Storm surge from Atlantic tropical cyclones (TCs) is a large driver of fatalities and damage, accounting for 32% of all continental US (CONUS) TC fatalities from 1963 to 2024. Previous work has shown that landfalling minimum sea level pressure (MSLP) predicts normalized TC damage better than maximum sustained wind (Vmax), in part reflecting the robust relationship between storm size and MSLP. Given that storm size is an important driver of storm surge, we find a significantly stronger relationship between peak storm surge and CONUS hurricane landfalling MSLP (r = −0.78) than landfalling Vmax (r = 0.68). Peak storm surge also significantly correlates with both normalized damage (rrank = 0.72) and direct fatalities (rrank = 0.51). ADCIRC-simulated peak storm surges show consistent relationships with MSLP, Vmax, normalized damage, and direct fatalities, reinforcing the observational results. These findings highlight landfalling MSLP as a more skillful predictor of hurricane hazards and impacts than Vmax.

Abstract The Beobachtung von Ozean und Wolken–Das Trans ITCZ Experiment (BOWTIE) field campaign investigated how convective storm dynamics interact with the ocean surface to shape the structure of the Atlantic intertropical convergence zone (ITCZ). Conducted aboard the German Research Vessel (R/V) Meteor during August and September 2024, the campaign targeted the full meridional extent of the ITCZ while transiting the tropical Atlantic from east to west. The research was driven by evidence suggesting that storm-scale dynamics is pivotal for shaping the broader structure of the ITCZ and its connection to global circulation patterns and energy transport. BOWTIE featured high-resolution atmospheric and oceanic profiling, with a particular focus on the coupled boundary layers. Observations included cloud and humidity profiles, winds, precipitation, sea surface temperature, and upper-ocean physical and biogeochemical properties. A suite of advanced instruments provided vertically resolved cross sections of convective environments and surrounding conditions. BOWTIE was part of the larger international Organized Convection and EarthCARE Studies over the Tropical Atlantic (ORCESTRA) initiative, which coordinated eight campaigns across the Atlantic. During the voyage, the R/V Meteor served as a platform for two additional ORCESTRA campaigns: Soundings and Turbulent eddy measurements in the ITCZ with a Network of Quadcopters (STRINQS), which deployed unmanned aerial vehicles for profiling near-storm environments, and Process Investigation of Clouds and Convective Organization over the Atlantic Ocean (PICCOLO), which brought Colorado State University’s Sea-Pol scanning dual-polarization C-band radar onboard. This article provides an overview of BOWTIE’s scientific goals, campaign design, and observing strategy and presents selected early results from the extensive dataset. The combination of in situ, airborne, and radar measurements offers new insight into how ocean–atmosphere interactions at convective scales shape the ITCZ’s broader structure and behavior. Significance Statement The intertropical convergence zone (ITCZ) plays a central role in shaping tropical rainfall and global circulation, yet the processes governing its structure and variability remain incompletely understood. The Beobachtung von Ozean und Wolken–Das Trans ITCZ Experiment (BOWTIE) field campaign provides a coupled observational view of the Atlantic ITCZ by combining ship-based atmospheric and oceanic measurements with scanning and profiling radar, autonomous platforms, and coordinated aircraft and satellite observations. By sampling the full meridional extent of the ITCZ over 40 days and nights, BOWTIE reveals how convective organization, boundary layer dynamics, and upper-ocean variability interact across spatial and temporal scales. These observations advance understanding of the physical processes that regulate tropical rain belts and their day-to-day variability.

