Details

Autor(en) / Beteiligte

• Betts, Alan K.
• Tawfik, Ahmed B.
• Desjardins, Raymond L.

Titel

Revisiting Hydrometeorology Using Cloud and Climate Observations

Ist Teil von

• Journal of hydrometeorology, 2017-04, Vol.18 (4), p.939-955

Ort / Verlag

Boston: American Meteorological Society

Erscheinungsjahr

2017

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• This paper uses 620 station years of hourly Canadian Prairie climate data to analyze the coupling of monthly near-surface climatewith opaque cloud, a surrogate for radiation, and precipitation anomalies. While the cloud–climate coupling is strong, precipitation anomalies impact monthly climate for as long as 5 months. The April climate has memory of precipitation anomalies back to freeze-up in November, mostly stored in the snowpack. The summer climate has memory of precipitation anomalies back to the beginning of snowmelt in March. In the warm season, mean temperature is strongly correlated to opaque cloud anomalies, but only weakly to precipitation anomalies. Mixing ratio anomalies are correlated to precipitation, but only weakly to cloud. The diurnal cycle of mixing ratio shifts upward with increasing precipitation anomalies. Positive precipitation anomalies are coupled to a lower afternoon lifting condensation level and a higher afternoon equivalent potential temperature; both favor increased convection and precipitation. Regression coefficients on precipitation increase from wet to dry conditions. This is consistent with increased uptake of soil water when monthly precipitation is low, until drought conditions are reached, and also consistent with gravity satellite observations. Regression analysis shows monthly opaque cloud cover is tightly correlated to three climate variables that are routinely observed: diurnal temperature range, mean temperature, and mean relative humidity. The set of correlation coefficients, derived from cloud and climate observations, could be used to evaluate the representation of the land–cloud–atmosphere system in both forecast and climate models.