Search

Context: Some insectivorous bats are some of the smallest flying endotherm. They have a high energy demand to maintain body temperature. Therefore, one can expect that larger animals of a species and larger species occur in colder environments as a result of improved energy conservation related to reduced surface to volume ratio in larger endotherm animals. Evidence of this general rule is scarce in bats, although Bergmann predicted this some 175 years ago for closely related species. Aims: In this work, I investigated whether bat body size increases with above-sea-level elevation-related temperature decrease for three closely related Australian bat species of the genus Vespadelus. The purpose of this was two-fold. First, to investigate whether there is a relationship between bat size and elevation by using more recent computational techniques of Bayesian multilevel modelling (BMM). Second, to provide an example of applying recent advances in BMMs to wildlife research and to predict potential consequences of climate warming for these bats. Methods: I investigated whether bat size relates to elevations of bat-capture locations. I included measurement errors for elevation and forearm length measurements by using a BMM in an high-performance computing environment. This model uses measurements of 775 bats from locations in the western slopes of the Australian Alps. Key results: The BMM analysis showed that bat forearm length increased 0.11 mm for every 100 m elevation, with a low standard error of 0.01 mm, indicating a high precision. The standard deviations of the variables species and sex within species were large. This means that they did not provide sufficient explantory power for the overall model and predictions to warrant inclusion. Conclusions: This study showed that there is a linear increase of bat size with elevation. This is the first study to show that bat size is related to elevation (and associated temperature decline) in three sympatric, closely related species of the same genus and it confirmed what Bergmann predicted over 175 years ago. Implications: Under a warming climate, the results predict that bats become smaller on average. When incorporating average temperature-lapse rate to calculate elevations that assume a 1.5 and 3°C change in future average climate, the study coarsely quantified reduction in suitable habitat for the largest of the three species, V. darlingtoni , of up to 3%. Some small insectivorous bats live at the edge of their physiological limits. Bergmann's rule implies that larger animals occur in colder environments. I showed that bat size is related to elevation, which confirms Bergmann's 175-year-old prediction. A Bayesian multilevel model showed an increase of 0.11 mm in forearm length per 100 m, meaning that bats are likley to become smaller with an increasing temperature. I use this to spatially estimate habitat loss. Image by Alexander Herr. [ABSTRACT FROM AUTHOR]

At an energy flux imbalance of about 1 W m−2, the ocean stores 90% of the heat accumulating by global warming. However, neither the causes of this nor the responsible geophysical processes are sufficiently well understood. More detailed investigations of the different phenomena contributing to the oceanic energy balance are warranted. Here, the role of low-level marine clouds in the air–sea interaction is analysed. TEOS-10, the International Thermodynamic Equation of State of Seawater—2010, is exploited for a rigorous thermodynamic description of the climatic trends in the lifted condensation level (LCL) of the marine troposphere. Rising sea surface temperature (SST) at a constant relative humidity (RH) is elevating marine clouds, cooling the cloud base, and reducing downward thermal radiation. This LCL feedback effect is negative and counteracts ocean warming. At the current global mean SST of about 292 K, the net radiative heat flux from the ocean surface to the LCL cloud base is estimated to be 24 W m−2. Per degree of SST increase, this net flux is expected to be enhanced by almost 0.5 W m−2. The climatic LCL feedback effect is relevant for the ocean's energy balance and may be rigorously thermodynamically modelled in terms of TEOS-10 equations. LCL height may serve as a remotely measured, sensitive estimate for the sea surface's relative fugacity, or conventional relative humidity. [ABSTRACT FROM AUTHOR]

This paper focuses on modeling surrender time for policyholders in the context of life insurance. In this setup, a large lapse rate at the first months of a contract is often observed, with a decrease in this rate after some months. The modeling of the time to cancelation must account for this specific behavior. Another stylized fact is that policies which are not canceled in the study period are considered censored. To account for both censoring and heterogeneous lapse rates, this work assumes a Bayesian survival model with a mixture of regressions. The inference is based on data augmentation allowing for fast computations even for datasets of over millions of clients. Moreover, frequentist point estimation based on Expectation–Maximization algorithm is also presented. An illustrative example emulates a typical behavior for life insurance contracts, and a simulated study investigates the properties of the proposed model. A case study is considered and illustrates the flexibility of our proposed model allowing different specifications of mixture components. In particular, the observed censoring in the insurance context might be up to $50\%$ of the data, which is very unusual for survival models in other fields such as epidemiology. This aspect is exploited in our simulated study. [ABSTRACT FROM AUTHOR]

Weighted mean temperature (Tm) is an important parameter in the water vapor inversion of global navigation satellite systems (GNSS). High-precision Tm values can effectively improve the accuracy of GNSS precipitable water vapor. In this study, a new regional grid Tm empirical model called the RGTm model over China and the surrounding areas was proposed by combining ERA5 reanalysis data, radiosonde data, and TanDEM-X 90m products. In the process of model establishment, we considered the setting of the reference height in the height correction formula and the bias correction for the Tm lapse rate. Tm values derived from ERA5 and radiosonde data in 2019 were used as references to validate the performance of the RGTm model. At the same time, the GPT3, GGNTm, and uncorrected seasonal model were used for comparison. Results show that compared with the other three models, the accuracy of the RGTm model's Tm was improved by approximately 12.21% (15.32%), 1.17% (3.09%), and 2.31% (5.05%), respectively, when ERA5 (radiosonde) Tm data were used as references. In addition, the introduction of radiosonde data prevented the accuracy of the Tm empirical model from being entirely dependent on the accuracy of the reanalysis data. [ABSTRACT FROM AUTHOR]

Mountains near the equator have very unique temperature regimes due to the tropical latitude and alpine altitude. How climate change will impact these temperature regimes is not clear as there are so few temperature records in these environments. This study attempts to characterize the near‐surface air and ground temperature regime for the Teleki Valley (3,200–4,200 m. a.s.l) on the western slope of Mount Kenya using a set of six temperature loggers placed at 200 m elevation intervals. Temperature was recorded at 30 min intervals from September 2021 to October 2022. The mean diurnal temperature range varied from 6°C to 14°C, with maximum daily swings in excess of 25°C. There was a distinct seasonality in temperature, with a hot season during the months of January‐March, where it was roughly 3°C warmer than the coolest months. Diurnal temperature swings were also highest in March. This seasonality is driven by radiation and moisture: higher radiation during the vernal equinox combined with clear skies during the dry season made for higher temperatures. Elevation was the dominant spatial gradient determining temperature regimes, but rock and vegetation cover played a large role in modifying near‐surface air and ground temperature. The highest elevation logger (4,200 m. a.s.l), measuring ground temperature, showed muted seasonal and diurnal swings, creating for warmer temperatures than lower elevation loggers. This demonstrates the importance of thermal refugia at these high elevations. This also makes predictions of plant and animal responses to climate change difficult, as uniform upward shifts would not necessarily maintain thermal niches. Plain Language Summary: Tropical alpine areas have few detailed records of climate particularly in the zone near the surface where most organisms live. This study investigated the near ground temperature regime of the Teleki Valley of Mount Kenya using loggers placed at 200 m elevation intervals. The results showed that temperature can vary considerably over short distances and over time, with marked daily and seasonal temperature patterns. Elevation, topography and cover drive these differences as do macro‐climactic factors of wind patterns and solar radiation. The implication is that biotic responses to climate change will not be straight‐forward but may be site‐ and species‐specific. Key Points: Near‐surface temperature regime of the alpine zone of Mount Kenya has a seasonal element that is driven in part by solar radiation changesElevation and topography drive spatial differences in temperature in the alpine zoneThere is a diversity of microclimates which may play a crucial role in survival of biotic communities under climate change [ABSTRACT FROM AUTHOR]

