Eastern Pacific Basin Research Articles

Quantifying Downstream Climate Impacts of Sea Surface Temperature Patterns in the Eastern Tropical Pacific Using Clustering

El Niño–Southern Oscillation (ENSO) phases and flavors, as well as off-equatorial climate modes, strongly influence sea surface temperature (SST) patterns in the eastern tropical Pacific and downstream climate. Prior studies rely on EOFs (which characterize fractional SST variance) to diagnose climate-scale SST structures, limiting the ability to link individual ENSO flavors with downstream phenomena. Hierarchical and k-means clustering methods are used to construct Eastern Pacific patterns from the ERSST dataset spanning 1950 to 2021. Cluster analysis allows for the direct linkage of individual SST years/seasons to ENSO phase, providing insight into ENSO flavors and associated downstream impacts. In this study, four clusters are revealed, each depicting unique SST patterns influenced by ENSO and Pacific Meridional Mode (PMM) phases. A case study demonstrating the utility of the clusters was also carried out using accumulated cyclone energy (ACE) in the Atlantic and Eastern Pacific basins. Results showed that Eastern Pacific (EP) El Niño suppresses Atlantic tropical cyclone (TC) activity, while Central Pacific (CP) La Niña enhances it. Further, EP El Niño, coupled with positive PMM, amplifies ACE. Ultimately, the methods used herein offer a cleaner analysis tool for identifying dominant SSTA patterns and employing those patterns to diagnose downstream climatic effects.

The characterization and impact of Aeolus wind profile observations in NOAA's regional tropical cyclone model (HWRF)

AbstractObservation system experiments (OSEs) are conducted to assess the potential impacts of horizontal line‐of‐sight wind profile observations from the Aeolus satellite on tropical cyclone (TC) forecasting. The OSEs utilize the operational Hurricane Weather and Research Forecasting (HWRF) model. The OSEs include 226 forecasts from seven TC cases in the Atlantic and Eastern Pacific basins. Comparisons between Aeolus and model background winds show that winds from Aeolus are consistently stronger than those from HWRF. Data assimilation statistics also demonstrate that the greatest potential impacts from the assimilation of Aeolus observations are likely to occur in the upper troposphere and lower stratosphere and within approximately 500 km from the TC centre. For TC forecasting applications, the assimilation of Aeolus observations improves TC intensity and size forecasts in the Eastern Pacific basin, while the results for track forecasts and results from the Atlantic basin are mixed. However, in both basins, the largest and most statistically significant, positive impacts from the assimilation of Aeolus observations occur when reconnaissance flight data are unavailable and during the early stages of TC development. The traditionally used forecast assessments of TC intensity, track and size are rooted in surface‐based metrics, and an additional investigation above the surface demonstrated larger improvements from assimilating Aeolus observations on TC wind structure above 400 hPa as compared to the lower troposphere. Several, different assessments throughout this study demonstrate higher uncertainty and the need for special consideration associated with assimilation techniques within 500 km from the TC centre.

Optimization and impact assessment of Aeolus HLOS wind assimilation in NOAA's global forecast system

