3.1.3 Total Precipitation

Together with evapotranspiration and condensation, precipitation is a key process in the global water cycle. Precipitation develops when droplets form in the atmosphere at below dewpoint temperatures and accumulate to be too heavy to remain suspended in mid-air.

Along with temperature, precipitation is one of the most commonly recorded meteorological quantities, with records dating back several centuries. The longest continuous observational record of precipitation in Canada dates back to 1840 when the Toronto Meteorological Observatory started operating a station at Fort York.

Precipitation is physically described as linear depth, usually recorded in millimetres (volume/area), kg m-2 (mass/area) (WMO, https://library.wmo.int/doc_num.php? explnum_id=3152). However, to address precipitation intensity, the linear depth is put in relation to time units to describe the flux, usually in millimetres per hour or per day (mm h-1, mm d-1, kg m-2 s-1). An equivalent amount of precipitation can be denoted in litres per square metre (l m-2). Snow depth is commonly measured in centimetres and can be translated to its Snow Water Equivalent (SWE) with the same units as liquid precipitation.

Precipitation develops in three main types: convective, stratiform and orographic rainfall, each involving a vertical upward motion of air. This uplift invokes cooling of the air, leading to condensation forming clouds and eventually droplets falling from the cloud. While convective and orographic rainfall can be spatially rather confined, stratiform precipitation may cover very large areas extending over hundreds of kilometres. The duration of precipitation events varies from minutes to days, where convective events may be short in duration and high in intensity, while stratiform and orographic events tend to last longer, with usually lower intensities. Orography usually plays a major role in the seasonal and spatial distribution of precipitation with upwind slope locations receiving abundant precipitation and lee sides remaining substantially dryer. The large variation of precipitation in time and space as well as the specifics of measuring it contributes to much larger uncertainties and errors in precipitation estimates when compared to temperature or other climate variables.

Climatological annual mean precipitation over Canada is shown in Figure 3.2. Coastal regions, in particular along the western cordillera in the West receive consistently larger amounts of precipitation than the central plains and the Great Lakes water bodies bring somewhat higher precipitation amounts to their Eastern surroundings. In the Canadian North, however, the precipitation along coasts is much less pronounced and a northward gradient towards much dryer conditions prevails, with less than 250 mm per year over the Canadian Arctic Archipelago. Generally, the winter season in Canada tends to provide lower amounts of precipitation than the summer season, to varying degrees and annual amplitudes of different magnitudes across the continent.

Figure 3.2 Climatological annual mean precipitation (1971-2000) over Canada. [Source: https://www150.statcan.gc.ca/n1/pub/16-201-x/2006000/4177438-eng.htm ]

The delivery of water through precipitation plays an important role for fauna and flora and the ecosystems that sustain them. Humans depend on precipitation for growing crops and livestock and exploit the water bodies fed by rainfall and snow melt for drinking water, industry, transport, and recreational activities. Water management is a crucial part of human activity and precipitation as its key driver is closely monitored. Both too much precipitation leading to floods as well as persistent periods without precipitation can cause critical conditions and even lead to catastrophic impacts, with flooding and droughts being some of the costliest natural disasters. Same of the most used precipitation climate indices are presented in Table 3.4.

Table 3.5 presents high-level information on the retained observation-based historical datasets, for total precipitation. For each dataset, a link to summaries describing the metadata is provided.

The following are notes about the different types of datasets and points that should be considered when selecting a historical precipitation dataset for application in the Canadian North.

a) Station data: Measurement of rainfall and snowfall is essential for water resources planning, agriculture, power generation, irrigation, flood control and forecasting. Compared to temperature, measuring precipitation precisely is a much more difficult effort. Rain gauges are containers of a defined size that will capture the falling rain or snow. The gauge is emptied and the water amount is measured at regular intervals. However, various factors affect the amount of precipitation recorded, with wind being the main source of errors in observations. Small amounts of the captured water are unrecorded as they wet the surface of the instrument. Some of the fallen rain will evaporate before it is recorded and heavy rain may lead to splashing of the droplets, also resulting in a measurement that is lower than the actual precipitation. Low intensity and short duration precipitation events may also be under-sampled with rain gauges. On the other hand, condensation inside the gauge may increase the observed amount of water. Overall, errors in precipitation measurements can amount to 4-6% depending on wind speed and rainfall intensity (Devine and Mekis, 2008). Snow measurement is even more difficult and errors in strong winds can exceed 20% (Goodison et al., 1998). The ECCC AHCCD (see Annex 7.2.4 and Mekis and Vincent, 2011) undergoes a separate adjustment of rain and snow in order to compensate for these errors, using spatially variable correction factors for snow due to regionally different snow densities. As the map of stations in Figure 1.1 shows, station density in northern regions of Canada is low, with tens to hundreds of kilometres between stations. Given the small spatial extent of precipitation from convective cells, precipitation station data may be misleading for nearby areas (while temperatures will vary little in adjacent areas of the station, the precipitation recorded may have only affected the station but other amounts or no precipitation may have occurred in nearby areas).

