Large-scale Atmospheric Conditions Research Articles

Weather Radars Reveal Environmental Conditions for High Altitude Insect Movement Through the Aerosphere

High-flying insects that exploit tropospheric winds can disperse over far greater distances in a single generation than species restricted to below-canopy flight. However, the ecological consequences of such long-range dispersal remain poorly understood. For example, high-altitude dispersal may facilitate more rapid range shifts in these species and reduce their sensitivity to habitat fragmentation, in contrast to low-flying insects that rely more on terrestrial patch networks. Previous studies have primarily used surface-level variables with limited spatial coverage to explore dispersal timing and movement. In this study, we introduce a novel application of niche modelling to insect aeroecology by examining the relationship between a comprehensive set of atmospheric conditions and high-flying insect activity in the troposphere, as detected by weather surveillance radars (WSRs). We reveal correlations between large-scale dispersal events and atmospheric conditions, identifying key variables that influence dispersal behaviour. By incorporating high-altitude atmospheric conditions into niche models, we achieve significantly higher predictive accuracy compared with models based solely on surface-level conditions. Key predictive factors include the proportion of arable land, altitude, temperature, and relative humidity.

Systematic Upstream Large-Scale Control of Subtropical Low-Cloud Properties

Abstract It has recently been shown that the slow adjustment of the atmospheric boundary layer (ABL) to perturbations of the large-scale atmospheric conditions translate into an upstream control of the low-cloud cover (LCC) variability at climatological timescales in the subtropics. In this study we expand upon this recent study to investigate upstream control of the climatology of low-cloud radiative effect (CRE), as well as cloud properties relevant to their radiative effect: cloud liquid water path (LWP), and cloud optical depth (COD).We use machine-learning statistical models with feature selection capabilities (random-forests) to determine the influence of the local and upstream large-scale conditions in monthly data. These conditions are determined using back-trajectories in monthly-mean wind fields. Total CRE, dominated by the shortwave contribution, exhibits a dependence on upstream Estimated Inversion Strength (EIS), but not on upstream Sea-Surface Temperature (SST) as LCC does. Upstream control of COD was present but was not as consistent as LCC or CRE across different regions and cloud regimes, while LWP does not exhibit strong upstream control at all. This implies that the control of other boundary layer properties could explain the differences in response between LCC and CRE to SST and EIS. Looking at five subtropical eastern basins, it appears that key lead times are region-specific. The inclusion of upstream control provides significant improvements to the predictive skill of statistical models over local linear regression, with improvements in variance explained well over 20% in many cases.

Investigation of tempo-spatial variability of the Black Sea hydrodynamics by means of neural networks

The tempo-spatial variability of the Black Sea surface circulation was investigated using satellite data from the Archiving, Validation, and Interpretation of Satellite Oceanographic data (AVISO) project. Self-Organizing Maps (SOMs) were utilized, a neural network-based method known for its unsupervised learning capabilities, to analyze and categorize the circulation patterns, for the very first time. To assess the clustering precision achieved by the SOM algorithms the Davies-Bouldin Index (DBI) was employed as an internal validation metric. Through this comprehensive analysis, six distinct spatial patterns were identified, each exhibiting unique temporal variabilities and occurrence rates. Pattern 1: Characterized by the Sevastopol Cyclonic and Batumi Dipole Eddies, occurring 21 % of the time; Pattern 2: Defined by the Cyclonic RIM Current and Anticyclonic Batumi Eddy, with a 16 % occurrence rate; Pattern 3: Consisting of Anticyclonic Sevastopol and Batumi Eddies, occurring 17 % of the time; Pattern 4: Featuring the Cyclonic RIM Current and Cyclonic Batumi Eddy, also with a 21 % occurrence rate; Pattern 5: Marked by the Anticyclonic RIM Current and Batumi Dipole Eddies, with a 15 % occurrence rate; Pattern 6: Characterized by the Anticyclonic RIM Current and Multi Eddies, occurring 10 % of the time. To further validate the identified patterns, their relevance for predicting the hydrodynamics of the Black Sea was examined. This was achieved by exploring potential correlations between these patterns and major climatological indices, such as the North Atlantic Oscillation (NAO), the East Atlantic/West Russian (EAWR) oscillation, and the El Niño-Southern Oscillation (ENSO). These indices are known to influence large-scale atmospheric and oceanographic conditions, and understanding their relationship with the identified patterns can enhance predictive models of Black Sea dynamics. The findings from this study provide valuable insights into the complex circulation patterns of the Black Sea and their temporal behaviors. The use of advanced neural network techniques such as SOMs, combined with rigorous validation methods like the DBI, underscores the robustness of the analysis. Moreover, the established connections with climatological indices offer a promising avenue for improving long-term forecasts and understanding the broader climatic impacts on the Black Sea's surface circulation.

