Nonlinear Impacts of Surface Exchange Coefficient

RESEARCH LETTER Nonlinear Impacts of Surface Exchange Coefficient 10.1029/2019GL085783 Uncertainty on Tropical Cyclone Intensity and Air‐ Key Points: • Tropical cyclone ocean feedback Sea Interactions have a negative impact on intensity Robert G. Nystrom1, Xingchao Chen1, Fuqing Zhang1, and Christopher A. Davis2 but the extent depends on the air‐sea exchange coefficients 1Department of Meteorology and Atmospheric Science, and Center for Advanced Data Assimilation and Predictability • Both tropical cyclone intensity and 2 the model‐parameterized surface Techniques, Pennsylvania State University, University Park, PA, USA, National Center for Atmospheric Research, drag coefficient impact the degree of Boulder, CO, USA storm‐induced ocean cooling • Intensity forecast uncertainty is not necessarily reduced in coupled Abstract Tropical cyclone maximum intensity is believed to result from a balance between the surface simulations if uncertainty to the surface drag coefficient exists friction, which removes energy, and a temperature/moisture (enthalpy) difference between the sea surface and the air above it, which adds energy. The competing processes near the air‐sea interface are

Supporting Information: controlled by both the near surface wind speed and the surface momentum (Cd) and enthalpy (Ck) exchange • Supporting Information S1 coefficients. Unfortunately, these coefficients are currently highly uncertain at high wind speeds. • Figure S1 • Figure S2 Tropical cyclone winds also apply a force on the ocean surface, which results in ocean surface cooling • Figure S3 through vertical mixing. Using coupled atmosphere‐ocean and uncoupled (atmosphere only) ensemble • Figure S4 simulations we explore the complex influence of uncertain surface exchange coefficients on storm‐induced • Figure S5 fi • Figure S6 ocean feedback and tropical cyclone intensity. We nd that the magnitude of ocean cooling increases • Figure S7 with storm intensity and Cd. Additionally, the simulated maximum wind speed uncertainty does not • Figure S8 necessarily decrease when ocean feedback are considered. Plain Language Summary The peak intensity of tropical cyclones is primarily the result of the Correspondence to: energy exchange between the ocean and atmosphere. Tropical cyclones obtain energy from the warm R. G. Nystrom, ocean they track over, while at the same time losing energy through surface friction. Additionally, strong [email protected] surface winds of tropical cyclones cause the ocean surface to cool—a negative impact on intensity. The complex tropical cyclone–ocean feedbacks are partially controlled by the surface wind speed and the Citation: efficiency of the energy exchange between the ocean and atmosphere. Unfortunately, air‐sea energy Nystrom, R. G., Chen, X., Zhang, F., & Davis, C. A. (2020). Nonlinear impacts exchange is highly uncertain, especially at high wind speeds, and ocean feedback have often been ignored in of surface exchange coefficient tropical cyclone modeling. Using coupled atmosphere‐ocean and uncoupled (atmosphere only) simulations, uncertainty on tropical cyclone we find that forecasted maximum wind speed uncertainty does not necessarily decrease when ocean ‐ intensity and air sea interactions. ‐ Geophysical Research Letters, 47, feedback are considered because of competing effects between ocean atmosphere energy transfer, ocean e2019GL085783. https://doi.org/ cooling, and storm intensity. 10.1029/2019GL085783

Received 10 OCT 2019 Accepted 10 JAN 2020 1. Introduction Accepted article online 13 JAN 2020 Tropical cyclones (TCs) present significant risk of strong winds, storm surge, and heavy precipitation to regions around the world (Knapp et al., 2010; Peduzzi et al., 2012). While the significant advancements in global numerical weather prediction over recent decades (Bauer et al., 2015) have helped to significantly improve TC track forecasts (Alley et al., 2019; Cangialosi, 2019), TC intensity forecasts have improved at a much slower rate (Cangialosi, 2019). The smaller realized improvements in TC intensity prediction are the result of a combination of factors including insufficient model resolution (e.g.,Davis et al., 2008; Jin et al., 2014; Nystrom & Zhang, 2019), errors in initial conditions (e.g.,Brown & Hakim, 2013; Emanuel & Zhang, 2016; Hakim, 2013; Nystrom et al., 2018; Torn, 2016; Van Sang et al., 2008; Zhang & Sippel, 2009), and errors in model physics (e.g.,Braun & Tao, 2000; Bu et al., 2014; Green & Zhang, 2013, 2014; Judt et al., 2015; Melhauser et al., 2017). While our understanding of TCs has evolved since originally proposed in the 1980s, the basic components of potential intensity (PI) theory (Emanuel, 1988; Emanuel, 1997; Emanuel & Rotunno, 2011) still provide a useful framework to understand the quasi steady‐state intensity of TCs (e.g., Bryan & Rotunno, 2009a,

