scholarly journals Estimation of near-surface air temperature lapse rates over continental Spain and its mountain areas

2018 ◽  
Vol 38 (8) ◽  
pp. 3233-3249 ◽  
Author(s):  
F. Navarro-Serrano ◽  
J. I. López-Moreno ◽  
C. Azorin-Molina ◽  
E. Alonso-González ◽  
M. Tomás-Burguera ◽  
...  
Keyword(s):  
Air Temperature ◽  
Surface Air Temperature ◽  
Mountain Areas ◽  
Near Surface ◽  
Lapse Rates

scholarly journals Reanalysis of 44 Yr of Climate in the French Alps (1958–2002): Methodology, Model Validation, Climatology, and Trends for Air Temperature and Precipitation

2009 ◽  
Vol 48 (3) ◽  
pp. 429-449 ◽  
Author(s):  
Yves Durand ◽  
Martin Laternser ◽  
Gérald Giraud ◽  
Pierre Etchevers ◽  
Bernard Lesaffre ◽  
...  
Keyword(s):  
Air Temperature ◽  
Numerical Models ◽  
Surface Air Temperature ◽  
Positive Trend ◽  
Mountain Areas ◽  
Near Surface ◽  
French Alps ◽  
Mountainous Regions ◽  
Temperature And Precipitation ◽  
Analysis Scheme

Abstract Since the early 1990s, Météo-France has used an automatic system combining three numerical models to simulate meteorological parameters, snow cover stratification, and avalanche risk at various altitudes, aspects, and slopes for a number of mountainous regions in France. Given the lack of sufficient directly observed long-term snow data, this “SAFRAN”–Crocus–“MEPRA” (SCM) model chain, usually applied to operational avalanche forecasting, has been used to carry out and validate retrospective snow and weather climate analyses for the 1958–2002 period. The SAFRAN 2-m air temperature and precipitation climatology shows that the climate of the French Alps is temperate and is mainly determined by atmospheric westerly flow conditions. Vertical profiles of temperature and precipitation averaged over the whole period for altitudes up to 3000 m MSL show a relatively linear variation with altitude for different mountain areas with no constraint of that kind imposed by the analysis scheme itself. Over the observation period 1958–2002, the overall trend corresponds to an increase in the annual near-surface air temperature of about 1°C. However, variations are large at different altitudes and for different seasons and regions. This significantly positive trend is most obvious in the 1500–2000-m MSL altitude range, especially in the northwest regions, and exhibits a significant relationship with the North Atlantic Oscillation index over long periods. Precipitation data are diverse, making it hard to identify clear trends within the high year-to-year variability.

Near-surface air temperature lapse rates in Xinjiang, northwestern China

2017 ◽  
Vol 131 (3-4) ◽  
pp. 1221-1234 ◽  
Author(s):  
Mingxia Du ◽  
Mingjun Zhang ◽  
Shengjie Wang ◽  
Xiaofan Zhu ◽  
Yanjun Che
Keyword(s):  
Air Temperature ◽  
Surface Air Temperature ◽  
Northwestern China ◽  
Near Surface ◽  
Lapse Rates

scholarly journals Modelling spatial patterns of near-surface air temperature over a decade of melt seasons on McCall Glacier, Alaska

2020 ◽  
Vol 66 (257) ◽  
pp. 386-400
Author(s):  
Patrick Troxler ◽  
Álvaro Ayala ◽  
Thomas E. Shaw ◽  
Matt Nolan ◽  
Ben W. Brock ◽  
...  
Keyword(s):  
Spatial Patterns ◽  
Air Temperature ◽  
Flow Line ◽  
Surface Air Temperature ◽  
Strong Relationship ◽  
Near Surface ◽  
Elevation Difference ◽  
Dependent Model ◽  
Glacier Flow ◽  
Lapse Rates

AbstractWe examine the spatial patterns of near-surface air temperature (Ta) over a melting glacier using a multi-annual dataset from McCall Glacier, Alaska. The dataset consists of a 10-year (2005–2014) meteorological record along the glacier centreline up to an upper glacier cirque, spanning an elevation difference of 900 m. We test the validity of on-glacier linear lapse rates, and a model that calculates Ta based on the influence of katabatic winds and other heat sources along the glacier flow line. During the coldest hours of each summer (10% of time), average lapse rates across the entire glacier range from −4.7 to −6.7°C km−1, with a strong relationship between Ta and elevation (R2 > 0.7). During warm conditions, Ta shows more complex, non-linear patterns that are better explained by the flow line-dependent model, reducing errors by up to 0.5°C compared with linear lapse rates, although more uncertainty might be associated with these observations due to occasionally poor sensor ventilation. We conclude that Ta spatial distribution can vary significantly from year to year, and from one glacier section to another. Importantly, extrapolations using linear lapse rates from the ablation zone might lead to large underestimations of Ta on the upper glacier areas.