Impact of Alaska atmospheric blocking on the carbon flux in the Northeast Pacific Ocean

The Northeast Pacific Ocean (NEP) is one of the important carbon sinks in the global ocean. The causes of carbon flux changes in this region have been widely studied, but the physical processes associated with large scale climate variability remain controversial primarily due to scarcity of spatially and temporally continuous observations. In this study, we constructed a high-resolution sea surface partial pressure of CO2 (pCO2) from satellite observations for the NEP from 2003 to 2020 using the machine learning based XGBoost model. By analyzing the interannual large-scale high-latitude atmospheric dynamics and ocean physical conditions over the NEP, we find that the CO2 flux density (FCO2) anomalies have a strong correlation with the Alaskan atmospheric blocking events. In the region north of 48°N, anomalous cyclones triggered by atmospheric blocking increased sea surface height (SSH), which reduced the replenishment of dissolved inorganic carbon (DIC) from deep seawater, leading to enhanced carbon uptake. By contrast, in the region south of 48°N, the increase in sea surface temperature (SST) triggered by atmospheric blocking reduced the solubility of CO2 in seawater, resulting in a decrease in regional carbon flux. These results provide new perspectives for better understanding and predicting the effects of high-latitude atmospheric dynamics on regional ocean carbon fluxes.

Atmospheric patterns associated with summer sub-daily rainfall extremes in western Europe

Large-scale atmospheric circulations are a significant driver of rainfall extremes. However, little attention has so far been devoted to understanding how large-scale circulation patterns influence sub-daily rainfall extremes. Using a gauge-based sub-daily rainfall dataset, we investigate the relationship between large-scale circulations and 3-hour extremes (defined here as ≥ 40 mm rainfall in 3 h) for western Europe. A set of 30 weather patterns (WPs) developed by the UK Met Office and reanalysis data of geopotential height at 500 hPa (z500) are used to represent large-scale atmospheric conditions. Strong associations with 3-hour extremes are found for a small number of WPs: over 50% of 3-hour rainfall extremes across Western Europe occur with just 5 WPs. Composites of z500 reveal the WPs resulting in southerly or south-westerly flow along the leading edge of a trough, accompanied by a ridge to the east or northeast, are most favourable for sub-daily rainfall extremes, with a statistically significant difference between the atmospheric conditions on WP days with a 3-hour extreme rainfall event compared to WP non-event days. Given that large-scale circulations are predictable much further in advance than individual extreme rainfall events, these identified relationships could therefore have important implications for forecasting, aiding in the early identification of periods with increased risk of short-duration rainfall extremes.