AbstractThe European Space Agency Aeolus mission launched the first‐of‐its‐kind space‐borne Doppler wind lidar in August 2018. The Aeolus Level‐2B (L2B) Horizontal Line‐of‐Sight (HLOS) wind observations are integrated into the NOAA Finite‐Volume Cubed‐Sphere Global Forecast System (FV3GFS). Components of the data assimilation system are optimized to increase the forecast impact from these Aeolus observations. Three observing‐system experiments (OSEs) are performed using the Aeolus L2B HLOS winds for the period of August 2–September 16, 2019: a baseline experiment assimilating all observations that are operationally assimilated in NOAA's FV3GFS but without Aeolus; an experiment adding the Aeolus L2B HLOS winds on top of the baseline configuration; and an experiment adding the Aeolus L2B HLOS winds on top of the baseline but also including a total least‐squares (TLS) regression bias correction applied to the HLOS winds. The variances of the Aeolus HLOS wind random errors (i.e., observation errors) are estimated using the Hollingsworth–Lonnberg (HL) method. Results from both OSEs demonstrate positive impact of Aeolus L2B HLOS winds on the NOAA global forecast. The largest impact is seen in the tropical upper troposphere and lower stratosphere where the Day 1–3 wind vector forecast root‐mean‐square error (RMSE) is reduced by up to 4%. Additionally, the assimilation of Aeolus impacts the steering currents ambient to tropical cyclones, resulting in a 15% reduction in track forecast error in the Eastern Pacific basin Day 2–5 forecasts, and a 5% and 20% reduction in track forecast error in the Atlantic basin at Day 2 and Day 5, respectively. In most cases, the additional TLS bias correction increases the positive impact of Aeolus data assimilation in the NOAA global numerical weather prediction (NWP) system when compared to the assimilation of Aeolus without bias correction.

Hurricane Forecasting: A Novel Multimodal Machine Learning Framework

Abstract This paper describes a novel machine learning (ML) framework for tropical cyclone intensity and track forecasting, combining multiple ML techniques and utilizing diverse data sources. Our multimodal framework, called Hurricast, efficiently combines spatial–temporal data with statistical data by extracting features with deep learning encoder–decoder architectures and predicting with gradient-boosted trees. We evaluate our models in the North Atlantic and eastern Pacific basins in 2016–19 for 24-h lead-time track and intensity forecasts and show they achieve comparable mean absolute error and skill to current operational forecast models while computing in seconds. Furthermore, the inclusion of Hurricast into an operational forecast consensus model could improve upon the National Hurricane Center’s official forecast, thus highlighting the complementary properties with existing approaches. In summary, our work demonstrates that utilizing machine learning techniques to combine different data sources can lead to new opportunities in tropical cyclone forecasting. Significance Statement Machine learning techniques have not been fully explored for improving tropical cyclone movement and intensity changes. This work shows how advanced machine learning techniques combined with routinely available information can be used to improve 24-h tropical cyclone forecasts efficiently. The successes demonstrated for 24-h forecasts provide a recipe for improving predictions for longer lead times, further reducing forecast uncertainties and benefiting society.

Important factors in the tracking of tropical cyclones in operational models

AbstractMultiple configurations of the Geophysical Fluid Dynamics Laboratory vortex tracker are tested to determine a setup that produces the best representation of a model forecast tropical cyclone center fix for the purpose of providing track guidance with the highest degree of accuracy and availability. Details of the tracking algorithms are provided, including descriptions of both the Barnes analysis used for center-fixing most variables and a separate scheme used for center-fixing wind circulation. The tracker is tested by running multiple configurations on all storms from the 2015-2017 hurricane seasons in the Atlantic and eastern Pacific Basins using forecasts from two operational National Weather Service models, the Global Forecast System (GFS) and the Hurricane Weather Research and Forecast (HWRF) model. A configuration that tracks only 850 mb geopotential height has the smallest forecast track errors of any configuration based on an individual parameter. However, a configuration composed of the mean of eleven parameters outperforms any of the configurations that are based on individual parameters. Configurations composed of subsets of the eleven parameters and including both mass and momentum variables provide results comparable to or better than the full 11-parameter configuration. In particular, a subset configuration with thickness variables excluded generally outperforms the 11-parameter mean, while one composed of variables from only the 850 mb and near-surface layers performs nearly as well as the 11-parameter mean. Tracker configurations composed of multiple variables are more reliable in providing guidance through the end of a forecast period than are tracker configurations based on individual parameters.

