Bringing statistics to storylines: rare event sampling for sudden, transient extreme events

Justin Finkel, Paul A. O'Gorman

A leading goal for climate science and weather risk management is to accurately model both the physics and statistics of extreme events. These two goals are fundamentally at odds: the higher a computational model's resolution, the more expensive are the ensembles needed to capture accurate statistics in the tail of the distribution. Here, we focus on events that are localized in space and time, such as heavy precipitation events, which can start suddenly and decay rapidly. We advance a method for sampling such events more efficiently than straightforward climate model simulation. Our method combines elements of two recent approaches: adaptive multilevel splitting (AMS), a rare event algorithm that generates rigorous statistics at reduced cost, but that does not work well for sudden, transient extreme events; and "ensemble boosting" which generates physically plausible storylines of these events but not their statistics. We modify AMS by splitting trajectories well in advance of the event's onset following the approach of ensemble boosting, and this is shown to be critical for amplifying and diversifying simulated events in tests with the Lorenz-96 model. Early splitting requires a rejection step that reduces efficiency, but nevertheless we demonstrate improved sampling of extreme local events by a factor of order 10 relative to direct sampling in Lorenz-96. Our work makes progress on the challenge posed by fast dynamical timescales for rare event sampling, and it draws connections with existing methods in reliability engineering which, we believe, can be further exploited for weather risk assessment.

Finkel, Justin, and Paul A. O’Gorman. “Bringing Statistics to Storylines: Rare Event Sampling for Sudden, Transient Extreme Events.” arXiv, February 2, 2024. https://doi.org/10.48550/arXiv.2402.01823.

Climate Change Contributions to Increasing Compound Flooding Risk in New York City

Ali Sarhadi , Raphaël Rousseau-Rizzi , Kyle Mandli , Jeffrey Neal , Michael P. Wiper , Monika Feldmann , and Kerry Emanuel

Efforts to meaningfully quantify the changes in coastal compound surge- and rainfall-driven flooding hazard associated with tropical cyclones (TCs) and extratropical cyclones (ETCs) in a warming climate have increased in recent years. Despite substantial progress, however, obtaining actionable details such as the spatially and temporally varying distribution and proximal causes of changing flooding hazard in cities remains a persistent challenge. Here, for the first time, physics-based hydrodynamic flood models driven by rainfall and storm surge simultaneously are used to estimate the magnitude and frequency of compound flooding events. We apply this to the particular case of New York City. We find that sea level rise (SLR) alone will increase the TC and ETC compound flooding hazard more significantly than changes in storm climatology as the climate warms. We also project that the probability of destructive Sandy-like compound flooding will increase by up to 5 times by the end of the century. Our results have strong implications for climate change adaptation in coastal communities.

Sarhadi, Ali, Raphaël Rousseau-Rizzi, Kyle Mandli, Jeffrey Neal, Michael P. Wiper, Monika Feldmann, and Kerry Emanuel. "Climate change contributions to increasing compound flooding risk in New York City." Bulletin of the American Meteorological Society 105, no. 2 (2024): E337-E356. DOI: https://doi.org/10.1175/BAMS-D-23-0177.1

Assessing community flood adaptation capacity to reduce flood losses

L. Cano Pecharroman

Flooding constitutes the largest proportion of economic losses from disasters in the US and is poised to become the main driver of disaster-driven economic loss worldwide. Community level flood planning has the potential to alleviate part of the problem. The US Community Rating System incentivizes community floodplain management practices in exchange for flood insurance premium rate discounts. The main goal of the program is to reduce and avoid flood damage to insurable property. Using multi-period matching and difference in differences this paper explores whether and to what extent this goal is achieved. Contrary to past evidence, this assessment finds mixed evidence on the potential that participating in the CRS decreases flood losses for communities that join the CRS. Further research is required to explore whether the program contributes to increasing community resilience aspects beyond decreasing damage to insurable property, and to determine who benefits from flood loss reduction.