Improving Typhoon Muifa (2022) Forecasts with FY-3D and FY-3E MWHS-2 Satellite Data Assimilation under Clear Sky Conditions

This study investigates the impacts of assimilating the Microwave Humidity Sounder II (MWHS-2) radiance data carried on the FY-3D and FY-3E satellites on the analyses and forecasts of Typhoon Muifa in 2022 under clear-sky conditions. Data assimilation experiments are conducted using the Weather Research and Forecasting (WRF) model coupled with the Three-Dimensional Variational (3D-Var) Data Assimilation method to compare the different behaviors of FY-3D and FY-3E radiances. Additionally, the data assimilation strategies are assessed in terms of the sequence of applying the conventional and MWHS-2 radiance data. The results show that assimilating MWHS-2 data is able to enhance the dynamic and thermal structures of the typhoon system. The experiment with FY-3E MWHS-2 assimilated demonstrated superior performance in terms of simulating the typhoon’s structure and providing a prediction of the typhoon’s intensity and track than the experiment with FY-3D MWHS-2 did. The two-step assimilation strategy that assimilates conventional observations before the radiance data has improved the track and intensity forecasts at certain times, particularly with the FY-3E MWHS-2 radiance. It appears that large-scale atmospheric conditions are more refined by initially assimilating the Global Telecommunication System (GTS) data, with subsequent satellite data assimilation further adjusting the model state. This strategy has also confirmed improvements in precipitation prediction as it enhances the dynamic and thermal structures of the typhoon system.

Enhancing Regional Climate Downscaling through Advances in Machine Learning

Abstract Despite the sophistication of global climate models (GCMs), their coarse spatial resolution limits their ability to resolve important aspects of climate variability and change at the local scale. Both dynamical and empirical methods are used for enhancing the resolution of climate projections through downscaling, each with distinct advantages and challenges. Dynamical downscaling is physics based but comes with a large computational cost, posing a barrier for downscaling an ensemble of GCMs large enough for reliable uncertainty quantification of climate risks. In contrast, empirical downscaling, which encompasses statistical and machine learning techniques, provides a computationally efficient alternative to downscaling GCMs. Empirical downscaling algorithms can be developed to emulate the behavior of dynamical models directly, or through frameworks such as perfect prognosis in which relationships are established between large-scale atmospheric conditions and local weather variables using observational data. However, the ability of empirical downscaling algorithms to apply their learned relationships out of distribution into future climates remains uncertain, as is their ability to represent certain types of extreme events. This review covers the growing potential of machine learning methods to address these challenges, offering a thorough exploration of the current applications and training strategies that can circumvent certain issues. Additionally, we propose an evaluation framework for machine learning algorithms specific to the problem of climate downscaling as needed to improve transparency and foster trust in climate projections. Significance Statement This review offers a significant contribution to our understanding of how machine learning can offer a transformative change in climate downscaling. It serves as a guide to navigate recent advances in machine learning and how these advances can be better aligned toward inherent challenges in climate downscaling. In this review, we provide an overview of these recent advances with a critical discussion of their advantages and limitations. We also discuss opportunities to refine existing machine learning methods alongside new approaches for the generation of large ensembles of high-resolution climate projections.

Northeast Pacific marine heatwaves associated with high-latitude atmospheric blocking

The Northeast Pacific Ocean (NEP) is one of the hotspots of marine heatwaves (MHWs) occurring in the global ocean. The causes of MHWs in this region have been widely investigated, but the physical processes underlying heatwaves and regional climate variability remain under debate. By analyzing interannual large-scale high-latitude atmospheric dynamics and oceanic physical conditions over the NEP, we show that winter-spring sea surface temperature (SST) anomalies are strongly correlated with winter-spring atmospheric blocking events over Alaska. The occurrence of weaker westerly wind over the subarctic region over the NEP during the period of the blocking, accompanies a shallower vertical mixed layer, less southward horizontal Ekman transport, and higher SST in the upper NEP. These findings establish a linkage between high-latitude atmospheric dynamics and subarctic oceanic conditions and reveal the physical mechanisms of this connection, providing new insight into the possible causes of MHW in the NEP.

