1. Introduction
In comparison with the states in the U.S. Southeast and Gulf Coasts, New York experiences fewer landfalling hurricanes, on average. New York is extremely vulnerable to hurricanes because of its geographic location and well-developed coastal communities, however. A single catastrophic storm can lead to enormous social and economic losses to the state. In the period since 1950, more than 50 tropical or post-tropical cyclones have affected New York (using the 150-km-buffer-zone definition discussed in section 2; Fig. 1a). The most notorious case is Hurricane Sandy (2012), which recorded wind gusts of 87 kt (100 mi h−1) (1 kt ≈ 0.51 m s−1), storm surge of 9 ft (1 ft ≈ 30.5 cm), and total flood levels of 14 ft above low tide in New York Harbor, along with more than $30 billion in economic losses for the state (Blake et al. 2013). Sandy’s impacts on New York highlight the importance of resilience and adaptation to hurricane risk under current and future climate conditions. Policies and practices toward this end will be most rational if they are informed by careful assessments of the likelihood of low-probability but high-impact storms.
(a) Search area(s) of New York storms. The gray tiles show the coastline of the New York, and the yellow, green, and dark-blue tiles show search areas with 50-, 100-, and 150-km buffer zones, respectively. Also shown are the return periods estimated using (b) 1951–2020 and (c) 1851–2020 observations. The legends in (b) and (c) show the number of New York storms selected within the corresponding search areas.
Citation: Journal of Applied Meteorology and Climatology 61, 6; 10.1175/JAMC-D-21-0173.1
The historical record is too short to be adequate for assessing tropical cyclone risk in New York; before Sandy, for example, there was no storm comparable to it in the modern record (the most comparable one occurred in 1821, before modern observations). The historical record is even less adequate for assessing the change in risk as the climate warms. Statistical–dynamical downscaling is one useful approach for this purpose (Emanuel et al. 2006). In this approach, synthetic storms are generated using a simplified dynamical hurricane model (Emanuel et al. 2008) or a physics-based statistical model (e.g., Lee et al. 2018; Emanuel 2017; Jing and Lin 2020) conditioned on large-scale environment from global climate models or reanalysis datasets. This approach allows a large number of synthetic storms to be generated cheaply and makes use of the climate models’ ability to project future climate while avoiding the limitations on their ability to simulate tropical cyclones due to their low spatial resolution. A statistical–dynamical downscaling approach has been used to assess some aspects of current climate hurricane risk for New York and its vicinity: Lin et al. (2010) coupled the downscaling model by Emanuel et al. (2006) with the hydrodynamic model known as Sea, Lake, and Overland Surges from Hurricanes (SLOSH; Jelesnianski et al. 1992) to examine the probability of New York, New York (hereinafter New York City), being affected by a catastrophic hurricane coastal flood event; using synthetic storm surface winds by Lin and Chavas (2012), Yeo et al. (2014) estimated the mean return interval of major hurricane wind speed to be 300 years for Long Island and 700 years for New York City.
As Earth’s climate warms due to increasing greenhouse gas concentrations, we expect storms to reach higher intensities (e.g., Knutson et al. 2010; Sobel et al. 2016; Emanuel 2021), bring heavier rainfall (e.g., Knutson et al. 2013, 2020b), and induce more severe coastal flooding due to sea level rise (Woodruff et al. 2013). Beyond those coarse expectations, hurricane hazard depends on the storms’ properties, that is, the statistics of their frequency, intensity, size, forward motion speed, heading, and other properties. Sea level rise is independent of the storms but makes a large contribution to the increase in the storm surge hazard. Considering only the effect of sea level rise, Lin et al. (2016) showed that the return period of Hurricane Sandy’s flood height decreased by a factor of 3 from year 1800 to 2000 and by factor of 4 from 2000 to 2100 under a moderate-emissions pathway; Reed et al. (2015) showed that the mean flood height increased by more than 1.2 m from the period of 850–1800 to 1970–2005. The projected changes in surge hazards can be amplified or dampened depending on the combined changes in storm properties (Lin et al. 2012; Reed et al. 2015; Lin et al. 2016; Lin and Shullman 2017; Garner et al. 2017; Marsooli et al. 2019).
In New York, we expect that climate change will lead to an increase in storm intensity (Garner et al. 2017), similar to what we expect at global or basin scales, but other aspects of the hazard may change in ways that either mute or amplify that intensity trend. A recent study by Murakami et al. (2020) detected a decreasing trend in observed hurricane frequency near New York during the period from 1981 to 2018. Kossin (2018) found a statistically significant decreasing trend in the observed translation speed of tropical cyclones globally. Hall and Kossin (2019) further suggested that, in the North Atlantic Ocean, hurricanes may have become more likely to stall near the coast, due not only to the reduction of storm translation speed but also to increasing probability of sharp changes in the direction of storm motion. Such stalling would be expected to increase rainfall hazard, since a stalling storm will drop more rain on the places over which it passes, all else equal. While Murakami et al. (2020), Kossin (2018), and Hall and Kossin (2019) did not attribute these observed trends to anthropogenic climate change, it is of obvious interest to examine whether these trends are expected to continue into the future. Using a downscaling approach, Garner et al. (2017) showed a reduction of hurricane frequency in New York from current climate to the late-twenty-first century due to track pattern changes, which may be related to the increasing likelihood of storms recurving toward Europe as a consequence of global warming (Barnes et al. 2013; Haarsma et al. 2013; Sainsbury et al. 2020). Garner et al. (2021) further showed that there is a decreasing trend in the density of tropical cyclones traveling close to New York City but an increasing trend for the portion of storms remains 150 km or farther from the city. Our recent statistical–dynamical downscaling work, on the other hand, suggests that changes in the long-term mean frequency of landfalling storms at a given location follow changes in the basinwide tropical cyclogenesis rate (Lee et al. 2020). This makes such changes very uncertain, because we currently lack a robust understanding of tropical cyclone frequency and its relation to climate (Walsh et al. 2016; Sugi et al. 2020; Vecchi et al. 2019; Sobel et al. 2021).