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− ÀÁ 2 Ck Ts To * V max ¼ k −k ; (1) Cd Ts

* where Ts is the sea surface temperature (SST), To is the outflow temperature, (k − k) is the air‐sea enthalpy disequilibrium, and Ck/Cd is the ratio of the surface enthalpy exchange coefficient (Ck) to the surface drag coefficient (Cd) (Emanuel, 1997). We would therefore expect the maximum wind speed to increase with the ratio of Ck/Cd. Arguably, the biggest unknown when applying the PI theory to real cases, however, is how to estimate the surface exchange coefficients (Ck/Cd). Accurate estimates of the surface exchange coefficients at high wind speeds have been challenging to obtain in both the laboratory and nature, because of the dangers of collecting in situ observations within the turbulent TC boundary layer. As a result, the surface exchange coefficients are highly uncertain and recent studies often disagree on the sign of the relationship between wind speed and the exchange coefficients (Black et al., 2007; Bell et al., 2012; Chen et al., 2018; Donelan, 2018; Donelan et al., 2004; Holthuijsen et al., 2012; Hsu et al., 2017; Komori et al., 2018; Powell et al., 2003; Troitskaya et al., 2012, 2016; Takagaki et al., 2016). Bell et al. (2012) attempted to estimate the surface exchange coefficients and suggested that the current uncertainty may be larger than 40%. Additionally, Nystrom and Zhang (2019) suggested that the impacts from uncertainty in the surface drag coefficient can be as influential as initial condition uncertainty in limiting the intensity predictability of a strong TC. Further complicating the problem, TC‐induced ocean feedback, specifically the ocean surface cooling induced by vertical mixing, can have significant negative consequences on TC intensity (Bender & Ginis, 2000; Chen et al., 2007; Cione & Uhlhorn, 2003; Mogensen et al., 2017; Price, 1981; Schade & Emanuel, 1999; Zarzycki, 2016), but the implications for the PI have been largely ignored. A few recent studies (e.g., Balaguru et al., 2015; Lin et al., 2013; Miyamoto et al., 2017) have attempted to include the negative ocean feedback into PI theory but have neglected the uncertainty in the surface exchange coefficients, which will likely impact TC intensity and ocean feedback in currently unknown ways (e.g., Fan et al., 2009). In this study, we will focus on the influence of uncertain surface exchange coefficients on the intensity of TCs and ocean feedback using both atmosphere‐ocean coupled and uncoupled (atmosphere only) simulations. Our primary goal is to determine the influence of uncertainties within the modeled surface exchange coefficients on TC intensity prediction in coupled and uncoupled simulations.

2. Modeling Methodology To examine the impacts of uncertainty in the surface exchange coefficients of momentum and enthalpy fluxes, we investigate both coupled atmosphere‐ocean and uncoupled (atmosphere only; fixed SST) simulations of Hurricane Patricia (2015). The ocean model used for the coupled simulations is the Regional Oceanic Modeling System (Shchepetkin & McWilliams, 2005), the atmospheric model is the Weather Research and Forecasting version 3.9.1 model (Skamarock et al., 2008), and the two models are coupled through the Coupled Ocean‐Atmosphere‐Wave‐Sediment Transport Modeling System (Warner et al., 2010). A figure depicting the domain configuration is shown in Figure S1 and further details of the model setup are also included in the supporting information. All simulations in this study have identical initial and boundary atmospheric and oceanic conditions, the details of which are included in the supporting information.

