10A.4 Identification of Physical Heterogeneities in Canadian High-Frequency Air Temperature Records

Wednesday, 15 January 2020: 3:45 PM
150 (Boston Convention and Exhibition Center)
Ana Žaknić-Ćatović, Univ. of Toronto, Scarborough, Toronto, Canada; and W. A. Gough

Handout (1.5 MB)

Apart from inhomogeneities in climatological records, often caused by human errors and measurement inconsistencies, physical heterogeneities in high-frequency air temperature data deserve closer scrutiny. This study examines multiple Canadian long-term air temperature records for the presence of physical heterogeneities based on the analysis of their diurnal air temperature patterns. A systematic departure from a radiatively induced sinusoidal pattern has been observed in 5% and 20% of examined temperature data. Linear features of this contrasting air temperature population are attributable to the advection of warm or cool air into a region which commonly causes a reverse order of diurnal temperature extrema, such as occurrence of a nighttime maximum and a daytime minimum.

Based on differences observed in their diurnal air temperature patterns, two distinct components of the air temperature sample are identified and assumed to be caused by different physical phenomena. The Radiative Temperature Component (RTC) is characterized by diurnal temperature changes that exhibit a sinusoidal pattern, with maximum air temperature typically occurring during the daytime, and the minimum air temperature during the nighttime. The Advective Temperature Component (ATC) is, in contrast, characterized by a linear air temperature pattern extending between the consecutive extrema.

The Linear Pattern Discrimination (LPD) algorithm, for the identification of four linear ATC cases in air temperature data, requires as the only input a chronologically ordered sequence of diurnal air temperature extrema at known times of occurrence. The LPD algorithm parses through the radiative sequence of air temperature-time extrema pairs, identified using the Climatological Observing Window Night and Day (COWN-D), for a preliminary discrimination between the RTC and ATC populations. In the following step, the ATC population is further subjected to the criteria for the identification of different linear cases, as illustrated in Figure 1 schematic.

In this study, the LPD algorithm, implemented in R-code, is applied to 65-year hourly temperature records of twenty-two Canadian stations for the separation of RTC and ATC populations and examination of incidences of specific ATC cases in data, prior to the statistical analysis. The ATC population count for each station is further partitioned into four seasonal components and finally, per specific case basis. An example of all four linear ATC cases identified in the Toronto air temperature sample is presented in Figure 2 while results of air temperature sample separation for all Canadian stations are presented in Table 1.

Contribution of the RTC sample in all Canadian air temperature data ranges between 80% and 95%. The ratio of RTC to ATC population appears to be reflective of the regional climate and specific local conditions. Northern locations have, in general, a higher ATC contribution to the total temperature sample while, for all locations, ATC samples in winter and fall combined are showing significantly higher counts than spring and summer seasons combined.

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