1. Introduction
Climate change is one of the main concerns for the sustainable development of our society. Climate change can impact not only the mean state, such as the global mean atmospheric temperature and global mean sea level, but also lead to extreme events. Tropical cyclones (TCs) are one of the most devastating extreme events. A recent report by the Intergovernmental Panel on Climate Change (IPCC; Collins et al. 2019) described the observed historical increases in TC winds and rainfall, and projected future increases in TC intensity under global warming scenarios. These changes in TC characteristics can have a significant impact on human society (WMO 2012; Needham et al. 2015; Khouakhi et al. 2017). Changes in TC characteristics associated with global climate change have been studied based on climate simulations conducted using global climate models (GCMs) (Yoshida et al. 2017; Roberts et al. 2020; Murakami et al. 2020). Therefore, improving GCM performance is important for reliable climate change impact assessment and adaptation against the varying impacts of TCs.
TCs generate extreme oceanic surface waves that cause coastal disasters (Shimozono et al. 2015; Kennedy et al. 2017; Cox et al. 2019; Mori et al. 2019). Oceanic surface waves are forced passively by ocean surface winds and thus TC modeling by GCMs, as well as regional weather/climate models, does not generally consider the feedback from ocean surface waves. However, feedback from ocean surface waves has been reported, and various wave-dependent atmosphere–ocean interactions have been comprehensively reviewed by Cavaleri et al. (2012). For example, the atmosphere–ocean momentum flux depends on ocean surface wave conditions, and the wave-dependent momentum flux has been studied in detail (Janssen 1991; Drennan et al. 2003; Högström et al. 2015; Potter 2015; Takagaki et al. 2016; Voermans et al. 2019; Chen et al. 2020). The atmosphere–ocean momentum flux is represented by the bulk formula and is parameterized generally by the wind speed in climate/weather models. In addition to wind-only parameterizations (e.g., Fairall et al. 1996), several parameterizations of atmosphere–ocean momentum flux considering wave conditions have been proposed in previous studies (Jones and Toba 2001; Taylor and Yelland 2001; Janssen 2004; Drennan et al. 2005; Högström et al. 2015; Patton et al. 2019). For example, momentum flux is assumed to be a function of wave age (Drennan et al. 2003), wave steepness (Taylor and Yelland 2001), energy of the equilibrium range of the wave spectrum (Janssen 2004), swell wave energy (Högström et al. 2015), and wind and wave directional misalignment (Patton et al. 2019). Although there have been several parameterizations of momentum flux either by wind only or by wind and waves, there is a lack of general consensus on momentum flux parameterization. Additionally, the behavior of the coefficient of momentum flux under wind speeds of more than 30 m s−1 is highly uncertain (Powell et al. 2003; Jarosz et al. 2007; Soloviev et al. 2014; Curcic and Haus 2020).
Experimental studies on TC modeling considering wave-dependent momentum flux using wave-coupled regional weather models have been conducted previously (Lee and Chen 2012; Chen et al. 2013; Kumar et al. 2020; Wu et al. 2020). However, Thomsen et al. (2014) concluded that the peak intensity cannot be affected by the drag coefficient, and the wave-coupled model is not necessarily required for better TC simulations. The importance of wave-model coupling for TC modeling is still unclear. In terms of GCM simulations with wave-dependent momentum flux, some studies conducted climate simulations and have reported the significant impacts of wave-dependent momentum flux on atmospheric climate (Janssen and Viterbo 1996; Fan et al. 2012; Shimura et al. 2017) and oceanic climate (Shimura et al. 2020). No studies have used wave-coupled GCMs to estimate the impact of wave-dependent momentum flux on TC characteristics. Studies of wave-dependent momentum flux using regional weather models usually focus on a few TC characteristics. On the other hand, climate studies using GCMs focus on the systematic characteristics of TCs. Therefore, the objective of this study was to estimate the systematic impacts of wave-dependent momentum flux on TC characteristics represented in the GCMs while simulating and analyzing 100 TCs.
