Table 3.6: Humidity variables and their properties (from Willett K. M., 2007)
| Variable | What does it measure? | Units | Used | How is it measured? |
|---|---|---|---|---|
| RH (relative humidity) | Closeness of the air to saturationCloseness of the air to saturation | % |
|
|
| q (absolute and specific humidity) | Absolute humidity is the ratio of the mass of water vapour to the total mass of moist air , while specific humidity is the ratio of the mass of water vapour to the mass of dry airAbsolute humidity is the ratio of the mass of water vapour to the total mass of moist air , while specific humidity is the ratio of the mass of water vapour to the mass of dry air | g kg-1 |
|
|
| Tdw (dew point temperature) | The atmospheric temperature lowered to the point of saturation | ᵒ C |
|
|
| Tw (wet-bulb temperature) | The temperature at which the measured air is saturated by evaporating water into it from the wet bulb | ᵒ C |
|
Directly from wet-bulb thermometers erived from psychrometric tables or combinations of other variables |
Some of these variables are directly measured at meteorological and climate stations, some are just output from forecast and climate models without a direct measurement at stations. However all of them are widely used in meteorological and climate fields. Water vapour plays a key role in determining the dynamical and radiative properties of the climate system and its transport around the atmosphere is a fundamental component of the hydrological cycle. Among the variables presented in Table 3.6, specific humidity is considered as very important in climate model evaluation and in the research field, and dew point temperature is of interest for assessing atmospheric soundings and the development of clouds and convection in models.
Relative humidity, dew point temperature and wet-bulb temperature provide important inputs to building envelope and heating/ventilation/air conditioning design as well as building energy modelling. Such data is frequently incorporated into “weather files” and ‘’design days’’ that serve as inputs in these applications. Such data could be potentially useful for northern users in assessment and design of current and future community housing, industrial and office sites as well as design of building retrofits for improved human comfort and energy efficiency.
Extreme values of relative humidity have significant impacts on human comfort conditions in terms of thermal stress as well as on the strength and frequency of forest fires. In high temperature conditions, high humidity inhibits evaporation, making cooling by perspiration less effective and can create heat stress and many health problems for people and animals. Fortunately, those conditions are rarely met in the Canadian North. Nevertheless, wildfires are experienced in a large part of Yukon and Northwest Territories (https://cwfis.cfs.nrcan.gc.ca/ha/nfdb ).
This section presents an inventory of historical datasets covering the Canadian North that have available specific and/or relative humidity. The information is summarized in Table 3.7. Surface relative humidity can also be estimated from 2 m air temperature and dew point temperature (e.g., https://www.weather.gov/media/epz/wxcalc/vaporPressure.pdf). Table 3.7 mentions if a dataset does not have available relative humidity but provides dew point temperature instead.
| name | source | data type | spatial domain | spatial resolution | temporal coverage | time step | data format | |
|---|---|---|---|---|---|---|---|---|
| MSC Observations | MSC/ECCC | Station data | Canada | Point data | Variable (1940 to present) | Hourly; Daily; Monthly | CSV; GeoJSON | details |
| Hydro-Québec Station data | Hydro-Québec | Station data | Northern Québec at hydroelectric stations | Point Data | Variable,1990 to present | Variable ( sub-daily to daily) | None | details |
| CRU CL v. 2.0 | CRU/University of East Anglia | Gridded observations | Global (land surface) | 10 arcminutes (0.1666667 degree) | 1961-1990 | Climatological means | ASCII | details |
| HadISDH | Met Office/Hadley Centre | Gridded observations; Station data | Global | 5° x 5° for gridded data and point for stations | 1973 - 2020 | Monthly | ASCII; NetCDF | details |
| ERA5 | ECMWF | Global atmospheric reanalysis | Global | 0.25° x 0.25° | 1950 - present | Hourly; Daily; Monthly | GRIB; NetCDF | details |
| CFSR | NCEP | Global atmospheric reanalysis | Global | 0.5° x 0.5° | 1979/01 to 2017/11 | Sub-daily; Monthly | GRIB | details |
| MERRA-2 | NASA | Global atmospheric reanalysis | Global | ½° latitude x ⅝° longitude | 1980/01 to present | Hourly; Daily; Monthly | NetCDF | details |
| JRA-55 | JMA | Global atmospheric reanalysis | Global | 0.6258ᵒ x 0.6258ᵒ | 1957/12 to 2021/05 | 3h; 6h; Daily; Monthly | NetCDF | details |
| ASRv2 | Byrd Polar Research Center/The Ohio State University; UCAR/NCAR | Regional reanalysis | Arctic | 15 km x 15 km | 2000/01 to 2016/12 | 3h; Monthly | NetCDF | details |
