Observational study of land-surface-cloud-atmosphere coupling on daily timescales

Our aim is to provide an observational reference for the evaluation of the surface and boundary layer parameterizations used in large-scale models using the remarkable long-term Canadian Prairie hourly dataset. First we use shortwave and longwave data from the Baseline Surface Radiation Network (BSRN) station at Bratt’s Lake, Saskatchewan, and clear sky radiative fluxes from ERA-Interim, to show the coupling between the diurnal cycle of temperature and relative humidity and effective cloud albedo and net longwave flux. Then we calibrate the nearby opaque cloud observations at Regina, Saskatchewan in terms of the BSRN radiation fluxes. We find that in the warm season, we can determine effective cloud albedo to ±0.08 from daytime opaque cloud, and net long-wave radiation to ±8 W/m2 from daily mean opaque cloud and relative humidity. This enables us to extend our analysis to the 55 years of hourly observations of opaque cloud cover, temperature, relative humidity, and daily precipitation from 11 climate stations across the Canadian Prairies. We show the land-surface-atmosphere coupling on daily timescales in summer by stratifying the Prairie data by opaque cloud, relative humidity, surface wind, day-night cloud asymmetry and monthly weighted precipitation anomalies. The multiple linear regression fits relating key diurnal climate variables, the diurnal temperature range, afternoon relative humidity and lifting condensation level, to daily mean net longwave flux, windspeed and precipitation anomalies have R2 values between 0.61 and 0.69. These fits will be a useful guide for evaluating the fully coupled system in models.

Note: On revision, the title changed to "Observational study of land-surface-cloud-atmosphere coupling on daily timescales"

Correction: An error in Eq(18b) has been corrected: the y-intercept was mis-copied from a spreadsheet. This does not affect this paper. However the error was propagated to Eq(3b) in Betts et al (2016), doi:10.3389/feart.2016.00001, where it introduced an offset error in Figure 5, so that Figure has been replaced.

Plain English Discussion

The Canadian Prairie data contains unique observations every hour of opaque reflective cloud since 1953. These have transformed our understanding of the daily cycle of temperature and humidity, because they tell us the heating by the sun in the daytime (clouds reflect sunlight), and the cooling of the Earth at night (clouds trap the Earth's heat). The day-night rise and fall of temperature and humidity depends first on clouds. But secondly it depends on other processes: the wind that stirs the air near the surface, how dry the air is, whether it is cloudier in the daytime or at night, and whether it has rained more than usual in the past month or two. With so much data, we can map the effects of all these on the daily climate - as a guide for our weather forecast models and our climate models.

DATA

Related Topics

• Boundary Layer,
• Canadian Prairies,
• Climate,
• Clouds,
• Datasets,
• Land-surface