A wide variety of wave-dependent momentum flux parameterizations are available, as described above. We employed the momentum flux parameterization, which is a function of misalignment between the wind and wave directions (Patton et al. 2019). It is known that a strong systematic misalignment between wind and wave directions exists in TCs, corresponding to the TC quadrant (Holthuijsen et al. 2012). The misalignment cannot be represented by the wind-only parameterization. We assessed whether systematic misalignment can result in systematic TC characteristic differences.
2. Method
a. Atmospheric global circulation model (AGCM)
The model framework of the AGCM and wave model are similar to our previous study (Shimura et al. 2017) but the spatial resolution is much finer so as to resolve TCs. The AGCM developed by the Meteorological Research Institute (MRI) of the Japan Meteorological Agency (Mizuta et al. 2012) was used. The MRI-AGCM is a global spectral model computing the dynamical processes in the wavenumber space. The maximum triangular wavenumber is 959. The physical processes were computed in a gridded space and the horizontal grid is 1920 × 960 corresponding approximately 0.1875° resolution (approximately 20 km) for both longitude and latitude. The model vertical layers consist of 64 layers. Climate simulations of the MRI-AGCM have been widely used for climate change assessments (Mizuta et al. 2017; Yoshida et al. 2017; Mori et al. 2021). The MRI-AGCM is described in detail by Mizuta et al. (2012).
b. Ocean wave model
Ocean waves were computed by the spectral wave model WAVEWATCH III (WW3), version 4.18 (Tolman 2014). The spatial resolution was 0.1875° in the global domain from 78°S to 75°N. The wave frequency space was 0.035–0.56 Hz, which was discretized in 30 logarithmic increments. The wave directional resolution was 10°. The evolution of the spectral density of waves can be represented by a balance of three main sources in deep water: wind input, wave dissipation, and nonlinear wave–wave interactions. For the wind input and dissipation terms, the source term package from Ardhuin et al. (2010), called ST4 in WAVEWATCH III, was used. The nonlinear wave–wave interaction was computed using the discrete interaction approximation proposed by Hasselmann and Hasselmann (1985).
c. Air–sea flux
d. Experimental configuration
TCs in the western North Pacific Ocean (i.e., typhoons) were used in this experiment. The 100 TCs that recorded the deepest minimum central pressure during 1982–2018 in the western North Pacific (100°E–180°, 0°–50°N) were selected. The selection was based on observations from the IBTrACS dataset (Knapp et al. 2010, 2018). For example, the top first, second, and third typhoons are Vanessa in 1984, Forrest in 1983, and Megi in 2010, respectively. The minimum central pressure of the 100 TCs ranged from 880 to 920 hPa, with an average of 909 hPa. The 100 TCs are listed in Table A1 in appendix A.
The initial time of AGCM computation is the first time that the central pressure reached less than 990 hPa. The initial condition data, such as three-dimensional data of wind, temperature, humidity, and land surface data, were derived from ERA5 (Hersbach et al. 2020). ERA5 is the latest reanalysis dataset provided by the European Centre for Medium Range Forecasts (ECMWF). The horizontal resolution is 0.25°, and the data are available from 1979 to the present. The boundary conditions of the AGCM, sea surface temperatures (SSTs), and sea ice concentrations were derived from the analysis data of the OISST (Reynolds et al. 2007). The monthly averaged SST and ice concentration were linearly interpolated to daily fields for input of AGCM following the protocol of Atmospheric Model Intercomparison Project (AMIP)-type climate simulation (Gates et al. 1999). For the initial condition of WW3, the local wave spectrum [fetch-limited Joint North Sea Wave Project (JONSWAP) spectrum] is calculated using the local wind speed and direction (Tolman 2014).
Experiments with and without two-way wave coupling were conducted targeting the 100 TCs described above. The computation time was set to 9 days, irrespective of the individual TC. The two-way coupling interval between the AGCM and WW3 was 20 min. The experiments with and without two-way wave coupling were denoted as ExpWave and ExpWind, respectively.
