Lapse Rate Research Articles

Temporal variability in air temperature lapse rates across the glacierised terrain of the Chandra basin, western Himalaya

Temporal variability in air temperature lapse rates across the glacierised terrain of the Chandra basin, western Himalaya

Understanding the Cascade: Removing GCM Biases Improves Dynamically Downscaled Climate Projections

AbstractPolarization surrounding bias correction (BC) in creating climate projections arises from its lack of physicality. Here, we perform and analyze 18 dynamical downscaling simulations (with and without BC) to better understand the physical impacts of BC, applied before downscaling, on regional climate output across the western United States. Without BC, downscaled precipitation is systematically and unrealistically wet biased compared to a hierarchy of observationally based datasets over the 1980–2014 period due to cascading mean‐state Global Climate Model (GCM) biases: (a) overly strong lower‐tropospheric lapse rates (5 K/km), (b) overly cold (2 K) tropospheric temperatures, and (c) anomalous mid‐tropospheric cyclonic vorticity advection. With BC, downscaled precipitation (snow) biases are virtually eliminated (halved). Identified GCM biases are common to the broader Coupled Model Intercomparison Project ensemble. Physical effects of BC on the quality of the regionalized projections, pending an evaluation of BC's distortion of the downscaled climate response, may motivate its broader application by dynamical downscalers.

Insights to the water balance of a Boreal watershed using a SWAT model

The hydrological characteristics of a watershed play a crucial role in shaping ecosystems within the Boreal zone and have a significant impact on regional environments. Knowing these characteristics, such as the distinctive topography, vegetation, soil composition, and climatic conditions in the Canadian Boreal ecozone, is essential for implementing sustainable water management. This study focuses on assessing the hydrological dynamics of the Upper Humber River Watershed (UHRW) in western Newfoundland, Canada, using the Soil and Water Assessment Tool (SWAT) model. The UHRW includes sub-basins and hydrological response units (HRUs), with diverse land uses, soil types, and slope characteristics. Key parameters influencing streamflow simulation were identified through sensitivity analysis, including the runoff curve number, the effective hydraulic conductivity, the temperature lapse rate, the soil evaporation compensation factor, and the available water capacity of the soil layer. The SWAT model, using data from the Reidville hydrometric station, shows favorable performance metrics, with R2 values of 0.79 and 0.83 during the calibration and evaluation periods, respectively. The model effectively captures seasonal and monthly flow patterns, displaying right-skewed distributions and seasonal variations. The analyzed hydrological parameters, such as precipitation, evaporation, transpiration, surface runoff, and groundwater flow, reveal their significant contributions to the water balance. The flow duration curve analysis indicates the model’s capability to estimate peak and low flows, with slight under-prediction during the recession phase. Seasonal analysis further supports the model’s performance, with positive Nash-Sutcliffe Efficiency (NSE) values ranging from 0.65 to 0.91. The study concludes that the SWAT model is suitable for simulating the hydrological processes in the studied watershed providing valuable insights for sustainable water resource management and decision-making in the UHRW. The results can be useful for other Boreal ecozone watersheds.

ClimateDT: A Global Scale-Free Dynamic Downscaling Portal for Historic and Future Climate Data

Statistical downscaling of climate data has been widely described in the literature, with the aim of improving the reliability of local climatic parameters from coarse-resolution (often >20 km) global datasets. In this article, we present ClimateDT, a dynamic downscaling web tool for monthly historical and future time series at a global scale. The core of ClimateDT is the 1 km 1981–2010 climatology from CHELSA Climate (version 2.1), where the CRU-TS layers for the period 1901-current are overlayed to generate a historic time series. ClimateDT also provides future scenarios from CMIP5 using UKCP18 projections (rcp2.6 and rcp8.5) and CMIP6 using 5 GCMs, also available on the CHELSA website. The system can downscale the grids using a dynamic approach (scale-free) by computing a local environmental lapse rate for each location as an adjustment for spatial interpolation. Local predictions of temperature and precipitation obtained by ClimateDT were compared with climate time series assembled from 12,000 meteorological stations, and the Mean Absolute Error (MAE) and the explained variance (R2) were used as indicators of performance. The average MAEs for monthly values on the whole temporal scale (1901–2022) were around 1.26 °C for the maximum monthly temperature, 0.80 °C for the average monthly temperature, and 1.32 °C for the minimum monthly temperature. Regarding monthly total precipitation, the average MAE was 19 mm. As for the proportion of variance explained, average R2 values were always greater than 0.95 for temperatures and around 0.70 for precipitation due to the different degrees of temporal autocorrelation of precipitation data across time and space, which makes the estimation more complex. The elevation adjustment resulted in very accurate estimates in mountainous regions and areas with complex topography and substantially improved the local climatic parameter estimations in the downscaling process. Since its first release in November 2022, more than 1300 submissions have been processed. It takes less than 2 min to calculate 45 locations and around 8 min for the full dataset (512 records).