For the possible influence of greenhouse gas forcing on tropical cyclone translation speed, the assessment by Knutson et al. (2020a) found that dynamical (regional and global) models showed no robust changes in global and basinwide projections (Knutson et al. 2013; Wu et al. 2014; Kim et al. 2018). In Lee et al. (2020), we projected a downward, albeit statistically insignificant, global trend. However, when focusing on the Texas area, Hassanzadeh et al. (2020), using the same statistical–dynamical model output, found an increase in storm motion in the late-twenty-first century, due to a stronger Atlantic subtropical high and a weakening North American monsoon, which together increase the northward steering winds over Texas as the climate warms. The difference between the results from Hassanzadeh et al. (2020) and from Lee et al. (2020) highlights that at the local scale, changes in the tropical cyclone characteristics may be different from those expected at basin or global scales. The climate change impacts on storm characteristics and the associated risk need to be studied at regional and local scales.
This study aims to investigate how anthropogenic warming may change hurricane wind hazard in New York, considering changes in a range of storm characteristics. We apply the Columbia (tropical cyclone) Hazard model (CHAZ), a statistical–dynamical downscaling model, developed by Lee et al. (2018), forced by environmental fields from five models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) (Taylor et al. 2012), following Lee et al. (2020). In the next section, we briefly describe the downscaling experiments, the models, the data, the analysis methods, and the definition of New York hurricanes we use. In section 3, we review the statistics of New York hurricanes in both observations and CHAZ simulations of the historical period. Section 4 contains an analysis of the projected influence of anthropogenic climate change, followed by a more detailed assessment of projected changes in impact intensity and county-level wind hazards in section 5. In section 6 we discuss and summarize our findings.
2. Methods
a. CHAZ CMIP5 downscaling
As mentioned above, we use CHAZ with inputs from five global models taken from the CMIP5 archive, following our previous work, Lee et al. (2020). The CHAZ CMIP5 downscaling generates synthetic storms in the historical (1951–2005) and future (2006–2100) periods, the latter under the RCP8.5 scenario. The RCP8.5 scenario may be overly pessimistic, but the intent is to understand the sensitivity of tropical cyclone risk to warming; we expect changes to be approximately linear in global mean surface temperature change, so that if future greenhouse gas emissions are lower in reality than in RCP8.5, the changes in risk will be proportionally smaller (or equivalently, changes projected for a particular time should be expected to instead occur later; Tebaldi and Arblaster 2014). The five chosen CMIP5 models are the National Center for Atmospheric Research (NCAR) Community Climate System Model, version 4 (CCSM4; Gent et al. 2011); the Geophysical Fluid Dynamics Laboratory Climate Model, version 3 (GFDL CM3; Donner et al. 2011); the Hadley Centre Global Environment Model, version 2 Earth System (HadGEM2-ES; Jones et al. 2011); the Max Planck Institution Earth System Model Medium Resolution (MPI-ESM-MR; Zanchettin et al. 2012); and the Model for Interdisciplinary Research on Climate, version 5 (MIROC5), from the University of Tokyo Center for Climate System Research, National Institute for Environmental Studies, Japan Agency for Marine-Earth Science, and Technology Frontier Research Center for Global Change (Watanabe et al. 2010). These five models sufficiently cover the spread of global annual frequency projections shown in Lee et al. (2020; see their Fig. 3).
The CMIP5 models provide CHAZ with large-scale environmental conditions including potential intensity (PI; Bister and Emanuel 2002); deep-layer (850–250 hPa) vertical wind shear; moisture variables, that is, column relative humidity and/or saturation deficit; the absolute vorticity at 850 hPa; and the steering flow (weighted-average 850- and 250-hPa winds). CHAZ seeds initial storm precursors such that the probability of genesis is proportional to an environmental index [tropical cyclone genesis index (TCGI); Tippett et al. 2011] that is computed here from the CMIP5 data and moves these seeds following the steering wind calculated using a beta-advection model (Emanuel 2008). Along the track, an environmentally forced stochastic autoregression model computes the intensity evolution (Lee et al. 2015, 2016). Humidity variables enter the CHAZ model in multiple ways: the intensity model uses area-averaged 500–300-hPa midlevel humidity (Lee et al. 2015), whereas TCGI uses grid values of either column relative humidity (CRH) or saturation deficit (SD; Camargo et al. 2014); SD is the difference between the column integrated water vapor and the same quantity at saturation, and the CRH is their ratio. Both are calculated following Bretherton et al. (2004). The responses of both versions of TCGI to global warming are discussed in Camargo et al. (2014) and Lee et al. (2020).
