Lapse Rate Research Articles

Assessing Radiative Feedbacks and Their Contribution to the Arctic Amplification Measured by Various Metrics

AbstractArctic amplification (AA), characterized by a more rapid surface air temperature (SAT) warming in the Arctic than the global average, is a major feature of global climate warming. Various metrics have been used to quantify AA based on SAT anomalies, trends, or variability, and they can yield quite different conclusions regarding the magnitude and temporal patterns of AA. This study examines and compares various AA metrics for their temporal consistency in the region north of 70°N from the early twentieth to the early 21st century using observational data and reanalysis products. We also quantify contributions of different radiative feedback mechanisms to AA based on short‐term climate variability in reanalysis and model data using the Kernel‐Gregory approach. Albedo and lapse rate feedbacks are positive and comparable, with albedo feedback being the leading contributor for all AA metrics. The net cloud feedback, which has large uncertainties, depends strongly on the data sets and AA metrics used. By quantifying the influence of internal variability on AA and related feedbacks based on global climate model ensemble simulations, we find that water vapor and cloud feedbacks are most heavily affected by internal variability.

A consistent treatment of mixed‐phase saturation for atmospheric thermodynamics

AbstractIce processes are integral to the formation and maintenance of high‐level clouds and can contribute substantially to the buoyancy of deep moist convection. These processes are represented in modern microphysics parameterisations but are commonly ignored in theoretical treatments of moist thermodynamics. While many thermodynamic variables can be defined uniquely for saturation with respect to liquid water and ice, it is desirable to account for the presence of mixed‐phase condensate, which is common in real clouds. Here, a simple yet novel representation of mixed‐phase saturation is introduced. Using this approach, analytical equations are derived for a variety of thermodynamic variables under mixed‐phase conditions. These include mixed‐phase versions of the saturation vapour pressure, saturated lapse rates, isobaric wet‐bulb temperature, and equivalent potential temperature. A new formulation of the ice–liquid water potential temperature is also presented, together with a method for calculating the mixed‐phase pseudo wet‐bulb temperature and wet‐bulb potential temperature. In addition, two new thermodynamic quantities are introduced: the isobaric saturation‐point temperature, a mixed‐phase analogue of the dew‐point and frost‐point temperatures, and the lifting saturation level, a mixed‐phase analogue of the lifting condensation and lifting deposition levels. Differences between each mixed‐phase variable and its liquid‐ and ice‐only counterparts are quantified across a wide range of atmospheric conditions. A Python library for evaluating these variables, developed during the course of this work, is also briefly introduced.

Improving Interpolating Accuracy of Weighted Mean Temperature by Using a Novel Lapse Rate Model in Compact VMF1 Product

AbstractIn GNSS (Global Navigation Satellite Systems) meteorology, the accuracy of precipitable water vapor (PWV) retrieved from the tropospheric delay of GNSS signals is affected by the conversion factor. Compact VMF1 product (known as GGOS Atmosphere data) provides high‐accuracy global grid‐wise weighted mean temperature (Tm) values, which can be utilized to calculate the conversion factor. However, the Tm provided in the compact VMF1 data are solely ground surface values. To enhance the performance of compact VMF1 product, a new Tm lapse rate model for each grid point was developed for the purpose of reducing its surface Tm to the elevation of the GNSS site. Then the reduced Tm values over the neighboring grid points together with horizontal interpolation were used to obtain the interpolated Tm for the GNSS station. The sample data for the development of the new model were the Tm profiles obtained from ERA5 monthly averaged data spanning 2009–2018. To assess the model's performance, global radiosonde data at 504 radiosonde stations spanning 2019–2021 were employed. Results demonstrated that implementing the Tm lapse rate model significantly enhanced the accuracy of interpolating Tm values for GNSS stations with substantial height disparities from adjacent grid points, thereby improving PWV conversion accuracy. This indicates that employing the new Tm lapse rate model to adjust surface Tm data in the compact VMF1 product holds promise for enhancing its utility in GNSS meteorology.