Contrasting Behaviors between the Rapidly Intensifying and Slowly Intensifying Tropical Cyclones in the North Atlantic and Eastern Pacific Basins

AbstractBased on 35-yr (1982–2016) best track and Statistical Hurricane Intensity Prediction Scheme data, this study examined climatology of rapidly intensifying (RI) and slowly intensifying (SI) events as well as their time evolutions of storm-related and environmental parameters for tropical cyclones (TCs) in both North Atlantic (AL) and eastern North Pacific (EP) basins. Major hurricanes were intensified mainly through RI while tropical depression and tropical storms were intensified through SI. The percentage of TCs that underwent RI peaks in the late hurricane season whereas the percentage of TCs that underwent SI peaks early. For the first time in the literature, this study found that RI events have significantly different storm-related and environmental characteristics than SI events for before-, during-, and after-event stages. In both AL and EP basins, RI events always intensify significantly faster during the previous 12 h, are located farther south, and have warmer sea surface and 200-hPa temperatures, greater ocean heat content, larger 200-hPa divergence, weaker vertical wind shear, and weaker 200-hPa westerly flow than SI events for all event-relative stages. In the AL basin, RI events have larger low-level and midlevel relative humidity and larger 850-hPa relative vorticity than SI events for all event-relative stages in the AL and most event-relative stages in the EP. RI events are associated with more convectively unstable atmosphere and are farther away from their maximum potential intensities than SI events for most event-relative stages in the AL and for all event-relative stages in the EP.

A Quantitative Tomotectonic Plate Reconstruction of Western North America and the Eastern Pacific Basin

AbstractPlate reconstructions since the breakup of Pangaea are mostly based on the preserved spreading history of ocean basins, within absolute reference frames that are constrained by a combination of age‐progressive hotspot tracks and paleomagnetic data. The evolution of destructive plate margins is difficult to constrain from surface observations as much of the evidence has been subducted. Seismic tomography can directly constrain paleotrench locations by imaging subducted lithosphere in the mantle. This new evidence, combined with the geological surface record of subduction, suggests that several intraoceanic arcs existed between the Farallon Ocean and North America during late Mesozoic times—in contrast to existing quantitative models that typically show long‐lived subduction of the Farallon plate beneath the continental margin. We present a continuously closing plate model for the eastern Pacific basin from 170 Ma to present, constrained using “tomotectonic analysis”—the integration of surface and subsurface data. During the Middle to Late Jurassic, we show simultaneous eastward and westward subduction of oceanic plates under an archipelago composed of Cordilleran arc terranes. As North America drifts westward, it diachronously overrides the archipelago and its arcs, beginning in the latest Jurassic. During and post‐accretion, Cordilleran terranes are translated thousands of kilometers along the continental margin, as constrained by paleomagnetic evidence. Final accretions to North America occur during the Eocene, ending ~100 Myr of archipelago override. This model provides a detailed, quantitative tectonic history for the eastern Pacific domain, paving the way for tomotectonic analysis to be used in other paleo‐oceanic regions.

Forecasts of Hurricanes Using Large-Ensemble Outputs

AbstractThis paper describes the development of a model framework for Forecasts of Hurricanes Using Large-Ensemble Outputs (FHLO). FHLO quantifies the forecast uncertainty of a tropical cyclone (TC) by generating probabilistic forecasts of track, intensity, and wind speed that incorporate the state-dependent uncertainty in the large-scale field. The main goal is to provide useful probabilistic forecasts of wind at fixed points in space, but these require large ensembles [O(1000)] to flesh out the tails of the distributions. FHLO accomplishes this by using a computationally inexpensive framework, which consists of three components: 1) a track model that generates synthetic tracks from the TC tracks of an ensemble numerical weather prediction (NWP) model, 2) an intensity model that predicts the intensity along each synthetic track, and 3) a TC wind field model that estimates the time-varying two-dimensional surface wind field. The intensity and wind field of a TC evolve as though the TC were embedded in a time-evolving environmental field, which is derived from the forecast fields of ensemble NWP models. Each component of the framework is evaluated using 1000-member ensembles and four years (2015–18) of TC forecasts in the Atlantic and eastern Pacific basins. We show that the synthetic track algorithm generates tracks that are statistically similar to those of the underlying global ensemble models. We show that FHLO produces competitive intensity forecasts, especially when considering probabilistic verification statistics. We also demonstrate the reliability and accuracy of the probabilistic wind forecasts. Limitations of the model framework are also discussed.

