ENSO Effect on East Asian Tropical Cyclone Landfall via Changes in Tracks and Genesis in a Statistical Model
By Hall, Timothy M | |
Proquest LLC |
ABSTRACT
Improvements on a statistical tropical cyclone (TC) track model in the western
(ProQuest: ... denotes formulae omitted.)
1. Introduction
There are many densely populated coastal regions that are susceptible to high fatalities and costly damage every year because of the frequent landfall of tropical cyclones (TC) that occur in the western
The
The advantage of using a statistical track model is the ability to translate what the TC activity-climate state relationships mean in terms of risk to humans. Obser- vational studies have used track shape (Elsner and Liu 2003; Camargo et al. 2007b,c) or track density (Wang and Chan 2002; Wu et al. 2004; Zhang et al. 2012) changes as a proxy for risk of TC landfall because data become sparse when considering only the observed landfalls on small coastal regions. Data limitations increase when considering different climate states. Most other
In previous work, Yonekura and Hall (2011, hereafter YH11) use a statistical track model to simulate the full life cycle of TCs in the WNP including an
One important aspect that was missing from the YH11 model is the seasonality of WNP TCs. Chan (2000) has shown that the effect of
In the following section, we describe the methods used to incorporate
2. Methods
a. Data
As in YH11, the TC track data used to construct the statistical track model are from the International Best- Track Archive for Climate Stewardship (IBTrACS; Knapp et al. 2010) database of 6-hourly storm positions. The storms from 1945 to 2007 are used for consistency with YH11.
To define the state of
Seasonally varying predictors are used in both the gen- esis and track-propagation component of the model. The main purpose of adding SST as a parameter in the genesis component and midlevel winds in the track-propagation component is to include the effect of seasonality on WNP TCs, thus we use climatological predictors. The interaction term between the climatological predictor and
For the genesis component, we use the monthly annual- cycle climatology of SST from the
The choice of 500-hPa zonal winds as a predictor for the track component is motivated by Chan and Gray (1982), who studied which wind levels most influence track displacement and speed. They found that the midlevel winds (700, 600, and 500 hPa) have the most influence. Chan (2000) then used 500-hPa wind anom- alies to explain changes in WNP TC tracks associated with different
b. Genesis
The genesis component of the model simulates the number of TCs per year and the day of year and for- mation location of each TC, all of which may be sensitive to
We have added seasonality to the genesis model of YH11. We include the local monthly annual-cycle cli- matology of SST as an independent predictor variable in the regression. We randomly select a day of year, and the SST value of that day fixes a Poisson rate for a formation event. Then we sample the probability and repeat until an event is realized. An ENSO-SST interaction term is also used as a predictor to account for the effect of yearly
... (1)
where r stands for a grid location, a specifies a year, m specifies a month, each b is a local regression coeffi- cient at r,ENSO(a) is the
c. Tracks
The track component of the model simulates the 6-hourly increment of a TC position as a function of location, day of year, and
The IBTrACS 6-hourly zonal and meridional dis- placement values are linearly regressed locally on their respective predictors. The distance weight determining the locality of regression is a Gaussian kernel with a length scale of 300 km. The length scale is determined objectively by out-of-sample likelihood maximization. At each 18 grid point, we archive values of the regression coefficients from the equations:
... (2)
dY(r,a) 5 b0y(r) 1bENSOy(r) 3ENSO(a), (3)
where dX(r, a, d)anddY(r, a) are the mean displace- ments in the zonal and meridional directions, respectively, at grid point r, year a, and day d.ENSO(a)istheENSO index for the year specified by year a,andu500(r, d)is the 500-hPa zonal wind at grid point r and day d from a daily climatology. Note that
The resulting fields of coefficients from regression Eqs. (2) and (3) are shown in Fig. 2, in which the con- tours are color filled where the coefficients are signifi- cantly different than zero at the 95% confidence level according to a two-tailed Student's t test at every grid point.
d. Lysis
Lysis, or death, of each TC is determined by com- paring a random uniform draw to a predetermined PDF of historical lysis probability at every track step. Land and ocean lysis is strictly separated according to a 0.258 land-ocean mask. See YH11 for details.