Variations of the zero degrees isotherm and environmental lapse rate recorded with ADSB and Mode S EHS messages

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

A Statistical Study of Polar Mesospheric Cloud Fronts in the Northern Hemisphere

AbstractComplex spatial structures in polar mesospheric cloud (PMC) images provide visual clues to the dynamics that occur in the summer mesosphere. In this study, we document one such structure, a PMC front, by analyzing PMC images in the northern hemisphere from the Cloud Imaging and Particle Size (CIPS) instrument onboard the aeronomy of ice in the mesosphere (AIM) satellite. A PMC front is defined as a sharp boundary that separates cloudy and mostly clear regions, and where the clouds at the front boundary are brighter than the clouds in the cloudy region. We explore the environment that supports the formation of PMC fronts using near‐coincident temperature and water vapor observations from the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) satellite instrument. A comparison of PMC front locations to near‐coincident temperature profiles reveals the presence of inversion layers at PMC altitudes. The adiabatic and superadiabatic topside lapse rates of these temperature inversions indicate that some of the identified inversion layers may have been formed by gravity wave (GW) dissipation. The structure of the squared buoyancy frequency profiles indicates a stable layer or thermal duct that can be associated with large‐amplitude mesospheric inversion layers (MILs) that extend large distances. These inversion layers may be conducive to horizontal wave propagation. We hypothesize that ducted GWs may be a formation mechanism of PMC fronts.

Sea ice feedbacks cause more greenhouse cooling than greenhouse warming at high northern latitudes on multi-century timescales

Abstract In contrast to surface greenhouse warming, surface greenhouse cooling has been less explored, especially on multi-century timescales. Here, we assess the processes controlling the pacing and magnitude of the multi-century surface temperature response to instantaneously doubling and halving atmospheric carbon dioxide concentrations in a modern global coupled climate model. Over the first decades, surface greenhouse warming is larger and faster than surface greenhouse cooling both globally and at high northern latitudes (45–90° N). Yet, this initial multi-decadal response difference does not persist. After year 150, additional surface warming is negligible, but surface cooling and sea ice expansion continues. Notably, the equilibration timescale for high northern latitude surface cooling (∼437 years) is more than double the equivalent timescale for warming. The high northern latitude responses differ most at the sea ice edge. Under greenhouse cooling, the sea ice edge slowly creeps southward into the mid-latitude oceans amplified by positive lapse rate and surface albedo feedbacks. While greenhouse warming and sea ice loss at high northern latitudes occurs on multi-decadal timescales, greenhouse cooling and sea ice expansion occurs on multi-century timescales. Overall, this work shows the importance of multi-century timescales and sea ice processes for understanding high northern latitude climate responses.

A novel method for detecting tropopause structures based on the bi-Gaussian function

Abstract. The tropopause is an important transition layer and can be a diagnostic of upper-tropospheric and lower-stratospheric structures, exhibiting unique atmospheric thermal and dynamic characteristics. A comprehensive understanding of the evolution of fine tropopause structures is necessary and primary for the further study of complex multi-scale atmospheric physical–chemical coupling processes in the upper troposphere and lower stratosphere. A novel method utilizing the bi-Gaussian function is capable of identifying the characteristic parameters of vertical tropopause structures and providing information on double-tropopause (DT) structures. The new method improves the definition of the cold-point tropopause and detects one (or two) of the most significant local cold points by fitting the temperature profiles to the bi-Gaussian function, which defines the point(s) as the tropopause height(s). The bi-Gaussian function exhibits excellent potential for explicating the variation trends of temperature profiles. The results of the bi-Gaussian method and lapse rate tropopause, as defined by the World Meteorological Organization, are compared in detail for different cases. Results indicate that the bi-Gaussian method is able to more stably and obviously identify the spatial and temporal distribution characteristics of the thermal tropopauses, even in the presence of multiple temperature inversion layers at higher elevations. Moreover, 5 years of historical radiosonde data from China (from 2012 to 2016) revealed that the occurrence frequency and thickness of the DT, as well as the single-tropopause height and the first and second DT heights, displayed significant meridional monotonic variations. The occurrence frequency (thickness) of the DT increased from 1.07 % (1.96 km) to 47.19 % (5.42 km) in the latitude range of 16–50° N. The meridional gradients of tropopause height were relatively large in the latitude range of 30–40° N, essentially corresponding to the climatological locations of the subtropical jet and the Tibetan Plateau.

