Gary Lackmann

The response of tropical cyclones (TCs) and their associated impacts to climate change has been and continues to be a major research focus. A large fraction of the economic loss associated with tropical cyclones occurs after landfall, yet there has been limited research to date to understand how post-landfall TC intensity and impacts may respond to climate change. Some recent studies present initial findings which indicate the potential for slower TC decay in a warmer climate, and that this effect is substantial and is already discernible in historical data (Li and Chakraborty 2020, hereafter LC20). Many factors and processes act in concert to determine the rate of post-landfall TC decay, complicating isolation of the effects of individual mechanisms. Previous studies of TC decay often emphasize local effects, such as land-surface and soil characteristics, terrain, and geographical location. Other effects, such as variations in extratropical interactions during or after landfall, have received less attention. In a series of recent papers on TC extratropical transition (ET) and climate change, the PI and collaborators found consistent evidence for a poleward shift in TC activity in the North Atlantic basin with climate warming. Composite analyses of TC-allowing global simulations under warming indicate strengthened jet interactions for ET events in a future, warmer climate, consistent with the northward shift in TC activity. This is also consistent with LC20, which recommends study of post-landfall extratropical interactions as an important research topic. Here, our proposed work includes a hierarchy of analysis methods and modeling strategies to answer the question of how post-landfall TC decay and impacts will change in a warming climate. In addition to impacts (wind and precipitation), we emphasize examination of changes in TC decay rate resulting from changes in landfall location and proximity to the jet stream, as this aspect builds directly on our recent ET work. Idealized and real-data case studies, TC-allowing global model simulations, and new CMIP6 high-resolution simulations will all be analyzed in the proposed work. Intellectual Merit: The proposed research is a logical extension of our recent work on climate change and ET, which found strong basin-to-basin sensitivity of ET responses to warming with effects maximized in the North Atlantic basin. The current proposal will leverage existing datasets and numerical modeling strategies to extend the ET research into the critically important post-landfall TC lifecycle. The PI has extensive experience with this topic, dating back to the Bosart and Lackmann (1995) study of a post-landfall TC re-intensification event; the PI has published several recent studies examining how weather extremes (including TCs and ET) respond to climate change. Broader Impacts: Enhanced TC-jet interactions and increased moisture content increase the possibility of reduced post-landfall TC decay rates. Any northward or inland extension of TC hazards increases risk exposure for potentially under-prepared populations. Knowledge of changes in TC decay rate and hazard distribution will inform resilience and mitigation efforts. Student professional development will benefit from collaborations with NCAR as well as state agencies. This project will support a current PhD student who is a member of two underrepresented groups.

Overview Warm season weather and climate extremes������������������flooding rains, heat waves, and droughts������������������ have devastating impacts on people and nature. These extremes and their impacts are expected to become more severe as Earth warms, a trend that is increasingly being observed. These phenomena challenge our scientific understanding and our modeling systems, because they involve disparate processes operating across wide ranges of scales, both spatially (regional to global) and temporally (convective to seasonal). Moreover, there is growing evidence of interactions among these scales. For example, the large-scale flow that results in a heat wave, which is then amplified by local interactions with the land surface, may, in turn, be modified by the presence of that regional heat. Likewise, the latent heating of the atmosphere associated with heavy rains may influence the circulation on much larger scales. Simulating such phenomena with sufficient veracity to address associated scientific questions and to project their responses to climate change, therefore, demands modeling approaches that span as wide a range of scales as is feasible, allowing model outputs to be interrogated at meso- or even cloud-scales. Here we propose a program of research, focused on how climate change will affect warm-season weather and climate extremes in North America. The research will comprise analyses of existing output of climate models contributed to the 6th Coupled Model Intercomparison Project (CMIP6) in addition to our own high-resolution (15-km grid) numerical experiments using the Model for Prediction Across Scales-Atmosphere (MPAS-A). These simulations will start from a 30-year baseline run simulating the current climate. Future simulations will include resimulations of extreme events native to the control run under future climate conditions and 30 free running warm season time-slices. Extreme events from the time-slice simulations will be re-simulated to enable hour-by-hour analyses of physical processes within the model to determine how they are modified by climate change. Event re-simulations at convection permitting resolution will also be explored. Intellectual Merit The intellectual merit of this research is in building a predictive understanding of future changes in warm-season climate extremes, by building on existing (CMIP) climate simulations and using modeling strategies that include the multiple relevant scales of the physical climate system that participate in these extremes and the interactions among them. The PI team������������������s expertise spans weather and climate dynamics, and the influence of climate change on extreme events. Broader Impacts Broader impacts of this work will include better informing the public about future climate extremes and developing future climate scientists. The latter will be accomplished via graduate student training, undergraduate research participation, and collaboration with the North Carolina Museum of Natural Sciences in activities for high school students that will introduce them, through hands-on activities, to climate science and will inform them about pathways to future careers in climate science, and STEM generally. An open data-access strategy that facilitates classroom and project use of weather and climate datasets, including those produced by this project, will help to build data science, programming, and analysis skills for undergraduate and graduate students at NC State and Northern Illinois Universities.