Performance of the HWRF Rapid Intensification Analog Ensemble (HWRF RI-AnEn) during the 2017 and 2018 HFIP Real-Time Demonstrations

Abstract The performance of the Hurricane Weather Research and Forecasting (HWRF) Model Rapid Intensification Analog Ensemble (RI-AnEn) is evaluated for real-time forecasts made during the National Oceanic and Atmospheric Administration (NOAA)’s 2018 Hurricane Forecast Improvement Program (HFIP) demonstration. Using a variety of assessment tools (Brier skill score, reliability diagrams, ROC curves, ROC skill scores), RI-AnEn is shown to perform competitively compared to both the deterministic HWRF and current operational probabilistic RI forecast aids. The assessment is extended to include forecasts from the 2017 HFIP demonstration and shows that RI-AnEn is the only model with significant RI forecast skill at all lead times in the Atlantic and eastern Pacific basins. Though RI-AnEn is overconfident in its RI forecasts, it is generally well calibrated for all lead times. Furthermore, significance testing indicates that for the 2017–18 Atlantic and eastern Pacific sample, RI-AnEn is more skillful than HWRF at all lead times and better than most of the other probabilistic guidance at 48 and 72 h. ROC curves reveal that RI-AnEn offers a good combination of sensitivity and specificity, performing comparably to SHIPS-RII at all lead times in both basins. With respect to specific high-impact cases from the 2018 Atlantic season, performance of RI-AnEn ranges from excellent (Hurricane Michael) to poor (Hurricane Florence). The multiyear assessment and results for two high-impact case studies from 2018 indicate that, while promising, RI-AnEn requires further work to refine its performance as well as to accurately situate its effectiveness relative to other RI forecasts aids.

220 Year Diatom δ18O Reconstruction of the Guaymas Basin Thermocline Using Microfluorination

AbstractThe removal of exchangeable oxygen from diatom opal prior to δ18Odiatom analysis is a crucial first step before analyzing frustule oxygen isotopes for paleoceanographic applications. We present the results of experiments that quantify the temperature‐dependent reactivity of biogenic silica with water under laboratory conditions. We demonstrate that controlled exchange between rinse water and diatom opal at room temperature results in predictable alteration of δ18Odiatom, after vacuum dehydroxylation. Diatom samples equilibrated with an 18O‐enriched δ18Oequil. water solution of +94.4‰ at ~21 °C for 70 hr prior to dehydroxylation yield δ18Odiatom data that can be directly interpreted with existing empirical δ18Odiatom versus temperature relationships. We compare sediment trap‐based δ18Odiatom temperature data with modern δ18Oforam temperatures. Finally, we present an ~220 year record of δ18Odiatom data from an eastern Pacific Guaymas Basin boxcore, analyzed using the microfluorination technique, that indicates diatom chlorophyll maximum (Chlmax) temperatures have shifted from a period during the late eighteenth and nineteenth centuries that was 8 °C cooler during the fall bloom relative to mean annual alkenone sea surface temperature, to the late twentieth century when we observe no difference between alkenone sea surface temperature and diatom Chlmax. These data suggest a twentieth century seasonal shift in the timing of fall upwelling and Guaymas Basin stratification breakdown that could be due to reduced upwelling efficiency and/or increased summer mixed layer thermal stratification.