PLR and take-off speed implications on noise contours and local gaseous emissions for SST business jet concept aircraft

DLR has established a simulation process to evaluate the landing and take-off of novel civil supersonic transport (SST) aircraft. This process was applied to two business jet concept aircraft in order to assess the certification noise levels according to novel regulations under discussion at ICAO CAEP. It has to be noted, that neither of the concept aircraft is optimized for low noise nor for low boom. At DLR, modifications to departure flight procedures are under investigation, i.e., an automated thrust reduction referred to as Programmed Lapse Rate and an increased take-off speed. These measures as proposed by NASA promise to reduce the certification noise levels for the two departure locations. For the two considered concept aircraft, promising procedures with reduced noise at the certification locations have been identified previously. This paper now addresses the impact of these promising departure procedures on noise contour areas and local gaseous emissions. Impact on EPNL and SEL contours and differences in CO2 and NOX are assessed along the simulated flights. It is demonstrated, that the proposed procedures do not only reduce noise at the certification locations but also noise contour areas and gaseous pollutants.

Impacts of Sampling and Storm-Motion Estimates on RUC/RAP-Based Discriminations of Nontornadic and Tornadic Supercell Environments

Abstract This study explores reasons for differences in discriminations of nontornadic and tornadic supercell environments between a recent study of field project (FP) radiosonde observations and RUC/RAP-based studies. Two differences are explored: 1) differences in the relative skill between near-ground and deeper-layer storm-relative helicity (SRH) and 2) differences in skill for storm-relative winds (SRWs) seen in observed soundings that are not seen in RUC/RAP-based analyses. Results show that RUC/RAP-derived near-ground SRH continues to show larger skill than deeper-layer SRH for springtime, afternoon/evening cases over the plains (the “FP” domain), although 0–1-km SRH becomes more skillful than 0–500-m SRH. The skill of kinematic variables decreases over the FP domain, as the skill of mixed-layer convective available potential energy (MLCAPE) and the percent of the low-level horizontal vorticity that is streamwise increase for significant tornadoes. Large skill is found for mean ground-relative winds (GRWs) over all layers tested, but the skill of SRWs using Bunkers motion is relatively small. The field project dataset is shown to be biased toward particularly high-end nontornadic supercells, with more tornado-favorable mixed-layer lifted condensation levels (MLLCLs), lapse rates, and low-level shear/SRH compared to the nontornadic cases in the RUC/RAP dataset over the FP domain. The skill of deeper-layer SRH, GRWs, SRWs, and MLCAPE is unusually large in the field project sample, which highlights variables that may increase the likelihood of tornadoes when other variables that relate to the supercell tornado production (low-level shear/SRH and MLLCLs) are already in a tornado-favorable range. The skill of deeper-layer kinematic variables is particularly evident when observed storm motions are used instead of Bunkers motion. Significance Statement Researchers continue to explore reasons why some storms produce tornadoes and others do not. Some recent studies show conflicting results, including the best layers to analyze the environmental wind shear and the ability of wind speeds relative to the storm to distinguish nontornadic from tornadic storms. One study is rooted in observations taken for field projects that targeted tornadic storms and the others in model analyses. Restricting model analyses to a spatiotemporal region that emulates field projects does not explain the disagreement but reveals how the skill of some other variables changes. The tendency for field projects to target particularly strong storms and the use of actual storm motions versus estimates of storm motions both contribute to the conflicting results.

Convection-permitting climate model representation of severe convective wind gusts and future changes in southeastern Australia

Abstract. Previous research has suggested that the frequency and intensity of surface hazards associated with thunderstorms and convection, such as severe convective winds (SCWs), could potentially change in a future climate due to global warming. However, because of the small spatial scales associated with SCWs, they are unresolved in global climate models, and future climate projections are uncertain. Here, we evaluate the representation of SCW events in a convection-permitting climate model (Bureau of Meteorology Atmospheric Regional Projections for Australia, BARPAC-M) run over southeastern Australia for the months of December–February. We also assess changes in SCW event frequency in a projected future climate for the year 2050 and compare this with an approach based on identifying large-scale environments favourable for SCWs from a regional parent model (BARPA-R). This is done for three different types of SCW events that have been identified in this region, based on clustering of the large-scale environment. Results show that BARPAC-M representation of the extreme daily maximum wind gust distribution is improved relative to the gust distribution simulated by the regional parent model. This is due to the high spatial resolution of BARPAC-M output, as well as partly resolving strong and short-lived gusts associated with convection. However, BARPAC-M significantly overestimates the frequency of simulated SCW events, particularly in environments having steep low-level temperature lapse rates. A future decrease in SCW frequency under conditions with steep lapse rates is projected by BARPAC-M, along with less frequent favourable large-scale environments. In contrast, an increase in SCW frequency is projected under conditions of high surface moisture, with more frequent favourable large-scale environments. Therefore, overall changes in SCWs for this region remain uncertain, due to different responses between event types, combined with historical model biases.