The frequency and intensity of both floods and droughts are expected to increase in response to climate change; however, significant uncertainties remain regarding regional changes, especially for extreme rainfall. In particular, North Carolina������������������s geographic position makes it vulnerable to several natural hazards that pose significant flooding risks, including hurricanes, severe thunderstorms, and large winter storms. The most obvious problems within NC in recent years are the pluvial and fluvial flooding from notable hurricanes which paralyzed the eastern NC highway system for days to weeks. The heavy rainfall associated with Hurricanes Floyd (1999), Matthew (2016), Florence (2018), and Dorian (2019) generated record-breaking fluvial flooding along key economic corridors including I-95, I-40, US-70, NC-12, and US-64, and created a chain of transportation infrastructure problems that affected emergency response operations and the transportation of goods. In particular, I-95 facilitates 40 percent of the Nation������������������s GDP while US-70 and I-40 are key routes for supporting the military, agriculture and the economy in eastern NC. Though hurricanes receive a lot of attention in resilient design, as they should, transportation engineers face additional challenges, including possible changes to rainfall intensity from localized thunderstorms and even drought. NC officials, recognizing the risks posed by a changing climate, developed Executive Order 80 (EO80) to help protect the people, natural environment, and economy of North Carolina. NC DOT is likewise working to implement solutions to become more resilient to weather extremes in a changing climate. This objective of this study is to improve confidence in climate change projections by quantifying future precipitation extremes within NC for resilient design (e.g., precipitation intensity, duration, frequency curves). This project will incorporate guidance developed for the National Cooperative Highway Transportation Research Board, NCHRP 15-61, with additional methods and numerical model experiments to improve confidence in future precipitation extremes, and to inform design concepts for potential future events.

Our ongoing research has identified characteristic synoptic and mesoscale weather patterns accompanying atmospheric rivers (ARs) in the eastern North Pacific, and has evaluated associated variations in predictability for these patterns. We see evidence that in some cases a particular mesoscale feature contributes substantially to the strength of AR moisture transport: A band of cyclonic, lower-tropospheric potential vorticity (PV) along the ���left bank��� of the AR. Using self-organizing maps (SOMs), we have explored the variation of predictive forecast skill metrics for the distinct synoptic patterns accompanying ARs, and we identified a set of cases that exhibits lower predictive skill in association with this feature. The PV configuration for a subset of these cases features an ���S-shaped��� lower-tropospheric PV pattern that resembles expectations for mesoscale frontal waves. Leveraging the CW3E West-WRF reforecast, Laurel DeHaan���s predictive skill metrics, and SOM analysis, we are now poised to pinpoint specific challenging patterns and the related physical processes that will potentially lead to improved model representations and forecasts for low-skill cases. This work improves situational awareness during operational AR forecasting by elucidating a priori which patterns are most likely to feature reduced forecast skill. Understanding patterns and processes related to reduced skill will also be useful in guiding AR reconnaissance data collection and may aid in the interpretation of ensemble sensitivity results. Improved process representation and initial conditions for West-WRF or other forecast models in these low-skill events would represent a substantial improvement in operational AR prediction.