3. Results
This study targeted past TCs using ERA5 reanalysis as the initial condition. This global model experiment did not intend to closely reproduce actual TCs by using procedures such as the lateral boundary condition constraint, assimilation, and nudging. Therefore, some TCs were not generated in a certain case. Only the selected cases, where the simulated TCs reached 960 hPa, were analyzed. As a result, 95 cases for both ExpWind and ExpWave were selected for analysis. The minimum central pressures of the 95 cases in the ExpWind ranged from 860 to 959 hPa, with an average of 907 hPa. Note that the average of all the observations was 909 hPa. The root-mean-square error of the minimum central pressure between the observations and ExpWind was 23 hPa. Thus, although this experiment did not aim to reproduce the detailed characteristics of individual TCs, the intercase-averaged intensity of strong TCs targeted in this study could be accurately represented. The results of the drag coefficient, TC intensity, and track are described below based on the 95 cases of ExpWind and ExpWave (the number of cases in which both ExpWind and ExpWave produced TCs is 93). The minimum pressures for both experiments are listed in Table A1 in appendix A.
a. Momentum flux under TC
(a) The relationship between friction velocities u*,n (red line) or u*,wave (shading) and wind speed U10,n, and (b) the relationship between drag coefficient Cm,n (red line) or Cm,wave (shading) and U10,n. The shading shows the data density at a log10 scale. The density is normalized by maximum density.
Citation: Journal of Climate 35, 2; 10.1175/JCLI-D-21-0362.1
The spatial pattern of the relationship between U10 and Cm,n or Cm,wave is shown. The results of ExpWave targeting Typhoon Vanessa were selected as an example. Figure 2 shows a snapshot of U10,
The snapshot of wind speed and momentum flux characteristics in ExpWave at 0600 UTC 28 Oct 1984: (a),(b) U10 (m s−1), (c),(d)
Citation: Journal of Climate 35, 2; 10.1175/JCLI-D-21-0362.1
Figure 2 shows an example of one such scenario. Here, the representative characteristics of 95 TC cases are shown. Figure 3 shows the composite map of
Composite maps of momentum flux characteristics in ExpWave: (a)
Citation: Journal of Climate 35, 2; 10.1175/JCLI-D-21-0362.1
b. TC intensity
The impacts of the momentum flux and drag coefficient differences on the TC intensity are shown in this section. The TC intensity represented by the central pressures and size of TCs were compared for all of the simulated TCs in ExpWave and ExpWind, as shown in Fig. 4. Figure 4a shows a histogram of the differences in the lifetime minimum (peak) central pressure (ExpWave minus ExpWind). The average and standard deviation of the difference were −1.0 and 6.8 hPa, respectively. There was a small difference in the average value. On the other hand, there was a difference of up to ±20 hPa between individual TCs. If an experiment is conducted using one or a few TCs, the result of wave-dependent momentum flux impacts on TC intensity is highly sensitive to the selected TCs. Analyzing a sufficient number of TCs can provide the systematic impacts of wave-dependent momentum flux on TCs, as shown here. In this study, it was found that there were no systematic differences in lifetime minimum central pressures. The differences in individual TCs between experiments will be examined in future studies. We found that the differences are not related with TC intensity itself at least.
The comparison of TC central pressure and size between ExpWind and ExpWave: (a) histogram of differences of lifetime minimum central pressure (ExpWave − ExpWind), (b) histogram of differences of lifetime mean central pressure (ExpWave − ExpWind), (c) the composite of time evolution of central pressure, and (d) the composite of time evolution of maximum wind speed radius at 850-hPa height. Blue and red lines in (c) and (d) are for ExpWind and ExpWind, respectively. The color shading indicates the range between the 10th and 90th percentiles.
Citation: Journal of Climate 35, 2; 10.1175/JCLI-D-21-0362.1
Although there is no systematic difference in the lifetime minimum central pressure, systematic differences were observed in lifetime “mean” central pressures. Figure 4b is the histogram of differences of lifetime “mean” central pressure (ExpWave − ExpWind). The average and standard deviation of the differences were −1.9 and 3.9 hPa, respectively. The lifetime mean central pressure of ExpWave tends to be deeper than that of ExpWind. The difference was significant at the 5% significance level of the t test. Figure 4c shows the composite of the time evolution of the central pressure for ExpWind and ExpWave. The timing of the lifetime minimum central pressure was set to zero. The figure also shows that the lifetime minimum was the same between experiments, but the central pressure of ExpWave was deeper than ExpWind during the lifetime, particularly 50 to 100 h before the timing of the peak and after the timing. This indicates that the TCs in ExpWave develop faster and dissipate more slowly than those in ExpWind. Figure 4d is the same as Fig. 4c but for Rmax. The Rmax of ExpWave was slightly smaller than that of ExpWind during the lifetime before the timing of the peak. This is because the TCs in ExpWave develop faster than ExpWind, as indicated above. The mean difference in Rmax was approximately 10 km. The Rmax of the timing of the most development showed no differences between ExpWind and ExpWave.