Retrieval of boundary layer precipitable water from GOES ABI using machine learning techniques

Abstract Low-level moisture is an important ingredient for forecasting severe storms, especially over the Great Plains where severe storms often develop along the dryline. Although ground-based observation systems such as lidar or radiosondes on weather balloons provide accurate information on low-level moisture, data are provided at limited locations, and the low temporal resolution of radiosondes makes it difficult to track a rapidly developing dryline. Geostationary satellites provide high spatial and temporal observation, but the channels of current geostationary satellites are mostly sensitive to water vapor at mid to upper levels. However, the split window difference (SWD) between the “clean” window channel (10.3 μm) and the “dirty” window channel (12.3 μm) is commonly used for estimating low-level water vapor. However, this estimation is complicated by surface temperature contributions, dependence on the lapse rate, and nonlinear relationships between SWD and moisture. This study applies machine learning techniques to infer boundary layer precipitable water (BLPW) from Geostationary Operational Environmental Satellite (GOES) Advanced Baseline Imager (ABI) data. Since there are few observations that cover wide regions for training convolutional neural networks, especially for the atmosphere above the surface, High-Resolution Rapid Refresh (HRRR) model outputs are used as the truth for training. Statistical results show that using ABI channel 13, 14, and 15 brightness temperatures, skin temperature, solar zenith angle, and GOES total precipitable water product as additional inputs show the lowest MSE. Case study results show that the model developed in this study is good at capturing strong moisture gradients and depicting a dryline.

Impacts of Atlantic meridional overturning circulation weakening on Arctic amplification

Enhanced warming of the Arctic region relative to the rest of the globe, known as Arctic amplification, is caused by a variety of diverse factors, many of which are influenced by the Atlantic meridional overturning circulation (AMOC). Here, we quantify the role of AMOC changes in Arctic amplification throughout the twenty-first century by comparing two suites of climate model simulations under the same climate change scenario but with two different AMOC states: one with a weakened AMOC and another with a steady AMOC. We find that a weakened AMOC can reduce annual mean Arctic warming by 2 °C by the end of the century. A primary contributor to this reduction in warming is surface albedo feedback, related to a smaller sea ice loss due to AMOC slowdown. Another major contributor is the changes in ocean heat uptake. The weakened AMOC and its associated anomalous ocean heat transport divergence lead to increased ocean heat uptake and surface cooling. These two factors are inextricably linked on seasonal timescales, and their relative importance for Arctic amplification can vary by season. The weakened AMOC can also abate Arctic warming via lapse rate feedback, creating marked cooling from the surface to lower-to-mid troposphere while resulting in relatively weaker cooling in the upper troposphere. Additionally, the weakened AMOC increases the low-level cloud fraction over the North Atlantic warming hole, causing significant cooling there via shortwave (sw) cloud feedback despite the overall effect of sw cloud feedback being a slight warming of the average temperature over the Arctic.

Two Perspectives on Amplified Warming over Tropical Land Examined in CMIP6 Models

