Combining sparse observations and reanalysis data for refining spatiotemporal variability in near‐surface air temperature lapse rates over China

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.

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

Journal of Applied Meteorology and Climatology ◽

10.1175/2007jamc1565.1 ◽

2008 ◽

Vol 47 (1) ◽

pp. 249-261 ◽

Cited By ~ 138

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.

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Best practices for estimating near‐surface air temperature lapse rates

International Journal of Climatology ◽

10.1002/joc.6668 ◽

2020 ◽

Cited By ~ 1

Author(s):

A. C. Lute ◽

John T. Abatzoglou

Keyword(s):

Best Practices ◽

Air Temperature ◽

Surface Air Temperature ◽

Near Surface ◽

Lapse Rates

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A statistical downscaling method for daily air temperature in data-sparse, glaciated mountain environments

Geoscientific Model Development ◽

10.5194/gmd-8-579-2015 ◽

2015 ◽

Vol 8 (3) ◽

pp. 579-593 ◽

Cited By ~ 10

Author(s):

M. Hofer ◽

B. Marzeion ◽

T. Mölg

Keyword(s):

Air Temperature ◽

Statistical Downscaling ◽

A Priori ◽

Surface Air Temperature ◽

Reanalysis Data ◽

Data Availability ◽

Skill Assessment ◽

Selection Strategy ◽

Near Surface ◽

Sd Model

Abstract. This study presents a statistical downscaling (SD) method for high-altitude, glaciated mountain ranges. The SD method uses an a priori selection strategy of the predictor (i.e., predictor selection without data analysis). In the SD model validation, emphasis is put on appropriately considering the pitfalls of short observational data records that are typical of high mountains. An application example is shown, with daily mean air temperature from several sites (all in the Cordillera Blanca, Peru) as target variables, and reanalysis data as predictors. Results reveal strong seasonal variations of the predictors' performance, with the maximum skill evident for the wet (and transitional) season months January to May (and September), and the lowest skill for the dry season months June and July. The minimum number of observations (here, daily means) required per calendar month to obtain statistically significant skill ranges from 40 to 140. With increasing data availability, the SD model skill tends to increase. Applied to a choice of different atmospheric reanalysis predictor variables, the presented skill assessment identifies only air temperature and geopotential height as significant predictors for local-scale air temperature. Accounting for natural periodicity in the data is vital in the SD procedure to avoid spuriously high performances of certain predictors, as demonstrated here for near-surface air temperature. The presented SD procedure can be applied to high-resolution, Gaussian target variables in various climatic and geo-environmental settings, without the requirement of subjective optimization.

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

Journal of Geophysical Research Atmospheres ◽

10.1002/jgrd.50553 ◽

2013 ◽

Vol 118 (14) ◽

pp. 7505-7515 ◽

Cited By ~ 40

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

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Do Changes in the Midlatitude Circulation Have Any Impact on the Arctic Surface Air Temperature Trend?

Journal of Climate ◽

10.1175/jcli3906.1 ◽

2006 ◽

Vol 19 (20) ◽

pp. 5422-5438 ◽

Cited By ~ 65

Author(s):

R. G. Graversen

Keyword(s):

Air Temperature ◽

Energy Transport ◽

Surface Air Temperature ◽

Arctic Region ◽

Reanalysis Data ◽

The Arctic ◽

Positive Trend ◽

Important Indicator ◽

Near Surface ◽

The Mean

Abstract The warming of the near-surface air in the Arctic region has been larger than the global mean surface warming. There is general agreement that the Arctic amplification of the surface air temperature (SAT) trend to a considerable extent is due to local effects such as the retreat of sea ice, especially during the summer months, and earlier melting of snow in the spring season. There is no doubt that these processes are important causes of the Arctic SAT trend. It is less clear, however, whether the trend may also be related to recent changes in the atmospheric midlatitude circulation. This question is the focus of the present paper. Model experiments have shown that in a warmer climate responding to, for example, a doubling of CO2, the atmospheric northward energy transport (ANET) will increase and cause polar SAT amplification. In the present study, the development of the ANET across 60°N and its linkage to the Arctic SAT have been explored using the ERA-40 reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF). It is found that during 1979–2001, the ANET has experienced an overall positive but weak trend, which was largest during the period from the mid-1980s to the mid-1990s. In addition, it is found that the Arctic SAT is sensitive to variability of the ANET across 60°N and hence to variability of the midlatitude circulation: A large ANET is followed by warming of the Arctic where ANET leads by about 5 days. The warming is located primarily north of the Atlantic and Pacific sectors, indicating that baroclinic weather systems developing around the Icelandic and Aleutian lows are important for the energy transport. Furthermore, it is suggested here that a small, but statistically significant, part of the mean Arctic SAT trend is linked to the trend in the ANET. Another important indicator of the midlatitude circulation is the Arctic Oscillation (AO). Through the 1980s and early 1990s the AO index has shown a positive trend. However, even though a part of the SAT trend can be related to the AO in localized parts of the Arctic area, the mean Arctic SAT trend shows no significant linkage to the AO.

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