The RAPSODI (Radiosonde Atmospheric Profiles from Ship and island platforms during ORCESTRA, collected to Decipher the ITCZ) radiosonde dataset was collected during the ORCESTRA field campaign in August and September 2024. It is designed to investigate the mechanisms linking mesoscale tropical convection to tropical waves and to air-sea heat and moisture exchanges that regulate convection and tropical cyclone formation. The campaign began at the Instituto Nacional de Meteorologia e Geof & iacute;sica (INMG) on Sal in the Cape Verde Islands, continued with ship-based observations aboard the German research vessel R/V Meteor during an Atlantic transect, and concluded at the Barbados Cloud Observatory (BCO) in the eastern Caribbean. Over the 52 d campaign, a total of 624 radiosondes were launched at high temporal frequency (typically every three hours), capturing high-resolution vertical profiles of temperature, humidity, pressure, and winds from three complementary platforms. The dataset encompasses raw, quality-controlled, and vertically gridded data, is detailed in this paper and offers a valuable resource for investigating the atmospheric structure and processes shaping tropical convection and the intertropical convergence zone (ITCZ). The datasets generated in this study include raw radiosonde measurements (Level 0), oscillating and merged radiosonde profiles (Level 1), and vertically gridded profiles (Level 2), which are publicly available via the ORCESTRA data portal and DOI-referenced archives (; https://ipfs.io/ipns/latest.orcestra-campaign.org/raw/BCO/radiosondes/, ; https://ipfs.io/ipns/latest.orcestra-campaign.org/raw/INMG/radiosondes/, ; https://ipfs.io/ipns/latest.orcestra-campaign.org/raw/METEOR/radiosondes/, ; https://doi.org/10.82246/BAFYBEIHXRAJOJUQZYX65QSO7AMA6NGVREETKDW3HQZX3SDZFB7LCMG6VAQ, ; https://doi.org/10.82246/BAFYBEIA34AUWYVBH2RQ7CN7AGUZZ7PULQ2KRDDDIEESM6KPYSI, ; https://doi.org/10.82246/BAFYBEID7CNW62ZMZFGXCVC6Q6FA267A7IVK2W, ).

During the Propagation of Intraseasonal Tropical Oscillations (PISTON) field campaign in the summers of 2018 and 2019 over the western North Pacific (WNP), many tropical cyclones (TCs) exhibited an elongated rainband in the southwestern (SW) quadrant, typically located 500-1000 km from the storm center. We refer to this feature as the "monsoon tail (MT)," hypothesizing that it forms through interactions between the TC circulation and monsoon southwesterlies. A notable case was Typhoon Jebi (2018), where a trailing rainband detached from the main circulation and persisted as widespread convection. This system developed a closed low-level circulation and was designated as Invest 98 degrees W by the Joint Typhoon Warning Center but ultimately failed to undergo tropical cyclogenesis (TCG). Motivated by these observations, we conducted a 40-yr climatological analysis and a case study of Jebi and Invest 98 degrees W. We found that approximately 80% of WNP TCs exhibited at least one MT event under a moderate threshold (5000 km2 of area, 6 h of duration). Convective asymmetry in the SW quadrant was most strongly correlated with zonal wind shear, while low-level monsoonal flow and thermodynamic factors played secondary roles. Vertical wind shear helped organize asymmetric convection downshear, enabling detached MT structures to resemble incipient disturbances. This dual role of vertical wind shear (VWS) and the preexisting TC circulation enhancing vorticity and moisture while imposing hostile shear was evident not only in the Invest 98 degrees W case but also in the climatological analysis using its location as an illustrative example. These findings highlight how MT rainbands may exhibit developmental potential yet remain inhibited from TCG by environmental shear from the parent TC.