The perturbations to Cd and Ck are generated by introducing three parameters (α,VC, and β) which modify the model representation of the surface exchange coefﬁcients presented in Chen and Yu (2016, 2017) for Cd and Weather Research and Forecasting isftcﬂx option 2 for Ck. The model Cd representation from Chen and Yu (2016, 2017) is chosen due to its consistency with recent laboratory experiments (e.g., Donelan et al., 2004; Donelan, 2018; Takagaki et al., 2012, 2016; Troitskaya et al., 2012, 2016). We have intentionally chosen not to also couple with a wave model, which may be more physically realistic, because it would not allow us

to control Cd explicitly as a function of the wind speed. In this study, the surface drag coefﬁcient is a function of wind speed and is described as

ÀÁ 2 –4 Cd ¼ α × max 5−0 :014 minðÞ V C; U10 þ 1 :033 minðV C; U10 þ 4 :895Þ ×10 ; (2)

where U10 is the 10‐m total wind speed. From equation (2) we can see that α acts as multiplicative factor on the drag coefﬁcient at all wind speeds and VC controls the wind speed at which the drag coefﬁcient will level

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off. The surface enthalpy exchange coefﬁcient in this study is separated into two equations which separately

describe the sensible (Ch) and latent heat (Cq) exchange coefﬁcients as follows:

Cd Ch ¼ β ÀÁand (3) þ 1=2 : 1=4 1=2− 1 Cd 7 3Re* Pr 5

Cd Cq ¼ β ÀÁ; (4) þ 1=2 : 1=4 1=2− 1 Cd 7 3Re* Sc 5

u*z * ¼ 0 where Re* is the roughness Reynolds number (Re v ; u* is the friction velocity, z0 is the surface roughness, and v is the kinematic viscosity of air), Pr = 0.71 (Prandtl number), and Sc = 0.6 (Schmidt number). For TCs, where fluxes of latent heat are significantly larger than sensible heat fluxes (e.g., Zhang et al., 2008), we can

assume that Ck ≈ Cq ≈ Ch. We can see from equations (3) and (4) that β acts as a multiplicative factor on Ck. Additionally, α and VC also have an inﬂuence on Ck,asCk is also a function of Cd.AsCd is increased, through increasing α or decreasing VC,Ck is also generally increased. However, Ck/Cd always decreases when Cd increases, by either α or VC. In this study, we perturb α,VC, and β independently and only one at a time unless otherwise speciﬁed. Motivated by the uncertainty estimates from many studies, for example, Bell et al. (2012), Holthuijsen et al. (2012), and Hsu et al. (2017), α and β values from 0.5 to 2.0, every 0.15, −1 and VC values from 30.0 to 74.0, every 4.4 ms , are tested. This results in 11 different experiments for each parameter and 33 total simulations for both the coupled and uncoupled experiments. The wind‐speed‐

dependent proﬁles of Cd,Ck, and Ck/Cd resulting from the chosen parameter values are shown in Figure S2.

3. Results In both the coupled and uncoupled simulations, the peak simulated intensity in terms of maximum wind speed or minimum central pressure is generally in qualitative agreement with the PI theory, despite the

simulated storm never reaching a true steady‐state (Fig. 1). As α,Cd, increases the maximum simulated wind speed (minimum central pressure) decreases (increases) for both the coupled and uncoupled simulations

(Figures 1a and 1d). Similarly, as VC increases, Cd decreases at high wind speeds, the maximum simulated wind speed (minimum central pressure) increases (decreases) for both the coupled and uncoupled simula-

tions (Figures 1b and 1e). Finally, as β,Ck, increases the maximum simulated wind speed (minimum central pressure) increases (decreases) for both the coupled and uncoupled simulations (Figures 1c and 1f). The track of the simulated storm is nearly identical for all forecasts regardless of ocean coupling/lack thereof or surface exchange coefﬁcient values (not shown). Additionally, it is shown that for all simulations the peak intensity is reduced in the coupled simulations, relative to the atmosphere only simulations (Figure 1). However, the magnitude of the reduction in intensity appears sensitive to both the intensity of the storm and the surface drag coefﬁcient. For the β ensemble,

where the Cd variation with wind speed is identical for all ensemble members, the magnitude of the reduction in storm intensity generally increases with the intensity of the storm (Figures 1c and 1f). For the smallest β value (0.5) the peak simulated maximum wind speed is reduced in the ocean coupled simulation by a max- − − imum of only 5 ms 1 (10%) over the ﬁrst 48 hr, in comparison to 28 ms 1 (22%) for the largest β value (2.0). The wind stress acting on the ocean and which is largely responsible for the SST cooling, through ocean vertical mixing, is a function of both the wind speed and the surface drag coefﬁcient as described by