Mechanisms of Future Changes in Equatorial Upwelling: CMIP5 Intermodel Analysis

AbstractThe future change in equatorial upwelling between 1971–2000 and 2071–2100 is investigated using data from 24 coupled climate models. The multimodel ensemble (MME) mean exhibits substantial equatorial upwelling decrease in the eastern Pacific and weaker decrease in the western Atlantic Ocean. The MME mean of upwelling change and intermodel variation of that are decomposed into distinct isopycnal and diapycnal components. In the Pacific, the diapycnal upwelling decreases near the surface, associated with a weakened Ekman pumping. The isopycnal upwelling decreases at depths of 75–200 m around the core of the Equatorial Undercurrent (EUC) due to flattening of the density layer in which it flows. Both the weakened Ekman pumping and the EUC flattening are induced by the locally weakened trade wind over the eastern Pacific basin. In the equatorial Atlantic, both the change in MME mean and the intermodel variation of upwellings are significantly related to the weakened trade wind and enhanced stratification, although these drivers are not independent. The results for the Pacific Ocean imply that future reduction in upwelling may have impacts at different depths by different mechanisms. In particular, the rapid warming of sea surface temperature in the eastern Pacific basin may be mainly caused by the near-surface diapycnal upwelling reduction rather than isopycnal upwelling reduction associated EUC flattening, which is important at deeper levels.

A hybrid deterministic and stochastic approach for tsunami hazard assessment in Iquique, Chile

The southern Peru and northern Chile coastal region is an active subduction zone that contains one of the most significant seismic gaps in the eastern Pacific basin (~ 17°S–~ 24°S). Although the gap was partially filled by the 2014 Mw 8.1 Iquique earthquake, there is still a high seismogenic potential to release a Mw ~ 9 earthquake in the near future; therefore, all the near-field coastal cities in the region face a latent tsunami threat. In this article, we propose a hybrid deterministic–stochastic multi-scenario approach to assess the current tsunami hazard level in the city of Iquique, an important commercial and industrial center of northern Chile that is home to 184,000 inhabitants. In our approach, we defined 400 stochastic, 10 deterministic and 10 homogeneous tsunamigenic earthquake scenarios, covering the entire area of the seismic gap. Based on the regional distribution of gravity anomalies and published interseismic coupling distributions, we interpreted the occurrence of four major asperities in the subduction interface of the seismic gap. The asperity pattern was used to construct a group of deterministic slip-deficit earthquake sources with seismic magnitudes ranging between Mw 8.4 and Mw 8.9. Additionally, we constructed 10 homogeneous slip scenarios to generate an inundation baseline for the tsunami hazard. Subsequently, following a stochastic scheme, we implemented a Karhunen–Loeve expansion to generate 400 stochastic earthquake scenarios within the same magnitude range as the deterministic slip-deficit sources. All sources were used as earthquake scenarios to simulate the tsunami propagation and inundation by means of a non-hydrostatic model (Neowave 2D) with a classical nesting scheme for the city of Iquique. We obtained high-resolution data for flow depth, coastal surface currents and sea level elevation. The results suggest that the peak slip location and shelf resonance play an important role in the calculated coastal flow depths. The analysis of the entire set of simulated stochastic earthquake scenarios indicates that the worst-case scenario for Iquique is a Mw 8.9 earthquake. This scenario presented a tsunami arrival time of ~ 12 min, which is critical for the evacuation process. In addition, the maximum wave height and tsunami flow depth were found to be ~ 10 m and ~ 24 m, respectively. The observed coastal resonance processes exhibit at least three destructive tsunami wave trains. Based on historical and instrumental catalog statistics, the recurrence time of the credible worst-case earthquake scenario for Iquique (Mw 8.9) is 395 years, with a probability of occurrence of ~ 11.86% in the next 50 years.

On the Extraordinary Intensification of Hurricane Patricia (2015). Part I: Numerical Experiments