Pecharroman, L. Cano. "Assessing community flood adaptation capacity to reduce flood losses." International Journal of Disaster Risk Reduction 99 (2023): 104114, https://doi.org/10.1016/j.ijdrr.2023.104114

Electric-gas infrastructure planning for deep decarbonization of energy systems

Rahman Khorramfar, Dharik Mallapragada, Saurabh Amin

The transition to a deeply decarbonized energy system requires coordinated planning of infrastructure investments and operations serving multiple end-uses while considering technology and policy-enabled interactions across sectors. Electricity and natural gas (NG), which are vital vectors of today’s energy system, are likely to be coupled in different ways in the future, resulting from increasing electrification, adoption of variable renewable energy (VRE) generation in the power sector and policy factors such as cross-sectoral emissions trading. This paper develops a least-cost investment and operations model for joint planning of electricity and NG infrastructures that considers a wide range of available and emerging technology options across the two vectors, including carbon capture and storage (CCS) equipped power generation, low-carbon drop-in fuels (LCDF) as well as long-duration energy storage (LDES). The model incorporates the main operational constraints of both systems and allows each system to operate under different temporal resolutions consistent with their typical scheduling timescales. We apply our modeling framework to evaluate power-NG system outcomes for the U.S. New England region under different technology, decarbonization goals, and demand scenarios. Under a global emissions constraint, ranging between 80%–95% emissions reduction compared to 1990 levels, the least-cost solution relies significantly on using the available emissions budget to serve non-power NG demand, with power sector using only 14%–23% of the emissions budget. Increasing electrification of heating in the buildings sector results in greater reliance on wind and NG-fired plants with CCS and results in similar or slightly lower total system costs as compared to the business-as-usual demand scenario with lower electrification of end-uses. Interestingly, although electrification reduces non-power NG demand, it leads to up to 24% increase in overall NG consumption (both power and non-power) compared to the business-as-usual scenarios, resulting from the increased role for CCS in the power sector. The availability of low-cost LDES systems reduces the extent of coupling of electricity and NG systems by significantly reducing fuel (both NG and LCDF) consumption in the power system compared to scenarios without LDES, while also reducing total systems costs by up to 4.6% for the evaluated set of scenarios.

Khorramfar, Rahman & Mallapragada, Dharik & Amin, Saurabh, 2024. "Electric-gas infrastructure planning for deep decarbonization of energy systems," Applied Energy, Elsevier, vol. 354(PA). https://doi.org/10.1016/j.apenergy.2023.122176.

Thunderstorm straight line winds intensify with climate change.

Andreas F. Prein

Straight line winds (SLWs), or non-tornadic thunderstorm winds, are causing widespread damage in many regions around the world. These powerful gusts are associated with strong downdraughts in thunderstorms, rear inflow jets and mesovortices. Despite their significance, our understanding of climate change effects on SLWs remains limited. Here, focusing on the central USA, a global hot spot for SLWs, I use observations, high-resolution modelling and theoretical considerations to show that SLWs have intensified over the past 40 years. Theoretical considerations suggest that SLWs should intensify at a rate of ~7.5% °C−1, yet the observed rates show a more pronounced increase of ~13% °C−1. The simulation results indicate a 4.8 ± 1.2-fold increase in the geographical extent affected by SLWs during the study period. These findings underscore the importance of incorporating intensifying SLWs into climate change adaptation planning to ensure the development of resilient future infrastructure.

Prein, A.F. Thunderstorm straight line winds intensify with climate change. Nat. Clim. Chang. (2023). https://doi.org/10.1038/s41558-023-01852-9

Uncertainties and sensitivities in the quantification of future tropical cyclone risk

Simona Meiler, Alessio Ciullo, Chahan M. Kropf, Kerry Emanuel & David N. Bresch

Tropical cyclone risks are expected to increase with climate change and socio-economic development and are subject to substantial uncertainties. We thus assess future global tropical cyclone risk drivers and perform a systematic uncertainty and sensitivity analysis. We combine synthetic tropical cyclones downscaled from CMIP6 global climate models for several emission scenarios with economic growth factors derived from the Shared Socioeconomic Pathways and a wide range of vulnerability functions. We highlight non-trivial effects between climate change and socio-economic development that drive future tropical cyclone risk. Furthermore, we show that the choice of climate model affects the output uncertainty most among all varied model input factors. Finally, we discover a positive correlation between climate sensitivity and tropical cyclone risk increase. We assert that quantitative estimates of uncertainty and sensitivity to model parameters greatly enhance the value of climate risk assessments, enabling more robust decision-making and offering a richer context for model improvement.

Meiler, S., Ciullo, A., Kropf, C.M. et al. Uncertainties and sensitivities in the quantification of future tropical cyclone risk. Commun Earth Environ 4, 371 (2023). https://doi.org/10.1038/s43247-023-00998-w

Dynamic compound droughts in the Contiguous United States

Ali Sarhadi, Reza Modarres, Sergio M. Vicente-Serrano

Severe meteorological and hydrological drought synergy contributes to adverse and large-scale social, economic, and environmental impacts beyond the individual occurrences. The risk, memory, and causality of this combination can be expressed by a compound dynamic perspective under a changing climate. In this study, we show that the concurrent risk of hydrological and metrological droughts has increased by up to 10% to 20% for moderate and severe events, and up to 8% to 12% for extreme events in recent decades, across watersheds in the western and southeastern Contiguous United States (CONUS).

A bivariate Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model also indicates that the dynamic compound droughts have strong short-term memory based on co-volatility, especially in the western CONUS. The results also suggest a short-memory causative dynamic mechanism, through which meteorological droughts may exponentially increase the occurrence of long-lasting and severe compound droughts, especially in the western CONUS. Given the broad impacts of extreme compound droughts, our findings have critical relevance for the ongoing proactive and long-term adaptive plans to mitigate adverse consequences, especially in the western territories of the CONUS.

Ali Sarhadi, Reza Modarres, Sergio M. Vicente-Serrano, Dynamic compound droughts in the Contiguous United States, Journal of Hydrology, Volume 626, Part A, 2023, 130129, ISSN 0022-1694, https://doi.org/10.1016/j.jhydrol.2023.130129.

On the Physics of High CAPE

Kerry Emanuel

Large values of Convective Available Potential Energy (CAPE) are an important ingredient for many severe convective storms, yet there has been comparatively little research on how, physically, such large values arise or why they take on the observed values and climatology. Here we build on recently published observational and theoretical work to construct a simple, one-dimensional coupled soil-atmosphere model of pre-convective boundary layer growth, driven by a single diurnal cycle of prescribed net surface radiation. Based on this model and previously published research, we suggest that high CAPE (>∼ 1000 J/Kg) results when air masses that have been significantly modified by passage over dry, lightly vegetated soils are advected over moist and or moderately vegetated soils and then exposed to surface solar heating. Several diurnal cycles may be needed to raise the moist static energy of the boundary layer to levels consistent with high CAPE. The production of CAPE and erosion of Convective Inhibition (CIN) are strongly affected by the potential temperature of the desert-modified air mass, the level of near-surface soil moisture (and root-zone soil moisture if significant vegetation is present), the type of soil, and the characteristics of the vegetation. Consequently, CAPE production and severe convective weather may be significantly affected by regional-scale land use changes and by climate change.

Emanuel, K., 2023: On the Physics of High CAPE. J. Atmos. Sci., https://doi.org/10.1175/JAS-D-23-0060.1, in press.

Physically Based Weather Hazard Modelling: Accounting for Climate Change

Kerry Emanuel

Emanuel, K., 2023: Physically Based Weather Hazard Modelling: Accounting for Climate Change. J. Catastrophe Risk and Resilience (Essay; Inaugural issue).

An Analytic Model for Tropical Cyclone Outer Winds

Timothy W. Cronin

The variation of Tropical cyclone azimuthal wind speed (V) with distance from storm center (r) is a fundamental aspect of storm structure with important implications for risk and damages. The theoretical model of Emanuel (2004, https://doi.org/10.1017/CBO9780511735035.010), which applies outside the rainy core of the storm, matches radiatively-driven subsidence and Ekman suction rates just above the boundary layer to obtain a nonlinear differential equation for dV/dr. This model is appealing because of its strong physical foundation but lacks a known analytic solution for V(r). In this paper, I obtain an analytic solution to V(r) for the Emanuel (2004, https://doi.org/10.1017/CBO9780511735035.010) outer wind model. Following previous work, I then use this solution to explore properties of merged wind models that combine the outer model with an inner model, which applies to the rainy core of a storm.

Cronin, T. W. (2023). An analytic model for Tropical cyclone outer winds. Geophysical Research Letters, 50, e2023GL103942. https://doi.org/10.1029/2023GL103942

Evolution of Convective Energy and Inhibition before Instances of Large CAPE

Philip Tuckman, Vince Agard, and Kerry Emanuel

We analyze the evolution of convective available potential energy (CAPE) and convective inhibition (CIN) in the days leading up to episodes of high CAPE in North America. The widely accepted theory for CAPE buildup, known as the advection hypothesis, states that high moist static energy (MSE) parcels of air moving north from the Gulf of Mexico become trapped under warm but dry parcels moving east from over elevated dry terrain. If and when the resulting CIN erodes, severe convection can occur due to the large energy difference between the boundary layer parcels and cool air aloft. However, our results, obtained via backward Lagrangian tracking of parcels at locations of peak CAPE, show that large values of CAPE are generated mainly via boundary layer moistening in the days leading up to the time of peak CAPE, and that a large portion of this moisture buildup happens on the day of peak CAPE. On the other hand, the free-tropospheric temperature above these tracked parcels rarely changes significantly over the days leading up to such occurrences. In addition, the CIN that allows for this buildup of CAPE arises mostly from unusually strong boundary layer cooling the night before peak CAPE, and has a contribution from differential advection of unusually warm air above the boundary layer to form a capping inversion. These results have important implications for the climatology of severe convective events, as it emphasizes the role of surface properties and their gradients in the frequency and intensity of high CAPE occurrences.

Tuckman, P., V. Agard, and K. Emanuel, 2023: Evolution of convective energy and inhibition before instances of large CAPE. Monthly Weather Review, 151, 321–338, https://doi.org/10.1175/MWR-D-21-0302.1.

Learning Inter-Annual Flood Loss Risk Models From Historical Flood Insurance Claims and Extreme Rainfall Data

Anamitra Saha, Sai Ravela

Flooding is one of the most disastrous natural hazards responsible for substantial economic losses. A predictive model for flood-induced financial damages is useful for many applications, such as climate change adaptation planning and insurance underwriting. This research assesses the predictive capability of regressors constructed on the National Flood Insurance Program (NFIP) dataset using neural networks (Conditional Generative Adversarial Networks), decision trees (Extreme Gradient Boosting), and kernel-based regressors (Gaussian Process). The assessment highlights the most informative predictors for regression. The distribution for claims amount inference is modeled with a Burr distribution permitting the introduction of a bias correction scheme and increasing the regressor's predictive capability. Aiming to study the interaction with physical variables, we incorporate Daymet rainfall estimation to NFIP as an additional predictor. A study on the coastal counties in the eight US South-West states resulted in an R2=0.807. Further analysis of 11 counties with a significant number of claims in the NFIP dataset reveals that Extreme Gradient Boosting provides the best results, that bias correction significantly improves the similarity with the reference distribution, and that the rainfall predictor strengthens the regressor performance.

Salas, J., Saha, A., & Ravela, S. (2022). Learning Inter-Annual Flood Loss Risk Models From Historical Flood Insurance Claims and Extreme Rainfall Data. arXiv preprint arXiv:2212.08660.

Downscaling Extreme Rainfall Using Physical-Statistical Generative Adversarial Learning

Anamitra Saha, Sai Ravela

Modeling the risk of extreme weather events in a changing climate is essential for developing effective adaptation and mitigation strategies. Although the available low-resolution climate models capture different scenarios, accurate risk assessment for mitigation and adaption often demands details that they typically cannot resolve. Here, we develop a dynamic data-driven downscaling (super-resolution) method that incorporates physics and statistics in a generative framework to learn the fine-scale spatial details of rainfall. Our method transforms coarse-resolution (0.25∘×0.25∘) climate model outputs into high-resolution (0.01∘×0.01∘) rainfall fields while efficaciously quantifying uncertainty. Results indicate that the downscaled rainfall fields closely match observed spatial fields and their risk distributions.

Saha, A., & Ravela, S. (2022). Downscaling Extreme Rainfall Using Physical-Statistical Generative Adversarial Learning. arXiv preprint arXiv:2212.01446.