2 2 τ ¼ −ρu* ¼ −ρCdU10 ; (5)

where τ is the wind stress, ρ is the air density, u* is the friction velocity, and U10 is the 10‐m total wind speed. Therefore, while the impact of differences in 10‐m wind speed will cause differences in the wind stress, Cd differences will also be shown to be important. The magnitude of τ and the ocean cooling, averaged within 50 km relative to the storm center, for the largest β simulation are more than double that of the smallest β simulation (Figures 2i and 2j) with the largest ocean cooling being located to the right of the storm track in both simulations (Figures 2k and 2l), in agreement with Price (1981) and Sanford et al. (2011). This agrees with the results of previous studies in which it has been demonstrated that the negative ocean feedback mainly result from the turbulent ocean mixing

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Figure 1. Simulated (a–c) maximum wind speed and (d–f) minimum central pressure for uncoupled (solid) and coupled (dashed) simulations with different model representation of the surface momentum (C ) and enthalpy (C ) flux coefficients, where (left) α, (center) V and (right) β are shown. Bottom panels of d k − C each row depict the (a–c) change (Coupled–Uncoupled) in maximum wind speed (ms 1) and (d–f) minimum central pressure (hPa). The darkest red line corresponds to (left) α=2.0, VC=74.0, and (right) β=2.0 and the darkest blue line to α=0.5, VC=30.0, and β=0.5. Black line depicts the best track estimate of the maximum wind speed and minimum central pressure. The horizontal grey lines (top row) denote the wind speed cut offs on the Saffir‐Simpson Scale.

caused by the atmosphere‐imposed wind stress and that the ocean cooling is roughly proportional to the intensity of the storm, when all else is equal (e.g., Mogensen et al., 2017; Price, 1981). For the α ensemble, in which the surface drag coefﬁcient has been perturbed, the simulation with the smallest α value (0.5) only exhibits a maximum decrease in the maximum simulated wind speed, relative to the − − uncoupled simulation, of 7 ms 1 (10%) over the ﬁrst 48 hr, in comparison to 20 ms 1 (27%) for the largest α − value (2.0) despite the TC in the α = 0.5 simulation being 13 ms 1 stronger than that in the α = 2.0 simulation at 48 hr (Figures 1a and 1d). For both α = 0.5 and α = 2.0 the greatest SST cooling is again observed to the right of the track, but the maximum observed SST cooling near the storm track is ~2.5 °C for α = 0.5 and ~3.5 °C for α = 2.0 (Figures 2c and 2d). The SST change is not found to always increase with the storm intensity in the α ensemble (Figure 2b), as it was for the β ensemble (Figure 2j). The wind stress acting on the ocean (τ) increases with α (Figure 2a), despite the intensity generally decreasing with α (Figures 1a and 1d). This domi-

nant contribution from Cd, rather than the 10 m wind speed, explains why the more intense TC in the low α (low Cd) simulation exhibits less SST cooling and less intensity reduction than the weaker TC in the high α (high Cd) simulation (Figures S3a–S3c), in agreement with Fan et al. (2009).

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Figure 2. (ﬁrst column) Average surface stress acting on the ocean within 50 km of the storm center and (second column) average change in SST within 50 km of the center from the uncoupled to coupled simulations. Dark red corresponds to the largest parameter values, while the smallest values are in dark blue. Map of change in SST from initial time to 48 hr for (third column) lowest parameter value and (last column) highest parameter value. Black lines depict the simulated track through 48 hr.

−1 For the VC ensemble, where Cd has been perturbed only for wind speeds greater than 30 ms , the change in SST is found to be nearly identical for all simulations (Figures 2f–2h) and the reduction in intensity, relative to the comparable uncoupled simulation, is also nearly identical (Figures 1b and 1e). The average τ values for

all VC simulations are found to be nearly identical (Figures 2e andS3d–S3f) and the area with Cd differences are limited. However, if a TC spends longer time as a major hurricane (e.g., Hurricane Dorian, 2019), or has a

larger area of hurricane strength winds, the potential differing ocean feedback caused by Cd differences at high wind speeds may become more important. To further quantify the inﬂuence of each parameter value on the storm intensity and SSTs, ensemble correlations are examined from the coupled ocean‐atmosphere simulations. Very strong correlations (magnitude greater than 0.75) exist beyond ~24 hr between all parameters and simulated maximum wind