The Alborz Mountains are some of the highest in Iran, and they play an important role in controlling the climate of the country's northern regions. The land surface temperature (LST) is an important variable that affects the ecosystem of this area. This study investigated the spatiotemporal changes and trends of the nighttime LST in the western region of the Central Alborz Mountains at elevations of 1500–4000 m above sea level. MODIS data were extracted for the period of 2000–2021, and the Mann–Kendall nonparametric test was applied to evaluating the changes in the LST. The results indicated a significant increasing trend for the monthly average LST in May–August along the southern aspect. Both the northern and southern aspects showed decreasing trends for the monthly average LST in October, November, and March and an increasing trend in other months. At all elevations, the average decadal change in the monthly average LST was more severe along the southern aspect (0.60°C) than along the northern aspect (0.37°C). The LST difference between the northern and southern aspects decreased in the cold months but increased in the hot months. At the same elevation, the difference in the lapse rate between the northern and southern aspects was greater in the hot months than in the cold months. With increasing elevation, the lapse rate between the northern and southern aspects disappeared. Climate change was concluded to greatly decrease the difference in LST at different elevations for April–July. [ABSTRACT FROM AUTHOR]

Lapse risk is a key risk driver for life and pensions business with a material impact on the cash flow profile and the profitability. The application of data science methods can replace the largely manual and time-consuming process of estimating a lapse model that reflects various contract characteristics and provides best estimate lapse rates, as needed for Solvency II valuations. In this paper, we use the Lasso method which is based on a multivariate model and can identify patterns in the data set automatically. To identify hidden structures within covariates, we adapt and combine recently developed extended versions of the Lasso that apply different sub-penalties for individual covariates. In contrast to random forests or neural networks, the predictions of our lapse model remain fully explainable, and the coefficients can be used to interpret the lapse rate on an individual contract level. The advantages of the method are illustrated based on data from a European life insurer operating in four countries. We show how structures can be identified efficiently and fed into a highly competitive, automatically calibrated lapse model. [ABSTRACT FROM AUTHOR]

Estimating isotopic changes of river water on the Tibetan Plateau (TP) remains highly uncertain due to variable water sources related with seasonality of atmospheric circulations and local kinetic fractionation. The in‐depth understanding of such changes is crucial for accurate palaeoaltimetry reconstruction and for river budget evaluation which related with surviving of one‐fifth of the world's population under accelerated warming. Here, we present a comprehensive river water isotopic composition dataset (δ18O, δD and d‐excess, 1852 samples) over the TP to systematically describe spatial distribution and explore possible controlling factors of isotopic data in theses rivers. Results showed that δ18O in river water range from −20.8‰ to −5.2‰ (latitudinally, 0.92‰ per degree), enriching northwardly in the seven exorheic river basins. The d‐excess values range from −19.7‰ to 20.5‰ with the pronounced low values in endorheic rivers basins. We identified that moisture sources and atmospheric transports leave significant signals in isotopic compositions of river water in the Upper Brahmaputra (UB), Upper Mekong (UM) and Upper Yangtze (UYA) basins, and local processes modify the isotopic signals mainly in the Upper Salween (US), Upper Indus (UI), Upper Yellow (UYE) and Endorheic Rivers (ER) basins. The δ18O‐elevation relationships are only established in the UB, UI and UYE with lapse rates of −4.4‰/km, −3.4‰/km and −3.5‰/km, respectively. We suggest that as changes of moisture sources and lapse rates in different basins, it is cautious to apply δ18O‐elevation lapse rates for palaeoaltimetry reconstruction. Our dataset will benefit regional studies of hydrological, biogeochemistry, palaeoaltimetry reconstruction and palaeoclimate. [ABSTRACT FROM AUTHOR]

Aim: In the face of ongoing climate warming, we wanted to quantify impacts on vegetation at one of the major climatic and biogeographical boundaries of Europe, the limit between the Mediterranean and Eurosiberian biogeographical regions. We analyse temperature and moisture requirements of plants along altitudinal gradients at regional scale in the period 1980–2020 and we explore if changes coincide with observed changes in the same regions in terms of measured climatic data. Location: Southern France. Time period: 1980–2020. Taxa: Vascular plants. Methods: We calculated shifts in plants' temperature and moisture requirements for a large floristic database from south‐eastern France (SIMETHIS) during the period 1980–2020 along altitudinal gradients by using ecological indicator values (EIV). Additionally, we analysed standardized weather station data from the same area and period, to investigate whether floristic changes are synchronized with climate changes. Results: Vegetation data suggest a linear increase in temperature requirements of plant communities from 1980 to 2020 with a greater change at low altitudes. Upward shifts in temperature requirements coincided with observed climate change although warming did not show a general trend towards greater increases at low altitudes. Data on vegetation and climate suggest an upward shift of respectively 150 and 300 m for the boundary between Mediterranean and temperate belts. Moisture requirements of vegetation indicate an increase of the frequency of dry adapted species at low altitudes but an increase towards higher moisture requirements at high altitudes. Comparing vegetation responses with climate data suggests that responses are faster at low altitudes. Main conclusions: Our analyses show that strong general changes in vegetation are underway and highlight faster responses of vegetation to warming in low altitudes compared to high altitudes and demonstrate the need for reliable data on vegetation and climate changes, especially on water balance. [ABSTRACT FROM AUTHOR]

The magnitude of tropical cooling during the Last Glacial Maximum (LGM; ∼19–26.5 ka) remains controversial, with sea‐surface temperatures cooling by several degrees less than most temperatures reconstructed at high elevations. To explain this discrepancy, past studies proposed a steeper (increased) lapse rate—the temperature decrease with elevation—during the LGM relative to today. For instance, LGM temperatures in East Africa reconstructed from branched GDGTs from multiple elevations support an ∼0.9°C/km increase in the lapse rate during the LGM relative to present day. Lapse rates are a critical part of the Earth's climate sensitivity and atmospheric energy transfer, and it is vital to know whether and by how much the tropical lapse rate steepened during the LGM. Here, we simulate LGM glacier extents in the Rwenzori Mountains of Uganda with and without a change in lapse rate using a range of temperature and precipitation estimates. We find that the lapse rate must have been steeper than present for glaciers to reach their LGM positions using available sea‐level temperature and precipitation estimates for East Africa. Plain Language Summary: Glaciers are sensitive to changes in temperature and precipitation, and during the last ice age (∼20,000 years ago), glaciers in the tropics grew in cold (5°C–9°C) and dry conditions while sea surface temperatures changed relatively little (∼1°C–3°C). Perhaps the temperature estimates are telling different stories, or perhaps they are both correct and the rate of cooling with elevation (lapse rate) was steeper (increased) during the drier conditions of the ice age. Here, we use a glacier model and a range of temperature, precipitation, and lapse rate estimates to grow glaciers to moraines (glacial deposits) that mark their ice age extent at 20,000 years ago. Results indicate that glaciers can reach the moraines using the modest sea surface temperature change and a steeper lapse rate, which is supported by available biogeochemical analysis in this area or a large change in temperature and no lapse rate change, which is not supported by sea surface temperature estimates. Climate alterations in the tropics have global implications, and understanding how the lapse rate can change with time and location is vital for informing climate change models. Key Points: Tropical glaciers in East Africa required significant cooling (5.4°C–7.3°C) to reach their Last Glacial Maximum (LGM) extentsA large magnitude of cooling in Afro‐alpine regions conflicts with Indian Ocean sea surface temperatures (1°C–3°C)A steeper (increased) lapse rate during the LGM could explain this altitudinal discrepancy [ABSTRACT FROM AUTHOR]