CO2 fluxes under different oceanic and atmospheric conditions in the Southwest Atlantic Ocean

The Southwest Atlantic Ocean (SAO) is one of the largest global carbon sink areas. Therefore, the main objective of this study is to investigate turbulent CO2 flux behavior and quantify it in the presence of an intense horizontal sea surface temperature (SST) gradient in the SAO under different atmospheric conditions. In-situ, satellite, and reanalysis data were used from October 14 to 27, 2018 to achieve this objective. The study area was divided into four areas based on satellite observations of SST, salinity, and chlorophyll. The CO2 flux was calculated using the eddy covariance method. During the experiment the area absorbing the most CO2 was the Brazil Current (BC) owing to its proximity to the chlorophyll-rich and less saline waters of the La Plata River and the cold and less saline waters from the Malvinas Current (MC). Moreover, intense wind speeds increased the CO2 flux between the ocean and atmosphere. The Brazil Malvinas Confluence (BMC) also behaved as a CO2 sink, and the modulation of CO2 fluxes was due to the intense horizontal gradient of SST together with the moderate surface wind and turbulence. During the experiment, the MC sequestered less carbon than other regions because of the presence of high-pressure atmospheric systems near the region, resulting in high atmospheric stability, that inhibited mass exchange between the ocean and atmosphere. Vertical mixing mechanisms were identified at the BMC on the cold side, over MC waters. However, in the BC waters, the marine atmospheric boundary layer was modulated by the high-pressure atmospheric system, which suppressed the turbulent mixing. However, the intense mass exchange between the ocean and atmosphere was inhibited, and the area behaved as a mild CO2 sink because of the high-pressure system. This research contributes to a better understanding of the role of the SAO in the global carbon balance in a climate change scenario, and we showed that area can act as a CO2 sink or source, depending on the large-scale atmospheric conditions acting.

The Effect of Moscow Megapolis on Warm-Season Precipitation Depending on Large-Scale Atmospheric Conditions

The Effect of Moscow Megapolis on Warm-Season Precipitation Depending on Large-Scale Atmospheric Conditions

The Effect of Moscow Megapolis on Warm-Season Precipitation Depending on Large-Scale Atmospheric Conditions

The effect of Moscow megapolis on precipitation of different intensity under contrasting physical–synoptic conditions was estimated. The analysis of long-term standard observations at weather stations in the Moscow Region and the data of high-resolution reanalysis ERA5 over 1988–2020 were used to demonstrate that the effect of the city on heavy precipitation is largest in the cases with higher static instability of the atmosphere, combined with a weak large-scale flow, high moisture content of the atmosphere, and the absence of pronounced frontal zones in the region. On the average over the study period, the excess of the total seasonal precipitation in Moscow relative to the background values over the Moscow region is 5.3%. It was found that the effect of the city on precipitation of various intensity is different: the precipitation of low and medium intensity was less in the city (statistically insignificant), while the heaviest precipitation (above 95 percentile) increased over the city by 11.6% above the background value.

A 16-year climatology of the link between dry air intrusions and large-scale dust storms in North Africa

Understanding the underlying processes causing large-scale dust storms and their transport in North Africa is essential for their accurate prediction. Different atmospheric mechanisms have been identified to govern the emission of dust and its transport, ranging in scale and season, from Rossby wave breaking to local, low-level cold and dry jets. Connecting these features in a Lagrangian sense is a coherent airflow from the midlatitude upper troposphere to the surface where dust concentrations peak. Such dry intrusions (DIs) are linked to reduced static stability and strong, dry winds and indeed have been recently shown to play a central role in four of the largest dust storm in the region. Yet, the climatological link between DIs and dust storms over a long time period has not been studied yet. The aim of this paper is to identify objectively dust events that co-occur with DIs, and understand their spatio-temporal characteristics and underlying dynamical drivers. Combining Lagrangian-based DI identification in ERA-Interim and dust optical depth data in CAMS reanalyses, 325 events during 2003–2018 are identified. The events occur mostly in late winter and spring, when they are also larger in size and last longer, compared to summer events. When occurring with DIs, dust optical depth is generally higher, compared to events that are not accompanied by DIs. We focus on March events, and find coherent large-scale precursors and atmospheric conditions of dust-DI events: a northward jet shift over the North Atlantic with anticyclonic Rossby wave breaking occurs on average 4–5 days prior to the events. The lower troposphere responds with dry and cold conditions in northwest Africa, coinciding with the outflow of the DI airstream that is initially guided by the upper trough. Finally, elevated near-surface dust concentrations prevail on the leading edge of the DI in tropical west Africa, and in northeast Africa, where dust is exported to the Mediterranean in association with a Mediterranean cyclone.

