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]

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]

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]

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]

Abstract 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.

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 refraction and extinction in the Earth’s atmosphere depend on the atmosphere’s structure, so it was natural to try to infer that structure from optical observations. Efforts to extract structure from observed refractions led to proof that this is possible only below the astronomical horizon. Direct studies of the real atmosphere show complicated, variable structure. The complex history of relations between structure and refraction is outlined by citing some important works.

Near-surface air temperature lapse rate (NSATLR) is one of the key criteria for judging atmospheric stability. It is an important model parameter in glacier, hydrology, ecology, climate, and meteorology in high mountains such as the Tibetan Plateau (TP). However, due to the complex interaction between the atmosphere and land surface, retrieving NSATLR from remotely sensed data is challenging. In this article, we develop a novel model based on the MODIS data to estimate the daily 5-km NSATLR over the TP. The input parameters of the model include digital elevation model and the near-surface air temperature estimated from the MODIS data. By combining moving window and specified criteria, the pixel-based NASTLR with a 5-km spatial resolution over the TP is estimated. In addition, we also estimate the NSATLR based on the meteorological data over the TP (NSATLRin situ) to evaluate the accuracy of NSATLR estimation model. Results demonstrate that the NSATLR estimate agrees well with NSATLRin situ in the eastern TP, with an overall root-mean-square error of 1.51 °C/km and a mean bias error of −0.78 °C/km. Larger deviation exists in the southern TP. Thanks to the evident advantages of simplicity, the availability of input parameters and the independence from the meteorological data, the developed model can be applied to larger areas.

Objective To understand the structural characteristics of lapsed apheresis donors in our blood center and provide guidance for further improving recruitment and retention strategies by retrospectively analyzing the characteristics of lapsed apheresis donors in our center in recent years. Methods The apheresis donation and lapsed apheresis donors in Zhejiang Blood Center from 2016 to 2020 were statistically analyzed, and the general information of lapsed donors, including gender, blood type, age, education level and occupation composition, were compared and analyzed. The lapse of novel and long-term donors with different frequency were retrospectively analyzed. The SPSS 19.0 statistical software package was used for data analysis. Results In 2020, the total lapse of apheresis donors decreased by 16.6% as compared with 2016(P

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.

Most climate models have shown that the Indian Summer Monsoon Rainfall (ISMR) will increase on account of global warming. The primary reason is the increase in column water vapor (CWV). The rainfall increase is not, however, proportional to the increase in column water vapor; for a given amount of CWV, rainfall will be lower in the future, according to model simulations. This suggests that other there are factors are at play. We have used a diagnostic model based on moist static energy (MSE) and moisture conservation to examine the impact of changes in the vertical lapse rate of temperature. Our results indicate that on decadal time scales, changes in ISMR are mediated by changes in CWV and temperature lapse rates. This is consistent with previous studies that showed the impact of column-mean temperature on the rainfall-CWV relationship. Findings are significant for climate model projections of ISMR, as correct estimation of lapse rate changes in models is necessary to predict ISMR changes accurately.

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]

The tropopause is described as the distinction between the troposphere and the stratosphere, and the tropopause height (TPH) is an indicator of climate change. GNSS Radio Occultation (RO) can monitor the atmosphere globally under all weather conditions with a high vertical resolution. In this study, four different techniques for identifying the TPH were investigated. The lapse rate tropopause (LRT) and cold point tropopause (CPT) methods are the traditional methods for determining the TPH based on temperature profiles according to the World Meteorological Organization (WMO) definition. Two advanced methods based on the covariance transform (CT) method are used to estimate the TPH from the refractivity (TPHN) and the TPH from the bending angle (TPHα). Data from the Sentinel-6 satellite were used to evaluate the different algorithms for the identification of the TPH. The analysis shows that the CPT height is greater than the LRT height and that the CPT is only valid in tropical regions. The CPT height, TPHN, and TPHα were compared with the LRT height. In general, the TPHα had the largest value, followed by the TPHN, and the LRT had the lowest value of TPH at and near the equator. In the equatorial region, the maximum TPH results from the TPHα (approximately 17.5 km), and at the poles, the minimum TPH results from the LRT (approximately 9 km). The results were also compared with the European Center for Medium-Range Weather Forecasts (ECMWF), and there was a strong correlation of 0.999 between the different approaches for identifying the TPH from RO and the ECMWF model. The identification of the TPH is critical for the transfer of mass, water, and trace gases between the troposphere and stratosphere.

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]

The refraction and extinction in the Earth's atmosphere depend on the atmosphere's structure, so it was natural to try to infer that structure from optical observations. Efforts to extract structure fromobserved refractions led to proof that this is possible only below the astronomical horizon. Direct studies of the real atmosphere show complicated, variable structure. The complex history of relations between structure and refraction is outlined by citing some important works. [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]

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.