The goal of this paper is to provide an initial assessment of water-vapor feedback (WVF) in humid urban heat island (UHI) environments based on temperature difference data. To achieve this, a novel temperature difference WVF model was developed that can analyze global and UHI local temperature difference data. Specifically, the model was applied to a comparative temperature literature study of similar cities located in humid versus dry climates. The literature study found that the daytime UHI ΔT was observed to be 3.3 K higher in humid compared to dry climates when averaged over thirty-nine cities. Since the direct measurement of WVF in UHI areas could prove challenging due to variations in the temperature lapse rates from tall buildings, modeling provides an opportunity to make a preliminary assessment where measurements may be difficult. Thus, the results provide the first available UHI ΔT WVF model assessment. The preliminary results find local water-vapor feedback values for wet-biased cities of 3.1 Wm−2K−1, 3.4 Wm−2K−1, and 4 Wm−2K−1 for 5 °C, 15 °C, and 30 °C UHI average temperatures, respectively. The temperature difference model could also be used to reproduce literature values. This capability helps to validate the model and its findings. Heatwave assessments are also discussed, as they are strongly affected by UHI water-vapor feedback and support the observation that humid regions amplify heat higher than UHIs in dry regions, exacerbating heatwave problems. Furthermore, recent studies have found that urbanization contributions to global warming more than previously anticipated. Therefore, cities in humid environments are likely larger contributors to such warming trends compared to cities in dry environments. These preliminary modeling results show concern for a strong local UHI water-vapor feedback issue for cities in humid environments, with results possibly over a factor of two higher than the global average. This assessment also indicates that albedo management would likely be an effective way to reduce the resulting WVF temperature increase. [ABSTRACT FROM AUTHOR]

Copyright of Hydrogeology Journal is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Fine‐scale climate information is critical to understand species–climate relationships. It is usually obtained by interpolating meteorological station data or by downscaling coarse‐gridded climate data. In mountain areas, however, the low station density and macroclimate variables used in coarse‐gridded climate products cannot reproduce fine‐scale variations caused by the complex topography and thus result in biased predictions of species responses to climate change. Here, we present an innovative method to estimate daily local air temperature at 100 m spatial resolution in the mountain region of South Tyrol (Central Alps, Italy), called the cloud‐corrected model. We introduce a correction factor that couples solar radiation and cloud cover data to improve air temperature predictions. Results are compared to models that either consider elevation only (lapse‐rate model) or elevation and solar radiation but not cloud cover (clear‐sky model) using a set of independent meteorological stations for validation. Moreover, all models were tested to predict critical phenological stages of the climate‐sensitive species Vitis vinifera. Over the vegetative period, the cloud‐corrected model significantly reduced the mean absolute error of predicted temperature compared with the lapse rate and clear‐sky model by 10 and 23%, respectively, and the bias by 93 and 90%, respectively. Solar radiation and cloud cover both strongly influenced local air temperature and their inclusion in temperature estimates greatly reduced systematic biases and improved predictions of important plant phenological stages by multiple days. Our method therefore provides a new way to include easily accessible topography and climate data into fine‐scale temperature predictions and accounts for important climate forcing factors that otherwise are often neglected. It combines efficiency and accuracy, because it limits data requirements but still operates on an ecologically relevant spatial scale. Thus, our approach offers promising opportunities to improve the understanding of species–climate relationships, especially in regions sensitive to the effects of climate change. [ABSTRACT FROM AUTHOR]

We revisit the fundamental principles of thermodynamic equilibrium in relation to heat transfer processes within the Earth's atmosphere. A knowledge of equilibrium states at ambient temperatures (T) and pressures (p) and deviations for these p-T states due to various transport 'forces' and flux events give rise to gradients (dT/dz) and (dp/dz) of height z throughout the atmosphere. Fluctuations about these troposphere averages determine weather and climates. Concentric and time-span average values (z, Δt)) and its gradients known as the lapse rate = d < T(z) >/dz have hitherto been assumed in climate models to be determined by a closed, reversible, and adiabatic expansion process against the constant gravitational force of acceleration (g). Thermodynamics tells us nothing about the process mechanisms, but adiabatic-expansion hypothesis is deemed in climate computer models to be convection rather than conduction or radiation. This prevailing climate modelling hypothesis violates the 2nd law of thermodynamics. This idealized hypothetical process cannot be the causal explanation of the experimentally observed mean lapse rate (approx.−6.5 K/km) in the troposphere. Rather, the troposphere lapse rate is primarily determined by the radiation heat-transfer processes between black-body or IR emissivity and IR and sunlight absorption. When the effect of transducer gases (H2O and CO2) is added to the Earth's emission radiation balance in a 1D-2level primitive model, a linear lapse rate is obtained. This rigorous result for a perturbing cooling effect of transducer ('greenhouse') gases on an otherwise sunlight-transducer gas-free troposphere has profound implications. One corollary is the conclusion that increasing the concentration of an existing weak transducer, i.e., CO2, could only have a net cooling effect, if any, on the concentric average (z = 0) at sea level and lower troposphere (z < 1 km). A more plausible explanation of global warming is the enthalpy emission 'footprint' of all fuels, including nuclear. [ABSTRACT FROM AUTHOR]

In the present work a recently developed product, the South American Mapping of Temperature (SAMeT), is presented. SAMeT minimizes certain deficiencies associated with both the eventual lack of observations and incorporates temperature corrections due to elevation or lapse rate of temperature (LRT) considerations. SAMeT is based on combinations of observed 2‐m temperatures and ERA5 reanalysis, as well as LRTs values computed from maximum (Tmax) and minimum (Tmin) temperatures. The LRT were computed using a simple linear regression on a 40‐year ERA5 reanalysis dataset and a digital elevation model dataset from Global Digital Elevation (GTOPO30). It was obtained that the computed LRT is consistently smaller than the standard LRT for all the seasons and studied regions. The South American territory was divided into four subdomains and the evaluation performed via a cross validation. This methodology consists in removing 90% of the observations to form OBS90 and generate the SAMeT fields. The remaining 10% (OBS10) is used for validation purposes. An additional dataset, hereafter referred to as OBS90i, is obtained using Kriging interpolation of OBS90 along with GTOPO30 and temperature adjustment by a standard LRT (−6.5°C·km−1). The evaluation period was from January 2011 to December 2013. Thus by using the standard LRT, a systematic negative bias for the temperature is obtained. On the other hand, ERA5 displays underestimation for Tmax and overestimation for Tmin. Furthermore, in comparative terms ERA5 displays the larger errors; thus, it is suggested that a bias remotion is important for any application involving these data. The systematic application of computed values for LRT in addition to a combination between observations and ERA5 data allows SAMeT to generate fields with smaller errors compared to observations than ERA5 while keeping spatial correlations. Thus, the present product brings an innovation to the temperature space fields for the South American region. [ABSTRACT FROM AUTHOR]

Freezing rain has been normally considered to be composed of supercooled raindrops when the 2 m air temperature (hereafter Tas ${T}_{as}$) is below freezing. However, according to a statistical survey of freezing rain observations in China from 2000 to 2019, we find that there were 656 events that occurred at Tas ${T}_{as}$ greater than 0°C (hereafter warm freezing rain and denoted by WFR), which account for 7% of the total freezing rain observations. Additionally, nearly 3% (266 observations) of freezing rain events occurred when the near‐surface wet‐bulb temperature was greater than 0°C. The modeling and sensitivity experiments on the nonequilibrium raindrop temperature (hereafter Tr ${T}_{r}$) show that the temperature difference between raindrops and the atmosphere is the main cause of WFR. The magnitudes of the ΔTar ${\Delta}{T}_{ar}$ (difference between raindrop temperature Tr ${T}_{r}$ and air temperature Ta ${T}_{a}$) and ΔTwr ${\Delta}{T}_{wr}$ (difference between Tr ${T}_{r}$ and wet‐bulb temperature Tw ${T}_{w}$) are determined by the raindrop diameter D, temperature lapse rate Г, and relative humidity RH. Increases of D and Г, and a decrease of RH enhance ΔTar ${\Delta}{T}_{ar}$ and ΔTwr ${\Delta}{T}_{wr}$ and thus the occurrence of WFR. Further simulations of 4 idealized and 370 real sounding profiles reveal that either the Ta ${T}_{a}$ or the Tw ${T}_{w}$ cannot properly distinguish the WFR events. When considering the temperature difference between raindrops and the atmosphere, the WFR can form by the "melting of solid hydrometeors" or "supercooled warm rain process." This study can also deepen our understanding of the conditions of WFR and freezing rain formation at different altitudes. Plain Language Summary: It has been normally thought that freezing rain occurs when the air temperature or wet‐bulb temperature is below 0°C. However, in our analysis of 9,312 freezing rain events in China over a 20‐year period, nearly 7% of freezing rain events occurred when the air temperature >0°C (called warm freezing rain [WFR]). Even use of the wet‐bulb temperature cannot explain all of the WFR events. Therefore, we developed a theoretical model to explain the formation mechanism of the WFR and analyze the key influencing factors. It is found that the temperature difference between the raindrop and the surrounding atmosphere is the main cause of WFR. An increase of raindrop diameter, an increase of atmospheric temperature lapse rate, and a decrease of relative humidity enhance the temperature difference between raindrops and the atmosphere, favoring the occurrence of WFR. Key Points: Approximately 7% of freezing rain events occur when the near‐surface air temperature is above 0°C (warm freezing rain)The temperature difference between raindrops and the atmosphere is the main cause of warm freezing rainThe critical impact factors affecting the temperature difference are the raindrop size, atmospheric temperature lapse rate, and relative humidity [ABSTRACT FROM AUTHOR]

