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The Arctic warms much faster than other places under increasing greenhouse gases, a phenomenon known as Arctic amplification (AA). Arctic positive lapse-rate feedback (LRF) and oceanic heating induced by sea-ice loss have been considered as major causes of Arctic warming and AA, and Arctic high atmospheric stability has been considered as a key factor for the occurrence of the bottom-heavy warming profile and thus positive LRF in the Arctic. Here we analyze model simulations with and without large AA and sea-ice loss and long-term changes in ERA5 reanalysis data to examine the relationships among Arctic sea-ice loss, stability, LRF, Arctic warming, and AA. Results show that the Arctic bottom-heavy warming profile and the resultant positive LRF are produced primarily by increased oceanic heating of the air due to sea-ice loss in Arctic winter, rather than high atmospheric stability. Without the oceanic heating induced by sea-ice loss, most Arctic climate feedbacks weaken greatly, and all other processes can only produce slightly enhanced surface warming and thus weak positive LRF under stable Arctic air. A non-convective Arctic environment allows the oceanic heating to warm near-surface air more than the upper levels, resulting in large positive LRF that roughly doubles the surface warming compared with the case without the LRF. We conclude that enhanced cold-season oceanic heating due to sea-ice loss is the primary cause of Arctic large positive LRF, which in turn allows the surface heating to produce more Arctic warming and large AA.

Precipitable water vapor (PWV) is an essential parameter in numerical weather prediction and climate research. Existing global empirical PWV models rely on a single coefficient for vertical adjustment and lack geographical differentiation. Therefore, this study developed the global PWV vertical adjustment model (GPWV-H) by considering the time-varying lapse rate using the fifth-generation European Centre for Medium-Range Weather Forecasts Atmospheric Reanalysis (ERA5) from 2012 to 2017. The performance of the GPWV-H model in vertical adjustment is evaluated using multi-source PWV data and compared with the conventional empirical model (EPWV-H). The numerical results are as follows: (1) The bias and root mean square (RMS) of the GPWV-H model are − 0.10/ − 0.35 mm and 1.43/1.07 mm, respectively, when ERA5 and radiosonde PWV profiles were used as reference which are 9.3 and 5.9% (in RMS) lower than EPWV-H model; (2) The GPWV-H model improved by 15.1–17.1 and 0.8–1.6% compared to the non-adjustment and the EPWV-H model, respectively, when interpolating Second Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) with various grid resolutions to radiosonde stations. These results indicate that the GPWV-H model outperforms the EPWV-H model regarding global PWV interpolation accuracy and stability and has a promising application tendency in global real-time and high-precision water vapor monitoring.

This study focuses on analyzing the altitude of the zero degrees isotherm and variations in the environmental lapse rate (ELR) using the publicly available data collected from Automatic Dependent Surveillance–Broadcast (ADS-B) and Mode S Enhanced Surveillance (EHS) messages emitted by airplanes over a five-month period in 2021. The data was gathered using a professional receiver stationed in Bucharest (Romania). The aviation messages were decoded and the air temperature and pressure were determined, at the location of the airplane. The method has the advantage of the continuous messages that are emitted by the aircrafts during flight that allow instantaneous determination of the meteorological parameters at no additional costs. It can also be extended to permit almost real time maps of the ELR. When data was analyzed and a standard ELR value of 6.5 K/km is employed it was observed that the mean altitude of the 0 degrees isotherm exhibits a seasonal increase during the summer months, with an average altitude of 2874.2 m. The highest recorded altitude of the 0 degrees isotherm was found to be 5346.8 m, near Alexandria city (Romania), on 22.07.2021. Using a standard Least Mean Square algorithm alongside the International Standard Atmosphere pressure formula, the ELR values were calculated from pressure measurements data. The resulting mean ELR for the five-month period was determined to be 5.1331 K/km, slightly lower than the standard value.