AbstractHurricane Patricia (2015) broke records in both peak intensity and rapid intensification (RI) rate over the eastern Pacific basin. All of the then-operational models predicted less than half of its extraordinary intensity and RI rate, leaving a challenge for numerical modeling studies. In this study, a successful 42-h simulation of Patricia is obtained using a quintuply nested-grid version of the Weather Research and Forecast (WRF) Model with the finest grid size of 333 m. Results show that the WRF Model, initialized with the Global Forecast System Final Analysis data only, could reproduce the track, peak intensity, and many inner-core features, as verified against various observations. In particular, its simulated maximum surface wind of 92 m s−1 is close to the observed 95 m s−1, capturing the unprecedented RI rate of 54 m s−1 (24 h)−1. In addition, the model reproduces an intense warm-cored eye, a small-sized eyewall with a radius of maximum wind of less than 10 km, and the distribution of narrow spiral rainbands. A series of sensitivity simulations is performed to help understand which model configurations are essential to reproducing the extraordinary intensity of the storm. Results reveal that Patricia’s extraordinary development and its many inner-core structures could be reasonably well simulated if ultrahigh horizontal resolution, appropriate model physics, and realistic initial vortex intensity are incorporated. It is concluded that the large-scale conditions (e.g., warm sea surface temperature, weak vertical wind shear, and the moist intertropical convergence zone) and convective organization play important roles in determining the predictability of Patricia’s extraordinary RI and peak intensity.

Barrier Layer Development Local to Tropical Cyclones based on Argo Float Observations

AbstractThe objective of this study is to quantify barrier layer development due to tropical cyclone (TC) passage using Argo float observations of temperature and salinity. To accomplish this objective, a climatology of Argo float measurements is developed from 2001 to 2014 for the Atlantic, eastern Pacific, and central Pacific basins. Each Argo float sample consists of a prestorm and poststorm temperature and salinity profile pair. In addition, a no-TC Argo pair dataset is derived for comparison to account for natural ocean state variability and instrument sensitivity. The Atlantic basin shows a statistically significant increase in barrier layer thickness (BLT) and barrier layer potential energy (BLPE) that is largely attributable to an increase of 2.6 m in the post-TC isothermal layer depth (ITLD). The eastern Pacific basin shows no significant changes to any barrier layer characteristic, likely due to a shallow and highly stratified pycnocline. However, the near-surface layer freshens in the upper 30 m after TC passage, which increases static stability. Finally, the central Pacific has a statistically significant freshening in the upper 20–30 m that increases upper-ocean stratification by ~35%. The mechanisms responsible for increases in BLPE vary between the Atlantic and both Pacific basins; the Atlantic is sensitive to ITLD deepening, while the Pacific basins show near-surface freshening to be more important in barrier layer development. In addition, Argo data subsets are used to investigate the physical relationships between the barrier layer and TC intensity, TC translation speed, radial distance from TC center, and time after TC passage.

Probabilistic Prediction of Tropical Cyclone Intensity with an Analog Ensemble

An analog ensemble (AnEn) technique is applied to the prediction of tropical cyclone (TC) intensity (i.e., maximum 1-min averaged 10-m wind speed). The AnEn is an inexpensive, naturally calibrated ensemble prediction of TC intensity derived from a training dataset of deterministic Hurricane Weather Research and Forecasting (HWRF; 2015 version) Model forecasts. In this implementation of the AnEn, a set of analog forecasts is generated by searching an HWRF archive for forecasts sharing key features with the current HWRF forecast. The forecast training period spans 2011–15. The similarity of a current forecast with past forecasts is estimated using predictors derived from the HWRF reforecasts that capture thermodynamic and kinematic properties of a TC’s environment and its inner core. Additionally, the value of adding a multimodel intensity consensus forecast as an AnEn predictor is examined. Once analogs are identified, the verifying intensity observations corresponding to each analog HWRF forecast are used to produce the AnEn intensity prediction. In this work, the AnEn is developed for both the eastern Pacific and Atlantic Ocean basins. The AnEn’s performance with respect to mean absolute error (MAE) is compared with the raw HWRF output, the official National Hurricane Center (NHC) forecast, and other top-performing NHC models. Also, probabilistic intensity forecasts are compared with a quantile mapping model based on the HWRF’s intensity forecast. In terms of MAE, the AnEn outperforms HWRF in the eastern Pacific at all lead times examined and up to 24-h lead time in the Atlantic. Also, unlike traditional dynamical ensembles, the AnEn produces an excellent spread–skill relationship.

