Historical climate indices based on adjusted and homogenized daily temperature and precipitation data
On this page
- Overview
- Source datasets
- Methods
- Station list
- Application
- Limitations
- Other considerations
- Use limitation
- Contact information
- References
1. Overview
Historical temperature and precipitation climate indices based on adjusted and homogenized daily temperature and precipitation data are available. Temperature indices based on homogenized daily maximum and minimum temperatures are available for 338 station locations across Canada. Precipitation indices based on adjusted daily rainfall, daily snowfall, and daily precipitation amounts are available at 463 station locations across the county. The time period of the dataset is station and variable dependant, and ranges between 1900 and 2016.
A range of impact-relevant historical climate indices are available, including indices that represent the counts of the number of days when temperature or precipitation exceeds (or is below) a threshold value; the lengths of episodes when a particular weather/climate condition occurs; and indices that accumulate temperature departures above or below a fixed threshold.
The indices are grouped into three sets: temperature indices sets A and B and precipitation indices. Set A of the temperature indices are based on fixed thresholds (e.g., the number of days with a maximum temperature above 30°C). Set B of the temperature indices are based on percentiles (e.g., 95th percentile of the daily winter maximum temperature). The precipitation indices are based on thresholds, fixed or station-dependant.
Please see Vincent et al. (2018)Footnote 1 for further details on the calculation and analyses of the historical daily temperature and precipitation indices.
Table 1. Main characteristics
| Variables | For the full variable list and definitions |
| Geographic area | Canada (land mass only) |
| Spatial resolution | Point locations |
| Time period | Time period varies per station, per variable with data availability ranging between 1900 and 2016 |
| Temporal resolution | Variable dependent (annual or seasonal) |
2. Source datasets
2.1 Adjusted and homogenized Canadian climate data: Daily temperature and precipitation
Adjusted and homogenized Canadian climate data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. AHCCD was developed to assess long-term trends in Canada’s climate, accounting for non-climatic factors. Long-term data records are often impacted by changes (e.g. site exposure, location, instrumentation, observer, and observing procedures) that are not related to climate. These non-climatic changes were detected and removed using statistical procedures. When necessary, data were corrected for some measurement techniques that are known to possibly lead to underestimations or overestimations. In addition, data from nearby stations were sometimes combined to create longer time series.
The method used to adjust and homogenize station data differs for temperature and precipitation, as summarized below. Full technical details may be accessed online.
2.1.1 Homogenized surface air temperature
The temperature indices in this dataset were produced from the homogenized surface air temperature data, which consists of monthly, seasonal and annual means of homogenized daily maximum, minimum and mean surface air temperatures (degrees Celsius) for 338 locations in Canada.
The time periods of the homogenized temperature data vary by location, with the oldest data available from the early 1840s at some stations to the most recent update in 2018. Observations at co-located sites were sometimes joined in order to create longer time series. Station data availability over most of the Canadian Arctic is restricted to the mid-1940s to present.
In the homogenized surface temperature datasets, non-climatic shifts were identified by comparing a station to other nearby stations. Techniques based on regression models were used to detect non-climatic shifts in temperature monthly series (Wang et al. 2007Footnote 2; Vincent, 1998Footnote 3). If a shift in the data was seen at one station, but not at nearby stations, it’s possible that the shift may be due to non-climatic causes. Once a shift has been identified, station metadata is used to identify what may have caused the shift, and what corrective measure should be applied. Following this, adjustments were applied to the daily temperatures to address the bias due to the non-climatic causes, if adjustments are warranted. A new procedure was applied to derive the adjustments; for more information, please see Wang et al. (2010)Footnote 4 and Vincent et al. (2012)Footnote 5.
Further, adjustments were applied to the daily minimum temperatures at synoptic stations (mainly airports) to address the bias caused by the change in observing time in July 1961 (Vincent et al. 2009)Footnote 6, prior to the homogeneity testing.
2.1.2 Adjusted precipitation
The precipitation indices in this dataset were produced from the adjusted precipitation data, which consist of monthly, seasonal and annual totals of daily adjusted rain, snow and total precipitation (millimetres) for 464 locations in Canada.
The time periods of the adjusted precipitation data vary by location, with the oldest data available from the early 1840s at some stations to the most recent update in 2016. Observations at co-located sites were sometimes joined in order to create longer time series. Station data availability over most of the Canadian Arctic is restricted to the mid-1940s to present.
The adjusted precipitation datasets account for a number of known errors in precipitation measurements. First, rain gauge measurements of precipitation are known to underestimate amount of actual precipitation due to the loss of rain water from the instruments during periods of high intensity rainfall (Molini et al. 2005)Footnote 7. Field experiments have been undertaken at various locations to quantify these biases and correct them for the types of rain gauges used by the Meteorological Service of Canada.
Second, ruler measurements have been used historically to measure snow depth and an assumed density of 100 kg m-3 was used to convert snow depth to snow water equivalent. However, AHCCD data use more accurate density estimates that vary geographically across the country. Snow tends to be denser in the east and north of the country, and less dense in the west. Also, daily precipitation amounts below a minimum measurable amount were set to a value of zero in the past. However, the accumulated impact of these trace amounts can become significant, especially in areas like the Arctic where precipitation amounts are low. Adjustments were applied to account for this underestimation by assigning a value to these trace days: 0.1 mm was applied for rain, whereas for snow the adjustment factor ranged from 0.03 to 0.07 mm depending on the station location. Finally, nearby observations were sometimes joined and adjustments were applied based on a simple ratio computed using available periods of overlapping data. For more information, please see Mekis and Vincent (2011)Footnote 8.
