Seasonal and Synoptic Variations in Near-Surface Air Temperature Lapse Rates in a Mountainous Basin

Abstract

To accurately estimate near-surface (2 m) air temperatures in a mountainous region for hydrologic prediction models and other investigations of environmental processes, the authors evaluated daily and seasonal variations (with the consideration of different weather types) of surface air temperature lapse rates at a spatial scale of 10 000 km 2 in south-central Idaho. Near-surface air temperature data (T max , T min , and T avg ) from 14 meteorological stations were used to compute daily lapse rates from January 1989 to December 2004 for a medium-elevation study area in south-central Idaho. Daily lapse rates were grouped by month, synoptic weather type, and a combination of both (seasonal-synoptic). Daily air temperature lapse rates show high variability at both daily and seasonal time scales. Daily T max lapse rates show a distinct seasonal trend, with steeper lapse rates (greater decrease in temperature with height) occurring in summer and shallower rates (lesser decrease in temperature with height) occurring in winter. Daily T min and T avg lapse rates are more variable and tend to be steepest in spring and shallowest in midsummer. Different synoptic weather types also influence lapse rates, although differences are tenuous. In general, warmer air masses tend to be associated with steeper lapse rates for maximum temperature, and drier air masses have shallower lapse rates for minimum temperature. The largest diurnal range is produced by dry tropical conditions (clear skies, high solar input). Cross-validation results indicate that the commonly used environmental lapse rate [typically assumed to be Ϫ0.65°C (100 m) Ϫ1] is solely applicable to maximum temperature and often grossly overestimates T min and T avg lapse rates. Regional lapse rates perform better than the environmental lapse rate for T min and T avg , although for some months rates can be predicted more accurately by using monthly lapse rates. Lapse rates computed for different months, synoptic types, and seasonal-synoptic categories all perform similarly. Therefore, the use of monthly lapse rates is recommended as a practical combination of effective performance and ease of implementation.

Fine-Scale Variations of Near-Surface-Temperature Lapse Rates in the High Drakensberg Escarpment, South Africa: Environmental Implications

“…The typically reported nocturnal cold air drainage reducing lapse rates at night, along with steeper lapse rates during the day, in summer, or associated with STEFAN W. GRAB / 511 enhanced cyclonic activity (low pressure and high relative vorticity) (e.g. Pepin, 2001;Blandford et al, 2008;Gillies et al, 2010;Bianco et al, 2011), are all to a considerable extent masked along the Drakensberg escarpment. This is owing to regional-scale westerly winds (particularly during the austral winter and spring) bringing dry air over and down the Great Escarpment, eradicating clouds and thus steepening lapse rates towards the upper escarpment zone.…”

“…Many studies examining lapse rates in mountain environments are typically at a regional scale (Ͼ10 km) (e.g. Fang and Yoda, 1988;Pepin, 2001;Blandford et al, 2008), yet it is argued that finer-scale (Ͻ10 km) analyses and modeling at the local valley scale may provide for smaller temperature prediction errors (e.g. Bolstad et al, 1998;Pagés and Miró, 2010).…”

“…In addition, the amplitude of climate change at lower altitudes may not represent those at higher altitudes, as surface temperature lapse rates (and indeed other climate parameters) are likely to vary in accordance with synoptic weather events, each of which is associated with particular moisture, airflow, radiation, and temperature patterns (cf. Pepin et al, 1999;Rolland, 2003;Hope, 2006;Blandford et al, 2008). Thus, should climate models project a higher future frequency for particular synoptic events, it may be possible, on such a basis, to model likely temperature changes at higher altitudes where mean contemporary temperature lapse rates are known for such events.…”

Investigating the Performance of Snowmelt Runoff Model Using Temporally Varying Near-Surface Lapse Rate in Western Himalayas

“…'Lapse rate' is defined as 'the decrease of an atmospheric variable with height, the variable being temperature, unless otherwise specified' 12 and it often refers to the environmental lapse rate or free atmospheric lapse rate in a vertical profile of the atmosphere 13 . Lapse rate can also refer to reduction of temperature at the different elevations at the surface of a mountainous terrain where it is termed 'near-surface lapse rate' 14,15 . The employment of environmental lapse rates to estimate the surface conditions has an implicit assumption that the terrain and surface processes are insignificant in determining surface temperatures, which is not a good approximation for temperature estimations in mountainous basins 14,16 .…”

Modeling Slope Environmental Lapse Rate (SELR) of temperature in the monsoon glacio-hydrological regime of the Himalaya

“…Many aspectes of elevation dependencies of surface temperature variations along the mountain slopes have been investigated 25 across various mountain ranges of the world (Richner and Phillips, 1984;Rolland, 2003;Pepin and Seidel, 2005;Blandford et al,2008;Kattel et al, 2013). Comparative studies of free air and surface temperature variations have amply demonstrated the significant differences between the two (Pepin and Losleben, 2002;Pepin and Seidel, 2005).…”