Upstream Large-Scale Control of Subtropical Low-Cloud Climatology

Abstract This study investigates the impact of the adjustment times of the atmospheric boundary layer (ABL) on the control of low-cloud coverage (LCC) climatology by large-scale atmospheric conditions in the subtropics. Using monthly data, we calculate back-trajectories and use machine learning statistical models with feature selection capabilities to determine the influence of local and upstream large-scale conditions on LCC for four physical cloud regimes: the stratocumulus (Sc) deck, the along-flow transition into the Sc deck (“Inflow”), the Sc-to-cumulus transition, and trade-cumulus clouds. All four regimes have unique local and upstream relationships with the large-scale meteorological variables within our parameter space, with upstream controls of LCC being the dominant processes in Sc deck and Sc-to-cumulus transition regimes. The time scales associated with these upstream controls across all regimes are consistent with known adjustment time scales of the ABL, determined in both modeling and observational studies. We find that low-level thermodynamic stratification (estimated inversion strength) is not the most important large-scale variable for LCC prediction in transition and trade-cumulus regimes despite its ubiquitous use as a proxy for LCC throughout the subtropics. Including upstream control provides significant improvements to the skill of statistical models predicting monthly LCC, increasing explained variance on the order of 15% in the Inflow, Sc deck, and transition regimes, but provides no improvement in the trade-cumulus regime.

Quasi-biennial modulation of rapidly intensifying tropical cyclones by the western North Pacific monsoon

Previous publications have highlighted the relationship between several climate modes such as El Niño–Southern Oscillation and tropical cyclones (TCs) experiencing rapid intensification (RI) over the western North Pacific (WNP), particularly on a 3–7-yr timescale. This study investigates the modulation of TCs experiencing RI by the WNP monsoon on biennial timescales. There is a significant positive relationship between rapidly intensifying TC (RITC) frequency over the WNP during July–November from 1980 to 2021 and the simultaneous WNP monsoon index. After classifying different WNP monsoon years on biennial timescales, we find significantly more TCs forming over the key region from 5°–25°N, 140°–160°E during strong WNP monsoon years. Some of these TCs then move westward into the portion of the WNP that climatologically has the most RI-favorable environmental conditions. Alternatively, other TCs forming in the key region move northward and undergo RI quickly after genesis, subsequently leading to an increase in rapidly intensifying WNP TC frequency. The WNP monsoon influences rapidly intensifying TC activity predominantly through modulation of large-scale atmospheric conditions. During strong WNP monsoon years, increased low-to-mid-level humidity, low-level vorticity and upper-level divergence and decreased vertical wind shear all favor TC genesis and RI development over the key region. A strong WNP monsoon is also associated with an anomalous 850-hPa cyclone, an anomalous 200-hPa anticyclone, increased 600-hPa moisture convergence and a decrease in the magnitude of 200-hPa winds over the key region. Our study highlights that the WNP monsoon significantly modulates TC and RITC activity at distinct timescales.

On Saharan Air Layer Stability and Suppression of Convection over the Northern Tropical Atlantic: Case Study Analysis of a 2007 Dust Outflow Event