The CHAZ CMIP5 projections for the global tropical cyclone activity and Northern Atlantic hurricane activity are discussed in Lee et al. (2020). Of particular note is that CHAZ projects an increase in Atlantic hurricane frequency if the TCGI depends on CRH but a decrease if the TCGI depends on SD (see their Fig. 3). The two versions of TCGI yield similar results for the historical period so that it is difficult to determine, using comparison with the historical period, which of the two future trends is correct. There is a theoretical argument to the effect that SD better reflects the thermodynamic inhibition of tropical cyclone formation—the import of midlevel air to the atmospheric boundary layer, which is related to the spinup time of TCs (Tang et al. 2016). In a moist-neutral environment, SD is proportional to the difference between the moist static energy of boundary layer and midlevel. Thus, at larger SD, greater surface fluxes are required to saturate the column, a prerequisite to genesis (Emanuel 2010, 2013). While this argument has been discussed in the abovementioned studies, the contribution of increasing saturation or entropy deficit to genesis relative to other environmental factors, especially under a warming climate, has not yet been quantified conclusively. In the context of empirical genesis indices, the answer may depend on the formulation of the index, including its functional form and the other choices of predictors. Using the normalized difference between the moist entropy of the middle troposphere and that of the boundary layer, the genesis potential index defined in Emanuel (2010) and used in Emanuel (2013, 2021) showed an upward trend in global (and North Atlantic) annual frequency as a consequence of warming. Broadly, we view the difference in sign in the genesis trend between the two sets of CHAZ simulations (SD and CRH) as consistent with the current state of the science, in that it is unclear—considering all lines of evidence, including high-resolution global models—whether we should expect an increasing or a decreasing number of tropical cyclones as the climate warms (Knutson et al. 2020b; Camargo and Wing 2021; Vecchi et al. 2019; Sobel et al. 2021). Thus, here we present both increasing or decreasing genesis scenarios when assessing New York hurricane risk under a changing climate.
The two sets of downscaling experiments conducted here are labeled “CHAZCRH” and “CHAZSD,” indicating which moisture variable is used in the TCGI. We will focus our analyses on three periods: the historical (HIST; 1951–2005), near future (RCP85nf; 2006–40), and late-twenty-first century (RCP85late; 2070–99). For each CMIP5 model, we run a set of 20 track ensembles, and for each track ensemble we have 40 intensity ensembles. The net simulation times, adding all ensemble members together for each set, are 260 000 years (i.e., 65 × 5 × 20 × 40) for HIST, 140 000 years for RCP85nf, and 120 000 years for RCP85late.
Because of biases in both the CMIP5 forcing and CHAZ, there are frequency biases in the simulations, defined as systematic differences between the HIST results and observations for the same historical period. We derive and apply a multiplicative bias correction factor to ensure that the simulated ensemble mean frequency of storms impacting New York with at least 34-kt storm intensity in the HIST simulations is the same as that of historically observed storms. The same correction factor is then applied to the future simulations RCP85nf and RCP85late. These frequency corrections, which in practice are applied by adjusting the number of years that the synthetic track data represent, are 0.35 and 0.55 for CHAZCRH and CHAZSD, respectively. The effective number of years for CHAZCRH and CHAZSD for the HIST period is therefore reduced to 91 000 (i.e., 260 000 × 0.35) and 143 000 (i.e., 260 000 × 0.55) years, for example. No bias correction is applied to other storm quantities, such as intensity and translation speed. CHAZ outputs synthetic storm intensity and position every 6 h. The storm translation speed and impact angle are derived from the storm position data.
b. Surface wind model fields
The two-dimensional wind field associated with each CHAZ synthetic storm impacting New York is computed as the sum of an azimuthally symmetric wind component and a simple left–right (relatively to storm forward direction) asymmetric wind component associated with the storm’s translation. Here, we imposed the translation speed to the symmetric wind as a function of radius loosely following Lin and Chavas (2012). We use two parametric wind models for the azimuthally symmetric component: a theory-based one by Chavas et al. (2015) (CLE15) and an empirical one by Willoughby et al. (2006) (W06). The CLE15 model consists of two theoretical solutions for the hurricane structure at the top of the boundary layer in the inner ascending (Emanuel and Rotunno 2011) and the outer descending regions (Emanuel 2004). The W06 model consists of three piecewise segments of continuous wind profiles: an inner eyewall segment where wind speed increases in proportion to a power of the storm radius, an outer eyewall segment that is designed to decay exponentially with the radial distance, and a radially varying polynomial ramp function that concatenates the first two segments. The radial resolution of both profiles is set as 2 km.
Both parametric models take the maximum azimuthally averaged wind speed Vmax and its corresponding radius Rmax as inputs. The Vmax is inferred from the storm intensity, and Rmax is computed using an empirical equation from Willoughby et al. (2006) [see their Eq. (7a)] and is a function of Vmax and latitude of storm center. Besides Vmax and Rmax, storm center latitude is also an input to both the CLE15 and W06 models. CLE5 can also include optional parameters such as the radiative subsidence rate, the surface drag coefficient, and the ratio of the surface drag coefficient and the enthalpy exchange coefficient. These parameters affect the shape of the symmetric wind profile. In this work, for simplicity, we keep these parameters constant, that is, the radiative subsidence rate is set to 2 × 10−3 m s−1, the drag coefficient is set to 1.5 × 10−3, and the ratio of exchange coefficients is set to 1.
Relative to CLE15, surface winds from W06 decay faster both toward the eye and outward from the radius of maximum wind (not shown). For a given set of input parameters, CLE15 generally produces a broader area of strong winds than W06 does. Consequently, the estimated wind hazards from CLE15 are expected be systematically more severe at high intensity thresholds (e.g., major hurricane wind strength). Assessments of both parametric wind models’ performances in comparison with observations can be found in the original publications. A recent study by Yang et al. (2022) compared both models with the 2000–14 HWIND data and showed that CLE15 performs better at radii in the range of 1–3 times Rmax whereas W06 performs better in the region for radii 3–8 times Rmax. We use both CLE15 and W06 here to examine the sensitivity of the results to the choice of the parametric wind model.
c. Observations
The climatology of the downscaled TCs from the HIST period is compared with observations from the International Best Track Archive for Climate Stewardship, version 04r00 (IBTrACS; Knapp et al. 2010). Specifically, we use 6-hourly storm positions and maximum wind speeds (kt) from the National Hurricane Center (NHC). The storm translation speed and impact angle are derived from the position data. We include storms that reach at least tropical storm intensity strength, 34 kt. When comparing with CHAZ HIST simulations, we use data from 1951 to 2020 as the reference historical period. We also use data from 1851 to 2020, both to examine the sensitivity of the observed New York storm frequency and impact intensity to the data coverage period, and to make use of all the available observations so that our analysis is comprehensive.