The Environmental Lapse Rate (ELR) depicts how the temperature near the surface varies with altitude and can be used for temperature downscaling coarse resolution data and for understanding boundary layer processes. We calculated the ELR using ERA5 reanalysis data, examined its temporal and larger-scale spatial variability, and found a prevalent seasonal ELR cycle over the Arctic. There are extensive positive ELR values resulting from pervasive inversions over most of the Arctic in winter; hence, we also explored the possible factors that lead to inversions in polar regions. Our results can serve as a reference for future research on the inversions in different morphological regions at different pressure levels. By improving the characterization of the ELR, we obtain a more explicit representation of the vertical temperature variation across the Arctic region and examine potential trends in ELR over time. Our results challenge the commonly assumed fixed ELR values that are typically used in the Arctic region in, for example, correcting ice-core temperature reconstructions or estimating higher-resolution runoff from land ice.

Despite strong terrain influences on the climate of the Appalachian Highlands in the eastern USA, few attempts have been made to systematically collect air and soil temperature data from summits and other high-elevation sites in this region. This paper reports on the Appalachian Highlands Environmental Monitoring (AHEM) mesoscale climate network, a series of 20 high-elevation sites recording temperature at hourly intervals from 1996 to 2008 on Appalachian summits along a 1500 km transect extending from Maine to North Carolina. Observations included air temperature, ground surface temperature, and soil temperature at 25 cm depth. Data were analyzed with respect to four issues: (1) accuracy of air temperature estimates and comparisons with previous studies; (2) relations between the altitude of the 0 °C mean annual air temperature and latitudinal position; (3) variations in frequency distributions of freeze–thaw days with latitude; and (4) the accuracy of an existing soil temperature classification scheme in the Appalachians. Analytic results include: (1) topographically informed interpolation techniques provide more accurate temperature estimates than traditional methods; (2) the elevation of the 0 °C mean annual air temperature decreases systematically with increasing latitude; (3) the frequency distributions of freeze–thaw days are related directly to latitudinal position; (4) classifications of mean annual soil temperature based on data from the 25 cm level are in general agreement with an existing U.S. Department of Agriculture soil-temperature map suggesting permafrost underlying high-elevation locations in the northern Appalachian Highlands..

This research aims to determine the types of thunderstorms formed in the thickness of the cloud (determine the Dry adiabatic lapse rate (DALR) and Environmental lapse rate (ELR)) in the case of precipitation during the day. Data were taken by Temperature, Dew point, Atmospheric Pressure, and Height from re-analysis by the (ECMWF) for the heights (0-18000) m, the levels of pressure (1000-100) mbar, low cloud cover data, and the characteristic days ((18, 24, 27) February, 28 April, and 25 November) of the year 2018 for Baghdad station were chosen to obtain the largest possible number of clouds and their diversity to use them in calculating the cloud cover and weather stability in terms of calculating the daily change, temperature, dew point in addition to calculating the low cloud cover with altitude and atmospheric instability. The Sigma Plot program was used in this research to determine the base of clouds and thunderstorms. The change in temperature, Dew point, clouds base, and altitude was determined, then the cloud thickness, types, and classification were calculated. The clouds found are strong thunderstorm clouds characterized by thickness and height, such as the clouds of Nimbostratus (Ns) and Cumulonimbus (Cb).