A prominent Saharan Air Layer (SAL) was detected over the Northern Atlantic from the West African Coast to the Caribbean Sea in 2007. Data was collected from the Aerosols and Ocean Science Expedition (AEROSE), which encountered a major dust outflow on 13 and 14 May 2007. These observational measurements came from onboard instrumentation and radiosondes that captured the dust-front event from 13 to 14 May 2007. Aerosol backscatter was confined within the Marine Boundary Layer (MBL), with layers detected up to 3 km. Aerosol Optical Depth (AOD) increased by one order of magnitude during the dust front, from 0.1 to 1. Downward solar radiation was also attenuated by 200 W/m2 and 100 W/m2 on the first and second days, respectively. A weaker gradient at and above 500 m from potential temperature profiles indicates a less-defined MBL, and an ambient air temperature of 26 °C on 14 May and 28 °C on 15 May were observed above 500 m, reinforcing the temperature inversion and static stability of the SAL. Subsequent days, clear and boundary-layer cloudy days were observed after the dust front. From 14 to 18 May, a Convective Inhibition (CIN) layer started to form at the top of the MBL, developing into a negative buoyancy from 17 to 23 May, and reinforcing the large-scale anticyclonic atmospheric conditions. These results show that the SAL acts as positive feedback on suppressing deep convection over the tropical Atlantic during this dust outflow and several days after its passage.

Statistical Framework for Western North Pacific Tropical Cyclone Landfall Risk through Modulation of the Western Pacific Subtropical High and ENSO

Abstract Seasonal predictions of tropical cyclone (TC) landfalls are challenging because seasonal landfall count not only depends on the number and spatial distribution of TC genesis, but also whether those TCs are steered toward land or not. Past studies have separately examined genesis and landfall as a function of large-scale ocean and atmospheric environmental conditions. Here, we introduce a practical statistical framework for estimating the seasonal count of TC landfalls as the product of a Poisson model for seasonal TC genesis and a logistic model for landfall probability. We compute spatial variations in TC landfall and genesis by decomposing TC activity in the western North Pacific (WNP) basin into 10° × 10° bins, then identify coherent regions where El Niño–Southern Oscillation (ENSO) and the western extent of the Pacific subtropical high (WPSH) have significant influences on seasonal landfall count. Our framework shows that ENSO and the WPSH are weakly related to basinwide landfalls but strongly related to regional genesis and landfall probability. ENSO modulates the zonal distribution of TC genesis, consistent with past work, whereas the WPSH modulates the meridional distribution of landfall probability due to variations in steering flow associated with the Pacific subtropical high. These spatial patterns result in four coherent subregions of the WNP basin that define seasonal landfall variations: landfall count increases in the southwestern WNP during a positive WPSH and La Niña, the south-central WNP during a positive WPSH and El Niño, the eastern WNP during a negative WPSH and El Niño, and the northern WNP during a negative WPSH and La Niña.

What caused the increase of tropical cyclones in the western North Pacific during the period of 2011–2020?

Based on satellite data after 1979, we find that the tropical cyclone (TC) variations in the Western North Pacific (WNP) can be divided into three-periods: a high-frequency period from 1979 to 1997 (P1), a low-frequency period from 1998 to 2010 (P2), and a high-frequency period from 2011 to 2020 (P3). Previous studies have focused on WNP TC activity during P1 and P2. Here we use observational data to study the WNP TC variation and its possible mechanisms during P3. Compared with P2, more TCs during P3 are due to the large-scale atmospheric favorable conditions of vertical velocity, relative vorticity and relative humidity. Warm sea surface temperature (SST) anomalies are found during P3 and migrate from east to west, which is also favorable for TC genesis. The correlation between the WNP TC frequency and SST shows a significant positive correlation around the equator and a significant negative correlation around 36°N, which is similar to the warm phase of the Pacific Decadal Oscillation (PDO). The correlation coefficient between the PDO and TC frequency is 0.71, above the 95% confidence level. The results indicate that the increase of the WNP TC frequency during 2011–2020 is associated with the PDO and warm SST anomalies.