The spatial distributions of sea surface wind speed and heat flux (total of latent and sensible flux), which is the energy source of the TC, are discussed next. Figure 5 shows the composite map of U10 and heat flux for the ExpWind cases and the differences between ExpWave and ExpWind. The composite map corresponds to the most developed stage during the time when TC reaches the lifetime minimum central pressure and 24 h prior, same as in Fig. 3, and for developing stages as 96–72 h before peak intensity. The contour indicates the value derived from ExpWind, and the color shade is the difference between experiments (ExpWave − ExpWind). At the most developed stage, the intercase-averaged maximum wind speeds of ExpWind and ExpWave were 58.6 and 57.7 m s−1, respectively (Fig. 5a). The intercase-averaged maximum wind did not differ between experiments as there was no systematic difference in the minimum central pressure, as shown in Fig. 4a. In terms of spatial distribution, not just for maximum wind speed, differences of ±2 m s−1 were observed in U10. The spatial distribution can be characterized by positive values close to the eye and negative values away from the eye, particularly for the second and fourth quadrants of the TC. Because the drag coefficient Cm,wave was larger than Cm.n, it was expected that U10 of ExpWave would be smaller than that of ExpWind. The cause of the higher wind speed of the ExpWave near the eye is not clear at this moment. Figure 5b shows the heat flux difference at the most developed stage. The heat flux of ExpWave was larger than that of ExpWind in the eye region by 90 W m−2, which is 10% of the ExpWind value. A Cm,wave value larger than Cm.n leads to a larger heat flux as the bulk transfer coefficient of heat depends on the drag coefficient. Despite the larger heat flux, the maximum intensity was not different because the maximum intensity at this mature stage is determined more by atmospheric and SST conditions than by the 10% change in the surface heat flux.
The composite map of U10 and heat flux (total of latent and sensible flux): (a) U10 at the most developed stage (m s−1), (b) heat flux at the most developed stage (W m−2), (c) U10 at the developing stage (m s−1), and (d) heat flux at the developing stage (W m−2). Contours indicate the ExpWind value, and color shades indicate differences (ExpWave − ExpWind). The distances are normalized by Rmax.
Citation: Journal of Climate 35, 2; 10.1175/JCLI-D-21-0362.1
Figures 5c and 5d are the same as Figs. 5a and 5b, but for the developing stage, namely 96 to 72 h before most development. The intercase-averaged maximum wind speeds of ExpWind and ExpWave were 27.6 and 29.4 m s−1, respectively (Fig. 5c). The fact that the wind speeds of ExpWave is larger than ExpWind is because the central pressure of ExpWave tends to be deeper than that of ExpWind, as shown in Fig. 4c. On the other hand, the wind speed of ExpWave was lower by 2 m s−1 in the area surrounding the TC owing to the larger drag coefficient. This faster development of ExpWave at this stage is due to the larger heat flux, as shown in Fig. 5d. The enhancement of heat flux was approximately 130 W m−2, which is 25% of the ExpWind value close to the TC eye. In addition, the enhancement was more remarkable on the left-hand side of the TC moving direction. This is related to the spatial pattern of the drag coefficient, as shown in Fig. 3, which shows the enhancement of drag at the left-hand side of the moving direction. The spatial pattern of the heat flux difference between ExpWave and ExpWind can affect the TC intensity significantly because of the enhanced heat flux concentrated in and near the eye region. Miyamoto and Takemi (2010) demonstrated that the heat flux within 7–8 times the radius of the maximum wind speed critically determines the intensity of TCs. Such an enhanced heat flux by 25% near the eye region, as shown in Fig. 5d, is responsible for the stronger intensity of the simulated TCs in the ExpWave at the developing stage. Simultaneously, the enhancement of drag can have a significant impact on TC development at the early stage. The enhancement of wind speed was approximately 2–3 m s−1, although there was no difference at the most developed stage.