Analytical and adaptable initial conditions for dry and moist baroclinic waves in the global hydrostatic model OpenIFS (CY43R3)

Abstract. This article presents a description of an analytical, stable, and flexible initial background state for both dry and moist baroclinic wave simulation on an aquaplanet in order to test the dynamical core of numerical weather prediction models and study the dynamics and evolution of extratropical cyclones. The initial background state is derived from an analytical zonal wind speed field, or jet structure, and the hydrostatic primitive equations for moist adiabatic and frictionless flow in spherical coordinates. A baroclinic wave can develop if a perturbation is added to the zonal wind speed field. This new baroclinic wave configuration has been implemented in the Open Integrated Forecasting System (OpenIFS) CY43R3, a global numerical weather prediction model developed by the European Centre for Medium-Range Weather Forecasts. In total, seven parameters can be used to control the generation of the initial background state and hence the development of the baroclinic waves in the OpenIFS configuration file: the jet's width, the jet's height, the maximum zonal mean wind speed of the jet, the horizontal mean of the surface virtual temperature, the surface relative humidity, the lapse rate, and the surface roughness. Nine dry and nine moist initial background states have been generated to test their stability without perturbations. The meteorological stability of the initial states is investigated by examining the spatial distributions of the equivalent potential temperature, the absolute vorticity, and the Brunt–Väisälä frequency. Moreover, the root mean square error (RMSE) of the zonal wind speed has been computed to assess their numerical stability. Finally, six dry and six moist initial background state have been used with an unbalanced perturbation to ensure that the baroclinic life cycles that develop are physically realistic. The resulting baroclinic wave is shown to be sensitive to the jet's width. This configuration for baroclinic wave simulations will be used to create a large ensemble of baroclinic life cycles to study how extratropical cyclones may evolve in the future.

Assessing Trial-by-Trial Electrophysiological and Behavioral Markers of Attentional Control and Sensory Precision in Psychotic and Mood Disorders.

The current study investigated the extent to which changes in attentional control contribute to performance on a visual perceptual discrimination task, on a trial-by-trial basis in a transdiagnostic clinical sample. Participants with schizophrenia (SZ; N = 58), bipolar disorder (N = 42), major depression disorder (N = 51), and psychiatrically healthy controls (N = 92) completed a visual perception task in which stimuli appeared briefly. The design allowed us to estimate the lapse rate and the precision of perceptual representations of the stimuli. Electroencephalograms (EEG) were recorded to examine pre-stimulus activity in the alpha band (8-13 Hz), overall and in relation to behavior performance on the task. We found that the attention lapse rate was elevated in the SZ group compared with all other groups. We also observed group differences in pre-stimulus alpha activity, with control participants showing the highest levels of pre-stimulus alpha when averaging across trials. However, trial-by-trial analyses showed within-participant fluctuations in pre-stimulus alpha activity significantly predicted the likelihood of making an error, in all groups. Interestingly, our analysis demonstrated that aperiodic contributions to the EEG signal (which affect power estimates across frequency bands) serve as a significant predictor of behavior as well. These results confirm the elevated attention lapse rate that has been observed in SZ, validate pre-stimulus EEG markers of attentional control and their use as a predictor of behavior on a trial-by-trial basis, and suggest that aperiodic contributions to the EEG signal are an important target for further research in this area, in addition to alpha-band activity.