3. Methods
A total of 44 historical climate indices were selected to address the needs for social and economic impact assessment in Canada. A list of these indices and their definitions are available. Please see the definition list for the thresholds and percentile definitions of each index in this dataset.
3.1 Temperature indices
The temperature indices are produced from homogenized daily maximum and minimum temperature data at 338 locations across Canada. Two sets of temperature indices were computed; Set A and Set B. Set A consists of 18 threshold-based indices, such as ‘summer days’ which is the number of days with a maximum temperature above 25 degrees Celsius. Set B consists of 16 percentile-based indices, such as the 5th percentile of maximum winter temperature. The temperature indices are based on an annual or seasonal basis, with most of Set A indices provided on an annual basis and all of Set B indices provided on a seasonal basis. When computing the indices, monthly values were set to the missing value code (-9999 or -9999.9) for months with more than three consecutive days or more than five random days with missing values. Annual and seasonal values were set to the missing value code if the value of any month was missing within a year or season (Vincent et al. 2018)Footnote 1.
3.2 Precipitation indices
The precipitation indices are produced from adjusted daily rainfall, daily snowfall, and daily total precipitation data at 464 locations across Canada. The adjusted daily total precipitation amounts include daily rainfall and snowfall water equivalent amounts. There are 10 precipitation indices which consist of wet, very wet, highest 1-day events, and dry conditions. The wet and dry condition indices are based on absolute value thresholds (e.g. the number of days with more than one millimeter of rain). The very wet indices are based on a 90th percentile threshold reference value (e.g. the number of days with rain greater than the 90th percentile). The 90th percentile reference values of rainfall, snowfall, and total precipitation were calculated over the 1961-1990 reference period using all daily events greater than or equal to 1 mm. The highest 1-day events indices are provided as amount of precipitation (i.e. highest 1-day rain in millimetres) (Vincent et al. 2018)Footnote 1.
Records of trace precipitation observations have not been consistent across the country and over time, and therefore a threshold of 1 mm was selected to identify days with precipitation (including rainfall, snowfall, and total precipitation). It should be noted however, that trace observations are important for seasonal or monthly precipitation accumulation amounts, especially in dry regions where trace amounts can be large relative to total precipitation amounts (Vincent et al. 2018)Footnote 1. When computing the indices, monthly values were set to the missing value code (-9999 or -9999.9) for months with more than three consecutive days or more than five random days with missing values. Annual and seasonal values were set to the missing value code if the value of any month was missing within a year or season (Vincent et al. 2018) Footnote 1.
4. Station list
A list of the AHCCD stations included in the temperature and precipitation indices datasets is available. The list may also be accessed in Microsoft Excel or csv format.
Homogenized temperature station list: Excel format CSV format
Adjusted precipitation station list: Excel format CSV format
5. Application
The historical climate indices dataset was developed to assist with social and economic climate change impact assessments and provide insight on changes in extreme climate conditions in Canada. Changes in these indices may impact important sectors of the Canadian economy including tourism, agriculture, energy and shipping (Vincent et al. 2018)Footnote 1. For example, hot days and hot nights indices may be used to assess impact on human health. Summer days, days with snowfall and heavy snowfall may be relevant to the tourism sector. Growing season indices may provide greater insight on the impacts to the agricultural sector. Changes in heating degree days (HDD), very cold days, and frost days may be relevant indices in determining impacts on energy consumption. Frost days and consecutive frost days indices are two example indices that may be relevant to the shipping industry, and changes in freeze-thaw days may impact road maintenance and maple syrup production. The importance of these climate indices are not limited to the aforementioned sectors, and likewise, the aforementioned indices may be relevant to not only the ones discussed but to a wide range of sectors. These indices may also be helpful in providing benchmarks for evaluating the performance of climate models, for example, by comparing with the models’ simulated historical climate changes in Canada. For further details on the historical trends of climate indices, please see Vincent et al. (2018)Footnote 1.
6. Limitations
It should be noted that there may be missing values in the AHCCD climate indices dataset, which may vary by variable, station and time. All missing values were set to the missing value code (-9999 or -9999.9). In addition, the AHCCD indices are site-specific datasets. A gridded climate indices dataset based on the observed historical adjusted and homogenized climate data has not been developed at this time. Nonetheless, it is worth noting that the Canadian gridded dataset (CANGRD) is available. CANGRD datasets include historical gridded temperature and precipitation anomalies, interpolated from AHCCD station data at a 50km resolution across Canada.
Historical simulations (1951-2005) of high-resolution statistically downscaled climate indices based on global climate model output are available (Li et al. 2018)Footnote 9. It should be noted that the statistically downscaled climate indices are based on model simulations and not the observed historical adjusted and homogenized climate data. More information on statistically downscaled climate indices is available here.
7. Other considerations
Users are urged to assess whether the AHCCD datasets are suitable for their application. AHCCD datasets differ from the official Meteorological Service of Canada in situ station records and therefore should not be used for legal purposes. Users interested in the original observations made at a given site should use the Meteorological Service of Canada station data.
Further, it should be noted that ongoing research may result in future revisions of the AHCCD dataset (e.g., updated methodologies) to provide a better spatial and temporal representation of the climate trends in Canada. This may result in future updates to the AHCCD climate indices dataset that reflect any future revisions of the AHCCD dataset.
8. Use limitation
Open Government Licence - Canada (http://open.canada.ca/en/open-government-licence-canada)