Quantifying rates of climate change in mountain regions is of considerable interest, not least because mountains are viewed as climate “hotspots” where change can anticipate or amplify what is occurring elsewhere. Accelerating mountain climate change has extensive environmental impacts, including depletion of snow/ice reserves, critical for the world's water supply. Whilst the concept of elevation‐dependent warming (EDW), whereby warming rates are stratified by elevation, is widely accepted, no consistent EDW profile at the global scale has been identified. Past assessments have also neglected elevation‐dependent changes in precipitation. In this comprehensive analysis, both in situ station temperature and precipitation data from mountain regions, and global gridded data sets (observations, reanalyses, and model hindcasts) are employed to examine the elevation dependency of temperature and precipitation changes since 1900. In situ observations in paired studies (using adjacent stations) show a tendency toward enhanced warming at higher elevations. However, when all mountain/lowland studies are pooled into two groups, no systematic difference in high versus low elevation group warming rates is found. Precipitation changes based on station data are inconsistent with no systematic contrast between mountain and lowland precipitation trends. Gridded data sets (CRU, GISTEMP, GPCC, ERA5, and CMIP5) show increased warming rates at higher elevations in some regions, but on a global scale there is no universal amplification of warming in mountains. Increases in mountain precipitation are weaker than for low elevations worldwide, meaning reduced elevation‐dependency of precipitation, especially in midlatitudes. Agreement on elevation‐dependent changes between gridded data sets is weak for temperature but stronger for precipitation.Plain Language Summary: Mountains cover a large part of the Earth's surface and harbor distinct ecosystems, hold most of snow and ice outside the polar regions, and provide water for billions of people. This research looks at recent climate changes in mountains and compares them with simultaneous changes in lowland regions using weather station data, large global data sets, and climate models. We examine changes since 1900, but also concentrate on the last 40 years since this is when many changes have started to accelerate. Nearly all regions of the globe are getting warmer. When we make local comparisons, mountain sites are usually warming faster than lower areas nearby. However, when we average data from all global mountains and compare them with those from all lowland areas, there is no significant difference. Rainfall/snowfall on the other hand is decreasing in some areas, and increasing in others. In nearly all cases the strongest increase is occurring in the lowland areas, with increases in the mountains being more subdued (if at all). One consequence of our findings is that stores of mountain snow and ice may decline even faster than previously assumed due to the combination of enhanced mountain warming and reduced elevation dependency of rainfall/snowfall.Key Points: Using station and gridded data sets, we compare global precipitation and temperature trends by elevationLocal comparisons of paired stations and regional comparisons using gridded data often show faster mountain than lowland warmingPrecipitation differences between mountains and adjacent lowlands are reducing, often driven by stronger precipitation increase in lowlands [ABSTRACT FROM AUTHOR]

Uneven and sparse surface air temperature (Ta) observations challenge the understanding of the atmosphere‐land interactions and global environmental changes. Lapse rate (γ) is thought of with great potential to obtain accurate long‐term and high‐resolution Ta dataset. However, previous studies on γ over China were solely based on observed data, which limited our understanding since few stations are distributed in western China. In this study, an improved procedure for estimating γ by combining sparse observations and newly released ERA5 reanalysis data was proposed. The spatial heterogeneity in γ over China and its influencing factors (e.g., topography, climate, and vegetation) were then explored. A re‐examination of long‐term spatiotemporal variations in γ and its potential driving forces over China was also performed at grid scales. The results demonstrated that our γ estimations reproduced similar patterns to those derived from observations. The subsequent analyses revealed that the annual and seasonal variability in γ varied substantially among different altitude gradients, land cover types, and climate zones. The vital role played by vegetation covers in regulating local γ was emphasized in this study, which was poorly assessed previously. Moreover, γ in low altitude zones showed lower values but strong temporal fluctuations and contributed more to the interannual variability in γ over China, which could be mainly attributed to human activities. The present findings can provide an alternative method for estimating γ and be further used to assess regional or global temperature changes. [ABSTRACT FROM AUTHOR]

Understanding of the tropical atmosphere is elaborated around two elementary ideas, one being that density is homogenized on isobars, which is referred to as the weak temperature gradient (WTG), and the other being that the vertical thermal structure follows a moist-adiabatic lapse rate. This study uses simulations from global storm-resolving models to investigate the accuracy of these ideas. Our results show that horizontally, the density temperature appears to be homogeneous, but only in the mid- and lower troposphere (between 400 hPa and 800 hPa). To achieve a homogeneous density temperature, the horizontal absolute temperature structure adjusts to balance the horizontal moisture difference. Thus, water vapor plays an important role in the horizontal temperature distribution. Density temperature patterns in the mid- and lower troposphere vary by about 0.3 K on the scale of individual ocean basins but differ by 1 K among basins. We use equivalent potential temperature to explore the vertical structure of the tropical atmosphere, and we compare the results assuming the temperature following pseudo-adiabat and reversible-adiabat (isentropic) with the effect of condensate loading. Our results suggest that the tropical atmosphere in saturated convective regions tends to adopt a thermal structure that is isentropic below the zero-degree isotherm and pseudo-adiabatic above it. However, the tropical mean temperature is substantially colder and is set by the bulk of convection, which is affected by entrainment in the lower troposphere. [ABSTRACT FROM AUTHOR]

In insurance, the surrender rate is an important variable that threatens the sustainability of insurers and determines the profitability of the contract. Unlike other actuarial assumptions that determine the cash flow of an insurance contract, however, it is characterized by endogenous variables such as people’s economic, social, and subjective decisions. Therefore, a microscopic approach is required to identify and analyze the factors that determine the lapse rate. Specifically, micro-level characteristics including the individual, demographic, microeconomic, and household characteristics of policyholders are necessary for the analysis. In this study, we select panel survey data of Korean Retirement Income Study (KReIS) with many diverse dimensions to determine which variables have a decisive effect on the lapse and apply the lasso regularized regression model to analyze it empirically. As the data contain many missing values, they are imputed using the random forest method. Among the household variables, we find that the non-existence of old dependents, the existence of young dependents, and employed family members increase the surrender rate. Among the individual variables, divorce, non-urban residential areas, apartment type of housing, non-ownership of homes, and bad relationship with siblings increase the lapse rate. Finally, among the financial variables, low income, low expenditure, the existence of children that incur child care expenditure, not expecting to bequest from spouse, not holding public health insurance, and expecting to benefit from a retirement pension increase the lapse rate. Some of these findings are consistent with those in the literature. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Estudios Andaluces is the property of Revista de Estudios Andaluces and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The growing relevance of risk-based valuations of insurance contracts has stimulated the extension of the traditional deterministic lapse rate models towards a dynamic modelling. A popular dynamic model uses deterministic lapse rates as base rates and dynamic adjustment factors, generally assuming a relationship between lapses and one or more economic factors to describe policyholder behaviour. This relationship is generally represented by an S-Shaped function. This implies a monotonic increase in lapse rate by increasing the economic variable, usually set equal to a "market spread" between a benchmark rate and the policy crediting rate. In this paper, we assume a different policyholder behaviour, based on the assumption that the policyholder does not modify his/her behaviour for small values of the market spread. Hence, for a better description of such behaviour, the double-sigmoid function appears to be more adequate. The double-sigmoid function is obtained as a combination of two logits in their sum or product. Theoretical features and practical applications of the model are discussed. [ABSTRACT FROM AUTHOR]