Impact of Atmospheric Compressibility and Stokes Drift on the Vertical Transport of Heat and Constituents by Gravity Waves

AbstractVertical transport of heat and atmospheric constituents by gravity waves plays a crucial role in shaping the thermal and constituent structure of the middle atmosphere. We show that atmospheric mixing by non‐breaking waves can be described as a diffusion process where the potential temperature (KH) and constituent (KWave) diffusivities depend on the compressibility of the wave fluctuations and the vertical Stokes drift imparted to the atmosphere by the wave spectrum. KH and KWave are typically much larger than the eddy diffusivity (Kzz), arising from the turbulence generated by breaking waves, and can exceed several hundred m2s−1 in regions of strong wave dissipation. We also show that the total diffusion of heat and constituents caused by waves, turbulence, and the thermal motion of molecules, is enhanced in the presence of non‐breaking waves by a factor that is proportional to the variance of the wave‐driven lapse rate fluctuations. Diffusion enhancements of both heat and constituents of 50% or more can be experienced in regions of low atmospheric stability, where the lapse rate fluctuations are large. These important transport effects are not currently included in most global chemistry‐climate models, which typically only consider the eddy diffusion that is induced when the unresolved, but parameterized waves, experience dissipation. We show that the theoretical results compare favorably with observations of the mesopause region at midlatitudes and describe how the theory may be used to more fully account for the unresolved wave transport in global models.

ERA5‐Based Significant Tornado Environments in Canada Between 1980 and 2020

AbstractThis study uses ERA5 close‐proximity soundings and associated convective parameters to characterize significant tornadic storm (F/EF2+) environments between 1980 and 2020 in parts of Canada. It is shown that ERA5 convective parameters are suitable to represent observed parameters, based on radiosonde comparisons. Results indicate that the eastern Canadian Prairies have nearly double the lifted condensation level with higher level of free convection compared to eastern Canada (southern Ontario/Quebec). Eastern Canada has more a humid boundary layer and free troposphere that can lead to warmer cold pools, favoring tornadogenesis. Central Canada (Manitoba) has the largest mixed‐layer (ML) convective available potential energy (CAPE) mainly due to a combination of regional differences in low level moisture and steeper mid‐level lapse rates in western Canada. Central continental U.S. and Canadian regions appear to have the highest (most negative) convective inhibition, leading to more explosive initiation. Mean bulk wind shear and storm relative helicity (SRH) increases from west to east, with eastern regions being significantly larger. The supercell composite and significant tornado parameters are generally less than U.S. magnitudes, particularly in western Canada, and would require recalibration for more practical use in Canada. Overall, western Canada significant tornadic storms are associated with more low‐level looping hodographs and dominated by thermodynamic influences compared to larger wind influences in eastern regions. This is likely due to more spring, late summer, and autumn events that typically have well‐developed synoptic systems (stronger wind shear) with overall less buoyant energy in eastern regions.

Generalized Gaussian signal detection theory: A unified signal detection framework for confidence data analysis.

Human decision behavior entails a graded awareness of its certainty, known as a feeling of confidence. Until now, considerable interest has been paid to behavioral and computational dissociations of decision and confidence, which has raised an urgent need for measurement frameworks that can quantify the efficiency of confidence rating relative to decision accuracy (metacognitive efficiency). As a unique addition to such frameworks, we have developed a new signal detection theory paradigm utilizing the generalized Gaussian distribution (GGSDT). This framework evaluates the observer's metacognitive efficiency and internal standard deviation ratio through shape and scale parameters, respectively. The shape parameter quantifies the kurtosis of internal distributions and can practically be understood in reference to the proportion of the Gaussian ideal observer's confidence being disrupted with random guessing (metacognitive lapse rate). This interpretation holds largely irrespective of the contaminating effects of decision accuracy or operating characteristic asymmetry. Thus, the GGSDT enables hitherto unexplored research protocols (e.g., direct comparison of yes/no vs. forced-choice metacognitive efficiency), expected to find applications in various fields of behavioral science. This article provides a detailed walkthrough of the GGSDT analysis with an accompanying R package (ggsdt). (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Dynamics and scaling of internally cooled convection

Our goal is to investigate fundamental properties of the system of internally cooled convection. The system consists of an upward thermal flux at the lower boundary, a mean temperature lapse-rate and a constant cooling term in the bulk with the bulk cooling in thermal equilibrium with the input heat flux. This simple model represents idealised dry convection in the atmospheric boundary layer, where the cooling mimics the radiative cooling to space notably through longwave radiation. We perform linear stability analysis of the model for different values of the mean stratification to derive the critical forcing above which the fluid is convectively unstable to small perturbations. The dynamic behavior of the fluid system is described and the scaling of various important measured quantities such as the total vertical convective heat flux and the upward mass flux is measured. We introduce a lapse-rate dependent dimensionless Rayleigh-number Raγ that determines the behavior of the system, finding that the convective heat-flux and mass-flux scale approximately as Raγ0.5 and Raγ0.7 respectively. The area-fraction of the domain that is occupied by upward and downward moving fluid and the skewness of the vertical velocity are studied to understand the asymmetry inherent in the system. We conclude with a short discussion on the relevance to atmospheric convection and the scope for further investigations of atmospheric convection using similar simplified approaches.