d. Definition of New York hurricane
Tropical cyclones affecting New York are defined here as those whose centers come within 150 km of the New York coastline (Fig. 1). Throughout the paper, we will use “New York hurricanes” and “New York storms” interchangeably to refer to all these storms collectively (i.e., not only for those storms whose wind speed exceeds 64 kt); statistics associated with specific intensity thresholds will be discussed explicitly. The observed New York storms are shown in Fig. 2a. To avoid missing storms that pass through the area between two 6-hourly data points, we first interpolate the 6-hourly data to 15-min intervals and identify the first points when storms enter the dark-blue area shown in Fig. 1. From those first points, we then resample data every 6 h from those 15-min data. New York hurricane characteristics are defined as the statistics of the storm intensity, translation speed, and heading from the resampled 6-hourly data within this 150-km-buffer-zone area.
The (a) 1951–2020 historical New York hurricane tracks colored by storm intensity in maximum wind speed (kt). (b) As in (a), but zoomed in to the New York area. (c) As in (b), but with only storms that caused large economic damage: Hurricanes Carol (1954), Enda (1954), Esther (1961), Belle (1976), Gloria (1985), Bob (1991), Floyd (1999), Irene (2011), and Sandy (2012). The bluish tiles in (b) and (c) are the 150-km search area discussed in section 2.
Citation: Journal of Applied Meteorology and Climatology 61, 6; 10.1175/JAMC-D-21-0173.1
e. Climate change impacts and hazard calculations
The influence of anthropogenic climate change on New York hurricane characteristics is quantified using the “climate change delta” (hereinafter sometimes “delta” for brevity), which is defined as the difference in values of a measure of interest (e.g., 10th percentile of storm intensity) between simulations of the historical and a projected future period, normalized by its historical value. To compare the delta with the natural variability, we calculate 95% confidence intervals using ensembles from intensity stochasticity and CMIP5 forcing. Our confidence in the sign of the change is greater if the 95% confidence interval does not include zero than if it does.
New York hurricanes’ impact intensities, landfall, and wind hazards are assessed using both the annual frequency of exceedance and the return period. The return period is the inverse of the annual frequency of exceedance (and vice versa). The impact intensity is the highest intensity reached by each New York storm while impacting the state (i.e., while it is within the buffer zone). The landfall hazard is based on the impact intensities from those storms that directly make landfall in the state, that is, those whose centers pass the gray area shown in the Fig. 1a. The county-level wind hazard is estimated using the maximum sustained surface wind each New York county experiences. Specifically, for a given storm at a given time, we first generate a wind field in a polar coordinate around the storm center, and then interpolate it to the municipal center of each county. By design, the county-level wind is derived from the higher-resolution portions of these wind maps when the municipal center lies close to the storm center and from the lower-resolution part when the municipal center lies away from the storm center. While it is possible to increase the spatial resolution used in the wind profile models we use here, these models provide only rough estimates of the radial profile of the real surface wind. They do not account for variations in surface roughness, for example, and thus the generated wind fields are smooth. The county-level wind calculation used here captures those features the CHAZ wind model aims to resolve—the radial variation of surface wind and the storm-induced asymmetries.
3. New York hurricanes in the historical period
The observed annual frequency of Atlantic hurricanes, basinwide, is ∼11 yr−1 (Fig. 3) from 1951 to 2020. The observed annual frequency of New York storms—those passing through the 150-km buffer zone—for all intensities is 0.7 (Fig. 4). Considering only storms with intensity above tropical storm strength (≥34 kt), it is 0.50, decreasing to 0.40 and 0.31 if 100- and 50-km buffer zones (green and yellow areas in Fig. 1a), respectively, are used. The annual frequency of storms actually making landfall in New York with at least tropical storm intensity is 0.07 (the gray area in Fig. 1a); tropical cyclones pass nearby offshore much more often than they actually make landfall. New York storms have intensities ranging from 15 to 110 kt, with a maximum frequency at 45–60 kt (Fig. 5a). These intensities are weaker than those over the tropical Atlantic (not shown), as expected because of the colder sea surface temperature and stronger environmental shear, both of which are unfavorable for hurricane development and intensification, in the midlatitudes. Over the 70-yr period, two New York storms achieved intensities greater than the category-3 threshold (≥96 kt); these are Hurricanes Carol (100 kt) and Edna (97 kt) in 1954.1 There have been two more category-3+ New York hurricanes in the longer period since 1851: the New England Gale of 1869 (100 kt) and the 1938 New England Hurricane, also known as the “Long Island Express” (105 kt; see Landsea et al. 2014).
Annual Atlantic Ocean hurricane frequency from observations (black) and CHAZ CMIP5 downscaling during HIST (gray), RCP85nf (blue), and RCP85late (red) from (a) CHAZCRH and (b) CHAZSD. The various degrees of shading show the 10th, 25th, 75th, and 90th percentiles of the ensembles.
Citation: Journal of Applied Meteorology and Climatology 61, 6; 10.1175/JAMC-D-21-0173.1
Annual New York storm frequency from observations (black dashed line) and CHAZ CMIP5 downscaling (boxplots) during HIST (gray), RCP85nf (blue), and RCP85late (red) from CHAZCRH and CHAZSD. The boxes show the 25th–75th percentiles of the ensemble spread, and the whiskers cover the 10th–90th-percentile range. Stars indicate the mean.
Citation: Journal of Applied Meteorology and Climatology 61, 6; 10.1175/JAMC-D-21-0173.1
Probability density function of New York hurricane (a) intensity (kt), (b) forward speed (km h−1), (c) heading (°), and (d) 6-h time derivative of the heading (°) from observations (black) and CHAZ simulations at HIST (gray), RCP85nf (blue), and RCP85late (red) periods. CHAZCRH and CHAZSD are combined for the bar plots. CHAZCRH and CHAZSD spread are indicated separately by the solid and dashed lines, respectively. Circular symbols on the upper part of each panel indicate the observed values of the notable New York storms as labeled in (d).