c. TC track
The impacts of the wave-dependent momentum flux on the TC tracks are discussed in this section. To the best of our knowledge, previous studies on wave impact on TC, based on regional weather/climate models, have not analyzed TC track differences. Figure 6 shows the relative locations of TCs of ExpWave and ExpWind at the same time. Figure 6a shows the location of the ExpWave relative to ExpWind eyes. The center of the figure represents ExpWind. The upward direction of the figure is the moving direction of the ExpWind TC. The markers are plotted for the relative locations of all the time steps of the 93 TCs. Note that the initial conditions are same between ExpWind and ExpWave, and thus the relative location becomes larger as TCs develop. It was observed that the relative location of ExpWave tends to be at the right hand and backward, in the fourth quadrant, of the moving direction of the ExpWind TCs. The percentages for each quadrant were 17% (first quadrant), 9% (second quadrant), 15% (third quadrant), and 31% (fourth quadrant). Note that the count of the location on the axis was 7%, and the central location percentage was 21%. Figure 6b shows the influence of the TC location differences shown in Fig. 6a on the spatial distribution of wind speed. Figure 6b is similar to Fig. 5a, but the composite of ExpWave was calculated based on the relative location of ExpWave TC to the ExpWind TC. In the composite map shown in Fig. 5a, the eye of the TC was set on the origin of the figure for both ExpWind and ExpWave. In Fig. 6b, the eye of the TC of ExpWind was set on the origin of the figure and that of ExpWave was set relative to ExpWind. Figure 5a shows the representative spatial pattern of the wind speed difference around the TC eye between ExpWind and ExpWave. On the other hand, Fig. 6b shows the wind speed difference while accounting for the location difference. Note that Fig. 6b is derived from the data of the most developed stage, similar to that shown in Fig. 5a. The wind speed difference can be characterized by positive values in the fourth quadrant and negative values in the center and in the second quadrant. This is because of the right-backward location of the ExpWave TC to ExpWind, as shown in Fig. 6a. The differences in composite wind speed ranged from approximately −17 to 4 m s−1, except for the TC eye area, which is the origin. The reason for the significantly larger negative values is that the relative location is highly variable, as shown in Fig. 6a, indicating that the positive value location also varies significantly in each case. On the other hand, the negative value location does not depend significantly on the relative location as the location of the TC center of ExpWind is fixed at the origin of the figure.
The differences in TC tracks between ExpWind and ExpWave: (a) the relative location of the eye of ExpWave to ExpWind (the contours give data density) and (b) the composite map of U10, which is similar to Fig. 5a, but the composite of ExpWave is calculated based on relative location of ExpWave TC to ExpWind. In (b), contours indicate the ExpWind value and color shading indicates differences (ExpWave − ExpWind). The distance is normalized by Rmax of ExpWind.
Citation: Journal of Climate 35, 2; 10.1175/JCLI-D-21-0362.1
The TC track differences depend on latitude. Figure 7 shows the relative locations of ExpWave to ExpWind depending on the latitude. The vertical axes of Figs. 7a and 7b indicate the horizontal and vertical locations shown in Fig. 6a, respectively, and the horizontal axis indicates the latitude. The distance is not normalized by Rmax, and the unit is kilometers. The occurrence density in Fig. 7 was drawn using the log10 scale, and the maximum value was log10(93) = 1.97 (note that the number of overlapped cases between ExpWind and ExpWave is 93, as described above). Similar to Fig. 6a, Fig. 7a shows that the relative location of ExpWave tends to be on the right-hand side of ExpWind. The relative location is on the right-hand side in the ExpWave at higher latitudes. Around Japan (35°–40°N), TCs of ExpWave are located approximately 80 km right of ExpWind, on average. This means that the TCs of ExpWave tend to pass at 80 km eastward to those of ExpWind around Japan. The average value at 35°N was 75 km, and the 10th–90th percentile ranged from −39 to 188 km. For the relative location parallel to TC translation (Fig. 7b), the backward shift of the TC of the ExpWave (i.e., slower northward movement) was more evident.