Researchers often use gridded datasets to develop statistical relationships between climate and natural resources at locations that are distant from weather stations. These gridded datasets may provide inaccurate estimates of temperature at sites with elevations that differ significantly from the elevation assigned to the corresponding grid cell. We assess the accuracy of three gridded climate datasets commonly used in ecological studies (800‐m resolution PRISM, 4‐km GRIDMET, and 1‐km Daymet) compared with a network of 153 temperature dataloggers arranged along elevation transects in Yellowstone National Park (Wyoming, Montana, Idaho). Measured lapse rates for monthly average daytime high temperatures were generally steeper (more cooling per unit of elevation increase) than lapse rates in gridded datasets and steeper than those in similar mountainous regions. Measured lapse rates for monthly average nighttime lows were similar to gridded data lapse rates. Our measured lapse rates are most useful for the adjustment of daytime highs from weather stations or gridded data during warmer months. Temperatures during cooler months sometimes are strongly affected by other factors. Ecologically relevant metrics calculated from temperatures adjusted with constant lapse rates can increase multiplicatively and by varying amounts from year to year. For example, high‐elevation estimates of climatic water deficit (CWD) calculated from gridded data that were not adjusted with our measured lapse rates were 2.5–4 times greater (depending on the year) than calculations from temperatures that were adjusted. Our results emphasize the importance of correcting grid‐based climate estimates for elevation in complex terrain when accurate, site‐specific data are required. When the elevation assigned to grid cells differs significantly from the elevation of points within the cell, the lapse rate obtained for data extracted from grids can be shallower than the true rate. We illustrate the implications of these findings with case studies in Yellowstone. [ABSTRACT FROM AUTHOR]

The geostrophic drag law (GDL), which predicts the geostrophic drag coefficient and the cross-isobaric angle, is relevant for meteorological applications such as wind energy. For conventionally neutral atmospheric boundary layers (CNBLs) capped by an inversion, the GDL coefficients A and B are affected by the inversion strength and latitude, expressible via the ratio of the Brunt–Väisälä frequency (N) to the Coriolis parameter (f). We present large-eddy simulations (LES) covering a wider range of N/|f | than considered previously, and show that A and B obtained from carefully performed LES collapse to a single curve when plotted against N/|f |. This verifies the GDL for CNBLs over an extended range of N/|f| within LES. Additionally, in agreement with atmospheric observations, we show that using A=1.9 and B=4.4 accurately predicts the geostrophic drag coefficient in the limit of weak inversion strength or high latitude (N∕|f| ≲ 300). However, due to the strong dependence of B on N/|f|, corresponding predictions for the cross-isobaric angle are less accurate. As we find significant deviations between the LES results and the original parameterization of the GDL for CNBLs, we update the corresponding model coefficients. [ABSTRACT FROM AUTHOR]

The near‐surface air temperature lapse rate is the predominant source of spatial temperature variability in mountains and controls snowfall and snowmelt regimes, glacier mass balance, and species distributions. Lapse rates are often estimated from observational data, however there is little guidance on best practices for estimating lapse rates. We use observational and synthetic datasets to evaluate the error and uncertainty in lapse rate estimates stemming from sample size, dataset noise, covariate collinearity, domain selection, and estimation methods. We find that lapse rates estimated from small sample sizes (<5) or datasets with high noise or collinearity can have errors of several °C km−1. Uncertainty in lapse rates due to non‐elevation related large‐scale temperature variability was reduced by correcting for spatial temperature gradients and restricting domains based on spatial clusters of stations. We generally found simple linear regression to be more robust than multiple linear regression for lapse rate estimation. Finally, lapse rates had lower error and uncertainty when estimated from a sample of topoclimatically self‐similar stations. Motivated by these results, we outline a set of best practices for lapse rate estimation that include using quality controlled temperature observations from as many locations as possible within the study domain, accounting for and minimizing non‐elevational sources of climatic gradients, and calculating lapse rates using simple linear regression across topoclimatically self‐similar samples of stations which are roughly 80% of the station population size. [ABSTRACT FROM AUTHOR]

In this study we derive the environmental lapse rate (ELR) from vertical profiles of temperature in the lower troposphere, applying it to downscale air temperature of the new European Centre For Medium‐Range Weather Forecasts (ECMWF) reanalysis ERA5, which replaces ERA‐Interim (ERAI). We focus over the western U.S. region, a data‐rich area with observations of daily maximum and minimum temperature (Global Historical Climatology Network) and snow depth and soil temperature. Observations indicate an ELR of −4.5 K·km−1 in the region, lower than the commonly used −6.5 K·km−1. ERA5 ELR agrees with the observational estimates, with some overestimation in winter and limitations in the diurnal variability. The elevation correction of ERA5 temperature using different ELR showed the benefits of deriving ELR fields from ERA5 vertical profiles, when compared with a constant ELR. Simulations with the ECMWF land surface model, at 9‐km resolution, driven by ERA5 using different ELR corrections showed the added value of the methodology, but the impact of different ELR corrections is limited. However, the validity of the downscaling method in reducing temperature to station altitude suggests that there is sufficient generality for application at kilometer and subkilometer resolutions. By comparing the estimated representativity errors of observations with reanalysis, the improvements from ERAI to ERA5 are mainly visible in the random component of the error. Large systematic biases remain, which require further attention from the modeling and data assimilation, and limit the potential benefits of ELR corrections. Key Points: Environmental lapse rate derived from atmospheric reanalysis vertical profiles agrees with observational estimatesSurface downscaling outperforms ERA5, but the impact of different ELR corrections to the driving data is reducedSystematic biases in ERA5 near‐surface temperature require further efforts from modeling and data assimilation [ABSTRACT FROM AUTHOR]

Vegetation recovery following landslides caused by typhoons has been monitored on Mikurajima Island, Japan. Despite having a temperature profile of a warm temperate zone, Mikura-jima has many plant species representative of a cool temperature zone. Temperature environment is an essential parameter for determining the development of vegetation and flora, but little data are available for the island. In the present study, we measured atmospheric temperature by thermographs at seven different elevations (130, 300, 400, 500, 600, 700 and 800 m) over a period of 5 years (from 2013 to 2017), and we determined the warm index, cool index and rate of decline with elevation. The lapse rate along elevation gradient was 0.98 °C per 100 m for elevations 500 m and below, and 0.66 °C per 100 m for elevations 500 m and above. Warm index (W.I.) was estimated to fall below 85 at elevations over about 800 m, which allows the existence of a cool temperature zone. The re-examination of temperature environment in the present study will facilitate planning of goals and evaluation or estimation of vegetational succession on Mikura-jima Island. [ABSTRACT FROM AUTHOR]