e. Simulations
Our goal is to estimate the effects of
We also evaluate each of the four model versions against historical landfalls. For this purpose, a separate set of simulations is performed: 500 realizations of the historical period 1945-2007 for each model version using the historical time series of the
f. Uncertainty and significance
There are two types of uncertainty to document. One is the uncertainty due to the stochastic nature of our simulations. For example, a single 1-yr simulation in a 1 2
To determine the second type of uncertainty, we perform a generalized jackknife significance test (
3. Results
In this section we first evaluate the model by com- paring historical simulations to direct observations. We then examine the impact of
a. Model evaluation and comparison
We now evaluate the model by comparing simulation results to observations using two diagnostics: 1) life spans at extreme
To evaluate the landfall characteristics of the model, we perform 500 full stochastic simulations of the his- torical period 1945-2007. We define a landfall as when a track crosses a 100-km coastal segment from ocean to land for the East Asian regions shown in the Fig. 3 inset. Figure 3 shows the average annual landfall rates per 100 km over the 500-member ensemble, the inner 95% spread about the mean, and the rates directly computed from IBTrACS. Landfall rates vary enormously by re- gion, depending on the orientation of the coast with respect to mean tracks and the proximity of the coast to regions of high activity. Peak landfall rates occur in
The model is in close agreement with observed landfall rates. In most regions the historical landfall rate falls in- side the 95% spread about the mean, indicating no model bias. There are, however, a few scattered regions of bias that are identified with letters that correspond to the coastal segments highlighted on the Fig. 3 map inset. Just north (Fig. 3a) and south (Fig. 3b) of the
b.
The modulation of TC tracks in the WNP due to
Figures 4a and 4b show the local mean displacement vectors for opposite
To better visualize the cumulative effect of these space- and time-dependent
These 10-day tracks can be divided into two broad categories, those that recurve, ultimately heading north- eastward, and those that remain on a comparably straight northwestward trajectory. We find that both track cate- gories occur in both
The straight-moving tracks pose the biggest landfall threat for
Before running full stochastic simulations, we first consider the combined landfall effect of
c.
We now turn to landfall rates using the fully stochastic model. The results for a neutral state (i.e.,
The Track-Only model shows more spatially varied
The landfall rates resulting from the inclusion of both track- and genesis-
To further highlight the significance of the
Track density is now examined to help interpret these landfall-
4. Discussion
We have constructed a statistical track model for TCs in the WNP with seasonal climate and
Stochastic simulations of TCs in extreme
The model is also evaluated by comparing simulated landfall rates to direct historical landfalls. Overall, the model performs well: in most regions historical landfall rates are inside the spread of model results across large ensemble simulations, indicating a lack of bias. There are a few regions where the model has biased landfall rates. These biases have been reduced, however, com- pared with the YH11 model version, which had no sea- sonality and no
Future directions for this work include developing a model component for the intensity and size of each TC, similar to Vickery and Twisdale (1995) and Vickery et al. (2000), to make a more complete risk analysis. Studies such as Irish et al. (2008) and Weisberg and Zheng (2006) show that it is the complex relationship between fac- tors such as translational speed, storm size, intensity, bathymetry, and orientation that determines damage. Further, modeling of other TC-climate relationships may also be explored in the future where the model is dependent on other climate states. Advances in the understanding of the relationships between WNP TC activity and the Madden-Julian oscillation, quasi-biennial oscillation (Ho et al. 2009), central Pacific SST warming events (Kim et al. 2011), and even the more remote Antarctic Oscillation (Ho et al. 2005) and Arctic Oscillation (Choi and Byun 2010) motivate their additional consideration as statistical predictors to further improve the accuracy of our model.
Acknowledgments. This work was supported in part by a grant from the NASA Applied Sciences program. We thank
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(Manuscript received
Corresponding author address:
E-mail: [email protected]
Copyright: | (c) 2014 American Meteorological Society |
Wordcount: | 7892 |
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