Citation: Journal of Applied Meteorology and Climatology 61, 6; 10.1175/JAMC-D-21-0173.1
New York hurricanes generally move faster than those in the tropics because the large-scale winds in midlatitudes are stronger than those in the tropics. The average observed New York storm’s translation speed is 38 km h−1 (Fig. 5b). The 90th percentile is 63 km h−1, and the 10th percentile is 18 km h−1 (Fig. 5b). Most New York storms pass through the area while recurving from a northwestward trajectory to a northeastward trajectory toward the mid-Atlantic; the mean heading is 52°, that is, moving from southwest toward northeast (Fig. 5c). The distribution of the 6-hourly rate of change of the heading suggests that for 50% of the 6-hourly sample the heading changes by less than 7° across a 6-h time interval and that fewer than 10% of New York storms change forward direction more than 25° (Fig. 5d). The rates of change of the heading are smaller than those for coastal Atlantic storms, which have a mean of 20° as reported by Hall and Kossin (2019).
Values of the most historically notable post-1951 New York storms’ characteristics are marked by circular symbols above the bar graphs in Fig. 5, and their tracks are shown in Fig. 2c: Hurricanes Carol (1954), Edna (1954), Esther (1961), Belle (1976), Gloria (1985), Bob (1991), Floyd (1999), Irene (2011), and Sandy (2012). Their intensities in the New York area range from 50 (Floyd) to 100 (Carol) kt, and their translation speeds vary from 20 (Esther) to 70 (Gloria) km h−1. The heading angle is the most tightly clustered of the parameters for this set of high-impact storms; nearly all of them approached New York from the south-southwest with heading of 0°–90°. The outstanding exception is Sandy, which made landfall while moving roughly from the southeast toward the northwest (300°); using a different (statistical) hazard model, Hall and Sobel (2013) estimated the return period for a storm with at least category-1 intensity making landfall as close to perpendicular to the New Jersey coast as Sandy did to be 700 years (95% confidence interval 400–1400 years) and argued that this steep angle was part of the reason for Sandy’s extreme storm surge.
By construction, the CHAZ CMIP5 simulations during the HIST period have the same mean annual frequencies of exceedance at 34 kt as observed. Figure 4 shows the frequencies of New York storms at all intensities after the above frequency adjustment. Considering the spread among the simulations forced by different climate models and by the stochastic terms in the CHAZ model, the observed frequency at all intensity is close to the median of the CHAZSD experiments and is above the median of the CHAZCRH. Without applying any bias correction to storm intensity or forward motion, the CHAZ CMIP5 historical runs capture the observed storm climatology reasonably well in both CHAZCRH and CHAZSD configurations (Fig. 5). CHAZ storms move slightly more slowly than those in the observations (the light-gray bars in Fig. 5b are shifted toward the left when compared with the black bars), however, which may be related to the facts that currently CHAZ does not include the process of extratropical transition and that CHAZ simulations do not include the remnants of landfalling storms. Both classes of storms are typically—if with important exceptions—weak, and also move faster than tropical systems, both of which tend to make them less damaging. CHAZ also has a greater frequency of storm headings toward the northwest, which may also be related to the lack of the above two classes of storms that tend to move northeastward. Representing these systems in CHAZ is a target for further model development but is outside the scope of this study.
4. Climate change impacts on New York hurricanes
The projections of annual New York storm frequency we obtain by downscaling CMIP5 results with CHAZ roughly follow those of mean Atlantic hurricane frequency (Fig. 3). From HIST to RCP85nf, the mean New York storm frequency at all intensities above tropical storm strength in CHAZCRH increases (from 0.60) by 0.08 ± 0.01, whereas it decreases by 0.12 ± 0.007 in CHAZSD (Fig. 4). In RCP85late, the annual frequency of those impacting the New York area decrease further, by an additional 0.3 ± 0.007, in CHAZSD. In CHAZCRH the ensemble spreads in the frequencies of both Atlantic hurricanes and New York storms increase, but the ensemble-mean values do not vary greatly from RCP85nf to RCP85late. Further analyses indicate that, for synthetic events in CHAZCRH with MIROC5 and CCSM4 forcing, the mean New York storm frequency decreases from RCP85nf (0.39 and 0.57) to RCP85late (0.30 and 0.40). This reduction in New York storm frequency is directly associated with the reduction (by a factor of approximately 2) of the annual Atlantic frequency from RCP85nf to RCP85late. The synthetic storm tracks in RCP85late also shift poleward when compared with those in the HIST and RCP85nf (not shown). Such poleward shifts are not as evident in storms downscaled with CCSM4 and MIROC5 forcings, an outcome that further contributes to the reduction of the New York storms in CHAZCRH downscaled from these two CMIP5 models.
Next, we examine the intensities, forward speeds, and headings of New York storms. For these three quantities, we use combined data from both CHAZ TCGI configurations because their distributions between CHAZCRH and CHAZSD are not substantially different, as shown by the vertical lines in Fig. 5. While there are shifts in the multimodel distributions of these characteristics from HIST to RCP85nf to RCP85late in Fig. 5, these changes are within the ensemble spread, indicated by the vertical lines. To examine whether these distributions are significantly different from each other, we apply the Kolmogorov–Smirnov test. The results (not shown) suggest that the distributions of these New York storm characteristics in the three climate periods considered here are indeed significantly different, with very small p values (<1.0 × 10−10). The only exceptions are the New York storm intensity distributions for HIST and RCP85nf, whose difference has a p value of 0.068, meaning that we cannot reject the null hypothesis that the two intensity distributions are indistinguishable at the 95% confidence level. To further quantify the climate change impacts on the distributions, we calculate climate change deltas for the 10th, 25th, 50th, 75th, and 95th percentiles for each of the storm characteristics, except for the heading, which has more than 70% of CHAZ HIST data clustered between 0° and 90° (move toward northeast). We use 25th, 50th, 75th, 80th, and 85th percentiles to cover a wider range of distributions.