We investigate determinants of lapse rates in the Chinese life insurance industry using firm-province level data from 2005 to 2013. We conduct a panel study, exploring significant regional differences and dramatic changes in demographic conditions during the urbanisation process in China. First, we find that the unemployment rate is positively related to lapse rates, and the driving force is migrant population rather than local urban residents. This provides evidence for the emergency fund hypothesis from a new perspective. Second, we find that an insurer's reputation is negatively linked to lapse rates. We define this as the 'reputation hypothesis'. Third, our findings are also consistent with the interest rate hypothesis. We extend the literature by decomposing life insurance products into three types and find that interest rates are positively (negatively) associated with lapse rates of investment-type (protection-type) products. Lapse rates of health products are not related to interest rates. Fourth, our empirical result suggests that high lapse rates can potentially weaken the insurers' financial soundness and harm new business. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Arctic glaciers have been, and are predicted to remain, an important sea‐level rise contributor over the 20th and 21st centuries. However, multi‐annual observations of their basic meteorological characteristics, which drive melt processes, are very sparse, contributing to uncertainties in sea‐level rise projections. This paper presents a methodology to process and analyse 6 years (September 2011–September 2017) of glacier meteorology data collected by an automatic weather station on the ablation zone of a small valley glacier in Svalbard (~79°N). The study focuses on the microclimate of the study glacier and its differences with that of large glaciers in the region, namely slightly increased summer air temperature with steep near‐surface lapse rates, frequently superadiabatic, reduced wind speed and incoming shortwave radiation flux. These differences are likely related to the adjacent complex relief and non‐ice‐covered surfaces within the basin of the study glacier and, as such, pose a challenge for accurate simulation of alpine glacier microclimates in regional melt assessments in the Arctic. The processing routine applied in this paper might serve as a reference for future similar studies. [ABSTRACT FROM AUTHOR]

Characterization of spatial and temporal variability of stable isotopes (δ18O and δ2H) of surface waters is essential to interpret hydrological processes and establish modern isotope–elevation gradients across mountainous terrains. Here, we present stable isotope data for river waters across Kyrgyzstan. River water isotopes exhibit substantial spatial heterogeneity among different watersheds in Kyrgyzstan. Higher river water isotope values were found mainly in the Issyk‐Kul Lake watershed, whereas waters in the Son‐Kul Lake watershed display lower values. Results show a close δ18O–δ2H relation between river water and the local meteoric water line, implying that river water experiences little evaporative enrichment. River water from the high‐elevation regions (e.g., Naryn and Son‐Kul Lake watershed) had the most negative isotope values, implying that river water is dominated by snowmelt. Higher deuterium excess (average d = 13.9‰) in river water probably represents the isotopic signature of combined contributions from direct precipitation and glacier melt in stream discharge across Kyrgyzstan. A significant relationship between river water δ18O and elevation was observed with a vertical lapse rate of 0.13‰/100 m. These findings provide crucial information about hydrological processes across Kyrgyzstan and contribute to a better understanding of the paleoclimate/elevation reconstruction of this region. [ABSTRACT FROM AUTHOR]

To date, influence of aerosols on the atmospheric stability and hence on the convection/precipitation is not well understood. From the detailed analysis carried out using high‐accuracy radiosonde and Aethalometer measurements, a significant decrease in the convective available potential energy has been noticed in association with the increase in surface concentration of black carbon (BC) for a restricted range of precipitable water vapor and under similar environmental background (wind speed, direction, and cloud cover) over Gadanki (13.5°N, 79.2°E), a tropical rural site in southern peninsular India. A number of case studies along with statistical analysis indicate a notable perturbation in the temperature profile associated with high‐surface BC concentration conditions compared to the regional mean concentration of the same. A discernible fall in the temperature lapse rate within and above the local boundary layer in association with the higher BC concentration has been observed, which in turn reduces the available potential energy for convection decreasing the positive buoyancy required for a rising moist air parcel. This observation has been noticed over a nonurban location, but the phenomenon is not location dependent and is more applicable to the urban regions where BC concentration is likely to be much higher. Plain Language Summary: Our daily needs of energy lead us to massive usage of fuel burning. These burnings release huge amount of tiny particles into the atmosphere, which significantly influence our environment. Black carbon (BC) is such a man‐made health hazardous pollutant aerosol particle which warms up our environment by absorbing solar radiation. Generally, the temperature of our surroundings decreases with height in a certain rate. Due to this nature of temperature in our atmosphere, the warm moist air from the ground can move upward and is cooled down. The upward movement of moist air eventually causes rainfall mostly during summer in tropical areas. But our present observation shows that higher amount of BC in our environment can disturb the normal upward movement of moist air by heating up the atmosphere. Thus, the increase in the amount of this man‐made pollutant particle affects not only human health but also the normality of our atmosphere disturbing its balance. Our study provides some important evidences on how BC particles can disturb the balance of our atmosphere. This observation has been done over a rural location, but the phenomenon should be more prominent over urban environment where BC is likely to be more in amount. Key Points: Influence of black carbon on the atmospheric instability is investigated under similar background conditionsIt is found that increase in surface black carbon affects the lower tropospheric instability over a regionThere is significant decrease in temperature lapse rate and convective available potential energy during increase in black carbon concentration [ABSTRACT FROM AUTHOR]

Ecological analyses often incorporate high‐resolution environmental data to capture species‐environment relationships in modelling applications, and downscaled climate data are increasingly being used for such analyses. While such data products provide high precision, the accuracy of these data is seldom directly tested. Consequently, introduced bias from downscaling algorithms may propagate through analyses that incorporate these data products. Here, we utilize data from the Foothills Climate Array (FCA), a mesoscale grid of 232 weather stations in the prairies and eastern slopes of the Rocky Mountains in southern Alberta, Canada, to evaluate several publicly available downscaled climate products. We consider daily, monthly, and annual records for a suite of temperature and humidity variables. The FCA data are ideal to evaluate climate downscaling because they contain multi‐year observations and cover a range of topographic conditions, from flat prairie grass‐ and croplands to mountainous terrain. We find that the downscaling algorithms improve the accuracy of climate variables over simple interpolations of low‐resolution data, but errors are often large at validation locations (e.g., several °C for temperature variables), and downscaled datasets show notable elevational and seasonal bias for all variables. A bias adjustment analysis demonstrates that such bias can be greatly reduced with relatively simple regression‐based models, even when only a small subset of observational data are used, provided they cover a relatively large spread of elevations. We discuss our findings in the context of climate change and ecological modelling and make general recommendations for consumers of downscaled climate data products. [ABSTRACT FROM AUTHOR]

—Monthly mean data of the ERA-Interim reanalysis (1979–2014) are used to estimate the lapse rate γ in the Northern Hemisphere high-latitude troposphere. The relationship between the lapse rate and surface air temperature Ts is analyzed in terms of interannual variability for different seasons. The study analyzes the ability of the HIRHAM5 regional climate model to simulate the features of lapse rate distribution in the Arctic troposphere and the parameter of its sensitivity to the variation in surface air temperature dγ/dTs that were derived from reanalysis data. The regional features of the link between the vertical stratification of tropospheric temperature and the Arctic Oscillation are revealed. The estimates obtained from reanalysis data and model simulations are especially significant as the Arctic climate has higher variability and sensitivity under global changes that is characterized by so called Arctic amplification. [ABSTRACT FROM AUTHOR]

The glaciers of Patagonia are the largest in South America and are shrinking rapidly, raising concerns about their contribution to sea level rise in the face of ongoing climatic change. However, modeling studies forecasting future glacier recession are limited by the scarcity of measured on‐glacier air temperatures and thus tend to use spatially and temporally constant lapse rates. This study presents 9 months of air temperature observations. The network consists of five automatic weather stations and three on‐glacier air temperature sensors installed on the South Patagonia Icefield along a transect at 48°45′S. Observed lapse rates are, overall, steeper on the east (−0.0072 °C/m) compared to the west (−0.0055 °C/m) and vary between the lower section (tongue, ablation zone) and the upper section (plateau, accumulation zone) of the glaciers. Warmer off‐glacier temperatures are found in the east compared to the west for similar elevations. However, on‐glacier observations suggest that the glacier cooling effect is higher in the east compared to the west. Through application of distributed temperature‐index and point‐scale energy balance models we show that modeled ablation rates vary by up to 60%, depending on the air temperature extrapolation method applied, and that melt is overestimated and sublimation is underestimated if the glacier cooling effect is not included in the distributed air temperature data. These results can improve current and future modeling efforts of the energy and mass balance of the whole South Patagonia Icefield. Key Points: Distinct lapse rates prevail on the east and west sides of the icefieldA strong glacier cooling effect relative to off‐glacier air temperature was observedAblation is estimated using temperature extrapolation approaches; divergent results highlight the need for realistic temperature distributions [ABSTRACT FROM AUTHOR]