Contributions from Changing Large-Scale Atmospheric Conditions to Changes in Scandinavian Temperature and Precipitation Between Two Climate Normals

Multidecadal changes in regional climate can occur as a forced response to changing greenhouse gases and aerosols, as a result of natural internal climate variability, or due to their combination. Internal climate variability is frequently associated with regional changes in large-scale circulation. We investigate how changes in Scandinavian temperature and precipitation conditions during 1961–2020 can be linked to changes in the atmospheric large-scale circulation. The study is based on data from the ERA5 reanalysis and on Swedish average conditions based on observations from the Swedish Meteorological and Hydrological Institute. In general, it is shown that all seasons have become warmer and there is a predominance for more precipitation in the last 30 years. The results also show a clear decrease in daily temperature variability for winter and an increase in summer while there is no similar systematic change for precipitation. Further, we use a circulation type classification technique for identifying ten different circulation types for each calendar month in the 1961–2020 period. Results indicate that changes between the two periods can partly be related to changes in large-scale circulation due to changes in the frequencies of different circulation types. However, it is also clear that the contribution from frequency-related changes to the total change is comparatively low for most months and that changes also within the circulation types are required to explain the total change. The main conclusion of the study is that during the last 30 years it has mostly been warmer than in the preceding 30 years for the same type of weather situation for all months in the year. Consequently, internal climate variability, as represented by changes in the large-scale atmospheric circulation, cannot explain the observed changes in the Scandinavian temperature and precipitation.

THE COUPLING BET WEEN CONVECTIVE VARIABILITY AND LARGE-SCALE FLOW PATTERNS OBSERVED DURING PISTON 2018-2019

AbstractThe Propagation of Intraseasonal Oscillations (PISTON) field campaign took place in the waters of the western tropical North Pacific during the late-summer and early-fall of 2018 and 2019. During both research cruises, the Colorado State University SEA-POL polarimetric C-Band weather radar obtained continuous 3D measurements of oceanic precipitation systems. This study provides an overview of the variability in convection observed during the PISTON cruises, and relates this variability to large-scale atmospheric conditions. Using an objective classification algorithm, precipitation features are identified and labeled by their size (isolated/sub-MCS/MCS) and degree of convective organization (nonlinear/linear). It is shown that although large mesoscale convective systems (MCS’s) occurred infrequently (present in 13% of radar scans), they contributed a disproportionately large portion (56%) of the total rain volume. Conversely, small isolated features were present in 91% of scans, yet these features contributed just 11% of the total rain volume, with the bulk of the rainfall owing to warm rain production. Convective rain rates and 30-dBZ echo top heights increased with feature size and degree of organization. MCS’s occurred more frequently in periods of low-level southwesterly winds, and when low-level wind shear was enhanced. By compositing radar and sounding data by phases of easterly waves (of which there were several in 2018), troughs are shown to be associated with increased precipitation and a higher relative frequency of MCS feature occurrence, while ridges are shown to be associated with decreased precipitation and a higher relative frequency of isolated convective features.

Characteristics of Cold Air Outbreak events and associated Polar Mesoscale Cyclogenesis over the North Atlantic region

AbstractEquatorward excursions of cold polar air masses into ice-free regions, so-called Cold Air Outbreaks (CAO), are frequently accompanied by the development of severe mesoscale weather features. Focusing on two key regions, the Labrador Sea and the Greenland/Norwegian Sea, we apply objective detection for both CAO events and polar mesoscale cyclones to outline the temporal evolution of CAO events and quantify associated mesoscale cyclogenesis. We introduce a novel metric, the CAO-depth, which incorporates both the static stability and the temperature of the air mass. The large-scale atmospheric conditions during the onset of CAO events comprise a very cold upper level trough over the CAO region and a surface cyclone downstream. As the CAO matures, the cold air mass extends southeastward, accompanied by lower static stability and enhanced surface fluxes. Despite the nearly 20 degrees difference in latitude, CAO events over both regions exhibit similar evolution and characteristics including surface fluxes and thermodynamic structure. About 2/3rd of the identified CAO events are accompanied by polar mesoscale cyclogenesis, with the majority of mesoscale cyclones originating inside the cold air masses. Neither the duration nor the maturity of the CAO event seems relevant for mesoscale cyclogenesis. Mesoscale cyclogenesis conditions during CAO events over the Labrador Sea are warmer, moister, and exhibit stronger surface latent heat fluxes than their Norwegian Sea counterparts.