Mesoscale snow bands have been the subject of extensive research over the past few decades, owing in large part to their ability to impart significant societal impacts. Previously, we developed an automated snowband detection algorithm, and applied it to produce an objective snow band climatology and to evaluate band predictability in the HRRR model. We also tested object-oriented band verification strategies. An additional component of the project explored two novel model diagnostics that show promise in the prediction of heavy snowfall: the vertical snow flux, and the depositional snow growth. Here, we extend these previous efforts into the realm of high-resolution ensemble prediction, with emphasis on ensemble verification and predictability, and on further exploration of model output diagnostics. Now that we������������������ve developed and tested automated detection methods for mesoscale snow bands, we are able to run the detection algorithm on model forecasts, and for the case of convection-allowing ensembles, we can use the algorithm to generate probabilistic snow band predictions. We propose novel visualization strategies for band forecasts, including paintball plots, glyphs, probabilistic heat maps, and ensemble surface slicing; we will draw on visualization and science communication expertise at NC State in other departments to optimize these displays. User ability to interpret such graphics is equally important to the development of visuals. In this regard, we propose to take advantage of the recent addition of science-communication expertise at NC State. Modern numerical weather prediction models often include sophisticated cloud and precipitation microphysical parameterizations (MP). Under prior support, we recognized that some crucial microphysical information that is computed with high accuracy in the Thompson MP is not written as output. We modified a recent release of the Weather Research and Forecasting model, configured similarly to the HRRR operational model, to output the vertical snow flux and also the depositional and total snow growth. This exercise proved to be revealing: These metrics show distinct signatures within and upwind of heavy snowfall, with lofting of snow being ubiquitous upwind of mesoscale snow bands. Furthermore, forecasters often utilize visualization software such as BUFKIT to subjectively evaluate the presence of depositional snow growth. We propose that it would be more reliable to output this quantity directly from the HRRR model, and visualize it as a 2- or 3-dimensional field. In an ensemble sense, this could be combined with the aforementioned methods to provide operational forecasters with a much more complete picture of the microphysical structure of winter storms. During previous collaborative research, we have established collaborations at the Weather Prediction Center (WPC), the Environmental Modeling Center (EMC), and several NWS Forecast Offices. We have participated in the WPC Winter Experiment in the past, and have been invited to participate again this winter (Feb 2019). Such participation would provide an ideal opportunity to implement and test our methods in an operational setting. Graduate student Jacob Radford, who completed his MS thesis under prior support on this topic, will continue as a PhD student; Jacob is highly capable with various computational platforms, and his expertise will allow us to directly implement any of the proposed methods found to be of operational utility.

Persistent anomalies in the atmospheric circulation are unusual states of the atmospheric flow and conditions that remain approximately fixed over periods longer than a few days. Such states disrupt the daily march of weather in the extratropics. Many impacts of weather on human and natural systems are cumulative: the desiccation of soils and vegetation in a drought, the saturation of the ground during an extended period of rain, and the toll taken on people and societal infrastructure by prolonged heat or cold. Thus, persistent anomalies produce significant human impacts. Understanding and ultimately projecting how the frequency, distribution, and intensity of persistent anomalies will change with changes in global climate is necessary for projecting the impacts of climate change on nature and society. Interactions with extratropical cyclones are critical for initiating and sustaining persistent anomalies. The work proposed here focuses on these interactions and builds on the PIs������������������ prior research exploring the changes in cyclones with climate. It was found that the increases with global warming in specific humidity leads to increased release of latent heat in storms, with consequent effects on storm structure and intensity. The representation of these effects was shown to exhibit strong sensitivity to model resolution. The studies proposed here are motivated by hypotheses that address the importance of cyclones, especially strong cyclones, for persistent anomalies, through their initiation and maintenance and through their conditioning of the large scale circulation on which persistent anomalies occur. Simulations will include ensemble studies of individual persistent anomaly events and the climatology of persistent anomalies in multi-year experiments, under present-day and future climate conditions. It is anticipated that model configurations (resolutions and choices of parameterizations) that produce better hindcasts of individual events will yield better climatologies of persistent anomalies under present-day conditions. These configurations can then be applied to future events and climatologies, by imposing global warming conditions. Results will be analyzed using potential vorticity and Rossby wave-breaking diagnostics and cyclone tracking. Key precursors to persistent anomalies in the case studies will be determined using model ����������������surgery���������������, in which dynamical features can be deleted from the initial conditions. A single-layer model will be used to obtain dynamical understanding of the importance of changes in cyclones and the background flow in producing changes in persistent anomalies.