The latitudinal position of the southeast of the Iberian Peninsula generates high average temperatures (between 25 and 30oC) during the summer season, although its complex orography and the important lapse rate (LR) give rise to important differences in minimum temperatures, influenced by intense processes of nocturnal temperature inversion (ITN) in inland valleys. The increase of new meteorological stations and the rise in the density of observation networks, together with the implementation of its own network in cold air pools (CAP) in the provinces of Murcia, Albacete or Jaén, leads to a greater understanding of the distribution of surface temperatures. The main objective of the research is the generation of cartography (thermographs) that reflects the distribution of night temperatures in the southeast of the Iberian Peninsula, carried out through the treatment of satellite products (VIIRS LST Band I5 of the SUOMI-NPP satellite). Together with the use of satellite images (thermography), it is possible to analyze in detail the thermal contrasts that exist during the hottest days of summer through 147 weather stations and recording thermometers (July 15 to August 15) of the year 2017 to 2020. Verification of the thermographs used (Band I5 product) shows a notable statistical adjustment (R2 of 0.87), with a Bias and RMSE of -0.2 and 1.83oC respectively. The cartography is carried out for the differences in the provinces that make up the SE of the Iberian Peninsula (Alicante, Albacete, Murcia, Jaén, Granada and Almería), with daily thermal differences in minimum temperatures of up to 23.4oC between inland valleys and coastal areas. [ABSTRACT FROM AUTHOR]

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]

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 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.

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]

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.

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]

Mountains of the arid Great Basin region of Nevada are home to critical water resources and numerous species of plants and animals. Understanding the nature of climatic variability in these environments, especially in the face of unfolding climate change, is a challenge for resource planning and adaptation. Here, we utilize an Embedded Sensor Network (ESN) to investigate landscape-scale temperature variability in Great Basin National Park (GBNP). The ESN was installed in 2006 and has been maintained during uninterrupted annual student research training expeditions. The ESN is comprised of 29 Lascar sensors that record hourly near-surface air temperature and relative humidity at locations spanning 2000 m and multiple ecoregions within the park. From a maximum elevation near 4000 m a.s.l. atop Wheeler Peak, the sensor locations are distributed: (1) along a multi-mountain ridgeline to the valley floor, located ∼2000 m lower; (2) along two streams in adjoining eastern-draining watersheds; and (3) within multiple ecological zones including sub-alpine forests, alpine lakes, sagebrush meadows, and a rock glacier. After quality checking all available hourly observations, we analyze a 12-year distributed temperature record for GBNP and report on key patterns of variability. From 2006 to 2018, there were significantly increasing trends in daily maximum, minimum and mean temperatures for all elevations. The average daily minimum temperature increased by 2.1°C. The trend in daily maximum temperatures above 3500 m was significantly greater than the increasing trends at lower elevations, suggesting that daytime forcings may be driving enhanced warming at GBNP’s highest elevations. These results indicate that existing weather stations, such as the Wheeler Peak SNOTEL site, alone cannot account for small-scale variability found in GBNP. This study offers an alternative, low-cost methodology for sustaining long-term, distributed observations of conditions in heterogeneous mountainous environments at finer spatial resolutions. In arid mountainous regions with vulnerable water resources and fragile ecosystems, it is imperative to maintain and extend existing networks and observations as climate change continues to alter conditions.

Delineating the boundary between troposphere and stratosphere in a chemistry transport model requires a state variable for each air mass that maps out the ever shifting, overlapping three-dimensional (3-D) boundary at each time step. Using an artificial tracer, e90, with surface sources and 90 day decay time, the model e90 tropopause matches the 1-D temperature lapse rate definition of the tropopause as well as the seasonal variation of ozone at this boundary. This approach works from equator to pole, over all seasons, unlike methods based on potential vorticity or ozone. By focusing on the time scales that separate stratosphere from troposphere, we examine the cause of ozone seasonality at the midlatitude tropopause, the oldest air in the troposphere (winter descent in the subtropics), and a north-south bias in the age of air of the lowermost stratosphere as evaluated using a northern tracer. The tracer e90 is invaluable in 3-D modeling, readily separating stratosphere from troposphere and a giving quantitative measure of the effective distance from the tropopause.

Abstract 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.

The near-surface lapse rate reflects the atmospheric stability above the surface. Lapse rates calculated from land surface temperature (γ Ts) and near-surface air temperature (γ Ta) have been widely used. However, γ Ts and γ Ta have different sensitivity to local surface energy balance and large-scale energy transport and therefore they may have diverse spatial and temporal variability, which has not been clearly illustrated in existing studies. In this study, we calculated and compared γ Ta and γ Ts at ~ 2200 stations over China from 1961 to 2014. This study finds that γ Ta and γ Ts have a similar multiyear national average (0.53 °C/100 m) and seasonal cycle. Nevertheless, γ Ts shows steeper multiyear average than γ Ta at high latitudes, and γ Ts in summer is steeper than γ Ta , especially in Northwest China. The North China shows the shallowest γ Ta and γ Ts , then inhibiting the vertical diffusion of air pollutants and further reducing the lapse rates due to accumulation of pollutants. Moreover, the long-term trend signs for γ Ta and γ Ts are opposite in northern China. However, the trends in γ Ta and γ Ts are both negative in Southwest China and positive in Southeast China. Surface incident solar radiation, surface downward longwave radiation and precipitant frequency jointly can account for 80% and 75% of the long-term trends in γ Ta and γ Ts in China, respectively, which provides an explanation of trends of γ Ta and γ Ts from perspective of surface energy balance. [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]