There are growing evidences that indicate the Himalayan region is warming rapidly with more warming in high elevation areas. The elevation-dependent warming (EDW) accelerates the rate of change in mountain ecosystems, including cryosphere, hydrology, biodiversity and socio-economic systems. Here, we present temperature lapse rates (TLRs) based on primary data from 21 stations for three elevation transects leading to treeline (Western Himalaya: WH; Central Himalaya: CH; Eastern Himalaya: EH) representing different climate regimes along the Indian region of Himalayan Arc. TLRs were calculated using high temporal resolution data collected for 2 years (2017–2018) from complex Himalayan terrain. The annual mean TLR increased with decreasing moisture, being markedly higher for dry WH transect (− 0.66 °C/100 m) than at moderately moist CH (− 0.52 °C/100 m) and characteristically moist EH transect (− 0.50 °C/100 m). The One-Way Analysis of Variance (ANOVA) confirms that the TLR varied spatially, declining from West to East across the Himalayan Arc, and significantly differed seasonally. The lowest mean TLRs were found during the winter season (EH: − 0.46 °C/100 m; CH: − 0.40 °C/100 m; WH: − 0.31 °C/100 m). The monthly TLR for EH transect varied within a narrower range (− 0.32 °C/100 m to − 0.54 °C/100 m), than for CH transect (− 0.24 °C/100 m to − 0.68 °C/100 m), and WH transect (− 0.26 °C/100 m to − 0.90 °C/100 m). The lowest monthly TLR occurred in December (− 0.24 °C/100 m to − 0.32 °C/100 m) for all three transects. The relationship of TLR with rainfall and saturation vapor pressure was analyzed for CH transect to find out influence of these factors on seasonal variation in lapse rate. Moisture, snow albedo and reflectance are the factors which largely control the TLR along the elevation transects. The shallow TLR and higher growing season temperature values (9.2 ± 1.8 °C, 10.0 ± 1.4 °C, and 7.8 ± 1.7 °C), than normally found at treelines, may suggest that treeline environment in Himalaya is warming more rapidly than lowland areas. TLR was lowest in December due to reduced albedo and EDW, which influence treeline dynamics, snow and moisture regime, surface energy balance, species distribution, and growing season of alpine vegetation. The findings of this study provide useful insights to re-parameterize the climate models over the Himalayan region. This study facilitates in improving interpolation of air temperature for ecological studies in un-gauged and data-sparse regions, especially for the alpine region of Himalaya where observed data are extremely scarce.

The Arctic warming response to greenhouse gas forcing is substantially greater than the rest of the globe. It has been suggested that this phenomenon, commonly referred to as Arctic amplification, and its peak in boreal fall and winter result primarily from the lapse‐rate feedback, which is associated with the vertical structure of tropospheric warming, rather than from the sea‐ice albedo feedback, which operates mainly in summer. However, future climate model projections show consistently that an overall reduction of sea‐ice in the Arctic region leads to a gradual weakening of Arctic amplification, thereby implying a key role for sea‐ice albedo feedback. To resolve this apparent contradiction, we conduct a comprehensive analysis using atmosphere/ocean reanalysis data sets and a variety of climate model simulations. We show that the Arctic Ocean acts as a heat capacitor, storing anomalous heat resulting from the sea‐ice loss during summer, which then gets released back into the atmosphere during fall and winter. Strong air‐sea heat fluxes in fall/winter in sea‐ice retreat regions in conjunction with a stably stratified lower troposphere lead to a surface‐intensified warming/moistening, augmenting longwave feedback processes to further enhance the warming. The cold‐season surface‐intensified warming/moistening is found to virtually disappear if ocean‐atmosphere‐sea ice interactions are suppressed, demonstrating the importance of ice insulation effect and ocean heat uptake/release. These results strongly suggest that the warm‐season ocean heat recharge and cold‐season heat discharge link and integrate the warm and cold season feedbacks, and thereby effectively explain the predominance of the Arctic amplification in fall and winter. Plain Language Summary The Arctic warms faster than the rest of our planet. Interestingly, this accelerated warming is most pronounced in boreal fall and winter, when the sea‐ice albedo feedback is not active due to a lack of sunshine, which has led numerous studies to emphasize the role of longwave feedback processes. Here, we present observational and modeling evidence that the seasonal evolution of Arctic amplification cannot be explained by a single mechanism such as lapse‐rate feedback or sea‐ice albedo feedback. We show that ocean‐atmosphere heat exchanges associated with sea‐ice reduction are essential for Arctic amplification and its seasonality. In particular, our analysis shows that the ocean heat capacitor mechanism links and integrates the warm and cold season feedbacks, thereby explaining the seasonal evolution of Arctic amplification, and its peak in the cold season. This connected nature of climate feedback processes in conjunction with insulation effect of sea ice implies a substantial weakening of Arctic amplification and its seasonal contrast in a future ice‐free climate as well as during ice‐free states in the geological past. Key Points Arctic warming amplification and its peak in fall and winter cannot be explained by a single mechanism such as lapse‐rate feedback Positive local longwave feedback processes in fall and winter are intricately linked to ocean‐to‐atmosphere heat and moisture fluxes The cold‐season peak can be explained by the ocean heat capacitor mechanism that links and integrates the warm and cold season feedbacks

