This varies greatly from day to day. When this lapse rate is averaged out for all places and times, it is called the Standard or Average Lapse Rate, which is around 3.
It's important to keep in mind that this lapse rate is determined by a vertically moving radiosonde. The air itself is not moving up or down. The lapse rate that occurs in a vertically moving air parcel in which no condensation is occurringl. The temperature change is related to the expansional cooling compressional warming that occurs when the air moves upward downward. It is entirely deterined by the pressure distribution in the atmosphere in question. The slope of the wet adiabats depend on the moisture content of the air.
Our atmosphere is divided into various layers. In the troposphere, the temperature decreases with an increase in height. This is called the normal lapse rate of temperature.
In troposphere, with increase in height, the density of air decreases and the temperature also starts falling.
For every metres, there occurs a 1 oC fall in temperature and this is known as the normal lapse rate of temperature. The lapse rate works mainly in the troposphere which results in various types of weather and climatic changes affecting the life on earth.
Begin typing your search term above and press enter to search. Press ESC to cancel. Skip to content Home Physics What is normal lapse rate of temperature? Ben Davis December 18, What is normal lapse rate of temperature? What is the environmental lapse rate? With lack of supporting data from these stations, possible factors driving these inter-annual changes could not be ascertained.
This result highlights the need for significant further research to build data and concepts for a comphrehensive atmospheric model valid across the elevation ranges of the Himalaya. The amount of water vapor in the atmosphere and its seasonal variations could be playing an important role in forcing the SELR variations. Some insights on these aspects are developed from the data generated from Dingad catchment.
Higher specific humidity during monsoon months of July and August is characteristic of this regime. Mean monthly specific humidity at 2, m a. Highest observed value of specific humidity was At higher elevation 3, m a.
It is observed that the higher elevation stations generally experience lower specific humidity throughout the year. Monthly mean relative humidity of the uppermost station in the monsoon regime 3, m a. Figure 6. Mean monthly specific humidity variations of A lower 2, m a. Standard lapse rate equations as described in Equations 2 and 4 were implemented to test their response along sections 1 of Beas basin Kasol—Manali using mean monthly temperature and SELR of —90 period. Seasonal variability is more pronounced for Equation 2 as it uses the estimate of change in saturation mixing ratio between the two stations for calculating the heat generated due to condensation process.
On the contrary, Equation 4 is more stable as it uses saturation vapor pressure value of single station for calculating the potential heat generated during condensation Figure 3. Developed indices for the Kasol m a. Derived monthly indices were tested in the same section by using decadal mean monthly temperature of Kasol m a.
The model performance is found to be good with a p -value of 5. Table 2. Table 3. Further, the model is tested for five individual years from to , with p -value ranging from 1. To assess the regional validity of the derived indices, the model is tested for section 1 of the Sutlej basin Kasol—Rakcham.
Testing was undertaken with decadal mean monthly temperature and SELR — as well as for individual years from to Model has performed well with p -value ranging from 1. Further, the indices were tested for the upper Ganga basin for 2 full years —03 and — and 4 years of summer ablation months May—October.
Results support the applicability of the indices developed in the Beas basin for upper Ganga region as well with p -value of 1. However, significantly higher RMSE values ranging from 1.
This suggests the need for refining the indices developed using small set of data of one section alone. In general, this exercise has established the applicability of the model across the study region under the influence of monsoons and for different time periods.
Details of error analysis from the preliminary test of the 5-year mean — SELR indices of Kasol—Manali section. The regional performance of the model has been further improved by K -fold cross validation utilizing all the available mean monthly temperature data of Sutlej—Beas basin from to period. In K -fold cross validation all the available data is partitioned into K equally sized sections and an iterative training and validation are performed by using K-1 folds for learning in each iterations and validated using the one held-out fold.
The monthly indices were refined by five-fold cross validation of both the sections in the basin. The cross validation statistics are presented in the Table 4 , which suggested consistent response of the model across the folds. Regional monthly indices are derived through this exercise by averaging the results Table 2.
These indices are tested with each test fold data as well as with two years of new data from Kasol—Rakcham section and for three sections in the upper Ganga basin.
The regional indices have shown better performance across the study sections Figure 7. The Tehri—Basecamp section and Tehri—Chorabari section showed significantly higher relative error of 15 and The performance of the model was further tested with the standard Environmental lapse rate of 6. This clearly indicates the significant advantage of using the proposed model over the standard environment lapse rate in the monsoon region on a monthly scale.