speed (Figure 3a). In agreement with the PI theory, larger β (larger Ck), smaller α, and larger VC (smaller Cd) simulations are associated with more intense TCs. Additionally, very strong negative correlations (less than −0.75) between α or β and the change in SST within 50 km relative to the storm center suggest that

increasing α or β results in greater SST cooling (Figure 3b). Ensemble correlations between VC and the change in SST are not found to be signiﬁcant. To assess whether the simulated reduction in SSTs may explain the simulated reduction in intensity, the ensemble correlation between the change in SST and the change in the maximum wind speed (Coupled– Uncoupled) are examined (Figure 3c). The positive correlations (greater than 0.5) between the change in SST and the change in maximum wind speed from the uncoupled to coupled simulations for the α or β ensembles suggests that a greater SST reduction is associated with a greater reduction in the maximum wind

speed. While the SST across the VC ensemble is poorly correlated with the maximum wind speed at all times, notable correlations are found between the SST and the maximum wind speed for both the α and β ensembles (Figure 3d). However, the SSTs are clearly unimportant in explaining the cause of the intensity variability across the β ensemble, as the negative correlations suggest that cooler SSTs cause a stronger storm, which does not make physical sense. The large correlations found between the SST and maximum wind speed for

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Figure 3. Ensemble correlations between (a) parameter values and simulated maximum wind speed (Vmax), (b) parameter values and the change in SST, (c) change in SST and change in Vmax from uncoupled to coupled simulations, and (d) SST and Vmax for the coupled simulations.

the β ensemble exist because a stronger storm has larger magnitude of ocean cooling. Conversely, the positive correlation between SST and maximum wind speed found for the α ensemble makes physical sense. The simulations with smaller α values are associated with less ocean cooling and thus greater SSTs and greater maximum wind speed. The SST is well correlated with the fluxes of latent and sensible heat (not shown), explaining the thermodynamic link to TC intensity. A priori one may hypothesize that as the surface coefficients are modified such that the intensity of the storm increases, the negative ocean feedback will be larger and the intensity will be reduced, thus reducing the ensemble intensity spread relative to the uncoupled simulations. However, we have already demonstrated

that the magnitude of SST cooling is not simply a function of intensity when Cd is perturbed. We examine the ensemble spread of maximum wind speed and minimum central pressure here as a proxy to evaluate the intensity uncertainty (Figure 4). We deﬁne a meaningful reduction in the TC intensity spread to be − one that is larger than the inherent intrinsic predictability limit of TC intensity, ~4 ms 1 (Emanuel & Zhang, 2016). For the β ensemble, the ensemble spread in maximum wind speed (minimum pressure) is

meaningfully reduced by ~16% (~35%) at 48 hr, demonstrating that the uncertainty only to Ck is buffered, at least to some extent, by the negative ocean feedback, although substantial ensemble spread still remains.

The α and VC ensemble, which perturb Cd, do not however demonstrate any meaningful reductions in intensity spread for the coupled simulations (Figure 4). In fact, between 39 and 48 hr, the α ensemble actually has larger intensity spread in the coupled simulations than the uncoupled simulations, suggesting that ocean

feedback may increase the intensity uncertainty in this case. For a given SST and Ck/Cd in the uncoupled simulations, the maximum intensity is suggested to be relatively predictable; the peak intensity is a bounded quantity which is largely predetermined by the environmental conditions (Kieu & Moon, 2016). However, in the coupled simulations the greater magnitude of SST cooling for the larger α simulations reduces the

boundary layer equivalent potential temperature (θe; Figure S4), thereby reducing the diabatic heating within the eyewall above the boundary layer and weakening the secondary circulation (e.g., Hack &

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Figure 4. Ensemble intensity spread for (left) maximum wind speed and (right) minimum central pressure from uncoupled (solid) and coupled (dashed) single‐parameter and multiparameter ensemble simulations.