The relative location of the eye of ExpWave to ExpWind depending on latitudes: (a) the relative locations normal to TC translation and (b) the relative locations parallel to TC translation. The color shading indicates data density, the green circle indicates the average in 5° bins, and the bar indicates the 10th–90th-percentile range.
Citation: Journal of Climate 35, 2; 10.1175/JCLI-D-21-0362.1
The representative TC tracks of the experiments and observations (IBTrACS) were compared. First, representative tracks were derived using clustering analysis from the observed data. The 93 TC tracks that could be generated in both experiments were used for clustering. Each TC track was divided into 50 equal-distance sections from genesis to lysis, and the k-means clustering method was applied to 93 sets of these 50 points of data. Four representative tracks were identified from this clustering exercise (Fig. 8). The number of TCs for clusters 1–4 were 29, 34, 27, and 3, respectively. The cluster numbers for individual TCs are listed in appendix A. The tracks of clusters 1–3 were similar to those of the three prevailing TC tracks shown by Wu et al. (2005). Tracks from ExpWind and ExpWave were also divided into 50 equal-distance sections, and the representative tracks for each experiment were derived from averaged locations for each of the 50 sections among TCs clustered in the same cluster. Figure 8 shows the representative tracks derived from IBTrACS, ExpWind, and ExpWave for the four clusters. In comparing the IBTrACS and the two experiments, it is seen that the two experiments qualitatively reproduced well the representative observed tracks. In comparing the two experiments, it is observed that every representative track of ExpWave was located on the right-hand side of the moving direction of ExpWind. The difference in representative tracks of ExpWave relative to ExpWind does not necessarily mean an improvement in TC track performance when compared with that of IBTrACS. However, the magnitude of the curvature of the ExpWind TCs was underestimated, which was found to be more accurate in the ExpWave (Fig. 8).
The representative TC tracks for IBTrACS, ExpWind, and ExpWave. The 93 TC tracks were clustered using the k-means method.
Citation: Journal of Climate 35, 2; 10.1175/JCLI-D-21-0362.1
The causes of TC track differences are discussed further. Environmental steering flow is the main driver of TC motion (Neumann 1992). Shimura et al. (2017) used a coarser version of the wave-coupled AGCM and showed that wave-dependent momentum flux can influence global large-scale circulation. In fact, the upper atmospheric flow was altered more in ExpWave as compared with that in ExpWind. Figure 9 shows the differences in averaged wind speeds between ExpWave and ExpWind (west–east component) at 500 hPa. The average was based on the wind speeds of all experimental cases from 24 to 216 h after the start of the computation. The differences can be seen at a global scale as the momentum flux is altered globally by the wave-dependent drag. The magnitude of the difference was more than 10% in the western North Pacific. These experiments started from the same initial condition, and the differences increased over time and saturated after 7 days. Therefore, it was considered that the differences are larger in the case of the free-running climate simulation. The environmental steering flow was calculated using the deep layer mean (Neumann 1992). The 500-km radius averaged flow at 850, 700, 500, 400, and 300 hPa was used. The layer flows were weighted by 150, 175, 150, 100, and 75 for 850, 700, 500, 400, and 300 hPa, respectively, based on Neumann (1992). These environmental steering flows were used to estimate the artificial TC trajectories. The trajectories were initiated at the locations of TC genesis from the TC track data. Figure 10 is the same as Fig. 7, but is derived from the steering flow–based TC trajectory. It can be seen that the tendencies of the TC track and steering flow–based trajectory are similar (Figs. 7 and 10). The steering flow–based trajectory of ExpWave also shows a relatively right-hand and backward location in comparison with that of ExpWind. This indicates that the environmental flow altered globally by wave-dependent drag has a significant contribution to TC track differences.
The differences in averaged wind speeds (west–east component) at 500-hPa height between ExpWave and ExpWind (ExpWave − ExpWind). The contours indicate the ExpWind values, and the color shades indicate the differences (m s−1).