Multivariate integer-valued time series with flexible autocovariances and their application to major hurricane counts

This paper examines a bivariate count time series with some curious statistical features: Saffir–Simpson Category 3 and stronger annual hurricane counts in the North Atlantic and eastern Pacific Ocean Basins. As land and ocean temperatures on our planet warm, an intense climatological debate has arisen over whether hurricanes are becoming more numerous, or whether the strengths of the individual storms are increasing. Recent literature concludes that an increase in hurricane counts occurred in the Atlantic Basin circa 1994. This increase persisted through 2012; moreover, the 1994–2012 period was one of relative inactivity in the eastern Pacific Basin. When Atlantic activity eased in 2013, heavy activity in the eastern Pacific Basin commenced. When examined statistically, a Poisson white noise model for the annual severe hurricane counts is difficult to resoundingly reject. Yet, decadal cycles (longer term dependence) in the hurricane counts is plausible. This paper takes a statistical look at the issue, developing a stationary multivariate count time series model with Poisson marginal distributions and a flexible autocovariance structure. Our auto- and cross-correlations can be negative and have long-range dependence; features that most previous count models cannot achieve in tandem. Our model is new in the literature and is based on categorizing and super-positioning multivariate Gaussian time series. We derive the autocovariance function of the model and propose a method to estimate model parameters. In the end, we conclude that severe hurricane counts are indeed negatively correlated across the two ocean basins. Some evidence for long-range dependence is also presented; however, with only a 49-year record, this issue cannot be definitively judged without additional data.

Rewriting the Tropical Record Books: The Extraordinary Intensification of Hurricane Patricia (2015)

Abstract Hurricane Patricia was a historic tropical cyclone that broke many records, such as intensification rate, peak intensity, and overwater weakening rate, during its brief 4-day lifetime in late October 2015 in the eastern Pacific basin. Patricia confounded all of the intensity forecast guidance owing to its rapid intensity changes. Fortunately, the hurricane-penetrating National Oceanic and Atmospheric Administration WP-3D and U.S. Air Force C-130 aircraft and the National Aeronautics and Space Administration WB-57 high-altitude jet, under support of the Office of Naval Research, conducted missions through and over Patricia prior to and during its extreme intensity changes on all 4 days, while an extensive array of pressure sensors sampled Patricia after landfall. The observations collected from these missions include traditional data sources such as airborne Doppler radar and flight-level instruments as well as new data sources like a high-density array of dropsondes released from high-altitude and wide-swath radiometer. The combination of data from these sources and from satellites provides an excellent opportunity to investigate the physical processes responsible for Patricia’s structure and evolution and offers the potential to improve forecasts of tropical cyclone rapid intensity changes. This paper provides an overview of Patricia as well as the data collected during the aircraft missions.

Impact of Storm Size on Prediction of Storm Track and Intensity Using the 2016 Operational GFDL Hurricane Model

Abstract The impact of storm size on the forecast of tropical cyclone storm track and intensity is investigated using the 2016 version of the operational GFDL hurricane model. Evaluation was made for 1529 forecasts in the Atlantic, eastern Pacific, and western North Pacific basins, during the 2014 and 2015 seasons. The track and intensity errors were computed from forecasts in which the 34-kt (where 1 kt = 0.514 m s−1) wind radii obtained from the operational TC vitals that are used to initialize TCs in the GFDL model were replaced with wind radii estimates derived using an equally weighted average of six objective estimates. It was found that modifying the radius of 34-kt winds had a significant positive impact on the intensity forecasts in the 1–2 day lead times. For example, at 48 h, the intensity error was reduced 10%, 5%, and 4% in the Atlantic, eastern Pacific, and western North Pacific, respectively. The largest improvements in intensity forecasts were for those tropical cyclones undergoing rapid intensification, with a maximum error reduction in the 1–2 day forecast lead time of 14% and 17% in the eastern and western North Pacific, respectively. The large negative intensity biases in the eastern and western North Pacific were also reduced 25% and 75% in the 12–72-h forecast lead times. Although the overall impact on the average track error was neutral, forecasts of recurving storms were improved and tracks of nonrecurving storms degraded. Results also suggest that objective specification of storm size may impact intensity forecasts in other high-resolution numerical models, particularly for tropical cyclones entering a rapid intensification phase.