Typhoon Hagibis (2019) explosively intensified by 100 kt in 24 h, greatly exceeding the conventional rapid intensification (RI) threshold of similar to 30 kt per 24 h. This study investigates how marine heatwave (MHW)-driven ocean surface and subsurface structures influence such explosive RI (ERI). Using the coupled Weather Research and Forecasting and 3D Price-Weller-Pinkel Ocean model, we compared a Hagibis control run with two sensitivity experiments using non-MHW sea surface temperatures (SSTs) and vertical temperature profiles from another RI case of Typhoon Jebi (2018). Hagibis intensifies dramatically faster under MHW conditions-about 20% faster in the baseline simulation, and 40% faster when realistic vertical ocean mixing in the surface layer is included-revealing the striking sensitivity of ERI to upper-ocean thermal anomalies. Strong latent heat fluxes supplied continuous energy to the lower troposphere, while elevated subsurface heat content sustained air-sea enthalpy exchange despite storm-induced SST cooling. These ocean-atmosphere interactions reorganized TC convection, strengthened updrafts, and fostered an environment conducive to ERI. We conclude that even a 1 degrees C increase in surface temperature is sufficient to modulate storm structure and intensity, amplifying ERI. As ocean temperatures continue to rise, these findings highlight the need to refine the definition of ERI and improve our understanding of TC-ocean interactions under increasing MHW influence.

Extreme rainfall is fundamentally driven by microphysical processes aloft, which can be strongly influenced by complex terrain. The 2022 Prediction of Rainfall Extremes Campaign in the Pacific (PRECIP 2022) provided a unique opportunity to study these relationships through the deployment of the NSF NCAR S-band dual-polarization radar (S-Pol) in Taiwan, a region characterized by steep topography and frequent extreme precipitation. This study examines a Mei-Yu front case characterized by widespread, long-duration rainfall with embedded deep convective cells occurring both upstream and over Taiwan's topography. Our objective is to link microphysical processes to the varying terrain in context of rainfall during this high-impact event. Microphysical characteristics are inferred from vertical gradients of dual-polarization radar variables and modeled drop size distribution (DSD) moments, supported by ground-based disdrometer observations that resolve both the drizzle and precipitation modes. A normalized double-moment generalized gamma DSD model is applied to S-Pol to obtain DSD moments that are linked to dominant microphysical processes aloft including radar-inferred graupel presence. Results show that collision coalescence is the most frequent warm-rain process in convection, contributing to large drop production and increased rainfall rates. Additionally, graupel presence is associated with precipitation enhancement, with the magnitude of graupel-related amplification increasing with terrain height. This study highlights the benefit of modeling both the drizzle and precipitation modes of the DSD using dual-polarization radars to connect microphysical processes aloft to complex terrain.

Abstract Extreme rainfall events in Taiwan pose significant forecasting challenges due to complex multiscale interactions. Although orographic lifting is known to trigger convection, its role in modifying atmospheric stability, specifically through the formation of moist absolutely unstable layers (MAULs), remains underexplored. This study presents the first investigation demonstrating that terrain can induce MAULs and enhance extreme rainfall in mountainous terrain in Taiwan, a mechanism not previously documented. Convection‐permitting simulations show that terrain‐driven moisture convergence and layer lifting promote deeper and more persistent MAULs. Removing the terrain substantially limits the MAUL development, associated with weaker rainfall. Furthermore, the MAUL volume rapidly increases prior to the most intense rainfall, suggesting its potentital as an indicator of extreme precipitation. These findings highlight the role of terrain in modulating both the thermodynamic environment and extreme rainfall, underscoring the importance of accurately representing orographic effects in numerical weather prediction.

