Estimation of near‐surface air temperature lapse rates over continental Spain and its mountain areas

Navarro-Serrano, Francisco; López‐Moreno, Juan I.; Azorín-Molina, César; Alonso‐González, Esteban; Tomás-Burguera, Miquel; Sanmiguel‐Vallelado, Alba; Revuelto, Jesús; Vicente‐Serrano, Sergio M.

doi:10.1002/joc.5497

Cited by 34 publications

(34 citation statements)

References 59 publications

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“…As mentioned in Section 3, at first the predictors that are correlated with the residual error (predictand) at 5% significance level were identified for monthly T̄ min , T̄ avg , and T̄ max and have been summarized in Tables (see Appendix ). The results of the correlation analysis are in agreement with the findings of earlier studies conducted for different study regions (Rolland, ; Kattel et al, ; Navarro‐Serrano et al, ). Across India, wind speed and air moisture content at surface and different pressure levels affect the lapse rate variation.…”

Section: Resultssupporting

confidence: 90%

“…Near surface air temperature lapse rate exhibits spatial and temporal variation because of synoptic weather conditions, location indicators, landuse/landcover, and topography (Li et al, ; Kattel et al, ; Bharath et al, ; Navarro‐Serrano et al, ). Many studies have analysed the spatio‐temporal variability of lapse rates, especially in complex terrains.…”

Section: Introductionmentioning

confidence: 99%

“…The lapse rate values were significantly correlated with cloud cover and actual vapour pressure lapse rate. Navarro‐Serrano et al () analysed the spatio‐temporal variability of lapse rate for continental Spain and its mountainous areas, and its dependence on synoptic atmospheric circulations. The results showed that the lapse rate values were weaker than the constant of −6.5°C/km, and were strongly related to circulation weather types.…”

Section: Introductionmentioning

confidence: 99%

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

Ojha

2019

Intl Journal of Climatology

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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. K E Y W O R D Scluster analysis, India, lapse rate, regions, temperature | INTRODUCTIONTemperature is an important variable in hydrological, agricultural and climate model studies, as it strongly influences the partitioning of energy and water fluxes between the land and the atmosphere. The estimates of hydrological processes such as evaporation and evapotranspiration that depend on temperature can improve streamflow prediction in a river basin. In agricultural models, the estimates of these processes can assist in modelling soil moisture dynamics, determining crop water requirement and irrigation scheduling. In climate change impact assessment studies, temperature is an important indicator of global warming (Bharath et al., 2016). Therefore, it is important for societal planning in water resources, hydropower, agriculture, ecology, and other areas, to accurately quantify temperature distribution over the region of interest (Minder et al., 2010).Numerous studies have focused on modelling spatial and/or temporal variation of temperature (e.g., Luterbacher et al., 2004;Toreti and Desiato, 2008;Bharath et al., 2016). However, modelling spatial-temporal variability of temperature remains a challenging task, particularly over large geographical areas with complex topography and varying climate. Dense networks of sensors with high temporal

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Section: Resultssupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Ojha

2019

Intl Journal of Climatology

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“…Even under stable atmospheric conditions, LRs can vary considerably from one season to another for maximum and minimum air temperature (Pepin, 2001; Marshall et al ., 2007; Kattel et al ., 2013). This notion has been confirmed across the global mountain systems, including the Rocky Mountains (Kunkel, 1989; Pepin and Losleben, 2002), the Himalayas (Immerzeel et al ., 2014; Romshoo et al ., 2018; Ojha, 2019) and the Alps (Dumas, 2013; Nigrelli et al ., 2017), along with other regions (e.g., the Iberian Peninsula; (Navarro‐Serrano et al ., 2018), as well as in the Antarctic Peninsula (Ambrozova et al ., 2019). In each region, the vertical distribution of air temperature varies between nighttime and daytime due to variations in heat fluxes between the atmosphere and the surface (Duane et al ., 2008; Gardner et al ., 2009).…”

Section: Introductionmentioning

confidence: 99%

Maximum and minimum air temperature lapse rates in the Andean region of Ecuador and Peru