In our earlier research on atmospheric rivers (ARs), we identified a particular mesoscale feature that contributes substantially to the strength of the moisture transport in the ARs: A band of cyclonic potential vorticity (PV) that represents the ����������������left bank��������������� of the AR. Inversion of the PV field shows that the poleward flow to the east of PV bands can contribute up to 40% of the strength of the low-level jet (LLJ; e.g., Lackmann 2002). A positive feedback can operate in these situations: The PV feature is the result of condensational heating, which is related to horizontal moisture flux convergence. However, the moisture flux itself is enhanced by this cyclonic PV feature. Strengthened moisture transport can result in enhanced condensational heating, further strengthening the PV anomaly in a positive feedback. It is understood that model representation of condensational heating, and its vertical profile, is highly uncertain and strongly dependent on details in model parameterization schemes. In our ongoing research, we are working to test the hypothesis that uncertainty in heating parameterization, and thus in representation in this lower PV strip, contributes significant forecast uncertainty for ARs. However, as the research advances, additional opportunities have come into focus, and these are the subject of the current proposal.

Severe storms in the Southeastern U.S. are associated with higher fatality rates and lower warning skill scores than those in the Plains. Many such events occur in environments characterized by large environmental vertical wind shear (����������������high shear���������������) but weak instability (����������������low CAPE���������������). These high-shear low-CAPE (����������������HSLC���������������) conditions are associated with both low predictability and high tornado warning false alarm rates. Our regional NWS collaborators have identified these issues as among the most pressing concerns for their WFOs. The goal of this proposal is to address several remaining gaps in our understanding of HSLC severe weather predictability and facilitate an effective transition of the results into operations. The NCSU research team is particularly well prepared to undertake work on this problem, having conducted a number of CSTAR studies specifically targeting HSLC severe weather, and having a long-established relationship with many regional NWS WFOs.

The purpose of this work is to quantify and elucidate the role of frontal and diabatic processes and mesoscale dynamics in atmospheric rivers (ARs). In addition to conceptual understanding, the proposed work would provide valuable outcomes related to real-time prediction, observational measurements, and model resolution requirements for ARs. The latter holds important implications for climate change studies of ARs.

The problem of tropical cyclones (TCs) and climate change has been the focus of intensive research in recent years, yet important knowledge gaps remain. Aspects of the problem that have received less attention include changes in the intensity and frequency of recurving tropical cyclones, and the question of how climate change will affect the intensity and frequency of systems undergoing extratropical transition (ET). Previous research identified the mitigating effect that a tropical upper-tropospheric warming maximum would have on future TC intensity. However, this warming maximum is strongest in the tropics and sub-tropics; TCs moving to higher latitudes would move into an environment that features less warming aloft. Thus, one hypothesis is that robust recurving TCs will show a greater intensity increase with warming than would TCs that remain in the tropics. Warming effects on TCs that undergo extratropical transition are challenging to ascertain owing to competing physical processes. If the hypothesis that recurving TCs will exhibit additional relative strengthening with warming is correct, a second hypothesis is that an increased fraction of TCs will undergo ET. Further, more intense precursor TCs could lead to stronger ET events. On the other hand, reduced TC frequency with warming would work to lower ET frequency. Changes in TC occurrence during early- or late-season months, and in geographical regions poleward of the traditional TC development regions are related to the above hypotheses, and would be examined using the same suite of numerical model simulations needed to test the hypotheses on recurving and ET. High-resolution model simulations of the North Atlantic and North Pacific basins must include the tropics, sub-tropics, and adjacent midlatitude regions in order to address the proposed questions. The Weather Research and Forecasting (WRF) model, in regional and global configurations, will be used in conjunction with the Pseudo-Global Warming approach to test the above hypotheses. The intellectual merit of this work is that it would address gaps in current understanding of (i) how climate change would influence the intensity and frequency of recurving and extratropically transitioning tropical cyclones, and (ii) geographical and seasonal changes in tropical cyclone activity with climate change. Broader impacts of the proposed project are that these weather systems feature heightened potential for societal impacts, and also in mentoring graduate and undergraduate researchers, improved understanding of climate change and its consequences, and testing the WRF model in climate applications. The proposed project is potentially transformative in addressing questions related to aspects of climate change that have not received sufficient research attention, yet which old important implications for climate change impact on society.