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

2008 ◽  
Vol 47 (1) ◽  
pp. 249-261 ◽  
Author(s):  
Troy R. Blandford ◽  
Karen S. Humes ◽  
Brian J. Harshburger ◽  
Brandon C. Moore ◽  
Von P. Walden ◽  
...  
Keyword(s):  
Air Temperature ◽  
Surface Air Temperature ◽  
Lapse Rate ◽  
Maximum Temperature ◽  
Weather Types ◽  
Near Surface ◽  
Air Masses ◽  
Synoptic Weather ◽  
South Central ◽  
Lapse Rates

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 km2 in south-central Idaho. Near-surface air temperature data (Tmax, Tmin, and Tavg) 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 Tmax 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 Tmin and Tavg 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 −0.65°C (100 m)−1] is solely applicable to maximum temperature and often grossly overestimates Tmin and Tavg lapse rates. Regional lapse rates perform better than the environmental lapse rate for Tmin and Tavg, 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.

scholarly journals REDCAPP (v1.0): parameterizing valley inversions in air temperature data downscaled from reanalyses

2017 ◽  
Vol 10 (8) ◽  
pp. 2905-2923 ◽  
Author(s):  
Bin Cao ◽  
Stephan Gruber ◽  
Tingjun Zhang
Keyword(s):  
Air Temperature ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Surface Effects ◽  
Model Parameters ◽  
Fine Scale ◽  
Mountain Areas ◽  
Near Surface ◽  
Cold Air ◽  
The Difference

Abstract. In mountain areas, the use of coarse-grid reanalysis data for driving fine-scale models requires downscaling of near-surface (e.g., 2 m high) air temperature. Existing approaches describe lapse rates well but differ in how they include surface effects, i.e., the difference between the simulated 2 m and upper-air temperatures. We show that different treatment of surface effects result in some methods making better predictions in valleys while others are better in summit areas. We propose the downscaling method REDCAPP (REanalysis Downscaling Cold Air Pooling Parameterization) with a spatially variable magnitude of surface effects. Results are evaluated with observations (395 stations) from two mountain regions and compared with three reference methods. Our findings suggest that the difference between near-surface air temperature and pressure-level temperature (ΔT) is a good proxy of surface effects. It can be used with a spatially variable land-surface correction factor (LSCF) for improving downscaling results, especially in valleys with strong surface effects and cold air pooling during winter. While LSCF can be parameterized from a fine-scale digital elevation model (DEM), the transfer of model parameters between mountain ranges needs further investigation.

scholarly journals Near-surface air temperature lapse rates in the mainland China during 1962-2011

2013 ◽  
Vol 118 (14) ◽  
pp. 7505-7515 ◽  
Author(s):  
Xiuping Li ◽  
Lei Wang ◽  
Deliang Chen ◽  
Kun Yang ◽  
Baolin Xue ◽  
...  
Keyword(s):  
Air Temperature ◽  
Mainland China ◽  
Surface Air Temperature ◽  
Near Surface ◽  
Lapse Rates

scholarly journals REDCAPP (v1.0): Parameterizing valley inversions in air temperature data downscaled from re-analyses

2017 ◽  
Author(s):  
Bin Cao ◽  
Stephan Gruber ◽  
Tingjun Zhang
Keyword(s):  
Air Temperature ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Surface Effects ◽  
Model Parameters ◽  
Fine Scale ◽  
Mountain Areas ◽  
Near Surface ◽  
Cold Air ◽  
The Difference

Abstract. In mountain areas, the use of coarse-grid re-analysis data for driving fine-scale models requires downscaling of near-surface (e.g. 2 m high) air temperature. Existing approaches describe lapse rates well but differ in how they include surface effects, i.e. the difference between the simulated 2 m and upper-air temperatures. We show that different treatment of surface effects result in some methods making better predictions in valleys while others are better in summit areas. We propose the downscaling method REDCAPP (REanalysis Downscaling Cold Air Pooling Parameterization) with a spatially variable magnitude of surface effects. Results are evaluated with observations (395 stations) from two mountain regions and compared with three reference methods. Our findings suggest that the difference between near-surface air temperature and pressure-level temperature (∆T) is a good proxy of surface effects. It can be used with a spatially-variable Land-Surface Correction-Factor (LSCF) for improving downscaling results, especially in valleys with strong surface effects and cold air pooling during winter. While LSCF can be parameterized from a fine-scale digital elevation model (DEM), the transfer of model parameters between mountain ranges needs further investigation.

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