Abstract Climate change projections show amplified warming associated with dry conditions over tropical land. We compare two perspectives explaining this amplified warming: one based on tropical atmospheric dynamics and the other focusing on soil moisture and surface fluxes. We first compare the full spatiotemporal distribution of changes in key variables in the two perspectives under a quadrupling of CO2 using daily output from the CMIP6 simulations. Both perspectives center around the partitioning of the total energy/energy flux into the temperature and humidity components. We examine the contribution of this temperature/humidity partitioning in the base climate and its change under warming to rising temperatures by deriving a diagnostic linearized perturbation model that relates the magnitude of warming to 1) changes in the total energy/energy flux, 2) the base-climate temperature/humidity partitioning, and 3) changes in the partitioning under warming. We show that the spatiotemporal structure of warming in CMIP6 models is well predicted by the inverse of the base-climate partition factor, which we term the base-climate sensitivity: conditions that are drier in the base climate have a higher base-climate sensitivity and experience more warming. On top of this relationship, changes in the partition factor under intermediate (between wet and dry) surface conditions further enhance or dampen the warming. We discuss the mechanistic link between the two perspectives by illustrating the strong relationships between lower-tropospheric temperature lapse rates, a key variable for the atmospheric perspective, and surface fluxes, a key component of the land surface perspective. Significance Statement Understanding what conditions give rise to the largest magnitude of warming in response to rising CO2 concentrations is not only scientifically important but also critical from a climate impact standpoint. Two main perspectives, one focusing on atmospheric dynamics and the other focusing on land surface processes, have been proposed to explain the stronger warming associated with drier conditions in the tropics. Here, we compare and contrast these two perspectives. We demonstrate that amplified warming in CMIP6 models can largely be predicted from base-climate dryness alone in both perspectives but is further modified based on changes in the partitioning of energy between temperature and moisture. We highlight the spatiotemporal conditions where assumptions in the two perspectives hold and where deviations occur within CMIP6 climate models.

Prospecting for Air Pollution Hot Spots in the Atmosphere using Lapse Rate

Prospecting for Air Pollution Hot Spots in the Atmosphere using Lapse Rate

Effects of Turbulence on Upper-Tropospheric Ice Supersaturation

Abstract Effects of turbulence on ice supersaturation at cirrus heights (>8 km) remain unexplored. Small-scale mixing processes become important for high Reynolds number flows, which may develop below the buoyancy length scale (10–100 m). The current study couples a stochastic turbulent mixing model with reduced dimensionality to an entraining parcel model to investigate, in large-ensemble simulations, how supersaturation evolves due to homogeneous turbulence in the stably stratified, cloud-free upper troposphere. The rising parcel is forced by a mesoscale updraft. The perturbation of an initially homogeneous vertical distribution of supersaturation is studied after a 36-m ascent in a baseline case and several sensitivity scenarios. Turbulent mixing and associated temperature fluctuations alone lead to changes in ensemble-mean distributions with standard deviations in the range 0.001–0.006, while mean values are hardly affected. Large case-to-case variability in the supersaturation field is predicted with fluctuation amplitudes of up to 0.03, although such large values are rare. A vertical gradient of supersaturation (≈10−3 m−1) is generated for high turbulence intensities due to the development of a dry-adiabatic lapse rate. Entrainment of slightly warmer (less than 0.1 K) environmental air into the parcel decreases the mean supersaturation by less than 0.01. Supersaturation fluctuations are substantially larger after entrainment events with an additional small offset in absolute humidity (by ±3.5%) between the parcel and environmental air. The predicted perturbations of ice supersaturation are significant enough to motivate studies of turbulence–ice nucleation interactions during cirrus formation that abandon the assumption of instantaneous mixing inherent to traditional parcel models. Significance Statement The purpose of our study is to investigate the effects of microscale turbulence on ice supersaturation in the upper troposphere. The associated variability in temperature and moisture fields is not resolved in cloud models and cannot easily be represented in terms of large-scale flow variables. We specify the conditions in which turbulent mixing and entrainment cause substantial variations in distributions of supersaturation. These include high turbulence intensity, strong atmospheric stability, and large moisture gradients. Our results suggest that turbulence may affect the strongly supersaturation-dependent ice formation processes in high-altitude clouds, pointing to the need to investigate cirrus formation in the presence of turbulence.

Role of Behavioral Economics in Predicting Life Insurance Policy Lapses in United Kingdom

Purpose: The aim of the study was to analyze the role of behavioral economics in predicting life insurance policy lapses in United Kingdom. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: In the United Kingdom, behavioral economics plays a significant role in predicting life insurance policy lapses. Key factors such as loss aversion, present bias, and financial literacy heavily influence policyholder decisions. Policyholders who are more loss-averse tend to maintain their policies, while those with a strong present bias are more likely to lapse, especially during financial stress. Additionally, lower financial literacy contributes to higher lapse rates, as individuals struggle to understand the long-term benefits of life insurance. Unique Contribution to Theory, Practice and Policy: Prospect theory, hyperbolic discounting & theory of planned behavior (TPB) may be used to anchor future studies on the role of behavioral economics in predicting life insurance policy lapses in United Kingdom. Insurers should tailor their communication strategies to address specific behavioral biases identified in policyholders. Policymakers should mandate the inclusion of financial literacy programs within the life insurance industry to educate consumers about the importance of maintaining their policies.

