Lapse Rate Research Articles

Target an arbitrary probability of response using weighted staircase procedures.

Threshold estimation procedures are widely used to measure the stimulus level corresponding to a specified probability of response. The weighted up-and-down procedure, familiar to many due to its use in standard pure-tone audiometry, allows the experimenter to target any probability of response by using different ascending and descending step sizes. Unfortunately, thresholds have a signed mean error that made using weighted staircases inadvisable. The current study evaluated a correction to eliminate the error. Monte Carlo simulations of weighted staircases were used to test the effectiveness of the proposed correction for yes-no and forced-choice tasks with Gaussian and log-Weibull psychometric functions. Results showed that the proposed correction was effective over a wide range of step size magnitudes and ratios with a symmetric psychometric function and less effective when there was asymmetry due to the shape of the function or a high guess or lapse rate. The proposed correction facilitates the use of weighted staircases to target an arbitrary probability of response.

Spatial and Temporal Variations of Surface Air Temperature (1962–2022) across Physiographic Regions in the Koshi Basin, Nepal

This study analyzed the seasonal and annual trends of maximum temperature (Tmax), minimum temperature (Tmin), and mean temperature (Tavg) across the physiographic regions (Himalaya, High Mountain, Middle Mountain, Siwalik, and Terai) of the Koshi Basin using observed data from 23 stations between 1962 and 2022. Missing temperature data were filled using the lapse rate method. The Mann-Kendall (M-K) test was employed to assess the consistency of the temperature dataset. The analysis revealed distinct regional and seasonal temperature trends in the Koshi Basin. The seasonal variations were prominent, especially in the Monsoon and Winter. In the Middle Mountain and High Mountain regions, Tmax increased significantly during the Pre-monsoon season, while Tmin decreased substantially during the Monsoon and Winter. Conversely, the Siwalik and Terai regions experienced more pronounced cooling Himalayas, especially during the Monsoon and Winter. Overall, the Himalayas and High Mountain regions exhibited a cooling trend until the late 1990s, followed by a warming trend. The Middle Mountain region demonstrated similar patterns, with a significant temperature increase after the 1990s. The Siwalik and Terai regions experienced a general cooling trend, although the Siwalik region exhibited some fluctuations. The significant regional variations in temperature trends were the key findings. Similarly, the Himalayas and High Mountains showed a cooling-warming shift in the late 1990s. All these findings underscore the importance of considering both regional and seasonal factors when studying temperature trends in the Koshi Basin.

Greenland Ice Cores Reveal a South‐To‐North Difference in Holocene Thermal Maximum Timings

AbstractHolocene temperature evolution remains poorly understood. Proxies in the early and mid‐Holocene suggest a Holocene Thermal Maximum (HTM) where temperatures exceed the pre‐industrial, whereas climate models generally simulate monotonic warming. This discrepancy may reflect proxy seasonality biases or errors in climate model internal feedbacks or dynamics. Using seasonally unbiased ice core reconstructions at NEEM, NGRIP, and Greenland Ice Sheet Project 2, we identify a Greenland HTM of ∼2°C above pre‐industrial, in agreement with other Northern Hemisphere proxy reconstructions. The firn‐based reconstructions are verified through borehole thermometry, producing a multi‐core, multi‐proxy reconstruction of Greenland climate from the last glacial to pre‐industrial. HTM timing across Greenland is heterogenous, occurring earlier at high elevations. Total air content measurements suggest a temperature contribution from elevation changes; regional oceanographic conditions, a weakened polar lapse rate, or variable near‐surface inversions may also be important sensitivities. Our reconstructions support climate simulations with dynamic Holocene vegetation, highlighting the importance of vegetation feedbacks.

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 shows the lowest mean-square error (MSE). Case study results show that the model developed in this study is good at capturing strong moisture gradients and depicting a dryline. Significance Statement Knowledge of low-level moisture is important for monitoring drylines and moisture convergence preceding convective initiation. Traditional observations lack the spatial resolution, spatial coverage, or temporal update frequency needed by forecasters. The latest generation of Geostationary Operational Environmental Satellite (GOES), the GOES-R series, provides high spatial resolution and rapid temporal updates. However, the estimation of low-level moisture using the Advanced Baseline Imager (ABI) is complicated by dependencies on other variables and nonlinear relationships. Machine learning is shown to provide good estimates of low-level moisture from ABI observations. This application of machine learning to GOES-R can improve the ability to monitor low-level moisture and forecast convective initiation.