Elevated duct is an atmospheric structure characterized by abnormal refractive index gradients, which can significantly affect the performance of radar, communication, and other systems by capturing a portion of electromagnetic waves. The South China Sea (SCS) is a high-incidence area for elevated duct, so conducting detection and forecasts of the elevated duct in the SCS holds important scientific significance and practical value. This paper attempts to utilize remote sensing techniques for extracting elevated duct information. Based on GPS sounding data, a lapse rate formula (LRF) model and an empirical formula (EF) model for the estimation of the cloud top height of Stratocumulus were obtained, and then remote sensing retrieval methods of elevated duct were established based on the Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data. The results of these two models were compared with results from the elevated duct remote sensing retrieval model developed by the United States Naval Postgraduate School. It is shown that the probability of elevated duct events was 79.1% when the presence of Stratocumulus identified using GPS sounding data, and the trapping layer bottom height of elevated duct well with the cloud top height of Stratocumulus, with a correlation coefficient of 0.79, a mean absolute error of 289 m, and a root mean square error of 598 m. Among the different retrieval models applied to MODIS satellite data, the LRF model emerged as the optimal remote sensing retrieval method for elevated duct in the SCS, showing a correlation coefficient of 0.51, a mean absolute error of 447 m, and a root mean square error of 658 m between the trapping layer bottom height and the cloud top height. Consequently, the encouraging validation results demonstrate that the LRF model proposed in this paper offers a novel method for diagnosing and calculating elevated ducts information over large-scale marine areas from remote sensing data.

Various environmental processes are strongly controlled by spatio-temporal variations of surface air temperature (hereafter temperature) in complex terrain. However, the usually scarce network of observations in high mountain regions does not allow for an investigation of the relevant micro-meteorological processes that result in complex temperature fields. Climate impact studies often utilize a constant lapse rate of temperature (LRT hereafter) in order to generate spatially distributed temperature data, although it is well acknowledged that LRTs feature a pronounced variability at spatial, seasonal, and diurnal scales. In this study, the Weather Research and Forecasting (WRF) model is used to understand the factors and processes influencing temperature and LRT in the Khumbu and Rolwaling regions of the central Himalayas. A high resolution simulation is performed for one complete year (June 2014–May 2015) in order to capture the entire seasonal cycle. To test the model response to land cover and terrain characteristics, additional simulations with adjusted surface conditions are conducted. Our results demonstrate the capability of WRF to reproduce the processes controlling LRT, although an LRT bias is detected during non-monsoon seasons. The simulated temperature fields feature two LRT minima (i.e. low temperature decrease with elevation) during Nov–Dec and monsoon season, and two LRT maxima (strong temperature decrease with elevation) during the early post-monsoon and pre-monsoon seasons. A steeper LRT (i.e. a rapid decrease of temperature with elevation) is found at high elevations (> 4500 m) while shallower LRT values (i.e. a slower decrease or even increase of temperature with elevation) are apparent at lower elevations. During the pre-monsoon season, high net insolation rates and a reduced latent heat loss from snow free surfaces cause strong sensible heating at low elevations, while the presence of snow at high elevations leads to reduced sensible heating. This strong contrast results in steeper LRT values. Early post-monsoon shows similar characteristics but with a reduced magnitude. The shallow LRT during monsoon season is shown to be caused by the large-scale moisture supply and the associated latent heat release at the Himalayan slopes. This effect is further intensified due to strong up-valley winds which contribute to a well-mixed troposphere. Temperature inversions associated with cold air pooling cause shallow LRT values in lower river valleys during Nov–Dec. The results suggest that the identified processes should be considered for downscaling applications, particularly if distributed temperature fields are required for climate impact investigations.