For storm intensity (Fig. 6a), the delta from HIST to RCP85nf is very small and the 95% confidence intervals indicate low confidence on the projected changes. On the other hand, the deltas at all percentiles robustly indicate an increase in New York storm intensity from HIST to RCP85late. In CHAZ, the synthetic storm intensity is directly affected by three environmental variables: PI, shear, and the midlevel relative humidity (Lee et al. 2016). From HIST to RCP85nf, there is an overall increase in PI and a decrease in midlevel relative humidity (Fig. 7). Both variables have climate change deltas smaller than 3%. While the upward trend in PI favors synthetic storms’ intensification, the downward trend in relative humidity hinders the intensification process. The shifts in the vertical wind shear are very small for RCP85nf and in the case of 10th, 75th, and 90th percentiles, and the 95% confidence interval crosses zero. These small changes in PI and the uncertainty in the shear projections around New York both help to explain the smallness of the deltas, their lack of significance, and their nonmonotonicity across different percentiles in Fig. 6a.
The delta (%) of New York hurricane (a) intensity, (b) forward speed, (c) heading, and (d) 6-h time derivative of the heading from HIST to RCP85nf and RCP85late at the percentiles indicated in the x-axis labels. Blue and coral colors indicate the climate change delta for RCP85nf and RCP85late, respectively, and the black lines show the 95% confidence intervals. The parentheses show percentile values from the historical simulations.
Citation: Journal of Applied Meteorology and Climatology 61, 6; 10.1175/JAMC-D-21-0173.1
As in Fig. 6, but for PI, shear, and midlevel relative humidity.
Citation: Journal of Applied Meteorology and Climatology 61, 6; 10.1175/JAMC-D-21-0173.1
In the case of RCP85late, on the other hand, Fig. 7 shows that there is a clear shift in the three environmental conditions relevant to intensity; with an increase of more than 10% in PI [as discussed in, e.g., Sobel et al. (2016)] for all percentiles, a decrease in vertical wind shear ranging from −5% to −15%, and a decrease of RH of roughly 5% at all percentiles. The downward trend in shear is consistent with studies that found a downward trend in vertical wind shear over North Atlantic (Haarsma et al. 2013; Liu et al. 2017; Michaelis and Lackmann 2019) and off the coast of the eastern United States (Ting et al. 2019). The trends in both PI and shear are favorable for storm intensification, overcome the effect of decreasing RH, and explain the high confidence in the increase in storm intensity shown in Fig. 6a and the associated clear shift to the right in Fig. 5a.
From HIST to RCP85nf and to RCP85late, CHAZ projects a decrease in New York storms’ forward speeds over all percentiles (Fig. 6b). The 95% confidence interval suggests high confidence in the downward trend for all percentiles but the 90th. There are also robust increases in the heading angle (Fig. 6c) and its rate of change (Fig. 6d). The decrease in forward motion speed is consistent with the changes in the mean steering flow speed shown in Fig. 8 (between RCP85late and HIST) and agrees with the observed decreasing trend in forward speed found by Hall and Kossin (2019) during the historical period. This, and the increase in the change rate of forward direction, imply an increasing probability of storms stalling around New York as the climate warms. The increasing trend in heading angles, on the other hand, implies increasing odds of storms affecting New York from the east and from the north, which is also consistent to the changes in the steering flow pattern (Fig. 8). Such changes, especially increasing odds of storms from the east (as with Sandy), appears at first glance to be inconsistent with recent studies suggesting that the odds of such Sandy-like tracks should decrease with warming (Barnes et al. 2013) and that Atlantic hurricanes are more likely to recurve toward Europe (Haarsma et al. 2013; Sainsbury et al. 2020) because of reduced frequency of blocking. It is not necessarily inconsistent, however, because we focus on changes in downscaled hurricanes’ heading angle over New York specifically. Barnes et al. (2013) examined changes in the large-scale atmospheric circulation without considering hurricanes per se, and Haarsma et al. (2013) investigated the track patterns of hurricanes over the whole Atlantic basin.
Changes of the mean steering flow (m s−1) between the late-twenty-first century and the historical period.
Citation: Journal of Applied Meteorology and Climatology 61, 6; 10.1175/JAMC-D-21-0173.1
5. Landfall and county-level wind hazards
Climate change influences on both the frequency and intensity of New York storms, as discussed in the previous section, are reflected in the return periods at which storms make landfall with a given intensity (meaning, maximum sustained surface wind) and at which a given surface wind speed threshold is exceeded at a given location. New York landfalling storms between 1951 and 2019 include Carol (1954), Diane (1955), Henry (1985), Bob (1991), Barry (2007), and Andrea (2013). Except for Henry, which had a landfall intensity of 30 kt, the other five storms’ winds exceeded the tropical storm threshold (34 kt) upon landfall in New York. The return periods derived from these observations are 12.8, 22.9, 30.3, and 48.7 years for Saffir–Simpson wind categories tropical storm plus (TS+; ≥34 kt), category 1 plus (Cat1+; ≥64 kt), category 2 plus (Cat2+; ≥83 kt), and category 3 plus (Cat3+; ≥96 kt), respectively (Table 1). The corresponding annual frequencies are 0.078, 0.044, 0.033, and 0.021. There are no observed return periods for Cat4+ (≥113 kt) and Cat5 (≥136 kt) storms because there have been no landfalling storms with these intensities in New York during the historical period. This does not mean that the probability of occurrence for such events is zero, however, as we will discuss later using the CHAZ data.