b) Gridded data: Climatological data are often required in spatially distributed form rather than in the point format of stations. As mentioned in Section 2.1, to describe the coverage of large areas such as watersheds or provinces, point data are often interpolated to establish a gridded dataset, i.e. 2D value fields, with the quality and utility of such a grid depending largely on the quality and the distribution of the input data. As mentioned above, precipitation is highly variable in time and space and, unlike temperature, is not continuous in time and space. This poses difficulties for the interpolation of station data onto a grid. Ideally, each cell of the grid would contain one or more stations so that the grid-cell information would be backed by actual observed values. This may be the case in areas of high station density and for grids of a few tens of kilometre grid sizes. However, northern Canada’s station network is very sparse so that, as a function of the grid's resolution, many grid cells will not be backed by actual station data. Particularly higher temporal resolution data must therefore be used with caution as the interpolation may create misleading precipitation information. At longer temporal aggregations the spatial field of actual precipitation becomes much smother, as over a month’s or a season’s time the statistics of precipitation distribution will be smoothed (with the exception of orographic precipitation which is highly governed by the terrain). Therefore, interpolated lower temporal resolution data like monthly values are more likely to reproduce a real representation of precipitation of an area.

c) Precipitation in reanalysis and reanalysis-based products: In areas with sparse station coverage such as the Canadian North, reforecasts and reanalysis datasets provide a valuable alternative to station data and gridded datasets derived therefrom (for a comprehensive overview of reanalysis products see Section 2.1.3). While the completeness of reanalysis datasets is a clear advantage, they need to be used with caution. Their validation shows different levels of reliability in both their intercomparison and within a given dataset. For example, Rapaić et al. (2015) report particularly important spatial, seasonal, and temporal variability for precipitation over the Canadian Arctic, most pronounced in mountain regions, coastal areas and over the Canadian Arctic Archipelago. The biases vary in time and space and some reanalyses have larger wet biases (CFSR, 20CR, Rapaić, et al., 2015). The results of the evaluation appear to change with application. For example, NARR precipitation validated favourably for the Canadian North (Rapaić et al., 2015, Keller and Wahl, 2021) is recommended to be taken with care for water cycle analysis over parts of Québec by Sabarly et al. (2016). Generally, the difference between the precipitation of the reanalyses (and other gridded data) increases after around 2003 due to a stark decrease in available stations and a change in the type and collections of data assimilated in the reanalysis (Rapaic et al., 2015). Authors recommend a multi-dataset approach to compensate for the differences found in the various reanalysis data.

d) Precipitation data derived from satellites: Satellite derived precipitation data are usually a combination of one or several precipitation data with estimates derived from satellite imagery. The temporal and spatial resolution depends on the instrument used in their production. Except for geostationary satellites, most satellites give near-global coverage on some repeat cycle. For example, some satellites may sample at least once per 100 km grid cell every sixteen days (CloudSat). Depending on the orbit, they will have more frequent sampling at some latitudes than others. Here we consider only datasets that have coverage up to at least 60˚N. However, a single satellite does not fly over a region more than twice per day, and the gaps between overpasses may miss short-lived precipitation events. This is why merged or “hybrid” precipitation datasets are useful, as they can combine the observations from multiple satellite platforms to fill in some of the gaps. Typically, passive microwave or infrared instruments are used to estimate precipitation. Infrared sensors estimate cloud top temperatures, and an algorithm is used to relate the measured temperature to a precipitation estimate. Microwave-based algorithms derive precipitation from both scattering and emission from hydrometeors and cloud droplets. Note that emission cannot be used to estimate precipitation over land due to the heterogeneity of the surface emissivity. Low intensity and short duration precipitation events tend to be under-sampled. Currently, there are no studies that evaluate those datasets over the Canadian North.

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