A Comparison between the Only Two Documented Tornado Outbreak Events in China: Tropical Cyclone versus Extratropical Cyclone Environments

Abstract This study documents the features of tornadoes, their parent storms, and the environments of the only two documented tornado outbreak events in China. The two events were associated with Tropical Cyclone (TC) Yagi on 12 August 2018 with 11 tornadoes and with an extratropical cyclone (EC) on 11 July 2021 (EC 711) with 13 tornadoes. Most tornadoes in TC Yagi were spawned from discrete minisupercells, while a majority of tornadoes in EC 711 were produced from supercells imbedded in QLCSs or cloud clusters. In both events, the high-tornado-density area was better collocated with the K index rather than MLCAPE, and with entraining rather than non-entraining parameters possibly due to their sensitivity to midlevel moisture. EC 711 had a larger displacement between maximum entraining CAPE and vertical wind shear than TC Yagi, with the maximum entraining CAPE better collocated with the high-tornado-density area than vertical wind shear. Relative to TC Yagi, EC 711 had stronger entraining CAPE, 0–1-km storm relative helicity, 0–6-km vertical wind shear, and composite parameters such as an entraining significant tornado parameter, which caused its generally stronger tornado vortex signatures (TVSs) and mesocyclones with a larger diameter and longer life span. No significant differences were found in the composite parameter of these two events from U.S. statistics. Although obvious dry air intrusions were observed in both events, no apparent impact was observed on the potential of tornado outbreak in EC 711. In TC Yagi, however, the dry air intrusion may have helped tornado outbreak due to cloudiness erosion and thus the increase in surface temperature and low-level lapse rate.

Preconvective environments of severe convective winds over North China and South China

This study aimed to identify the environmental discrepancy between severe convective wind (SCW) and climate background over North China and South China and between SCW occurring in these two regions, which were prone to SCW in China. 17 parameters from 11,727 (13130) climate background soundings and 4404 (2513) SCW soundings in North (South) China were quantitatively investigated. These parameters were grouped as significant important parameters (SIP), moderate important parameters (MIP), and weak important parameters (WIP). The skill in discriminating the environments between SCW and climate background steadily decreased from SIP to MIP and then to WIP. In North China, SCW typically occurred under moderate to severe deep wind shear and low convective available potential energy (CAPE) conditions. However, in South China, SCW tended to occur at low CAPE and moderate deep wind shear, as well as moderate CAPE and moderate to severe deep wind shear. Compared to the background climate, the SCW environment was associated with considerable instability, high shear, and dry and cold air in the middle layer over both North and South China. A substantial variation in wind shear was identified between the SCW and background in North China, whereas a considerable difference in comprehensive conditions involving CAPE and wind shear was detected in South China. The SCW environment is characterized by a steeper lapse rate in the middle layer as well as a higher Total Totals index in North China than in South China, while the opposite was true for CAPE between two regions. In general, the thermal parameter discrimination was larger than the shear metric discrimination when comparing the soundings for the two regions.