Citation: Journal of Climate 35, 2; 10.1175/JCLI-D-21-0362.1
As in Fig. 7, but for the differences in steering flow–based TC trajectories between ExpWind and ExpWave depending on latitudes.
Citation: Journal of Climate 35, 2; 10.1175/JCLI-D-21-0362.1
The other main driver of TC motion is the latitudinal difference of the Coriolis parameter, denoted as the beta effect (Chan 2005). The beta effect forces the TCs in the Northern Hemisphere to move northwestward. The magnitude of the beta effect depends on the TC size, and a larger TC is more affected by the beta effect than a smaller TC. Figure 4 shows the systematic differences in TC size during the developing stage, indicating that the TC size of ExpWave was smaller than that of ExpWind. An idealized experiment for the beta effect was conducted by Chan and Williams (1987). Similar idealized experiments, assuming that Rmax values are 100 and 80 km and maximum wind speeds are 30 and 33 m s−1, respectively, according to the differences between ExpWind and ExpWave, were conducted in this study. The velocities of northwestward motion due to the beta effect were 6.7 and 5.6 km h−1 and the directions were 31.4° and 29.7° (anticlockwise from north) for ExpWind and ExpWave, respectively. Therefore, the beta effect differences related to the TC size at the developing stage can be attributed to the relative right-backward motion of ExpWave TC to ExpWind TC.
4. Discussion
In this study, ocean surface waves were considered to be atmosphere–ocean interactions. On the other hand, ocean temperature cooling by strong wind-forced upper ocean mixing has been studied from the viewpoint of atmosphere–ocean interaction effects on TC climatology (Schade and Emanuel 1999; Zarzycki 2016). A comparison between the effects of wave-dependent drag and strong wind-forced upper ocean mixing was conducted using the same experimental framework described above. The prescribed daily SST interpolated from monthly OISST data was used in ExpWind and ExpWave. Here, the slab ocean model was coupled to the MRI-AGCM, and the SST was altered depending on the atmospheric conditions. The slab ocean model was proposed by Zarzycki (2016). The slab ocean model can account for SST cooling under strong wind conditions as well as SST changes due to heat exchange at the atmosphere–ocean interface. Although three-dimensional ocean response to TC winds is significant (Mogensen et al. 2017), the slab ocean model is efficient for climate simulations because of light computational cost and can give first-order estimation of ocean cooling impacts on TCs. Urano et al. (2018) modified the slab model proposed by Zarzycki (2016). The details of the slab ocean model are provided in appendix B. An experiment similar to ExpWind and ExpWave was conducted based on the slab-ocean model coupled MRI-AGCM. The experiment was denoted as ExpSlabO. The results for ExpSlabO are presented in Fig. 11. Figure 11 shows the differences in the lifetime minimum central pressure and TC tracks between ExpWind and ExpSlabO, as shown in Figs. 4a and 7a. Figure 11a shows that upper ocean cooling by strong winds leads to a weaker central pressure (Fig. 11a). This is because ocean cooling reduces the heat supply from the ocean to the TC. The average difference in the lifetime minimum central pressure was 2.9 hPa and the standard deviation was 6.5 hPa. On the other hand, the TC tracks of ExpSlabO were almost the same as those of ExpWind (Fig. 11b). This result indicates that ocean coupling can reduce peak intensity and does not affect TC tracks, which is contrary to the results of the wave-dependent momentum flux.
As in Figs. 4a and 7a, but for the differences between ExpSlabO and ExpWind.
Citation: Journal of Climate 35, 2; 10.1175/JCLI-D-21-0362.1
5. Conclusions
The systematic characteristics of TCs represented in the GCM are important for reliable climate change impact assessment. MRI-AGCM and WAVEWATCH III were coupled by incorporating the wave-dependent momentum flux, and historical TCs were simulated using the model. In a comparison between AGCM and wave-coupled AGCM, the systematic impacts of wave-dependent momentum flux on TC characteristics were estimated. Previous studies on the impacts of wave-dependent momentum flux on TCs targeted only a few TCs. In this study, 100 TCs recording the deepest minimum central pressures from 1982 to 2018 in the western North Pacific (100°E –180°, 0°–50°N) were targeted. The overall tendency of the 100 TCs was analyzed, and the systematic impacts were estimated.