The presence of clouds lowers climate sensitivity in the MPI-ESM1.2 climate model

Abstract. Clouds affect the sensitivity of the climate system by changing their distribution, height, and optical properties under climate change. Although the precise magnitude remains uncertain, the direct cloud response to an external forcing is known to be destabilising. Additionally, clouds have a masking effect on CO2 forcing and can influence other feedback mechanisms such as the surface albedo feedback. To understand the overall impact of clouds, we compute how much the equilibrium climate sensitivity (ECS) to a doubling of CO2 changes when clouds are made transparent to radiation in an Earth system model (MPI-ESM1.2, the Max Planck Institute for Meteorology Earth System Model version 1.2). In practice, to stabilise the model climate at near-preindustrial temperatures, the solar constant was reduced by 8.8 %. Our experiments reveal that clouds exert a stabilising influence on the model, with a clear-sky ECS of 4.29 K, which is higher than the corresponding full-sky ECS of 2.84 K, contrasting with their direct destabilising effect. Detailed partial radiative perturbation diagnostics show that beyond directly amplifying warming by themselves, clouds also strengthen the negative lapse rate and positive water vapour feedbacks, while strongly damping the positive albedo feedback. These findings highlight the complex role of clouds in modulating climate sensitivity.

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.

Geostatistical Interpolation Approach for Improving Flood Simulation Within a Data‐Scarce Region in the Tibetan Plateau

ABSTRACTThe complex orography of the Tibetan plateau (TP) and the scarcity and uneven spatial distribution of meteorological stations present significant challenges in accurately estimating meteorological variables for hydrological simulations. This study aims to enhance the accuracy of daily precipitation and temperature interpolation for hydrological simulations in the Lhasa River Basin (LRB), particularly during flood events. We evaluate and compare the performance of deterministic Inverse Distance Weighting—IDW and geostatistical (Ordinary Kriging—OK and Kriging with External Drift—KED) interpolation methods for estimating precipitation and temperature patterns. Subsequently, we investigate the influence of different interpolation methods on hydrological simulations by using the interpolated meteorological data as input for the Water Balance Simulation Model (WaSiM) to simulate daily discharge in the LRB. Our results revealed that geostatistical methods, specifically OK and KED, are more effective in capturing the spatial variability and anisotropy inherent in precipitation patterns influenced by the Indian summer monsoons. In addition, the KED method effectively captured the daily variation of the temperature lapse rate, indicating the inadequacy of using a constant lapse rate for hydrological modelling in high‐elevation regions like the TP. The geostatistical technique outperformed the Deterministic method, with KED realising the best temperature and precipitation interpolation performance based on cross‐validation results. However, although KED provides superior results based on cross‐validation performance, applying its precipitation interpolation as input into WaSiM led to the poorest discharge simulation. The combination of OK for precipitation and KED for temperature produced the most accurate discharge simulations in the LRB, highlighting the importance of not solely relying on cross‐validation results but also considering the practical implications of interpolation methods on hydrological model outputs. Our study offers a robust framework for improving flood simulations and water resource management in a data‐scarce, high‐elevation region like the TP.

Enhancing GNSS tropospheric delay corrections through an innovative lapse rate grid and adiabatic modelling

Since measurements of meteorological parameters like air pressure, water vapor pressure, and temperature are typically not conducted at the receiving antenna’s height, accurate vertical adjustments are indispensable for the tropospheric delay calculation in GNSS applications. We developed an enhanced GPT3 model named as GPT3a, incorporating a new temperature lapse rate grid model and the adiabatic method. The capabilities of GPT3a in predicting atmospheric parameter profiles are assessed by comparison with radiosonde data, NCEP reanalysis data, and GNSS data. Compared with GPT3, IGPT, UNB3, and the constant model with a value of 6.5 K/km, the accuracy of the GPT3a is improved by 50 %, 26 %, 21 %, and 31 % respectively in predicting lapse rate. The RMSEs of GPT3a, GPT3 and IGPT, across temperature profiles (4.2 K, 10.7 K and 6.6 K, respectively), pressure profiles (5.7 hPa, 18.7 hPa and 6.8 hPa, respectively), and ZHD profiles (16.4 mm, 36.4 mm and 17.5 mm, respectively), demonstrate that GPT3a performs superiorly to the GPT3 and IGPT models. Moreover, in the GNSS-based water vapor retrieval, when there is a large height difference between GNSS sites and the model, the GPT3a obviously outperforms GPT3. In short, GPT3a can be used as an enhancement of GPT3 to support GNSS positioning, GNSS meteorology and atmospheric research, which extends the applicability of GPT3 beyond the Earth’s surface to airspace.

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.

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