Abstract The Geostationary Lightning Mapper (GLM) provides continuous, high-resolution lightning observations that enable a novel investigation of lightning attributes in pretropical cyclogenesis environments. Compositing and tracking methods from the Tracking and Object-Based Analysis of Clouds (tobac) Python package enable the consideration of lightning lifetime alongside GLM-measured area and optical energy. We first apply the object-based framework to lightning observed prior to the formation of Tropical Storm Claudette (2021), a genesis case within range of the Next Generation Weather Radar (NEXRAD) network. Collocated ground-based, dual-polarization radar observations suggest that small-area, low-energy lightning is indicative of stronger convection and updrafts, based on composite vertical radar profiles of tobac lightning features. We then apply the physical interpretations of these lightning attributes to lightning 72 h prior to genesis and within 200 km of the best track invest center for five North Atlantic disturbances. Developing disturbances include Claudette (2021), Ida (2021), Earl (2022), and Beryl (2024), which we compare against nondeveloping AL96 (2024). Small-area, low-energy lightning, previously associated with stronger updrafts and convection, is the dominant lightning mode for the four developing cases with large-area, high-energy lightning also identified at various times. Lightning activity appears to be modulated by deep-layer vertical wind shear, and electrified convection coincides with improvements in convective organization in multiple instances. While more work is needed to evaluate lightning attributes across a larger composite of disturbances, this study offers a novel characterization of lightning in pretropical cyclogenesis environments for the North Atlantic to aid in the understanding and forecasting of tropical cyclone genesis. Significance Statement This study investigates lightning characteristics in five North Atlantic tropical disturbances, four of which became tropical storms, to better understand and forecast tropical storm formation. We apply a novel approach that uses advanced observations from the Geostationary Lightning Mapper and tracking abilities from the Tracking and Object-Based Analysis of Clouds Python package. This framework enables a detailed characterization of lightning that considers lightning area, optical energy, and lifetime, providing more information than previously used flash counts and rates. These expanded lightning attributes may be relevant for understanding and forecasting tropical storm formation. Future work will use this framework to assess lightning across a larger sample of tropical disturbances to evaluate lightning trends across a wider range of cases.

To enable the study of the climatology of convective and stratiform precipitation in tropical cyclones, this work develops a convective-stratiform precipitation-type classification model for the Global Precipitation Measurement satellite Microwave Imager (GMI) from the Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset. The model uses the random forest classifier and takes as input the various brightness temperature observations from the GMI and calculated texture information. The optimal model setup is found through experimenting with the minimum number of samples in a leaf node and model calibration to prioritize classification accuracy. The final model is the sigmoid-calibrated model with a minimum number of samples in a leaf node of four. Overall, the model does well at capturing the general precipitation structure of tropical cyclones. Model deficiencies are likely due to issues with nonuniform beam filling. Finally, a dropout experiment is performed to investigate the most important predictors. The most important predictors are the 36.64-and 89.0-GHz brightness temperatures and texture information. While texture information are important, brightness temperature observations at observing frequencies of less than 89.0 GHz have a more significant impact toward model skill in classifying precipitation type from passive microwave observations.

Abstract. As part of BOWTIE (German: Beobachtung von Ozean und Wolken - Das Trans ITCZ Experiment), the German research vessel FS Meteor navigated the moist tropics of the Atlantic Ocean for 40 days in summer 2024, with an east-west trajectory. The journey started in the port of Mindelo, Cape Verde, on August 16, and finished in the port of Bridgetown, Barbados, on September 24. The objective was to measure properties of the atmosphere, upper-ocean, and air-sea interface within the Intertropical Convergence Zone (ITCZ), under a variety of wind, convection, and sea surface temperature regimes. Using a set of 29 instruments/platforms, BOWTIE sampled the near-surface conditions of the atmosphere and ocean with high temporal resolution. This included continuous measurements of: the 2-D wind field within the lowest 2 km of the atmosphere, near-surface ocean currents, cloud and precipitation properties. Profiles of the ocean state and atmospheric thermodynamics and kinematics were obtained both continuously and at discrete intervals. Furthermore, dedicated stations sampled biochemical properties of the upper-ocean. Complementing BOWTIE observations, FS Meteor hosted further dedicated field campaigns for 3D cloud and precipitation properties, as well as intensive measurements of the atmospheric boundary layer. This manuscript provides an overview of the extensive instrumentation and data collected during BOWTIE. In addition, it addresses two key aspects based on these observations. First, it examines the range and uncertainties of selected quantities measured by multiple instruments, including column-integrated water vapor, rain detection, surface ocean currents, and sea surface temperature. Second, it illustrates the diversity of sampled weather regimes through two representative cases: calm doldrum conditions and a gusty, precipitating convective state.