The wave-dependent momentum flux, considering wind and wave direction misalignment, was used. The larger the wave age and misalignment, the larger the drag coefficient. The drag coefficient at the left-hand side of the TC was enhanced by the wave condition. This is because of the longer period swell and larger misalignment of wind and wave directions. The enhancement ratio increased toward the outer region, and was higher than 2 in the outer region than at 200 km from the TC eye.
The wave-dependent momentum flux does not have any impact on the peak TC intensity. This is because the drag coefficients at the maximum wind are not different between experiments because of the alignment between wind and wave directions at the location. On the other hand, when focusing on individual TCs, the standard deviation of the difference in peak central pressure was 7 hPa, and the maximum difference reached 20 hPa. Therefore, several TCs need to be analyzed for systematic impacts, as in this study. The wave-dependent momentum flux can have a significant impact on TC development at the early development stage and not at the most developed stage. Although systematic differences in TC intensity at most developed stages were not observed, systematic differences in TC tracks between AGCM and wave-coupled AGCM were observed. The TC tracks of the wave-coupled AGCM tend to pass in a relatively eastward direction in comparison with that of the uncoupled AGCM. TCs of wave-coupled AGCM tend to pass at 80 km east to those of uncoupled AGCM around Japan. This is because the wave-dependent momentum flux can alter the environmental steering flow and reduce the beta effect of smaller TCs at the early development stage of coupled AGCM as compared with that in the uncoupled AGCM. The impacts on environmental steering flow are difficult to estimate by regional climate/weather model studies as environmental flow is forced by lateral boundary conditions. Systematic differences in TC tracks have a significant impact on climate change assessment, such as extreme sea level changes in coastal regions due to climate change.
It is known that a strong systematic misalignment between wind and wave directions exists in TCs, corresponding to the TC quadrant. Therefore, it is worth investigating the impacts of the wind-wave misalignment on TCs, as in this study. However, the parameterization of wave-dependent momentum flux used in this study was developed by large eddy simulation under the relative low wind conditions, which is function of wave age and wind-wave directional misalignment (Patton et al. 2019). It is not without problems to apply the parameterization to very high wind speed due to wave breaking/sea sprays. When wave breaking becomes frequent in high wind condition, sea spray is generated and contributes to air–sea momentum and heat flux significantly (Veron 2015). Sea spray may potentially explain the reduction of the drag coefficient at high wind speeds more than 30 m s−1 (e.g., Powell et al. 2003), which is not considered in this study. Recent studies (Magnusson et al. 2019; Li et al. 2021) incorporated the reduction of the drag coefficient into the wave-coupled model and improved the performance of TC simulations. Moreover, momentum flux calculated in the wave model using more complex wave state information, two-dimensional wave spectra, can be used for air–sea flux in the atmospheric model (Janssen 1991). The impacts of different parameterizations of wave-dependent momentum flux considering sea spray effects, reduction of drag coefficient at high wind speeds, and the two-dimensional wave spectra need to be investigated in the future because the impacts of wave-dependent momentum flux on TCs are significant as reported in this study.
Acknowledgments
Author Shimura was supported by JSPS KAKENHI (Grants 19K15099, 18H03791, and 19H00782) and JST FOREST Program (JPMJFR205R). This research was supported by the Integrated Research Program for Advancing Climate Models (TOUGOU; Grant JPMXD0717935498) by the Ministry of Education, Culture, Sports, Science, and Technology (MEXT).
Data availability statement.
The data created in this study are available in the Zenodo repository (https://doi.org/10.5281/zenodo.4724365). ERA-5 reanalysis data were obtained from the Copernicus Climate Change Service (C3S) Climate Date Store (https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset). The IBTrACS data were obtained from https://www.ncdc.noaa.gov/ibtracs/.
APPENDIX A
List of TCs
Table A1 lists the TCs used in this study. The 100 TCs that recorded the 100 deepest minimum central pressures from 1982 to 2018 in the western North Pacific Ocean (100°E–180°, 0°–50°N) were selected. The genesis time, name, minimum central pressure, and track cluster number are listed.
Lists of numerical experiments.
APPENDIX B
Description of the Slab Ocean Model
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