Climate velocities and species tracking in global mountain regions

Mountain ranges contain high concentrations of endemic species and are indispensable refugia for lowland species that are facing anthropogenic climate change1,2. Forecasting biodiversity redistribution hinges on assessing whether species can track shifting isotherms as the climate warms3,4. However, a global analysis of the velocities of isotherm shifts along elevation gradients is hindered by the scarcity of weather stations in mountainous regions5. Here we address this issue by mapping the lapse rate of temperature (LRT) across mountain regions globally, both by using satellite data (SLRT) and by using the laws of thermodynamics to account for water vapour6 (that is, the moist adiabatic lapse rate (MALRT)). By dividing the rate of surface warming from 1971 to 2020 by either the SLRT or the MALRT, we provide maps of vertical isotherm shift velocities. We identify 17 mountain regions with exceptionally high vertical isotherm shift velocities (greater than 11.67 m per year for the SLRT; greater than 8.25 m per year for the MALRT), predominantly in dry areas but also in wet regions with shallow lapse rates; for example, northern Sumatra, the Brazilian highlands and southern Africa. By linking these velocities to the velocities of species range shifts, we report instances of close tracking in mountains with lower climate velocities. However, many species lag behind, suggesting that range shift dynamics would persist even if we managed to curb climate-change trajectories. Our findings are key for devising global conservation strategies, particularly in the 17 high-velocity mountain regions that we have identified.

Abrupt warming and alpine glacial retreat through the last deglaciation in Alaska interrupted by modest Northern Hemisphere cooling

Abstract. Alpine glacier-based temperature reconstructions spanning the last deglaciation provide critical constraints on local to regional climate change and have been reported from several formerly glaciated regions around the world yet remain sparse from high-northern-latitude regions. Using newly and previously 10Be-dated moraines, we report paleo-glacier equilibrium line altitudes (ELAs) for 15 time slices spanning the Last Glacial Maximum (LGM) to the Little Ice Age (LIA) for a valley in the western Alaska Range. We translate our ELA reconstructions into a proxy for summer temperature by applying a dry adiabatic lapse rate at each reconstructed ELA relative to the outermost LIA moraine. We observe ∼4 °C warming through the last deglaciation at our site that took place in two steps following initial gradual warming: ∼1.5 °C abrupt warming at 16 ka, ∼2 kyr after the onset of global CO2 rise, and ∼2 °C warming at ∼15 ka, near the start of the Bølling. Moraine deposition and modest summer cooling during Heinrich Stadial 1 and the early Younger Dryas (YD) suggest that despite these events being expressed more strongly in wintertime, the classic blueprint of North Atlantic climate variability extends to the western Arctic region.

Aggregate lapsation risk

We study aggregate lapsation risk in the life insurance sector. We construct two lapsation risk factors that explain a large fraction of the common variation in lapse rates of the 30 largest life insurance companies. The first is a cyclical factor that is positively correlated with credit spreads and unemployment, while the second factor is a trend factor that correlates with the level of interest rates. Using a novel policy-level database from a large life insurer, we examine the heterogeneity in risk factor exposures based on policy and policyholder characteristics. Young policyholders with higher health risk in low-income areas are more likely to lapse their policies during economic downturns. We explore the implications for hedging and valuation of life insurance contracts. Ignoring aggregate lapsation risk results in mispricing of life insurance policies. The calibrated model points to overpricing on average. In the cross-section, young, low-income, and high-health risk households face higher effective mark-ups than the old, high-income, and healthy.

The Physics behind Precipitation Onset Bias in CMIP6 Models: The Pseudo-Entrainment Diagnostic and Trade-Offs between Lapse Rate and Humidity

Abstract Conditional instability and the buoyancy of plumes drive moist convection but have a variety of representations in model convective schemes. Vertical thermodynamic structure information from Atmospheric Radiation Measurement (ARM) sites and reanalysis (ERA5), satellite-derived precipitation (TRMM3b42), and diagnostics relevant for plume buoyancy are used to assess climate models. Previous work has shown that CMIP6 models represent moist convective processes more accurately than their CMIP5 counterparts. However, certain biases in convective onset remain pervasive among generations of CMIP modeling efforts. We diagnose these biases in a cohort of nine CMIP6 models with subdaily output, assessing conditional instability in profiles of equivalent potential temperature, θe, and saturation equivalent potential temperature, θes, in comparison to a plume model with different mixing assumptions. Most models capture qualitative aspects of the θes vertical structure, including a substantial decrease with height in the lower free troposphere associated with the entrainment of subsaturated air. We define a “pseudo-entrainment” diagnostic that combines subsaturation and a θes measure of conditional instability similar to what entrainment would produce under the small-buoyancy approximation. This captures the trade-off between larger θes lapse rates (entrainment of dry air) and small subsaturation (permits positive buoyancy despite high entrainment). This pseudo-entrainment diagnostic is also a reasonable indicator of the critical value of integrated buoyancy for precipitation onset. Models with poor θe/θes structure (those using variants of the Tiedtke scheme) or low entrainment runs of CAM5, and models with low subsaturation, such as NASA-GISS, lie outside the observational range in this diagnostic.