Estimates of the surface wind field in a tropical cyclone (TC) are required in real time by operational forecast centers to warn the public about potential impacts to life and property. In‐situ aircraft data must be adjusted from flight level to surface using wind reductions (WRs) since the aircraft cannot fly too low due to safety concerns. Current operational WRs do not capture all the variability in the TC surface wind field. In this study, an observational data set of Stepped Frequency Microwave Radiometer (SFMR) surface wind speeds that are collocated with flight‐level predictors is used to analyze the variability of WRs with respect to aircraft altitude and TC storm motion and intensity. The Surface Winds from Aircraft with a Neural Network (SWANN) model is trained on the observations with a custom loss function that prioritizes accurate prediction of relatively rare high‐wind observations and minimization of variance in the WRs. The model is capable of learning physical relationships that are consistent with theoretical understanding of the TC boundary layer. Radar‐derived wind fields at flight level and independent dropwindsonde in‐situ surface wind measurements are used to validate the SWANN model and show improvement over the current operational procedure. A test case shows that SWANN can produce a realistic asymmetric surface wind field from a radar‐derived flight‐level wind field which has a maximum wind speed similar to the operational intensity, suggesting promise for the method to lead to improved real‐time TC intensity estimation and prediction in the future.

This study investigates the microphysical and kinematic characteristics in extreme afternoon thunderstorm rainfall during Taiwan‐Area Heavy rain Observation and Prediction Experiment/Prediction of Rainfall Extremes Campaign In the Pacific IOP 2. The high‐quality S‐Pol radar observations and multi‐Doppler winds provided valuable information about the convective organization over complex terrain. There were two episodes of heavy rainfall in this event. Episode 1 (1200–1400 LST, Local Standard Time) featured multiple cell merger (MCM) favored by terrain‐induced circulation. Around the time of MCM, the enhanced ZDR region (>1 dB) broadened horizontally to ∼8 km in width at 5.5 km above mean sea level (AMSL). Afterward, maximum vertical velocity increased dramatically to ∼20 m s −1 and graupel reached up to 12 km AMSL. In contrast, Episode 2 (1500–1700 LST) exhibited isolated cells with weak updrafts (<10 m s −1 ) and more snow aloft. The merged ZDR columns coincided with MCM occurrence, preceding peaks in both vertical velocity and rainfall intensity. Building on emerging research investigating the relationship between ZDR column size and severe weather in the US, this study suggests that wide merged ZDR columns may be relevant to severe storms over complex terrain in Taiwan, highlighting their potential utility as indicators of storm intensification.

Crustaceans account for similar to 25 % of seafood consumption worldwide, however global concerns related to seafood safety have been raised, especially in terms of metal contaminants. Edible tissue concentrations of metals were examined in commercially important velvet crab Necora puber sampled around the Orkney islands, Scotland. Tissue concentrations of non-essential metals (cadmium and lead) were compared to permissible levels (< 0.50 mg.kg(-1) wet wt., European Commission). Cadmium concentrations were below this in all crabs sampled, whilst lead was above this limit in two N. puber individuals from one location. No correlation was observed between metal concentrations and other biometric factors, including the presence of black spot shell disease and claw deformities. Sex differences in tissue levels were observed with female crabs accumulating more cadmium, copper, manganese, and cobalt. This study provides relevant baseline data regarding metal accumulation in N. puber to inform future, recommended monitoring studies.

Abstract. We describe, at an elementary level, the spatially varying properties of the ocean that physical ocean models represent, the principles they use to evolve these properties with time, the physical phenomena that they simulate, and some of the roles these phenomena play within the Earth system. We also describe, in some technical detail, the methods and approximations that the models use and the difficulties that limit their accuracy or reliability.