Navarro-Serrano

López‐Moreno

Domínguez‐Castro

et al. 2020

Intl Journal of Climatology

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To know the vertical distribution of air temperature is complex, and this is necessary for different applications. The main explanatory variable of air temperature is elevation above sea level, whose relationship with air temperature is measured by air temperature lapse rates (LRs). LRs can vary considerably spatiotemporally due to a wide spectrum of geographical, environmental, and other atmospheric factors. Our study presents the first comprehensive assessment of spatiotemporal changes of LRs over the Tropical Andes. Our study is focused on the Peruvian and Ecuadorian Andes, divided in two subregions by the parallel 9.5° (i.e., north and south). Maximum and minimum air temperatures were employed from 115 quality checked weather stations, for the period 1994–2011. Maximum (LRmax) and minimum (LRmin) air temperature lapse rates have been calculated for the whole study period. The effects of seasonality, humidity content and ENSO on the variability of LRs have been analysed. Results show that LRs have large spatiotemporal variability, since references values of LRmax range from −3.57 (South, dry season) to −4.77 (North, wet season), and LRmin range from −3.78 (North, dry season) to −4.93°C·km−1 (South, dry season), in function of season and subregion. Results indicate that the ENSO phases contribute significantly to the variability of southern subregion LRs. This study also presents that minimum air temperatures were more unpredictable than maximum air temperatures in terms of error and uncertainty, as a consequence of the larger spatial variability of nocturnal air temperatures, mainly influenced by local topography. In conclusion, this work goes deeper into the need to obtain precise LRs adapted to the study region, and shows that the use of standard LR values can cause significant failures in modelling air temperature in regions of complex terrain, such as the Andes.

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“…The model was therefore applied over Médécourbe and Picot areas using a precipitation gradient with respect to the altitude similar to that of the Ossoue glacier. However, the temperature lapse rate was estimated to be 5.8 °C/1000 m for the Médécourbe and Picot valleys that are oriented to the North (Navarro-Serrano et al 2018).…”

Section: Glaciological Model Validationmentioning

confidence: 97%

Glacier fluctuations during the Late Glacial and Holocene on the Ariège valley, northern slope of the Pyrenees and reconstructed climatic conditions

et al. 2020

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The glacier evolution from two valleys located near Vicdessos, French Pyrenees, is deciphered from 17 in situ cosmic ray exposure (CRE) dating. In the Picot valley, Late Glacial glacier advances were documented during Heinrich Stadial 1 (HS1), while a rock glacier developed or was reactivated during the mid-Holocene. In the upper Médécourbe valley, the largest visible ice extent occurred during the Younger Dryas (YD) or earlier. At least two moraines formed during the early Holocene were dated, while an undated moraine located close to the head of the catchment may have been formed either during the Late Holocene or the Little Ice Age. A mass balance model suggests that a temperature about 5.1 °C cooler than today, without precipitation change, would be necessary to form a moraine at the base of the Picot catchment during HS1 at 2000 m a.s.l. A temperature about 3.9 °C cooler than today is necessary to explain a moraine formed during the Late Glacial-YD transition at Médécourbe at 2200 m. Comparing CRE dating from Picot and Médécourbe with those in the Pyrenees and the Alps highlights original glacial patterns. In the Picot catchment whose summit is below the current regional ELA, the absence of YD and Late Holocene moraines is consistent with the general low-altitude deglaciation trend documented in the northern and southern slope of the Pyrenees, but differs from high-altitude Pyrenean and Alpine records. However, due to specific geomorphological conditions, the glacial evolution of the Médécourbe valley took place differently compared to other low-altitude catchments in the Pyrenees. Article Highlights • CRE dating revealed moraines formed during the Heinrich Stadial 1(HS1) and the Late Glacial-Younger Dryas (YD) transition in Ariège valley (French Pyrenees). • About 5.1 °C and 3.9 °C cooler than today without change in precipitation would explain the HS1 and Late Glacial-YD transition moraines formation. • The evolution of the glacier fluctuation from the deglaciation may depend on the size of the accumulation area.

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Estimation of near‐surface air temperature lapse rates over continental Spain and its mountain areas

Cited by 34 publications

References 59 publications

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

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

Maximum and minimum air temperature lapse rates in the Andean region of Ecuador and Peru

Glacier fluctuations during the Late Glacial and Holocene on the Ariège valley, northern slope of the Pyrenees and reconstructed climatic conditions

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