Mean annual frequency (Freq) and return periods (RP; yr) of New York landfall storms exceeding TS+ and different Saffir–Simpson categories (labels Cat1+–Cat5) from observations (Obs) and from CHAZ CMIP5 downscaling. Here, “−Inf” means no storm at that intensity threshold. The climate change deltas (%) are shown in parentheses.
The observation-derived return periods are specific to the search area, that is, landfall versus over a buffer zone, as well as the data coverage period. Using the 150-km-buffer-zone definition, the return periods of the impact intensity are 2, 5.7, 12.8, and 27.9 years for Saffir–Simpson wind categories from TS+ to Cat3+ (Table 2). The return period of 27.9 years for Cat3+ may seem small given that there have been no storms at this intensity passing through the 150-km buffer zone of New York since 1954, but there were two in that year. This return period should probably be viewed as highly uncertain, given the strong low-frequency variability, as well as the fact that the two 1954 storms (Carol and Edna) were both only very marginally category 3 at landfall, and the uncertainty in intensity estimates is easily large enough that we cannot be certain these storms exceeded the (admittedly arbitrary) 96-kt threshold (Torn and Snyder 2012; Landsea and Franklin 2013).
Return periods estimated using synthetic events from CHAZCRH and CHAZSD for both landfall storms and storms passing the 150-km buffer zone in HIST are shown in Fig. 9 and Tables 1 and 2, too. CHAZCRH suggests a return period of 13, 50, 117, 236, 730, and 3947 for New York landfall storms from TS to Cat5 intensity thresholds, respectively. While the numbers are different, CHAZSD estimates return periods of landfall intensity at similar orders. With the 150-km-buffer-zone definition, synthetic New York storms from CHAZCRH and CHAZSD both estimate quite similar return periods for TS+ to Cat3+ intensity thresholds to the observation-derived values and estimates return periods of ∼80 and ∼500 yr for Cat4+ and Cat5, respectively.
The return period of landfall intensity computed from observations (solid black curve with dots) and (a) CHAZCRH and (b) CHAZSD experiments in HIST (gray), RCP85nf (blue), and RCP85late (red). (c),(d) As in (a) and (b), but for impact intensity using the 150-km-buffer-zone definition.
Citation: Journal of Applied Meteorology and Climatology 61, 6; 10.1175/JAMC-D-21-0173.1
In the two future periods, CHAZCRH projects that return periods at fixed intensity thresholds will decrease, that is, that hurricane hazard will increase at most of the intensity thresholds. In contrast, CHAZSD projects that the same return periods will increase in the future, that is, that hurricane hazard will decrease, at all intensity thresholds. This projected reduction is consistent with the overall decrease of New York storm frequency shown in Fig. 4. The relative magnitudes of these changes in the return periods in CHAZSD are much larger than those in CHAZCRH, as well as opposite in sign; this again is consistent with the changes in the basinwide and New York storm frequency shown in Figs. 3 and 4, in which the forced trends are larger in CHAZSD than in CHAZCRH.
The projected changes in the return period of the impact intensity are associated with substantial projected changes in the county-level wind hazards. Similar to the statewide impact intensity return periods, the county-level wind hazards may also be sensitive to search radius and thus we use 150-km-buffer-zone definition.2 In the HIST period, wind hazards from both CHAZSD and CHAZCRH have the highest annual frequencies of exceedance, and thus shortest return periods, in Suffolk County (at all Saffir–Simpson categories), followed by Nassau, Queens, Kings, New York, Bronx, and Richmond Counties (Fig. 10a). According to the CHAZ results, there is a small but nonzero probability that these counties will experience winds of major hurricane force (Fig. 10d). The relative ranking of wind hazard, between the counties of Kings, New York, Bronx, and Richmond, varies by intensity threshold, but the counties that can experience major hurricanes is neither sensitive to CHAZ TCGI configuration nor to the choice of the parametric wind models. Table 3 shows the wind hazard for Suffolk County. The estimated return periods from CLE15 and W06 are very similar, but we can still see the systematic differences in the estimated wind profiles between CLE15 and W06, as discussed in section 2. These differences are reflected in the derived return periods, in that CLE15 predicts slightly shorter return periods than W06 does (although with a few exceptions, such as those for category-5 hurricanes).
The return period of New York counties experiencing Saffir–Simpson (top) Cat1+ and (bottom) Cat3+ surface wind threshholds for (a),(d) HIST and the climate change deltas (b),(e) RCP85nf and (c),(f) RCP85late.
Citation: Journal of Applied Meteorology and Climatology 61, 6; 10.1175/JAMC-D-21-0173.1
As in Table 2, but for Suffolk County surface winds from New York storms. The italic font in the climate change delta indicates that the 95% confidence interval of the delta does not include zero.
In the two simulated future periods, the wind hazard in Suffolk County overall increases (i.e., decreasing return periods) in CHAZCRH, while the opposite occurs in CHAZSD, as with the statewide metrics shown above. An exception is that the wind hazard for category-5 hurricanes in CHAZCRH decreases in RCP85late, which may be related to the uncertain changes in the storm intensity in the near future as shown in Fig. 6a. The 95% confidence interval analyses of the climate change delta suggest a low confidence in such reduction, though. The climate change deltas of the annual frequency range from ∼6% to 14% from HIST to RCP85nf and from ∼29% to 57% from HIST to RCP85late in CHAZCRH. In CHAZSD, they are by ∼−13% to −22% in RCP85nf and can be as large as −100% in RCP85late, with little sensitivity to the choice of wind model (Table 3). The trends in wind hazard in other counties are qualitatively similar to those in Suffolk County; the projected risk increases with time in CHAZCRH and decreases in CHAZSD. As an example, Table 4 shows the return periods, annual frequency, and deltas for Nassau, Queens, Kings, New York, Bronx, and Richmond Counties from CHAZCRH using CLE15. Also considering CHAZCRH synthetic storms with W06 (Fig. 10) shows that the deltas of the annual frequency increases for Cat1+ and Cat3+ over New York’s whole coast.