Understanding the geographic distribution of tropical cyclone formation for applications in climate models

Projections of Tropical cyclone (TC) formation under future climate scenarios are dependent on climate model simulations. However, many models produce unrealistic geographical distributions of TC formation, especially in the north and south Atlantic and eastern south Pacific TC basins. In order to improve confidence in projections it is important to understand the reasons behind these model errors. However, considerable effort is required to analyse the many models used in projection studies. To address this problem, a novel diagnostic is developed that provides compelling insight into why TCs form where they do, using a few summary diagrams. The diagnostic is developed after identifying a relationship between seasonal climatologies of atmospheric variables in 34 years of ECMWF reanalysis data, and TC detection distributions in the same data. Geographic boundaries of TC formation are constructed from four threshold quantities. TCs form where Emanuel’s Maximum Potential Intensity, $$V_{{PI}}$$ , exceeds $$40\,\,{\text{ms}}^{{ - 1}}$$ , 700 hPa relative humidity, $$RH_{{700}}$$ , exceeds 40%, and the magnitude of the difference in vector winds between 850 and 200 hPa, $$V_{{sh}}$$ , is less than $$20~\,\,{\text{ms}}^{{ - 1}}$$ . The equatorial boundary is best defined by a composite quantity containing the ratio of absolute vorticity $$(\eta )~$$ to the meridional gradient of absolute vorticity ( $$\beta ^{*}$$ ), rather than $$\eta$$ alone. $${\beta ^*}$$ is also identified as a potentially important ingredient for TC genesis indices. A comparison of detected Tropical Depression (TD) and Tropical Storm (TS) climatologies revealed TDs more readily intensify further to TS where $${V_{PI}}$$ is elevated and $${V_{sh}}$$ is relatively weak. The distributions of each threshold quantity identify the factors that favour and suppress TC formation throughout the tropics in the real world. This information can be used to understand why TC formation is poorly represented in some climate models, and shows potential for understanding anomalous TC formation behaviour in the real world.

Biogeographical barriers to dispersal and rare gene flow shape population genetic structure in red‐footed boobies ( Sula sula )

Aim The ecological and biogeographical factors that influence population differentiation in seabirds are likely to be too complex and nuanced to be captured by many commonly held models of population differentiation. Pelagic seabirds exhibit high natal philopatry but can disperse long distances, potentially leading to long periods of genetic isolation with rare gene flow between isolated populations. Here, we assess levels of population genetic structure and gene flow in red-footed boobies (Sula sula) to test two hypotheses: (1) terrestrial and marine barriers to gene flow have caused population genetic differentiation among red-footed booby populations, and (2) despite high genetic differentiation among populations, rare gene flow still occurs among populations. Location Tropical oceans. Methods We genotyped 282 red-footed boobies at microsatellite, nuclear intron and mitochondrial markers. We applied basic tests of population differentiation, such as analysis of molecular variance, and applied modern statistical phylogeographical methods (e.g., structure, BayesAss) to investigate the history of gene flow. Results Red-footed boobies form four major genetic populations that correspond to oceanographic regions (Atlantic, Indian, Western+Central Pacific and eastern Pacific Oceans). However, gene flow has occurred recently among these populations. Further differentiation was found within ocean basins. Although the levels of gene flow within ocean basins are more difficult to assess, gene flow appears to have occurred between genetically differentiated Indian Ocean colonies. Main conclusions The population genetic structure of red-footed boobies has been driven primarily by major continental and marine barriers to gene flow that also drive differentiation in other pelagic seabirds (e.g., the Isthmus of Panama and eastern Pacific basin). However, dispersal across marine barriers (but not major continental barriers) appears to have been relatively common. We suggest that such patterns of long-term isolation and rare gene flow may be common to pelagic seabirds.