CANGRD

1. Overview of CANGRD

  • Purpose: Provides spatially interpolated climate anomalies for analyzing long-term climate trends in Canada.

  • Key Characteristics:

    • Gridded dataset (not raw station data).

    • Uses 50 km resolution grid across Canada.

    • Time coverage: Temperature anomalies from 1948–2018, Precipitation from 1948–2014.

    • Reference period for anomalies: 1961–1990 (WMO standard).

  • Primary Applications: Climate trend analysis, research, policy development, and regional climate assessments.

Key Dataset Features

Feature

Description

Spatial Resolution

50 km grid spacing

Geographical Coverage

Canada (land areas only)

Time Period

1948 – Present (updated annually)

Variables

Temperature and Precipitation Anomalies

Temporal Resolution

Monthly, Seasonal, Annual

2. Datasets and Formats

CANGRD provides gridded temperature and precipitation anomaly datasets, which are derived from AHCCD station data.

2.1 Canadian Gridded Temperature Anomalies

  • Description: Gridded monthly, seasonal, and annual mean daily maximum and minimum temperature anomalies.

  • Anomalies Calculation: Difference between the observed value and the 1961–1990 reference period.

  • Time Coverage: 1948–2018 (updated annually).

2.2 Canadian Gridded Precipitation Anomalies

  • Description: Gridded monthly, seasonal, and annual total precipitation anomalies.

  • Anomalies Calculation:

    • Expressed as a percentage (%) difference from the 1961–1990 reference period.

    • Adjusted for changes in observation methods (rain gauge corrections, undercatch bias corrections).

  • Time Coverage: 1948–2014 (data updates may be delayed due to station reductions).

2.3 Trend of Mean Temperature (1948–2018)

  • Trend Data: Represents long-term seasonal and annual temperature changes over Canada.

  • Units: Expressed in °C per decade.

  • Method Used: Kendall Linear Trend Analysis.

2.4 Trend of Total Precipitation (1948–2012)

  • Trend Data: Represents seasonal and annual trends of relative total precipitation change.

  • Units: Expressed as a % change per decade.

  • Method Used: Kendall Linear Trend Analysis.

3. Methods

3.1 Interpolation Method

  • Process: Converts point-based climate station data (AHCCD) into a continuous grid.

  • Technique Used: Gandin’s Optimal Interpolation (OI) Method:

    • Ensures spatial consistency and smoothness.

    • Uses climate station data from AHCCD as input.

    • 50 km resolution ensures regional trends are preserved.

3.2 Trend Calculation

  • Approach: Seasonal and annual trends calculated using the Kendall Linear Trend Analysis.

  • Temperature Trend: Change in mean temperature anomalies over time.

  • Precipitation Trend: Change in relative total precipitation (%) over time.

  • Missing Data Consideration: Some grid points may lack data in certain years.

4. Applications of CANGRD

  • Climate Change Analysis:

    • Tracking temperature and precipitation trends across Canada.

    • Understanding regional climate variability.

    • Assessing the impact of climate change on ecosystems.

  • Government Policy & Adaptation Planning:

    • Informing Canada’s climate adaptation strategies.

    • Supporting carbon emission reduction targets.

    • Contributing to national and regional climate assessments.

  • Disaster Risk Management:

    • Identifying regions vulnerable to droughts and extreme weather.

    • Providing long-term climate projections for emergency preparedness.

  • Scientific Research & Education:

    • Used in climate modeling studies.

    • Supports environmental impact assessments.

    • Basis for Canadian Climate Trends and Variations Bulletin (CTVB).

5. Limitations

  • CANGRD provides climate anomalies, NOT absolute temperature and precipitation values.

  • Interpolation uncertainties exist in data-sparse regions (e.g., Arctic, remote areas).

  • Precipitation anomalies are affected by station density and observation method changes.

  • CANGRD does NOT include sub-daily climate variables (e.g., hourly or extreme event data).

  • For site-specific station data, AHCCD should be used instead.

6. Future Considerations

  • Ongoing improvements in interpolation techniques.

  • Potential updates to include gridded humidity and wind speed anomalies.

  • Enhancements in sea-ice and snowpack trend representation.

7. Data Access & References

7.1 Data Access

7.2 References

  • Gandin, L.S. (1965). Objective Analysis of Meteorological Fields.

  • Mekis, É., & Vincent, L. A. (2011). Second Generation Adjusted Daily Precipitation Dataset for Canada.

  • Bretherton, F.P., Davis, R.E., & Fandry, B. (1976). Objective Analysis Techniques for Oceanographic Data.

8. Conclusion

The Canadian Gridded Data (CANGRD) provides a spatially consistent, long-term climate dataset essential for analyzing historical temperature and precipitation trends across Canada. With its high-resolution grid, robust interpolation techniques, and bias-corrected data, CANGRD serves as a critical tool for climate research, disaster risk management, and policy planning.

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