Schubert, 1986). The difference in comparison to the β ensemble is that the SST and θe are decreased most in weaker simulations of the α ensemble due to larger τ, which is directly linked to larger Cd (Figures 2 and S3). − Additionally, because the vertical wind shear increases similarly for all ensemble members from near 0 ms 1 − at 24 hr to near 8 ms 1 by 48 hr (not shown), the differences in simulated TC intensity beyond 24 hr may be caused by a combination of differences in ocean cooling and in differences in how the simulated TC interacts with vertical wind shear (e.g., Reasor et al., 2004). The net result is an increase in ensemble intensity spread for the α ensemble. To quantify the impact of uncertainties to all three parameters simultaneously, a 25‐member multiparameter ensemble forecast was conducted with both coupled and uncoupled simulations. Both the coupled

and uncoupled forecast pairs used identical random parameter values for α and β from 0.5 to 2.0 and VC from 30.0 to 74.0 (Figure S5). Overall, ensemble forecast results and the sensitivity to Ck and Cd in the multiparameter ensemble are similar to those in the single‐parameter ensembles (Figures S6–S8). The minimum pressure spread is reduced by an average of 10% in the coupled ensemble, relative to the uncoupled, but the maximum wind speed spread is unchanged (Figure 4) and the spread is still large for both metrics.

4. Summary and Discussion Using ocean‐atmosphere coupled and uncoupled simulations (with ﬁxed SSTs) of Hurricane Patricia (2015), the impacts of uncertainty within the model's physical representation of the surface exchange processes on TC intensiﬁcation were examined. In both coupled and uncoupled simulations, uncertainty in the surface

enthalpy (Ck) or surface momentum (Cd) exchange coefﬁcient results in notable TC intensity uncertainty. Additionally, we demonstrated that while all ocean coupled forecasts resulted in weaker TC intensity than uncoupled forecasts—as a result of negative ocean feedback—the magnitude of intensity reduction is a func-

tion of both the storm intensity and the surface momentum exchange coefﬁcient. When Cd is unchanged the simulated TC is found to decrease most in intensity when the storm is strongest. When Cd is changed the expected relationship between ocean cooling and TC intensity no longer holds. Despite a greater Cd value resulting in a lower TC intensity, we ﬁnd that the magnitude of SST cooling is increased with increasing

Cd. We further demonstrate that the extent to which TC intensity is reduced from the uncoupled to the coupled simulation can be well explained by the negative ocean feedback (decreased SSTs) and that physically realistic correlations exist between SST and TC intensity when the SST differences are realized through differences in the surface momentum exchange coefﬁcient. After highlighting a portion of the complex nature of TC air‐sea interactions, we address the potential implications on TC intensity predictability. Unfortunately for the prospects to ignore the importance of the current surface exchange coefﬁcient uncertainties, the forecasted maximum wind speed (minimum cen- −1 tral pressure) spread in coupled simulations with uncertainty to both Ck and Cd can be larger than 15 ms (20 hPa). Additionally, the uncertainty in simulated maximum wind speed is not meaningfully reduced,

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relative to the inherent predictability limit, when Cd is perturbed. The results of this study demonstrate that the current uncertainty regarding the values of Cd and Ck have the potential to limit the intensity predictability of TCs in ocean‐atmosphere coupled and uncoupled (atmosphere only; fixed SST) simulations. Broadly speaking, we expect that numbers of other strong TCs, which are slower moving (Price, 1981; Bender et al., 1993), are larger (Schade & Emanuel, 1999), and/or have a shallower ocean mixed layer (Mao et al., 2000; Mogensen et al., 2017; Price, 1981) to be more sensitive to the model representation of the air‐sea momentum exchange than Patricia (2015), which was rather small and had a relatively deep ocean mixed layer (Foltz & Balaguru, 2016). Additionally, with recent studies suggesting both an increase in strong TCs (Knutson et al., 2010) and a slowdown in translation speed (Kossin, 2018) with a warming climate, we suggest that the uncertainty in the surface exchange coefficients at high wind speeds and the complex coupled air‐sea interactions may become increasingly important. Future works to extend the results of the current study to other TCs will help in further understanding of TC air‐sea interactions and their influence on TC predictability. The results of this study may also be sensitive to the atmospheric model's surface layer and boundary layer model parameterizations (e.g., Braun & Tao, 2000; Smith et al., 2014; Zhang et al., 2015), each of which deserve future exploration. Lastly, the surface exchange coefficient‐driven differences in ocean feedbacks, combined with the recent advances in coupled data assimilation (Chen & Zhang, 2019; Li & Toumi, 2018; Penny & Hamill, 2017; Sluka et al., 2016), also highlight a potential opportunity. It may be possible in the near future to utilize ocean observations—in addition to available atmospheric observations—to constrain the uncertainty in the surface exchange coefficients using a well‐developed coupled ocean‐atmosphere ensemble data assimilation system through parameter estimation (Aksoy et al., 2006a, 2006b; Hu et al., 2010).

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