How is the relationship between rainfall and water vapor in the Indian monsoon influenced by changes in lapse rate during global warming?

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.

A comprehensive semigray climate model

A climate model is developed for Earth climate history simulations or snapshots of possible conditions on Earthlike exoplanets. It includes estimates for shortwave and longwave optical thickness based on data from Venus, Earth, and Mars; expressions for atmospheric shortwave absorption and surface convective heat loss; climate feedbacks due to water vapor, ice-albedo, clouds, and lapse rate; and a new model for planetary and surface albedo which takes account of surface cover, Rayleigh scattering, and differing wavelength fractions due to primary spectral class. While somewhat complex, it is still orders of magnitude faster than full-spectrum methods or radiative-convective convergence. The model can be modified for use with tidally locked planets, and is here applied to Proxima Centauri b as an example.

Climatology of the Elevated Mixed Layer over the Contiguous United States and Northern Mexico Using ERA5: 1979–2021

Abstract Elevated mixed layers (EMLs) influence the severe convective storm climatology in the contiguous United States (CONUS), playing an important role in the initiation, sustenance, and suppression of storms. This study creates a high-resolution climatology of the EML to analyze variability and potential changes in EML frequency and characteristics for the first time. An objective algorithm is applied to ERA5 to detect EMLs, defined in part as layers of steep lapse rates (≥8.0°C km−1) at least 200 hPa thick, in the CONUS and northern Mexico from 1979 to 2021. EMLs are most frequent over the Great Plains in spring and summer, with a standard deviation of 4–10 EML days per year highlighting sizable interannual variability. Mean convective inhibition associated with the EML’s capping inversion suggests many EMLs prohibit convection, although—like nearly all EML characteristics—there is considerable spread and notable seasonal variability. In the High Plains, statistically significant increases in EML days (4–5 more days per decade) coincide with warmer EML bases and steeper EML lapse rates, driven by warming and drying in the low levels of the western CONUS during the study period. Additionally, increases in EML base temperatures result in significantly more EML-related convective inhibition over the Great Plains, which may continue to have implications for convective storm frequency, intensity, severe perils, and precipitation if this trend persists. Significance Statement Elevated mixed layers (EMLs) play a role in the spatiotemporal frequency of severe convective storms and precipitation across the contiguous United States and northern Mexico. This research creates a detailed EML climatology from a modern reanalysis dataset to uncover patterns and potential changes in EML frequency and associated meteorological characteristics. EMLs are most common over the Great Plains in spring and summer, but show significant variability year-to-year. Robust increases in the number of days with EMLs have occurred since 1979 across the High Plains. Lapse rates associated with EMLs have trended steeper, in part due to warmer EML base temperatures. This has resulted in increasing EML convective inhibition, which has important implications for regional climate.

Black Carbon along a Highway and in a Residential Neighborhood during Rush-Hour Traffic in a Cold Climate

Short-term exposure to ultra-fine Black Carbon (BC) particles produced during incomplete fuel combustion of wood and fossil fuel has been linked to respiratory and cardiovascular diseases, hospitalizations and premature deaths. The goal of this research was to assess traffic-related BC in a cold climate along an urban highway and 300 m into an adjacent residential neighborhood. BC was measured with an aethalometer (MA350, Aethlabs) along the main traffic artery in geothermally heated Reykjavík, the capital of Iceland (64.135° N–21.895° W, 230,000 inhabitants). Stationary monitoring confirmed that traffic was the dominant source of roadside BC in winter, averaging 1.0 ± 1.1 µg/m3 (0.6 and 1.1 µg/m3 median and interquartile range; 28,000 vehicles/day). Inter-day variations in BC were primarily correlated to the atmospheric lapse rate and wind speed, both during stationary and mobile campaigns. During winter stills, BC levels surpassed 10 µg/m3 at intersections and built up to 5 µg/m3 during the afternoon in the residential neighborhood (adjacent to the highway with 43,000 vehicles/day). The BC penetrated deeply into the neighborhood, where the lowest concentration was 1.8 µg/m3 within 300 m. BC concentration was highly correlated to nitrogen dioxide (r > 0.8) monitored at the local Urban Traffic Monitoring site.