Table 4. K -fold cross validation statistics of the Kasol—Manali and Kasol—Rakcham sections. Figure 7. Relationship between modeled and observed SELR using regional indices derived by K -fold cross validation.
Regional indices were tested against each test folds of Kasol—Manali A and Kasol—Rakcham B sections as well as for the new data set of and Regional indices were further tested for different profiles of the upper Ganga basin C—E showing the significant improvement achieved through cross validation and its regional validity.
Table 5. Tela—Basecamp 2,—3, m a. Model performance against the standard environmental lapse rate 6. Regional mean monthly SELR indices were further tested for daily SELR estimation in the three study basins for selected years based on the daily data availability. It is found that the higher elevation stations have number of missing data either of maximum or minimum temperature, especially for the Manali station.
The results show lower correlation and higher RMSE ranging between 1. However, the p -value shows significant improvement for daily model results. The daily SELR modeling of the Kasol—Manali section is carried out for —88 and — periods and the relative error ranges from Average relative error of the Ganga basin section was On a daily basis, the model error accounted for Hence, it can be considered that the daily residuals are forced by the daily LCL variations and other local factors.
The results of monthly and daily SELR modeling are promising while considering the large number of possible factors influencing temperature lapse rates along the mountain slopes Marshall et al. Further improvement to the proposed model could be achieved by developing appropriate data and methods to link daily LCL variations and associated dynamics with other local land surface processes.
Figure 8. Model has been sucessful in estimating the temporal mean lapse rates but not suceeded in capturing the day to day SELR variations D. Table 6. Seasonal variations of valley scale SELR observed for Beas, Sutlej and upper Ganga basins in the present study is comparable with the observations reported from Neaplease Himalaya Kattel et al.
Long-term consistency of seasonal responses as shown in the present study also underlines this aspect. Significantly lower SELR of the nival—glacier regime section 2 comparable to the SALR persisting across the seasons is another important characteristic identified for the first time.
Dominance of the large scale circulation over local slope and valley winds on temperature lapse rate is reported from Alps also Kirchner et al. Lower mean monthly SELR of nival—glacier regime section 2 equaling plausible SALR for corresponding pressure levels throughout the year Figure 9 is another clear indication of the role of the orographic lifting of the air mass in regulating the SELR and forcing the deviation from the standard environmental lapse rate of 6.
Higher inter-annual variability of SELR in the nival—glacier regime as observed for Beas and Sutlej basins is indicative of the influence of more complex local processes driving these changes in the upper reaches. This raises questions regarding its use for modeling snow and glacier melt, especially for modeling future runoff and glacier fluctuations.
Various researchers have shown that the glacier and snowmelt estimation by degree day method is highly sensitive to the near surface temperature lapse rates Marshall et al. This result points toward the need for re-visiting the benchmark glacier monitoring strategy Fountain et al. The results suggest that the use of valley scale SELR having higher inter-annual stability may be more appropriate for extrapolating the temperature to the higher elevations until the challenging task of development of a full atmospheric model which encapsulate the various local processes over a complex terrain driving the SELR across the mountain elevations takes place.
Figure 9. Yellow outline represents the SELR range of section 1 during the monsoon months and blue outlines for section 2. Observed seasonal SELR variations in the study region range from 8. This difference could be forced by the orographic lifting of the air mass along the mountain slopes. In the case of displacement of air parcel along a vertical air column, such variations in the lapse rate occur above and below the LCL Ahrens, Analysis in the Sutlej and Dingad catchment suggests that the same processes are followed by the air parcel while being lifted along the mountain slopes by the orography as well.
Significant correlation between LCL at 2, m a. A major process consuming significant energy within the parcel is the re-evaporation of condensed precipitation while falling through the warmer layers below Dolezel, We propose that the rate of re-evaporation of water droplet, governed by the seasonal variations in the LCL could be playing an important role in determining the seasonal variations in the valley scale section 1 SELR.
This process has been incorporated in the proposed model through the monthly SELR indices and explains why similar seasonal SELR responses exist across the study region.
This implies that a significant portion of the latent heat released at the higher altitude region during March, April, May, and June months is consumed for re-evaporation and forcing the lapse rate along the mountain slopes to the higher side.
While during the monsoon months, orographic lifting of the incoming moisture forces LCL closer to the mountain slopes and most of the latent heat released is available for warming the region and reducing the temperature gradient. The amount of water vapor in the atmosphere during winter months is significantly less than the summer months and higher elevations have less water vapor as compared to the lower elevation stations Figure 6A.