As in Table 3, but for Nassau, Queens, Kings, New York, Bronx, and Richmond Counties from CHAZCRH using CLE15.
Although the return periods reported in Tables 3 and 4 are not sensitive to the choice of the wind model, they are sensitive to how we calculate the radius of the maximum wind (RMW). Predicting the RMW based on other storm parameters is challenging because RMW is hard to observe directly (Chavas and Knaff 2021) and thus we test the sensitivity of using constant prescribed RMWs, with values between 50 and 150 km (not shown). As expected, the larger the RMW, the shorter the return period and the higher the wind risk. With large RMWs (e.g., 80 km), the systematic differences between the CLE15 and W06 also become more evident, with much shorter return periods in CLE15 than in W06. Given that our interest is in the climate change delta, we compare the deltas derived from constant RMWs with those using RMWs calculated based on Willoughby et al. (2006). As an example, we focus on counties whose return periods at Cat1+ are lower than 500 years (Fig. 11). At this threshold, the climate change deltas for these counties are not sensitive to the RMW. The deltas become more sensitive to the RMW at Cat3+ thresholds, and at larger RMWs (130 and 150 km) with larger increase in the return period values.
Return periods (in gray bars) for New York county-level wind hazards from CHAZCRH using HIST and climate deltas for (a),(b) rcp85nf and (c),(d) rcp85late using wind profiles from Willoughby et al. (2006). Cases with RMWs obtained from W06 are shown in black, and those considering constant RMWs are in colored dots. Cat1+ [in (a) and (c)] and Cat3+ [in (b) and (d)] thresholds are shown for both future scenarios.
Citation: Journal of Applied Meteorology and Climatology 61, 6; 10.1175/JAMC-D-21-0173.1
6. Conclusions
This study has investigated New York State’s hurricane hazard over the historical period and under projected future conditions. We use the Columbia University’s CHAZ model to downscale model output from CMIP5, considering historical (1951–2005), near-future (2006–40), and late-twenty-first century (2070–99) periods as well as historical observations. Future periods are represented by the RCP8.5 scenario, with the goal of characterizing the response to global warming (while recognizing that warming may occur more slowly than projected if future emissions are lower than assumed in RCP8.5 and the models’ climate sensitivities are correct). We examine a range of storm characteristics: frequency, intensity, forward speed, and heading.
In the late-twenty-first century, the results robustly project an increase in New York storm intensity. We attribute this increase to increasing potential intensity and decreasing vertical wind shear in the vicinity. We also find a decreasing trend in storm translation speed and an increasing trend in heading, suggesting an increasing probability that storms will approach New York slowly and from the east. In the nearer future, the projected climate change influences are relatively small and less robust.
A major source of uncertainty in New York hurricane hazard is the projection of basinwide hurricane frequency over the North Atlantic. As described in our previous work (Lee et al. 2020), CHAZ projects either an increase or a decrease in this frequency, depending on which humidity variable is used—column relative humidity or saturation deficit—in the model’s environmental index for the probability of cyclogenesis. The current state of the science, in our view, does not allow us to draw a firm conclusion as to which is correct, so we report projected changes in the landfall and wind hazards separately under each of the two assumptions here. As expected, New York hurricane impact intensity and county-level wind hazards increase in CHAZCRH and decrease in CHAZSD as the climate warms. This underlying epistemic uncertainty is probably best understood using a storyline approach (Shepherd et al. 2018), and our presentation of both sets of results embodies this view. On the other hand, the changes in wind hazard are not too sensitive to which of the two parametric wind models (CLE15 and W06) considered here is used.
Although past studies have investigated climate change impacts of storms affecting the state, these studies have focused on storm surge hazard in New York City (e.g., Lin et al. 2012; Garner et al. 2017) using the downscaling model developed by Emanuel et al. (2006) and Emanuel (2008). Our work uses a different downscaling model (CHAZ) and focuses on county-level wind hazards across the state. Because of the broader areal coverage, the estimated impact frequencies obtained here are greater (equivalently, the return periods are shorter) than those reported in these previous studies. For example, we estimated an 0.6 annual frequency for New York State storms whereas Lin et al. (2012) estimated 0.34 for the New York City area (0.26 with observed data for the period 1951–2020). Differences in the downscaling models, the models used for surface wind estimation, and the specific climate models likely also contribute to differences. We view it as desirable for multiple studies to approach the problem from different perspectives and using different tools to allow for characterization of uncertainty, much as is done with multimodel ensembles such as CMIP for the global climate change problem as a whole.
The 97 kt is from interpolation, and it would be 100 kt if we use the last recorded value before entering the blue area in Fig. 1a.
Using a smaller search radius, we may miss ones whose center is outside the search radius for a county but the wind field was strong and large. Here, we tested a larger search radius (up to 300 km) and found that the county-level wind hazards are less sensitive to the increasing search radius after 150 km.
Acknowledgments.
The research was supported by the New York State Energy Research and Development Authority under the Research Grant NYSERDA 103862. This work was also partially supported by NASA Grant 80NSSC17K0196.
Data availability statement.
CHAZ is an open-sourced model (https://github.com/cl3225/CHAZ). CMIP5 data are archived and available online (https://esgf-node.llnl.gov. IBTrACS data are available at https://www.ncdc.noaa.gov/ibtracs/). The underlying data can be accessed online (https://github.com/cl3225/JAMC_2022_NewYork).
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