AbstractTropical cyclone (TC) track forecasting provides essential guidance for coastal communities. However, track forecast errors still occur, highlighting the need for continued research into error sources. Piecewise potential vorticity (PV) inversion is used systematically to quantitatively diagnose errors in track forecasts in four models during the 2017 Atlantic hurricane season. The deep layer mean steering flow (DLMSF) provides a sufficient proxy for hurricane movement, and DLMSF errors are correlated with TC track errors. Analysis of track forecasts for Hurricanes Harvey, Irma, and Maria reveals that their track errors are attributed to steering errors caused by misrepresentations of specific pressure systems. Harvey's westward track error in the GFS resulted from zonal wind errors from the Continental High, while Irma's northward track error in the SHiELD gfsIC resulted from meridional wind errors in the Bermuda High and Continental High. Maria's southward track error in the IFS resulted from meridional wind errors in the Bermuda High and a misrepresentation of Jose to Maria's northwest. The mean absolute error of the DLMSF shows that the Bermuda High contributed the most to steering flow errors in the cases examined. Our results show that piecewise PV inversion can identify the sources of biases in TC track forecasts. The correction of these biases may lead to improved track forecasts. Quantitative diagnostics presented here provide useful information for future model development.

Maerl beds are listed as a priority marine feature in Scotland. They are noted for creating suitable benthic habitat for diverse communities of fauna and flora and in supporting a wide array of ecosystem services. Within the context of climate change, they are also recognised as a potential blue carbon habitat through sequestration of carbon in living biomass and underlying sediment. There are, however, significant data gaps on the potential of maerl carbon sequestration which impede inclusion in blue carbon policy frameworks. Key data gaps include sediment thickness, from which carbon content is extrapolated. There are additional logistical and financial barriers associated with quantification methods that aim to address these data gaps. This study investigates the use of sub-bottom profiling (SBP) to lessen financial and logistical constraints of maerl bed sediment thickness estimation and regional blue carbon quantification. SBP data were cross validated with cores, other SBP data on blue carbon sediments, and analysed with expert input. Combining SBP data with estimates of habitat health (as % cover) from drop-down video (DDV) data, and regional abiotic data, this study also elucidates links between abiotic and biotic factors in determining maerl habitat health and maerl sediment thickness through pathway analysis in structural equation modelling (SEM). SBP data were proved to be sufficiently robust for identification of maerl sediments when corroborated with core data. SBP and DDV data of maerl bed habitats in Orkney exhibited some positive correlations of sediment thickness with maerl % cover. The average maerl bed sediment thickness was 1.08 m across all ranges of habitat health. SEM analysis revealed maerl bed habitat health was strongly determined by abiotic factors. Maerl habitat health had a separate positive effect on maerl bed sediment thickness.

This study introduces a novel concept of 'Adaptively Stacked' Species Distribution Models (AS-SDMs) to predict blue carbon habitat distribution, abundance, carbon stocks, and carbon sequestration potential in Orkney. ASSDMs are built from Weighted Boosted Regression Trees (WBRTs) that adaptively stack blue carbon sediment thickness, sediment carbon content, and sequestration potential to predicted abundance. A novel method to describe substrate types by relative inputs of mud, sand, and gravel is detailed that better characterises the determining factors of seagrass, maerl, and horse mussel abundance. This study also introduces a novel use of indexes to mitigate double counting issues of mixed species distribution models. Seagrass, maerl, horse mussel, and mixed seagrass and maerl (SGM) habitats are estimated to cover a maximum area of 657 km(2) in Orkney, have a total sediment carbon stock of 16 Mt. C, and sequester 6000 t C yr(-1). Applying a conservative threshold of 50 % abundance to habitat predictions, six key potential areas of blue carbon offset projects are identified. These areas cover just over 9 km(2), have a total carbon stock of 330,000 t C, and sequester 330 t C yr(-1). When applied to UK carbon credit value, assuming integration with voluntary markets and compliance with accreditation criteria, the habitats in these areas have a potential value of 24.5 pound million. If applied as annual values, these areas have carbon stocks with a potential value of 0.93 pound million yr(-1) and a carbon sequestration potential value of 24,000 pound yr(-1).

