Daily and seasonal variation of the surface temperature lapse rate and 0°C isotherm height in the western subtropical Andes

Abstract

The spatial distribution of surface air temperatures is essential for understanding and modelling high-relief environments. Good estimations of the surface temperature lapse rate (STLR) and the 0 C isotherm height (H0) are fundamental for hydrological modelling in mountainous basins. Although STLR changes in space and time, it is typically assumed to be constant leading to errors in the estimation of direct-runoff volumes and flash-floods risk assessment. This paper characterizes daily and seasonal temporal variations of the in-situ STLR and H0 over the western slope of the subtropical Andes (central Chile). We use temperature data collected during 2 years every 10 min by a 16 sensors network in a small catchment with elevations ranging between 700 and 3,250 m. The catchment drains directly into Santiago, the Chilean capital with more than seven million inhabitants. Resulting values are compared against those obtained using off-site, operational data sets. Significant intraand inter-day variations of the in-situ STLR were found, likely reflecting changes in the low-level temperature inversion during dry conditions. The annual average in-situ STLR is −5.9 C/km during wet-weather conditions. Furthermore, STLR and H0 estimations using off-site gauges are extremely sensitive to the existence of gauging stations at high elevations.

Future Changes in the Free Tropospheric Freezing Level and Rain–Snow Limit: The Case of Central Chile

“…Santo Domingo, a coastal site at 33.65 • S, 71.61 • W (75 m ASL), is the only radiosonde station operated by DMC in this region (black start in Figure 2), with launches twice daily at 12:00 and 00:00 UTC. In all cases, there is only one level in which the air temperature profile crosses the 0 • C, even if there is an inversion in dry days because they are warm and low [10], so the free tropospheric H 0 was obtained unambiguously from direct interpolation using the temperature and geopotential height of the levels just above and below 0 • C. On a given day, a mean value of H 0 was calculated using the 00:00 UTC (8:00 PM of the previous day), 12:00 UTC (8:00 AM of the current day) and 00:00 UTC (8:00 PM of the current day) values, and then pooled into the wet or dry groups according to the concurrent rainfall data at Santo Domingo. In stations with surface data only (surface air temperature, SAT), we estimated the freezing level during rainy days using a moist adiabatic lapse rate (Γ moist ≈ 6.5 • C/km) as H 0s f c = SAT/Γ moist + H G , which proved to be a good approximation in this region [10,31].…”

“…Given the nearly two-dimensional nature of the Andes cordillera, extending almost straight north-south along its subtropical portion, and its proximity to the Pacific shoreline (Figure 2), most of the subsequent analyses are performed using the along-coast (latitudinal) profile of H 0 . We acknowledge that the coastal, free tropospheric value of H 0 during a particular storm can differ from the actual snow line over the western slope of the Andes [7,10] but any coast-to-Andes difference is likely to exist both in present and future climates, so it will not preclude exploring the climate change impact upon the freezing level.…”

“…Although cloud microphysics and airflow-induced localized cooling can depress the elevation of the rain-snow limit over the terrain relative to the free tropospheric H 0 during a precipitation event, both heights are closely tied between storms (e.g., [4,5,9,10]). Furthermore, because the processes behind such offset are only weakly dependent on air temperature [7], the free tropospheric H 0 suffices to track the variability and change of the rain-snow limit over terrain in the large-scale climate change context addressed in this work.…”

Contrasting Impacts of a Hotter and Drier Future on Streamflow and Catchment Scale Sediment Flux in the High Andes

“…For the Chilean catchments, we obtained monthly PET from a temperature‐based compilation via CAMELS_CL. Temperature inputs to the HBV models were scaled vertically across each catchment elevation band using a lapse rate of 0.6°C per 100 m, similar to observed average lapse rates in the region (Ibañez et al., 2020). A precipitation gradient of 10% per 100 m was used to reflect orographic effects in the Andes (Viale & Garreaud, 2015).…”

The Swiss Alpine zero degree line: Methods, past evolution and sensitivities

“…Despite the relevance of the ZDL in mountainous regions, only few studies have so far investigated its spatial variability, its long-term changes and its sensitivity to regional climate change. Existing works are often related to the analysis of temperature lapse rates for example, in the Andes (Carrasco et al, 2005;Bradley et al, 2009;Schauwecker et al, 2017;Ibañez et al, 2020), the Himalayas (Kattel et al, 2013), the Cascade Mountains (Minder et al, 2010), the Carpathian mountains (Micu et al, 2020) and the European Alps (Rolland, 2003;Kirchner et al, 2013;Hiebl and Schöner, 2018). For the latter case, the recent CH2018 Climate Scenarios for Switzerland found an increasing ZDL under future climate change with uplift rates depending on the specific greenhouse gas scenario considered (CH2018, 2018.…”