .

[10]  B. Arheimer,et al.  Evolving Climate Services into Knowledge–Action Systems , 2019, Weather, Climate, and Society.

[11]  Neville Nicholls,et al.  Clivar/GCOS/WMO Workshop on Indices and Indicators for Climate Extremes Workshop Summary , 1999 .

[12]  David D. Parrish,et al.  NORTH AMERICAN REGIONAL REANALYSIS , 2006 .

[13]  Miriam Schmidts Esri ArcGIS Desktop Associate Certification Study Guide , 2013 .

[14]  B. J. Kilonsky,et al.  A Climate Data Portal , 2001, MTS/IEEE Oceans 2001. An Ocean Odyssey. Conference Proceedings (IEEE Cat. No.01CH37295).

[15]  Alex J. Cannon,et al.  Hydrologic extremes – an intercomparison of multiple gridded statistical downscaling methods , 2016 .

[16]  Alex J. Cannon,et al.  Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes? , 2015 .

[17]  Robert M. Graham,et al.  Comparison of ERA5 and ERA-Interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: effects on sea ice thermodynamics and evolution , 2019, The Cryosphere.

[18]  C. Zammit,et al.  Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China , 2014 .

[19]  T. Carter,et al.  GUIDELINES ON THE USE OF SCENARIO DATA FOR CLIMATE IMPACT AND ADAPTATION ASSESSMENT Version 1 December 1999 Task Group on Scenarios for Climate Impact Assessment Intergovernmental Panel on Climate Change , 2000 .

[20]  Uang,et al.  The NCEP Climate Forecast System Reanalysis , 2010 .

[21]  A. Thomson,et al.  The representative concentration pathways: an overview , 2011 .

[22]  W. Peltier,et al.  Uncertainty in Future Summer Precipitation in the Laurentian Great Lakes Basin: Dynamical Downscaling and the Influence of Continental-Scale Processes on Regional Climate Change , 2017 .

[23]  F. Giorgi,et al.  Addressing climate information needs at the regional level: the CORDEX framework , 2009 .

[24]  Guohe Huang,et al.  Ensemble Projections of Regional Climatic Changes over Ontario, Canada , 2015 .

[25]  D. Lettenmaier,et al.  Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs , 2004 .

[26]  Jinhong Zhu,et al.  Regional climate change projections for the Northeast USA , 2008 .

[27]  Remko Uijlenhoet,et al.  Evaluation of a bias correction method applied to downscaled precipitation and temperature reanalysis data for the Rhine basin , 2010 .

[28]  Tomasz Mrozewski Climate change data , 2019, Bulletin - Association of Canadian Map Libraries and Archives (ACMLA).

[29]  John F. B. Mitchell,et al.  The next generation of scenarios for climate change research and assessment , 2010, Nature.

[30]  R. K. Dixon,et al.  Mitigation and Adaptation Strategies for Global Change , 1998 .

[31]  M. Déqué,et al.  Intercomparison of statistical and dynamical downscaling models under the EURO- and MED-CORDEX initiative framework: present climate evaluations , 2016, Climate Dynamics.

[32]  D. Maraun Bias Correcting Climate Change Simulations - a Critical Review , 2016, Current Climate Change Reports.

[33]  Keywan Riahi,et al.  Power-generation system vulnerability and adaptation to changes in climate and water resources , 2016 .

[34]  D. P. Stone The Intergovernmental Panel on Climate Change (IPCC) , 2015 .

[35]  Ross D. Brown,et al.  Evaluation of CORDEX-Arctic daily precipitation and temperature-based climate indices over Canadian Arctic land areas , 2018, Climate Dynamics.

[36]  José M. Gutiérrez,et al.  VALUE: A framework to validate downscaling approaches for climate change studies , 2015 .

[37]  C. Deser,et al.  Uncertainty in climate change projections: the role of internal variability , 2012, Climate Dynamics.

[38]  Vijayalakshmi Atluri,et al.  Workshop Summary , 1998, DBSec.

[39]  Huaiping Zhu,et al.  Downscaling RCP8.5 daily temperatures and precipitation in Ontario using localized ensemble optimal interpolation (EnOI) and bias correction , 2018, Climate Dynamics.

[40]  G. Huang,et al.  High‐resolution temperature and precipitation projections over Ontario, Canada: a coupled dynamical‐statistical approach , 2015 .

[41]  N. Madras,et al.  Trend in frequency of extreme precipitation events over Ontario from ensembles of multiple GCMs , 2016, Climate Dynamics.

[42]  J. Seibert,et al.  Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods , 2012 .

[43]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .