Future economic damage from tropical cyclones: sensitivities to societal and climate changes

The social costs of tropical cyclones

Tropical cyclones (TCs) can adversely affect economic development for more than a decade. Yet, these long-term effects are not accounted for in current estimates of the social cost of carbon (SCC), a key metric informing climate policy on the societal costs of greenhouse gas emissions. We here derive temperature-dependent damage functions for 41 TC-affected countries to quantify the country-level SCC induced by the persistent growth effects of damaging TCs. We find that accounting for TC impacts substantially increases the global SCC by more than 20%; median global SCC increases from US$ 173 to US$ 212 per tonne of CO2 under a middle-of-the-road future emission and socioeconomic development scenario. This increase is mainly driven by the strongly TC-affected major greenhouse gas emitting countries India, USA, China, Taiwan, and Japan. This suggests that the benefits of climate policies could currently be substantially underestimated. Adequately accounting for the damages of extreme weather events in policy evaluation may therefore help to prevent a critical lack of climate action.

Observed increases in North Atlantic tropical cyclone peak intensification rates

Quickly intensifying tropical cyclones (TCs) are exceptionally hazardous for Atlantic coastlines. An analysis of observed maximum changes in wind speed for Atlantic TCs from 1971 to 2020 indicates that TC intensification rates have already changed as anthropogenic greenhouse gas emissions have warmed the planet and oceans. Mean maximum TC intensification rates are up to 28.7% greater in a modern era (2001-2020) compared to a historical era (1971-1990). In the modern era, it is about as likely for TCs to intensify by at least 50 kts in 24 h, and more likely for TCs to intensify by at least 20 kts within 24 h than it was for TCs to intensify by these amounts in 36 h in the historical era. Finally, the number of TCs that intensify from a Category 1 hurricane (or weaker) into a major hurricane within 36 h has more than doubled in the modern era relative to the historical era. Significance tests suggest that it would have been statistically impossible to observe the number of TCs that intensified in this way during the modern era if rates of intensification had not changed from the historical era.

Improved Transferability of Data-Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data-driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a "sample selection bias." In this article, we enhance data-driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.

Projecting the effect of climate change on residential property damages caused by extreme weather events

New Zealand's public insurer for natural hazards, the Earthquake Commission (EQC), provides residential insurance for some weather-related damage. Climate change and the expected increase in intensity and frequency of extreme weather-related events are likely to translate into higher damages and thus an additional financial liability for the EQC. We project future insured damages from extreme precipitation events associated with future projected climatic change. We first estimate the empirical relationship between extreme precipitation events and the EQC's weather-related insurance claims based on a complete dataset of all claims from 2000 to 2017. We then use this estimated relationship, together with climate projections based on future greenhouse gases concentration scenarios from six different dynamically downscaled Regional Climate Models, to predict the impact of future extreme precipitation events on EQC liabilities for different time horizons up to the year 2100. Our results show predicted adverse impacts that vary over time and space. The percent change between projected and past damages-the climate change signal-ranges between an increase of 7%-8% in liabilities for the period 2020 to 2040, and between 9% and 25% higher for the period 2080 to 2100. We also provide detail caveats as towhy these quantities might be mis-estimated. The projected increase in the public insurer's liabilities could also be used to inform private insurers, regulators, and policymakers who are assessing the future performance of both the public and private insurers that cover weatherrelated.

The critical role of cloud-infrared radiation feedback in tropical cyclone development

The tall clouds that comprise tropical storms, hurricanes, and typhoons-or more generally, tropical cyclones (TCs)-are highly effective at trapping the infrared radiation welling up from the surface. This cloud-infrared radiation feedback, referred to as the "cloud greenhouse effect," locally warms the lower-middle troposphere relative to a TC's surroundings through all stages of its life cycle. Here, we show that this effect is essential to promoting and accelerating TC development in the context of two archetypal storms-Super Typhoon Haiyan (2013) and Hurricane Maria (2017). Namely, this feedback strengthens the thermally direct transverse circulation of the developing storm, in turn both promoting saturation within its core and accelerating the spin-up of its surface tangential circulation through angular momentum convergence. This feedback therefore shortens the storm's gestation period prior to its rapid intensification into a strong hurricane or typhoon. Further research into this subject holds the potential for key progress in TC prediction, which remains a critical societal challenge.

Rainfall, groundwater, and surface water isotope data from extreme tropical cyclones (2016-2019) within the Caribbean Sea and Atlantic Ocean basins

Under a changing climate, projections estimate that over the next thirty years, extreme Tropical Cyclones (TCs) will increase in frequency, with two to three times more Category 4 and 5 hurricanes in the Atlantic basin between 20°N and 40°N. In recent years, the Caribbean Sea and Atlantic Ocean basins have experienced several extreme TCs, resulting in extensive human, ecological, and economic damage [1], [2], [3]. To improve understanding of TCs and their potential impacts in the face of climate change, physically based understanding of past climate and modern TC dynamics is necessary. Despite the well-known Atlantic hurricane season, surface observations of the isotopic evolution of TC's moisture and the propagation of isotopically distinct pulses across surface and subsurface water reservoirs are lacking. In this data article, we provide novel high frequency sampling of surface rainfall isotope compositions (δ¹⁸O, δ²H, and d-excess in ‰) for Hurricanes Otto (Costa Rica, 2016), Nate (Costa Rica, 2017), Irma (Cuba and The Bahamas, 2017), Maria (Cuba and The Bahamas, 2017), and Dorian (The Bahamas, 2019). These five TCs were characterized by unprecedented impacts during continental and maritime landfalls and passages. In total, 161 surface rainfall samples were collected in passive devices [4] with event-based and daily frequencies, resulting in the first surface isotopic tempestology anatomy across the Caribbean Sea and Atlantic Ocean basins to date. Derived rainfall from TCs often results in large input amounts of isotopically distinct water over an area from few hours to several days, and therefore this unique isotope composition is propagated through surface and shallow subsurface reservoirs. Our data also include spring (N=338) and surface water (N=334) isotope compositions following the impact of Hurricane Otto and Tropical Storm Nate in central Costa Rica. As this region is well-known for its diverse rainfall dynamics and as a climate change 'hot spot' [5], [6], [7], our data provide an opportunity to improve and complement modern and past climate interpretations often derived from satellite products and calcite-δ¹⁸O paleoclimatic archives in light of climatic forcing, TC rainfall amounts and recharge rates, and the hypothesized climatic-induced decline of past Mesoamerican civilizations.

Deciphering key processes controlling rainfall isotopic variability during extreme tropical cyclones

The Mesoamerican and Caribbean (MAC) region is characterized by tropical cyclones (TCs), strong El Niño-Southern Oscillation events, and climate variability that bring unique hazards to socio-ecological systems. Here we report the first characterization of the isotopic evolution of a TC (Hurricane Otto, 2016) in the MAC region. We use long-term daily rainfall isotopes from Costa Rica and event-based sampling of Hurricanes Irma and Maria (2017), to underpin the dynamical drivers of TC isotope ratios. During Hurricane Otto, rainfall exhibited a large isotopic range, comparable to the annual isotopic cycle. As Hurricane Otto organized into a Category 3, rapid isotopic depletion coupled with a decrease in d-excess indicates efficient isotopic fractionation within ~200 km SW of the warm core. Our results shed light on key processes governing rainfall isotope ratios in the MAC region during continental and maritime TC tracks, with applications to the interpretation of paleo-hydroclimate across the tropics.

“Reconstruction of precipitation variability from oxygen isotopes in the Mesoamerican and Caribbean region is made difficult by the occurrence of tropical cyclones. Here, the isotopic evolution of a tropical cyclone is studied in detail which helps disentangle the key processes governing rainfall isotope variability in the region.”

Integrating a Resilience Scorecard and Landscape Performance into a Geodesign Process

Uncertainty about the impacts of sea level rise make the ability to forecast future spatial conditions a necessary planning/design tool. Geodesign integrates multiple fields of science with change/impact models and planning/design strategies. Proactive planning analyses such as newly developed scorecards allow for plan evaluation; design strategies can now be quantitatively assessed using landscape performance calculators. Neither have been explored as Geodesign tools. A Geodesign process was developed using the resilience scorecard to assess flood vulnerability using projections for the 100 year floodplain with sea level rise by 2100. Projections were used as a guide to develop a resilient master plan for League City, TX, USA. Future impacts of the plan are projected using landscape performance measures.

Trends in the consecutive days of temperature and precipitation extremes in China during 1961-2015

BACKGROUND AND AIMS

Consecutive climatic extremes have more intense impacts on natural ecosystems and human activities than occasional events. There were many studies about the frequency or intensity of extreme weather events, but few focused on the consecutiveness or continuousness of climatic extremes. We analyzed the temporal and spatial distributions and tendencies in the consecutive temperature and precipitation extremes in China during 1961-2015.

METHODS

Daily temperature and precipitation data at 1867 meteorological stations over China was used and four consecutive indices of climate extremes, i.e. cold spell duration indicator (CSDI), warm spell duration indicator (WSDI), consecutive dry days (CDD) and consecutive wet days (CWD), were calculated by RClimDex 1.0. Linear trends in the time series of consecutive days of temperature and precipitation extremes were examined and their statistical significance was evaluated using Mann-Kendall test.

RESULTS AND DISCUSSION

There were obvious differences in the spatial distributions of consecutive days of climate extremes in China. During 1961-2015, CSDI and CWD decreased significantly at rates of 0.9 and 0.1 days per decade respectively, while WSDI increased significantly at rate of 0.8 days per decade in China. Spatially, CSDI decreased at rates of 0-3.0 days per decade in almost all parts of China, and WSDI increased at rates of 0-2.0 days per decade in most parts of China. The spatial trends of CDD and CWD were significant only in several regions of China. CSDI and WSDI had higher percent changes than those of CDD and CWD. Changes in the CSDI and WSDI were associated with large-scale oceanic and atmospheric circulation oscillations, such as Atlantic Multidecadal Oscillation (AMO), El Niño/Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). With global warming, there will be fewer cold extremes, more frequent hot extremes and precipitation extremes.

CONCLUSIONS

Given the increases in the frequency and intensity of some consecutive climatic extremes and an increasing physical exposure and socio-economic vulnerability to such extremes in China, more strategies and capacities of mitigation and adaptation to consecutive climatic extremes are essential for the local government and climate-sensitive sectors.

Development of an Asset Value Map for Disaster Risk Assessment in China by Spatial Disaggregation Using Ancillary Remote Sensing Data

The extent of economic losses due to a natural hazard and disaster depends largely on the spatial distribution of asset values in relation to the hazard intensity distribution within the affected area. Given that statistical data on asset value are collected by administrative units in China, generating spatially explicit asset exposure maps remains a key challenge for rapid postdisaster economic loss assessment. The goal of this study is to introduce a top-down (or downscaling) approach to disaggregate administrative-unit level asset value to grid-cell level. To do so, finding the highly correlated "surrogate" indicators is the key. A combination of three data sets-nighttime light grid, LandScan population grid, and road density grid, is used as ancillary asset density distribution information for spatializing the asset value. As a result, a high spatial resolution asset value map of China for 2015 is generated. The spatial data set contains aggregated economic value at risk at 30 arc-second spatial resolution. Accuracy of the spatial disaggregation reflects redistribution errors introduced by the disaggregation process as well as errors from the original ancillary data sets. The overall accuracy of the results proves to be promising. The example of using the developed disaggregated asset value map in exposure assessment of watersheds demonstrates that the data set offers immense analytical flexibility for overlay analysis according to the hazard extent. This product will help current efforts to analyze spatial characteristics of exposure and to uncover the contributions of both physical and social drivers of natural hazard and disaster across space and time.

Physical understanding of the tropical cyclone wind-pressure relationship

The relationship between the two common measures of tropical cyclone intensity, the central pressure deficit and the peak near-surface wind speed, is a long-standing problem in tropical meteorology that has been approximated empirically yet lacks physical understanding. Here we provide theoretical grounding for this relationship. We first demonstrate that the central pressure deficit is highly predictable from the low-level wind field via gradient wind balance. We then show that this relationship reduces to a dependence on two velocity scales: the maximum azimuthal-mean azimuthal wind speed and half the product of the Coriolis parameter and outer storm size. This simple theory is found to hold across a hierarchy of models spanning reduced-complexity and Earth-like global simulations and observations. Thus, the central pressure deficit is an intensity measure that combines maximum wind speed, storm size, and background rotation rate. This work has significant implications for both fundamental understanding and risk analysis, including why the central pressure better explains historical economic damages than does maximum wind speed.

Tropical cyclone intensity is commonly measured by both central pressure and maximum wind speed, yet the physical relationship between the two is not understood. Here the authors show that the central pressure is an intensity measure that depends on maximum wind speed and the product of storm size and background rotation rate.

Increased threat of tropical cyclones and coastal flooding to New York City during the anthropogenic era

In a changing climate, future inundation of the United States' Atlantic coast will depend on both storm surges during tropical cyclones and the rising relative sea levels on which those surges occur. However, the observational record of tropical cyclones in the North Atlantic basin is too short (A.D. 1851 to present) to accurately assess long-term trends in storm activity. To overcome this limitation, we use proxy sea level records, and downscale three CMIP5 models to generate large synthetic tropical cyclone data sets for the North Atlantic basin; driving climate conditions span from A.D. 850 to A.D. 2005. We compare pre-anthropogenic era (A.D. 850-1800) and anthropogenic era (A.D.1970-2005) storm surge model results for New York City, exposing links between increased rates of sea level rise and storm flood heights. We find that mean flood heights increased by ∼1.24 m (due mainly to sea level rise) from ∼A.D. 850 to the anthropogenic era, a result that is significant at the 99% confidence level. Additionally, changes in tropical cyclone characteristics have led to increases in the extremes of the types of storms that create the largest storm surges for New York City. As a result, flood risk has greatly increased for the region; for example, the 500-y return period for a ∼2.25-m flood height during the pre-anthropogenic era has decreased to ∼24.4 y in the anthropogenic era. Our results indicate the impacts of climate change on coastal inundation, and call for advanced risk management strategies.

Projections of future extreme weather losses under changes in climate and exposure

Many attempts are made to assess future changes in extreme weather events due to anthropogenic climate change, but few studies have estimated the potential change in economic losses from such events. Projecting losses is more complex as it requires insight into the change in the weather hazard but also into exposure and vulnerability of assets. This article discusses the issues involved as well as a framework for projecting future losses, and provides an overview of some state-of-the-art projections. Estimates of changes in losses from cyclones and floods are given, and particular attention is paid to the different approaches and assumptions. All projections show increases in extreme weather losses due to climate change. Flood losses are generally projected to increase more rapidly than losses from tropical and extra-tropical cyclones. However, for the period until the year 2040, the contribution from increasing exposure and value of capital at risk to future losses is likely to be equal or larger than the contribution from anthropogenic climate change. Given the fact that the occurrence of loss events also varies over time due to natural climate variability, the signal from anthropogenic climate change is likely to be lost among the other causes for changes in risk, at least during the period until 2040. More efforts are needed to arrive at a comprehensive approach that includes quantification of changes in hazard, exposure, and vulnerability, as well as adaptation effects.

Evaluation of the productivity decrease risk due to a future increase in tropical cyclone intensity in Japan

A number of scientists have recently conducted research that shows that tropical cyclone intensity is likely to increase in the future. This would result in an increase in the damage along with a decrease in economic productivity due to precautionary cessation of the economic activity of the affected areas during the passage of the cyclone. The economic effect of this stop in economic activity is a phenomenon that has not received much attention in the past, and the cumulative effect that it can have on the Japanese economy over the next 75 years has never been evaluated. The starting point for the evaluation of the economic risks is the change in the patterns of tropical cyclone intensity suggested by Knutson and Tuleya. The results obtained show how a significant decrease in the overall productivity of the country could be expected, which could lower GDP by between 6% and 13% by 2085.