Temporal and spatial changes in the environmental lapse rate distribution over the Arctic

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

Paleoaltimetry and paleotectonic reconstruction using triple oxygen and hydrogen isotopes: Depleted δ18O and δD values in ignimbrites of Verkhneavachinskaya caldera record collisional uplift during Miocene arc accretion to Kamchatka

Here we use δ18O and δD values as tools to investigate the paleo-altitude and the origin of large-volume (120 km3, 10 × 12 km) ignimbrites of Verkhneavachinskaya caldera (cf. Verkhne-Avachinskaya) (VC) in eastern Kamchatka, formed the in Late Miocene 5.8 Ma. The basaltic-andesitic intracaldera ignimbrite deposit exhibits low δ18O values, reaching −5.03‰, and δD values of −182‰ across a 1.2 km depth range in several sampled sections. The results support a massive meteoric-hydrothermal system throughout the cooling history of the thick intracaldera ignimbrite deposit. Using triple oxygen isotope data we estimate that the δ18O values of altering meteoric water are as low as −19‰ to −23‰, much lower than modern precipitation of −14‰, or − 16‰ estimated for the 2–3 °C warmer Kamchatka climate of the late Miocene. Therefore, the VC meteoric-hydrothermal system depended on high-altitude precipitation and glaciers, and altitudinal isotopic lapse rates suggest a paleo-altitude of 3.5 km at 5.8 Ma during caldera formation. These elevations exceed the modern by 1.5 km and provide a unique snapshot into an evolving landscape and paleo-environment of eastern Kamchatka at 6–5 Ma as the dynamic outcomes of accretion of the Kronotski arc. In particular, the existence of such a high plateau is in line with contemporary accretionary tectonics: accretion of the Shipunsky peninsula of this accreting arc terrain, ∼120 km to the east of the VC, and evidence of contemporaneous exhumation of high-grade Ganal amphibolites to the west. We conclude that the eruption of large-volume mafic ignimbrites that formed VC caldera was syncollisional or intracollisional in nature, likely requiring delamination of thickened crust to account for both uplift and magmatism.

Evaluation and refinement of ERA5-land 2 m atmospheric temperature in GNSS precipitable water vapor

Since the land 2 m air temperature from European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5-land) is vulnerable to interference by regional climate characteristics and local topography, how to objectively and accurately evaluate its accuracy and then establish a refined model is the key to improve its multifaceted and deep-level applications. This study proposed a systematic and complete refinement method for ERA5-land air temperature based on the densely distributed weather stations in Shandong Province in eastern China, including the compensation of elevation matching bias (EMB) caused by ASTER GDEM and the overall refinement using the remove-and-restore model (RRM). Taking the hottest July and the coldest December in this area, surface air temperature, atmospheric weighted mean temperature (Tm) and GNSS precipitable water vapor (PWV) were used to verify the feasibility of this method. The results show that: (1) Whether the temperature is high in July or low in December, the accuracy can be improved by about 42.2 % when the refinement method was used. Especially for high altitude areas (the summit of Mount Tai, 1553 m above sea level), the accuracy can be improved by 74.5 %. (2) In contrast to only the temperature lapse rate (TLR) correction, the average root mean square (RMS) can be reduced by 7.4 % and 60.4 % in July and 7.0 % and 59.5 % in December for the met stations less than 150 m in altitude and the peak station of Mount Tai, respectively, when the new method was used. (3) Compared with the Tm and PWV computed by interpolated temperature without any correction, the accuracy of Tm can be improved by 31.2 % and 34.1 % in July and December for weather station in the altitude of 100 ∼ 350 m after the TLR correction, EMB compensation and RRM refinement successively. Meanwhile, the accuracy of PWV can be improved by 47.8 % in July and 42.0 % in December, respectively, and especially for PWV high-value moments. Besides, after correction by RRM, both Tm and PWV in low altitude areas achieve at least 20 % accuracy improvement. In summary, the systematic and complete refinement method proposed in this study are important to significantly improve the ERA5-land 2 m temperature availability and GNSS PWV accuracy, and the compensation of EMB is mainly effective for high altitude areas.

An Improved Remote Sensing Retrieval Method for Elevated Duct in the South China Sea

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