Surface Temperature Lapse Rate Research Articles

Indian Himalayan Timberline Ecotone in Response to Climate Change – Initial Findings

This article enumerates the findings of a team research on the Indian Himalayan timberline ecotone, with focus on three sites (located in Kashmir, Uttarakhand and Sikkim). Timberline elevation increased from west to east, was higher in the warmer south aspect than the cooler north aspect, and was generally depressed. Betula, Abies, Rhododendron and Juniperus were important treeline genera. The Himalaya has not only the highest treelines (Juniperus tibetica, at 4900 m), but also the widest elevational range (>1700 m). Remotely sensed data revealed that the timberline is a long, twisting and turning ecotone, traversing a length of 8–10 km per km horizontal distance. Surface temperature lapse rate in the monsoonal regions was lower (–0.53°C/100 m elevation) than generally perceived and varied considerably with season, being the lowest in December. The Himalayan treeline species are not water-stressed at least in monsoonal regions, predawn tree water potential seldom getting below –1 MPa. The upward advance of Rhododendron campanulatum (a krummholz species) may deplete alpine meadows with climatic warming. Tree-ring chronology indicated that winter warming may be favouring Abies spectabilis . Early snowmelt increased growth period and species richness. Treelines generally are stable in spite of decades of warming. Dependence of people on timberline was still high; so economic interventions are required to reduce the same.

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

AbstractThe 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 intra‐ and 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.

A new approach for modeling near surface temperature lapse rate based on normalized land surface temperature data

The modeling of Near-Surface Temperature Lapse Rate (NSTLR) is of great importance in various environmental applications. This study proposed a new approach for modeling the NSTLR based on the Normalized Land Surface Temperature (NLST). A set of remote sensing imagery including Landsat images, MODIS products, and ASTER Digital Elevation Model (DEM), land cover maps, and climatic data recorded in meteorological stations and self-deployed devices located in the three study area were used for modeling and evaluation of NSTLR. First, the Split Window (SW) and Single Channel (SC) algorithms were used to estimate LST, and the spectral indices were used to model surface biophysical characteristics. The solar local incident angle was obtained based on topographic and time conditions for different dates. In the second step, the NSTLR value was calculated based on the LST-DEM feature space at the regional scale. The LST was normalized relative to the surface characteristics based on Random Forest (RF) regression and the NSTLR was calculated based on the NLST-DEM feature space. Finally, the coefficient of determination (R2) and Root Mean Square Error (RMSE) between the modeled NSTLR and the observed NSTLR were calculated to evaluate the accuracy of the modeled NSTLR. The mean values of R2 between DEM and NLST were improved 0.3, 0.42 and 0.35, rather than between DEM and LST for the study area A, B and C, respectively. The R2 and RMSE between the observed NSTLR and the Landsat derived NSTLR based on NLST for the study area A (B, C) were improved 0.30 (0.26, 0.35) and 0.81 (0.80, 0.94) °C Km−1, respectively, rather than the Landsat derived NSTLR based on LST. Also, for the study area A, the R2 and RMSE between the observed NSTLR and the MODIS derived NSTLR based on NLST in spring, summer, autumn and winter were improved 0.17, 0.12, 0.10, and 0.22; and 0.51, 0.44, 0.27, and 0.51 °C Km−1, respectively, rather than the MODIS derived NSTLR based on LST. Model assessment results (R2 and RMSE) and comparing modeled NSTLR (all strategies) with observed NSTLR, for both Landsat and MODIS, showed that the use of NLST instead of LST, significantly improved the accuracy of the obtained NSTLR in the mountainous regions.

Identification of homogeneous regions of near surface air temperature lapse rates across India

Spatial variation of near surface temperature lapse rate is usually analysed by subjectively dividing the study area in to different sub‐regions based on political or physiographic boundaries. This can often lead to unreliable results as near surface temperature lapse rate largely depends on regional conditions. In this study, homogeneous regions of near surface air temperature lapse rate across India have been identified for monthly mean minimum, mean, and mean maximum temperature using a probabilistic Gaussian mixture model clustering approach. For the delineated regions, lapse rates are estimated using weighted linear regression model. The weights corresponding to each station were obtained from the clustering approach. Results indicate that regions with uniform lapse rate vary across different months and a minimum of three regions is obtained for maximum temperature in April month. Further, temperature lapse rate estimates exhibit spatial and temporal variability, and are less steep than the Environmental lapse rate (−6.5°C/km). The maximum spatial variability in lapse rate is observed in May for both maximum and minimum temperature. The delineation of entire India into homogeneous regions improves temperature interpolation when compared to results obtained by considering entire India as one region. A maximum improvement of 34% in root mean square error is observed in prediction of monthly mean temperature in March. The identified regions and the associated lapse rates are expected to improve the prediction of near surface temperature at ungauged locations across India.