High-latitude systems in northwestern Canada and Alaska have warmed rapidly. The aim of this study was to examine how a remotely sensed proxy of vegetation productivity varied among mountain ranges with respect to elevation and climate from 2002–2017. Our study area included high-latitude mountains in Alaska, USA, and Yukon Territory, Canada, ranging from cold arctic mountains in the tundra biome to warmer interior mountains areas within the boreal biome. We used the annual maximum Normalized Difference Vegetation Index (NDVI) data from the 250-m MODIS NDVI product as a proxy of maximum growing season photosynthetic activity. The long-term (16-year) and interannual pattern of maximum NDVI was investigated with respect to elevation, July temperature, and July precipitation classes within four climatic mountain regions. The July temperature lapse rate was consistently linear, whereas the long-term maximum NDVI lapse rate was nonlinear. At lower elevations, the high-precipitation region had the highest NDVI, whereas the interior mountains region had the highest NDVI at higher elevations. The long-term maximum NDVI was negatively correlated with July precipitation for areas with July temperature below 12°C. Above 12°C, NDVI was positively correlated with July precipitation, with the greatest rate of NDVI increase with precipitation at the warmest July temperature class. The pattern of interannual peak NDVI with respect to July temperature was not as strong as the long-term pattern; however, the only interannual negative correlation between peak NDVI and July temperature was at lower elevations within the interior mountains. We concluded that among a regional climatic gradient of mountain areas, low growing season temperature and length were likely constraining vegetation productivity, and lower growing season moisture may be an important constraint at the warmest interior mountains region. [ABSTRACT FROM AUTHOR]

The association between elevation and temperature is analysed by simple linear correlations across several spatial scales. The minimum (tn) and maximum (tx) temperatures (response variables), expressed at two time scales (monthly and daily), are observed for 102 weather stations in east central France from 1980 to 2014 (12,784 days). Elevation (explanatory variable) is provided at 10 resolutions: 50, 100, 200, 500 m, 1, 2, 4, 8, 12, and 16 km. The coefficient of determination, R2, is used to determine which resolution gives the best results. The slope given by the regression is used to assess the drop in temperature per unit of elevation (temperature lapse rate [TLR]). In most situations, monthly and daily temperatures are optimally explained by the finest (50 m) resolution: the R2 is, respectively, 0.53 and 0.24 for tn and 0.78 and 0.39 for tx. The coarser resolutions produce results of much lower quality. However, in one circumstance (monthly mean of tn), the highest R2 value is obtained for the 4‐km resolution, which is a meaningful result as current regional climate models now achieve similar resolutions. Both monthly and daily TLRs of tn and tx are, on average, slightly lower than −0.5 °C/100 m at 50‐m resolution. The TLR decreases with resolution: it is only −0.23 °C/100 m for tn and −0.13 °C/100 m for tx at 16‐km resolution. Other insightful results involve the influence of the topographical context, which shows some additional effect with that of elevation and which was quantified through partial correlations. Influence of DEM spatial resolution on the altidunal temperature lapse rate. [ABSTRACT FROM AUTHOR]

Recent investigations of high‐resolution temperature patterns employing dense observational networks have improved our understanding of fine‐scale climatic influences on hydrological and ecological processes. Few of these investigations, however, have focused on high‐latitude environments. In this study—one of the first to evaluate drivers of fine‐scale temperature patterns in a non‐glaciated high‐latitude environment—we evaluated physiographic controls on summer temperature while accounting for free‐air temperature conditions along a north–south transect of temperature sensors on Mt. Healy, at the eastern edge of Denali National Park and Preserve in Alaska, United States. Although the study was limited to summer, when near‐surface temperature inversions are rare, we identified average daily minimum temperature (Tmin) lapse rates shallower than −2 °C/km over five summers. Lapse rates of average temperature (Tavg) were steeper (−5.3 °C/km), but still did not approach the global average lapse rate of −6.5 °C/km. We also identified positive effects of solar radiation on both Tmin and Tavg, variation in Tmin and Tavg lapse rates across a range of synoptic temperature conditions, and variation in Tmin lapse rates across terrain convergence values. Finally, we compared relationships between elevation and temperature from transect sensors and a gridded product. Over 15 months of comparison, sensor Tavg was 0.6 °C cooler than gridded data at similar elevations. Sensor lapse rates were on average 0.5 °C steeper than those from gridded data but only significantly so in the cool month of June 2014. These differences highlight the need for additional instrumentation across this remote and topographically variable region to improve accuracy and reduce biases in gridded climate products. [ABSTRACT FROM AUTHOR]

Land Surface Temperature (LST) is considered important in monitoring the energy flux between the land surface and atmosphere. Due to the diversity of topography in Iran and its effect on the climate diversity, we decided to study the effect of topography on the LST variations. To this end, the LST digital data derived from the observations of the MODIS Terra and Aqua were used. The results indicated that, during the daytime, from sea level up to a height of 400 meters, the LST increased, and then the temperature decreased with increasing altitude, and up to a height of 3000 meters, there was a strong correlation between the two. LST lapse rate was more during the daytime compared with that of the night time and it was more during the winter compared with the summer. LST lapse rate showed larger variability in diurnal cycle, but its monthly patterns were similar in different aspects. The aspect had substantial effect on LST inversion elevation. Furthermore, the inverse relationship between LST and slope was strong in slopes up to 20°. [ABSTRACT FROM AUTHOR]

Although the mean environmental lapse rate (MELR) value (a linear decrease of −6.5 °C/km) is the most widely used, near‐surface (i.e., non‐free atmosphere) air temperature lapse rates (NSLRs; measured at ~1.5 m height) are variable in space and time because of their dependence on topography and meteorological conditions. In this study we conducted the first analysis of the spatial and temporal variability of NSLRs for continental Spain and their relationship to synoptic atmospheric circulation (circulation weather types [CWTs]), focusing on major mountain areas including the Pyrenees, Cantabrian, Central, Baetic, and Iberian ranges. The results showed that the NSLR varied markedly at spatial and seasonal scales and depended on the dominant atmospheric conditions. The median NSLR values were weaker (less negative) than the MELR for the mountain areas (Pyrenees −5.17 °C/km; Cantabrian range −5.22 °C/km; Central range −5.78 °C/km; Baetic range −4.83 °C/km; Iberian range −5.79 °C/km) and for the entire continental Spain (−5.28 °C/km). For the entire continental Spain the steepest NSLR values were found in April (−5.80 °C/km), May (−5.58 °C/km), and October (−5.54 °C/km) because of the dominance of northerly and westerly advections of cold air. The weakest NSLR values were found in July (−4.67 °C/km) and August (−4.78 °C/km) because of the inland heating, and in winter because of the occurrence of thermal inversions. As the use of the MELR involves the assumption of large errors, we propose 1 zonal, 12 monthly, 11 CWTs, and 132 hybrid monthly–CWTs NSLRs for each of the mountain ranges and for the entire continental Spain. More regional studies are urgently needed to accurately assess the NSLR as a function of atmospheric circulation conditions. [ABSTRACT FROM AUTHOR]