| NARR | NCEP | Regional reanalysis | North America | 32 km x 32 km | 1979/01 to 2021/04 | Sub-daily; Monthly | GRIB | details |
| RDRSv2 | CCMEP/ECCC | Regional reanalysis | North America | 10 km x 10 km | 2000 -2017,(1980 - 1999 pending) | Hourly | RPN | details |
| 20CRv3 | CIRES; NOAA; DOE | Global atmospheric reanalysis | Global | T254 (approximately 75 km at the equator) | 20CRv3.SI is available for years 1836-1980 and 20CRv3.MO is available for years 1981-2015 | 3h; Daily; Monthly | NetCDF | details |
| ERA5-Land | ECMWF | Land surface reanalysis/model | Global (land only) | 0.1° x 0.1° (9 km) | 1950 - present | Hourly; Monthly | GRIB; NetCDF | details |
| AgERA | ECMWF | Re-gridded reanalysis | Global (land only) | 0.1° x 0.1° | 1979- present | Daily | NetCDF | details |
| AgCFSR | NASA | Re-gridded reanalysis with corrections | Global (land only) | 0.25° × 0.25° | 1980-2010 | Daily | NetCDF | details |
| AgMERRA | NASA | Re-gridded reanalysis with corrections | Global (land only) | 0.25° × 0.25° | 1980-2010 | Daily | NetCDF | details |
| GMFD | Princeton University | Re-gridded reanalysis with corrections | Global (land only) | 0.25° × 0.25° | 1948-2016 | 3h; Daily; Monthly | NetCDF | details |
| CRU JRA v2.1 | CRU/University of East Anglia | Re-gridded reanalysis with corrections | Global (land only) | 0.5° × 0.5° | Jan.1901 - Dec. 2020 | 6h | NetCDF | details |
| S14FD | DIAS | Re-gridded reanalysis with corrections | Global | 0.5° x 0.5° | 1958-2013 | Daily | NetCDF | details |
The following summarizes the points that should be considered when a dataset is selected for historical climate analyses of surface humidity or climate indices computation in northern Canada:
a) The Fire Weather Index (FWI) is a numeric rating of fire intensity, and it is used as a general index of fire danger throughout the forested areas of Canada. Its calculation requires daily values of temperature, relative humidity, wind speed, and 24-hour precipitation that were taken at solar noon, when the sun is at its peak directly overhead. Therefore, historical estimations of FWI will need series of sub-daily data that allows the estimation of solar noon variables. Just a limited number of stations and reanalyses-based datasets provide relative humidity at sub-daily temporal resolution and there are no comparison studies available on their performance over northern Canada for FWI computation.
b) The estimation of historical trends for relative humidity and the relative humidity evaluation of reanalyses and models in northern Canada are complicated by issues with the instruments measuring relative humidity in cold climate (Déry and Steiglitz, 2002). Icing and reservoir freezing were found to be particular problems for automatic stations in Canada if instruments were not checked regularly. It is recommended to use statistical methods to detect and adjust for artificial discontinuities in individual station records (homogenization). Homogenization, while unlikely to remove all non-climatic discontinuities in the data, produces a dataset that is far more robust (Willett, 2007). Wijngaarden and Vincent (2005) and Vincent et al., (2007) provide information on a homogenized data set of relative humidity and dew point temperatures for 75 stations in Canada. A significant negative step due to the replacement of the psychrometer by the dewcel instruments was observed for relative humidity at 52 stations, mostly in the Canadian North. For the dewpoint time series, the step for the introduction of the dewcel was observed at nine stations (mostly located in the northeast). Very few significant steps were detected in the specific humidity time series, because in cold temperatures, the specific humidity values are very low and do not vary much (Vincent et al., 2007). More information is provided in the Annex 7.3.1 which is describing MSC station measurements for humidity.
c) HadISDH dataset of monthly means of surface humidity was designed as a gridded product for studying large scale trends and variability, and assessing the validity of climate models. Stations incorporated in the product were homogenized and quality controlled. The effect of topographic elevation on spatial continuity in humidity is complex and relatively unstudied. In HadISDH, the interpolation is realized into a coarse global grid (5° x 5° spatial resolution). A single coarse-resolution grid box may incorporate a number of stations at different elevations (Willett et al., 2014). However, many studies mentioned that for trend analyses, anomalies are preferable to absolute values because they largely remove station specific variability (including variation due to elevation). HadISDH dataset includes anomalies values as well as absolute values.
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