However, lower temperatures ensure lower LCL during this period. We suggest that the higher LCL and higher valley scale SELR during April, May, and June months is very critical for higher elevation cryospheric region as it contributes to the shift in the warmest months from May to June at the lower elevations to July to August at the higher elevations. However, Kirchner et al. This also suggests that the surface conditions and other local processes such as cold air pooling and differences in net radiation have more influence on the daily SELR.
The present study establishes a major process driving the valley scale slope lapse rate of the monsoon region of the Himalaya. Figure We have seen that the seasonal SELR variations have regional characteristics and are linked with the seasonal moisture conditions as well as seasonal variations in the regional LCL. Therefore, we further studied how the lapse rates derived from reanalysis and climate model compares with the observed SELR. Environmental lapse rate of the free atmosphere is calculated from the ERA—interim reanalysis Dee et al.
The RCM inputs will be important information for providing finer scale regional climate accounting for regional feedbacks, physical processes and dynamical forcing. ERA—Interim and model details are not provided here as it is not the core focus of this paper. Discussion is limited to the comparison with corresponding initial and boundary conditions from ERA—interim and corresponding station observations. It is important to mention here that various reanalysis are amalgamations of observed station records, and satellite information, and uses different mathematical and statistical algorithms to generate the reanalysis data.
These are also not discussed here in detail as it is out of the scope of the present work. However, better quality of the ERA—interim data over other reanalysis data is proven as it uses observed surface temperature records during its preparation Simmons et al.
This particular fact is very important for the Himalayan region due to paucity of the observation network and may give a benchmark for future research in the absence of such records. Corresponding ERA—interim forcing to simulate the REMO regional climate model is expected to provide more exhaustive information over the study region. It is observed that the ERA—Interim give same lapse rate for different altitude sections and fails to capture the unique SELR response of the nival—glacier section section 2.
Whiteman and Hoch showed that the relationship between the pseudo-vertical temperature and radiosondes improves with elevation and steepness of the slope. Fiddes and Gruber have extensively shown the downscaling method of climate variables from coarser to finer resolution over heterogeneous topographic regions. It could be inherent that during the preparation of the reanalysis, most of the sub-grid scale processes are not being captured within the resolution of reanalysis.
This suggests that the improvement is needed on this front as well and development of a full atmospheric model integrated with the processes described in this paper and other land surface processes of the mountain slopes are essential to capture the observed SELR variations shown in the present study.
Gerlitz showed how ERA—Interim reanalysis data over a complex central Himalayan terrain can be used for estimating near surface temperatures by coupling it with a fuzzified regression tree model with local terrain parameters as potential predictors. Considering the limitations of ERA—Interim reanalysis described above, such methods could be further improved by the proposed approach in this paper.
The present study shows that the influence of monsoons in the seasonal variations of SELR of temperature is not only limited to the Nepal Himalaya but also extends across the upper Ganga and Sutlej—Beas basins in the western Himalaya.
SELR constrained to the nival—glacier regime of the study region is found to be significantly lower than the valley scale lapse rate and closer to the SALR across the seasons. The observed elevation dependent variations and seasonal variations of the valley scale SELR in the monsoon regime of the Himalaya amply demonstrates that the use of standard environmental lapse rate for temperature extrapolation is not appropriate for the region.
It is shown that the model performance is satisfactory across the study region and also for different time periods. It is also shown that the model performs much better than the standard environmental lapse rate of 6. Local surface energy balance including net radiation and turbulent heat fluxes and other local effects are believed to be the primary determinant of surface temperature and its vertical gradient Marshall et al. However, close estimate of the model and the observations as well as SELR linkages with seasonal LCL variations as shown in the study indicate that the seasonal variations in the moisture dynamics and the condensation regimes governed by the orographic lifting have an overriding influence in determining the valley scale SELR in the monsoon regime of the western Himalaya.
The influence of the surface conditions and other local processes such as cold air pooling and differences in net radiation are more evident on the daily SELR variations. The valley scale lapse rate are found to be much stable inter-annually as compared to the nival—glacier system SELR. This study put forward significant new insights on the SELR variability in the western Himalayan region and provides a foundation for the future work. This study also highlights the need for resurgent studies on temperature lapse rate dynamics across the various climate regimes of the data sparse Himalayan region for better understanding of the orographic forcing and its future directions.
RT conceived the study, collected field data, conducted the analysis, and prepared the manuscript. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. RT acknowledges the support and encouragement of Dr. Sharad K. Authors thank Ms. Anila Romil for providing English edits of the manuscript.
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