An Improved Analytical Solution for the Temperature Profile of Ice Sheets

The one‐dimensional steady state analytical solution of the energy conservation equation obtained by Robin (1955, https://doi.org/10.3189/002214355793702028) is frequently used in glaciology. This solution assumes a linear change in surface velocity from a minimum value equal to minus the mass balance at the surface to zero at the bed. Here we show that this assumption of a linear velocity profile leads to large errors in the calculated temperature profile and especially in basal temperature. By prescribing a nonlinear power function of elevation above the bed for the vertical velocity profile arising from use of the Shallow Ice Approximation, we derive a new analytical solution for temperature. We show that the solution produces temperature profiles identical to numerical temperature solutions with the Shallow Ice Approximation vertical velocity near ice divides. We quantify the importance of strain heating and demonstrate that integrating the strain heating and adding it to the geothermal heat flux at the bed is a reasonable approximation for the interior regions. Our analytical solution does not include horizontal advection components, so we compare our solution with numerical solutions of a two‐dimensional advection‐diffusion model and assess the applicability and errors of the analytical solution away from the ice divide. We show that several parameters and assumptions impact the spatial extent of applicability of the new solution including surface mass balance rate and surface temperature lapse rate. We delineate regions of Greenland and Antarctica within which the analytical solution at any depth is likely within 2 K of the actual temperatures with horizontal advection.

A New Methodology for Estimating the Surface Temperature Lapse Rate Based on Grid Data and Its Application in China

Land surface temperature (LST) is an important parameter in the study of the physical processes of land surface. Understanding the surface temperature lapse rate (TLR) can help to reveal the characteristics of mountainous climates and regional climate change. A methodology was developed to calculate and analyze land-surface TLR in China based on grid datasets of MODIS LST and digital elevation model (DEM), with a formula derived on the basis of the analysis of the temperature field and the height field, an image enhancement technique used to calculate gradient, and the fuzzy c-means (FCM) clustering applied to identify the seasonal pattern of the TLR. The results of the analysis through the methodology showed that surface temperature vertical gradient inversion widely occurred in Northeast, Northwest, and North China in winter, especially in the Xinjiang Autonomous Region, the northern and the western parts of the Greater Khingan Mountains, the Lesser Khingan Mountains, and the northern area of Northwest and North China. Summer generally witnessed the steepest TLR among the four seasons. The eastern Tibetan Plateau showed a distinctive seasonal pattern, where the steepest TLR happened in winter and spring, with a shallower TLR in summer. Large seasonal variations of TLR could be seen in Northeast China, where there was a steep TLR in spring and summer and a strong surface temperature vertical gradient inversion in winter. The smallest seasonal variation of TLR happened in Central and Southwest China, especially in the Ta-pa Mountains and the Qinling Mountains. The TLR at very high altitudes (>5 km) was usually steeper than at low altitudes, in all months of the year.

山区地表温度垂直递减率的时空分布特征——以祁连山为例

山区地表温度垂直递减率的时空分布特征——以祁连山为例

Surface temperature lapse rates over complex terrain: Lessons from the Cascade Mountains

The typically sparse distribution of weather stations in mountainous terrain inadequately resolves temperature variability. Accordingly, high‐resolution gridding of climate data (for applications such as hydrological modeling) often relies on assumptions such as a constant surface temperature lapse rate (i.e., decrease of surface temperature with altitude) of 6.5°C km−1. Using an example of the Cascade Mountains, we describe the temporal and spatial variability of the surface temperature lapse rate, combining data from: (1) COOP stations, (2) nearby radiosonde launches, (3) a temporary dense network of sensors, (4) forecasts from the MM5 regional model, and (5) PRISM geo‐statistical analyses. On the windward side of the range, the various data sources reveal annual mean lapse rates of 3.9–5.2°C km−1, substantially smaller than the often‐assumed 6.5°C km−1. The data sets show similar seasonal and diurnal variability, with lapse rates smallest (2.5–3.5°C km−1) in late‐summer minimum temperatures, and largest (6.5–7.5°C km−1) in spring maximum temperatures. Geographic (windward versus lee side) differences in lapse rates are found to be substantial. Using a simple runoff model, we show the appreciable implications of these results for hydrological modeling.

Using satellite thermal infrared imagery to study boundary layer structure in an Antarctic katabatic wind region

We use snow surface temperatures obtained from thermal infrared (TIR) satellite imagery, together with radiosonde profiles of free-air temperature and high-resolution topographic data to study the thermal structure of the atmospheric boundary layer in a coastal region ofEast Antarctica. Surface temperatures over a coastal ice shelf are shown to be significantly lower than those observed on the lower part of the adjoining coastal slopes as a result of the strong surface temperature inversion that forms over the ice shelf. Between 400 and 1500 m elevation the surface temperature lapse rate is close to the dry adiabatic value while the free-air temperature profile is significantly stable over this height range. We argue that this implies that the strength of the surface inversion increases with increasing elevation. Above 1500 m the surface temperature lapse rate becomes significantly superadiabatic and the coldest surface temperatures are found a few 10s of kilometres inland of the highest topography. The technique may prove useful for studying boundary layer structure in other regions of Antarctica where suitable high-resolution topographic data are available.