Annual Climatology of the Diurnal Cycle on the Canadian Prairies

We show the annual climatology of the diurnal cycle, stratified by opaque cloud, using the full hourly resolution of the Canadian Prairie data. The opaque cloud field itself has distinct cold and warm season diurnal climatologies; with a near-sunrise peak of cloud in the cold season and an early afternoon peak in the warm season. There are two primary climate states on the Canadian Prairies, separated by the freezing point of water, because a reflective surface snow cover acts as a climate switch. Both cold and warm season climatologies can be seen in the transition months of November, March and April with a large difference in mean temperature. In the cold season with snow, the diurnal ranges of temperature and relative humidity increase quasi-linearly with decreasing cloud, and increase from December to March with increased solar forcing. The warm season months, April to September, show a homogeneous coupling to the cloud cover, and a diurnal cycle of temperature and humidity that depends only on net longwave. Our improved representation of the diurnal cycle shows that the warm season coupling between diurnal temperature range and net longwave is weakly quadratic through the origin, rather than the linear coupling shown in earlier papers. We calculate the conceptually important 24-h imbalances of temperature and relative humidity (and other thermodynamic variables) as a function of opaque cloud cover. In the warm season under nearly clear skies, there is a warming of +2oC and a drying of -6% over the 24-h cycle, which is about 12% of their diurnal ranges. We summarize results on conserved variable diagrams and explore the impact of surface windspeed on the diurnal cycle in the cold and warm seasons. In all months, the fall in minimum temperature is reduced with increasing windspeed, which reduces the diurnal temperature range. In July and August, there is an increase of afternoon maximum temperature and humidity at low windspeeds, and a corresponding rise in equivalent potential temperature of 4.4K that appears coupled to increased precipitation. However overcast skies are associated with the major rain events and higher windspeeds.

Correction: An error in Eq(3b) has been corrected, which affected Figure 5, which has been replaced. This error came from Eq(18b) in Betts et al (2015), doi:10.3389/feart.2015.00013, where it has also been corrected (where no Figures were affected). The units on DTR in Figure 21 have also been corrected

Plain English Discussion

Since 1953, the 15 climate stations on the Canadian Prairie have made unique observations every hour of opaque reflective cloud, as well as pressure, temperature, humidity, wind, precipitation and snow depth. These cloud observations 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 strongly on clouds. But this daily cycle also varies between summer and winter, and is especially dependent on whether there is snow on the ground which strongly reflects sunlight. This is the sixth paper in a series using these measurements. Snow cover acts as a climate switch which drops the mean temperature by 10C (18F). This is easy to see for months like November and March when there is snow cover for only part of the month on the Prairies. Snow cover also switches the effect of clouds. In the warm season (April to October) when there is no snow cover, the afternoon maximum temperature goes up steadily with less cloud cover (because the clouds reflect sunlight), but the minimum temperature near sunrise barely changes. The reverse is true in the cold season with snow cover. Reduced cloud cover means the sunrise minimum temperature falls sharply, because the earth cools rapidly at night under a clear sky; while the daytime maximum temperature also falls - but rather less. With 240000 days of data, we can map all these effects on the daily climate, and also show how they also depend on the surface wind that stirs the air near the surface. This work is a guide for improving the effect of clouds in the models that forecast weather and climate.

Related Topics

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