Airborne Doppler radar provides detailed and targeted observations of winds and precipitation in weather systems over remote or difficult-to-access regions that can help to improve scientific understanding and weather forecasts. Quality control (QC) is necessary to remove nonweather echoes from raw radar data for subsequent analysis. The complex decision-making ability of the machine learning random-forest technique is employed to create a generalized QC method for airborne radar data in convective weather systems. A manually QCed dataset was used to train the model containing data from the Electra Doppler Radar (ELDORA) in mature and developing tropical cyclones, a tornadic supercell, and a bow echo. Successful classification of-96% and-93% of weather and nonweather radar gates, respectively, in withheld testing data indicate the generalizability of the method. Dual-Doppler analysis from the genesis phase of Hurricane Ophelia (2005) using data not previously seen by the model produced a comparable wind field to that from manual QC. The framework demonstrates a proof of concept that can be applied to newer airborne Doppler radars. SIGNIFICANCE STATEMENT: Airborne Doppler radar is an invaluable tool for making detailed measurements of wind and precipitation in weather systems over remote or difficult to access regions, such as hurricanes over the ocean. Using the collected radar data depends strongly on quality control (QC) procedures to classify weather and nonweather radar echoes and to then remove the latter before subsequent analysis or assimilation into numerical weather prediction models. Prior QC techniques require interactive editing and subjective classification by trained researchers and can demand considerable time for even small amounts of data. We present a new machine learning algorithm that is trained on past QC efforts from radar experts, resulting in an accurate, fast technique with far less user input required that can greatly reduce the time required for QC. The new technique is based on the random forest, which is a machine learning model composed of decision trees, to classify weather and nonweather radar echoes. Continued efforts to build on this technique could benefit future weather forecasts by quickly and accurately quality-controlling data from other airborne radars for research or operational meteorology.

Abstract Ocean ventilation translates atmospheric forcing into the ocean interior. The Southern Ocean is an important ventilation site for heat and carbon and is likely to influence the outcome of anthropogenic climate change. We conduct an extensive backwards‐in‐time trajectory experiment to identify spatial and temporal patterns of ventilation. Temporally, almost all ventilation occurs between August and November. Spatially, “hotspots” of ventilation account for 60% of open‐ocean ventilation on a 30 years timescale; the remaining 40% ventilates in a circumpolar pattern. The densest waters ventilate on the Antarctic shelf, primarily near the Antarctic Peninsula (40%) and the west Ross sea (20%); the remaining 40% is distributed across East Antarctica. Shelf‐ventilated waters experience significant densification outside of the mixed layer.

The vertical structure of the tropical cyclone (TC) vortex can be quantified throughout the TC life cycle via the dynamic height of the vortex (DHOV) metric, which is sensitive to the rate of decay of the tangential wind field with height. Observed storms always possessed a high DHOV value prior to periods of rapid intensification (RI). When limited to vertically-aligned TCs where the low- to mid-level vortex tilt magnitude is small, all DHOV values are found to be large enough for RI. Vortex tilt results from environmental vertical wind shear (VWS) and a similar relationship is found in an ensemble of TCs simulated in a moderate shear environment. Once vortex tilt decreases, both the observed and ensemble TCs exhibit a concurrent increase of DHOV and intensity, indicating the metric provides useful information about changing vertical structure in both tilted and aligned TCs. The growth of DHOV during RI is closely coupled with a strengthening warm core at the upper levels. A simulation with an upper-level jet of VWS is used to better understand the importance of the upper levels during RI by disrupting vortex development there. DHOV and intensity of the TC are effectively capped in the jet simulation relative to its counterpart in a control simulation, indicating shear can limit TC height without appreciable low- to mid-level tilt. Differences in kinematic and thermal structure between the jet and control runs are found from 12- to 16-km altitude, suggesting the importance of warming near the tropopause in powerful TCs.