In this study, Land Surface Temperature (LST) and its lapse rate over the mountainous Kashmir Himalaya was estimated using MODIS data and correlated with the observed in-situ air temperature (Tair) data. Comparison between the MODIS LST and Tair showed a close agreement with the maximum error of the estimate ±1°C and the correlation coefficient >0.90. Analysis of the LST data from 2002-2012 showed an increasing trend at all the selected locations except at a site located in the southeastern part of Kashmir valley. Using the GTOPO30 DEM, MODIS LST data was used to estimate the actual temperature lapse rate (ATLR) along various transects across Kashmir Himalaya, which showed significant variations in space and time ranging from 0.3°C to 1.2°C per 100 m altitude change. This observation is at variance with the standard temperature lapse rate (STLR) of 0.65°C used universally in most of the hydrological and other land surface models. Snowmelt Runoff Model (SRM) was used to determine the efficacy of using the ATLR for simulating the stream flows in one of the glaciated and snow-covered watersheds in Kashmir. The use of ATLR in the SRM model improved the R2 between the observed and predicted streamflows from 0.92 to 0.97. It is hoped that the operational use of satellite-derived LST and ATLR shall improve the understanding and quantification of various processes related to climate, hydrology and ecosystem in the mountainous and data-scarce Himalaya where the use of temperature and ATLR are critical parameters for understanding various land surface and climate processes. [ABSTRACT FROM AUTHOR]

Abstract: Although gridded air temperature data sets share much of the same observations, different rates of warming can be detected due to different approaches employed for considering elevation signatures in the interpolation processes. Here we examine the influence of varying spatiotemporal distribution of sites on surface warming in the long‐term trend and over the recent warming hiatus period in China during 1951–2015. A suspicious cooling trend in raw interpolated air temperature time series is found in the 1950s, and 91% of which can be explained by the artificial elevation changes introduced by the interpolation process. We define the regression slope relating temperature difference and elevation difference as the bulk lapse rate of −5.6°C/km, which tends to be higher (−8.7°C/km) in dry regions but lower (−2.4°C/km) in wet regions. Compared to independent experimental observations, we find that the estimated monthly bulk lapse rates work well to capture the elevation bias. Significant improvement can be achieved in adjusting the interpolated original temperature time series using the bulk lapse rate. The results highlight that the developed bulk lapse rate is useful to account for the elevation signature in the interpolation of site‐based surface air temperature to gridded data sets and is necessary for avoiding elevation bias in climate change studies. [ABSTRACT FROM AUTHOR]

Copyright of Revista Ciência e Natura is the property of Revista Ciencia e Natura and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Ciência e Natura is the property of Revista Ciencia e Natura and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Based on data collected from 90 meteorological stations distributed in Indian Himalayas, two precipitation regimes has been identified: (i) Monsoonal Precipitation Regime (MPR), in which annual precipitation is high, generally from about 1500 mm to over 3000 mm, and 60-80% of which occurs during the monsoon months; and (ii) Non-monsoonal Precipitation Regime (NMPR), in which annual precipitation is generally below 1000 mm, and the contribution of monsoon months to annual precipitation is less than 50%. While MPR in the characteristic precipitation regime of the areas in south of the main Himalayan ranges, MPR generally occurs in areas north of the main Himalayan ranges or in southern slopes shielded from the monsoon thrust by high mountain ranges. Because of a stronger influence of westerlies in NMPR, winter and spring precipitations exceed the monsoon precipitation. Thus the two precipitation regimes differ, not only in the amount of annual precipitation, but also in its seasonal distribution. Temperature lapse rate is lower during monsoon season (e.g. in Nepal, 0.46°C/100 m altitude during the monsoon, compared to 0.49°C-0.64°C/100 m during other seasons), that is why the upper altitudinal limit of temperatures, warm enough to support tree growth, is relatively higher in MPR. The nature of precipitation regime influences the distribution of trees. Distribution of certain tree species, such as Pinus wallichiana and Cedrus deodara is particularly linked to NMPR, while some widely occurring Himalayan species, like Pinus roxburghii and Quercus leucotrichophora tend to avoid NMPR. [ABSTRACT FROM AUTHOR]

All numerical weather prediction (NWP) models inherently have substantial biases, especially in the forecast of near-surface weather variables. Statistical methods can be used to remove the systematic error based on historical bias data at observation stations. However, many end users of weather forecasts need bias corrected forecasts at locations that scarcely have any historical bias data. To circumvent this limitation, the bias of surface temperature forecasts on a regular grid covering Iran is removed, by using the information available at observation stations in the vicinity of any given grid point. To this end, the running mean error method is first used to correct the forecasts at observation stations, then four interpolation methods including inverse distance squared weighting with constant lapse rate (IDSW-CLR), Kriging with constant lapse rate (Kriging-CLR), gradient inverse distance squared with linear lapse rate (GIDS-LR), and gradient inverse distance squared with lapse rate determined by classification and regression tree (GIDS-CART), are employed to interpolate the bias corrected forecasts at neighboring observation stations to any given location. The results show that all four interpolation methods used do reduce the model error significantly, but Kriging-CLR has better performance than the other methods. For Kriging-CLR, root mean square error (RMSE) and mean absolute error (MAE) were decreased by 26% and 29%, respectively, as compared to the raw forecasts. It is found also, that after applying any of the proposed methods, unlike the raw forecasts, the bias corrected forecasts do not show spatial or temporal dependency. [ABSTRACT FROM AUTHOR]

ABSTRACT The near surface air temperature is the primary indicator for climate change. Reanalysis as the surrogates for large-scale observations are widely used in the Tibetan Plateau because of the sparse meteorological network. However, an average bias of −3.54 °C and root-mean-square error ( RMSE) of 4.31 °C were found between ERA-Interim monthly 2-m temperature and observation over the Tibetan Plateau, which indicated that a correction procedure for ERA-Interim is necessary before local scale applications. To overcome this challenge, a robust elevation correction method is developed to downscale ERA-Interim 2° × 2° monthly 2-m temperature data based on ERA-Interim internal vertical lapse rates. This method is validated against 80 meteorological stations from 1979 to 2013 located in 26 ERA-Interim grid cells. It is also compared with other four correction methods, which are using different lapse rate schemes such as fixed monthly lapse rates, surface lapse rates calculated from the meteorological stations (within a single grid or with neighbouring sites), as well as a third-order curvilinear function of ERA-Interim pressure level data. The results indicate that the correction method using ERA-Interim internal vertical lapse rates cannot only significantly reduce the bias (89%) and RMSE (62%) for the original ERA-Interim data, but also capture the inter-annual variations for the plateau-wide climatology very well. The seasonal and annual temperature warming trends are also modelled encouragingly compared with other four methods. The strongest advantage of this method is that it is independent of local meteorological stations. Therefore, it is possible to extrapolate ERA-Interim temperature data for any other high mountain areas where no measurements exist. This work will help the scientific community identify the most proper and easiest method to downscale reanalysis temperature data for climate impact assessments at the site or regional scale. [ABSTRACT FROM AUTHOR]

Copyright of Annals of the American Association of Geographers is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

ABSTRACT Insufficient availability of weather stations recording air temperature is a common problem in many alpine regions. The low station density combined with the high variability of air temperature means that interpolated fields based on simple or more complex interpolation techniques are unlikely to be representative of the real patterns of air temperature. In this study, a novel method was developed to tackle this problem, following initial investigation of lapse rate variability in the study domain: the alpine Clutha catchment, New Zealand. Owing to a series of complexities in lapse rate variability, a multi-layer approach was adopted to produce 1 km2 daily fields of maximum (Tmax) and minimum air temperature (Tmin). The first layer of the Tmax and Tmin models was calculated using a trivariate thin-plate spline, which was constrained to the elevations of the continuous network to avoid unrealistic extrapolation. To compensate for missing continuous high elevation records, two lapse rate models were implemented to scale air temperature above the first layer. The two lapse rate models were based on the dominant processes driving lapse rate variation, which were found to be cold air ponding (Tmin) and spatial differences in relative humidity (Tmax). Independent station records were used to assess accuracy and compare the resultant fields to an existing product (the Virtual Climate Station Network) and a more conventional method based on a bivariate spline and a constant lapse rate. The validation revealed that the new methods developed here have led to a substantial improvement in producing spatial estimates of Tmax, with a mean root mean square error ( RMSE) of 2.38 °C, while progress in regard to Tmin was more limited (mean RMSE of 2.93 °C). As such, this work demonstrates that inclusion of the driving processes controlling lapse rates in interpolation routines can lead to improvements in accuracy. [ABSTRACT FROM AUTHOR]