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1 The influence of ocean on Typhoon Nuri (2008) 2 Supplementary Material (SM) 3 4 J. Sun1 and L.-Y. Oey*2,3 5 1: Center for Earth System Science, Tsinghua University; 6 2: IHOS and Atmospheric Science Department, National Central University; 7 3: Atmospheric & Oceanic Sciences Program, Princeton University 8 9 *Corresponding Author: [email protected]; 10

11 Summary

12 This supplementary material file describes (Section 1) the ocean model used to provide the

13 SST for the WRF simulation (experiments pom* in main text table 1), including model

14 validation using the Argo data; and (Section 2) changes in SST in South Sea and western

15 Pacific by vertical mixing by wind. More details are given in Sun et al [2015].

16 1. The North Pacific Ocean model

17 In order to account for the dynamical evolution of the upper-ocean state due to Typhoon

18 Nuri, we ran the North Pacific Ocean model of Oey et al [2013, 2014] which then provided the

19 SST for input into WRF.

20 a. Ocean model descriptions:

21 The model is based on the parallel version of the Princeton Ocean Model (POM). Air-sea

22 coupling was not included, and we focused on separating the effects of SST from effects of

23 internal variability. With coupling, winds produced by internal variability would modify SST’s

24 which in turn would change the internal variability of the storm, making it difficult to separate

25 their effects. The domain included the entire North Pacific Ocean from 99°E-70°W and from P a g e | 2

26 15°S-72°N (Fig.SM-1). The WRF domain is therefore wholly embedded within the large domain

27 of the model ocean which therefore provided smooth and dynamically consistent ocean variables

28 across the WRF’s lateral boundaries during the integration. The ocean model has 0.1° × 0.1°

29 horizontal resolution and 41 vertical sigma levels, and the ETOPO2 topography was used

30 (http://www.ngdc.noaa.gov/mgg/fliers/01mgg04.html). The sigma cells were finer,

31 logarithmically distributed near the free surface where the first grid cell was approximately 1~10

32 m below the surface depending on the water depth. A fourth-order scheme was used to minimize

33 the sigma-level pressure-gradient error [Berntsen and Oey 2010]. The Mellor and Yamada’s

34 [1982] turbulence closure scheme was modified to include turbulence energy due to breaking

35 waves near the surface [Craig and Banner 1994; Mellor and Blumberg 2004]. To account for

36 mixing in stable stratification [e.g. internal waves; MacKinnon and Gregg, 2003], Mellor’s [2001]

37 modification of a Ridchardson-number-dependent dissipation was used. The Smagorinsky’s

38 [1963] shear and grid-dependent horizontal viscosity was used with a nondimensional coefficient

39 = 0.1; the corresponding horizontal diffusivity is made 10 times smaller. The Oey et al’s [2007]

40 wind-drag formula with high wind-speed limit was used [Powell et al 2003] to specify the wind

41 stress using six-hourly cross-calibrated multi-platform wind [CCMP; Atlas et al. 2011]. However,

42 near the eye-wall, the CCMP wind was weaker than data from the IBTrACS, by as much as 50%

43 (e.g. 20 vs 43 m s-1); a correction to the CCMP data was therefore applied during Nuri. The

44 observed maximum wind speed, minimum sea level pressure (SLP), and location of the center of

45 Nuri from the IBTrACS data were used in the vortex model of Holland [1980] to estimate the P a g e | 3

46 typhoon’s wind. The radius of maximum wind speed of 30 km, required in the Holland model,

47 was also assumed; this value was close to the radius of the eye wall in the simulated Nuri (see

48 below). The six-hourly synthetic wind was then merged with the CCMP wind using a Gaussian

49 weight with e-1-decay radius of 350 km determined by trials and errors, such that there was a

50 smooth transition from Holland to CCMP wind at distances far from the typhoon’s center. The

51 merged wind was then linearly interpolated at each time step to force the model. Other details of

52 the model can be found in Oey et al [2013, 2014].

53 b. Ocean initialization:

54 The ocean model was run from August 6th through 25th 2008 to cover the period of Nuri

55 (August 18th - 22nd 2008). Initial ocean fields on August 6th were therefore required. Typhoon

56 Nuri passed over a region known for deep Z26 and active mesoscale variability [Oey et al 2013;

57 Chang and Oey 2014]. These upper-layer ocean conditions were included by initializing the

58 ocean model on August 6th 2008 with the results of a POM run with data assimilation. Altimetry

59 data from AVISO (http://www.aviso.oceanobs.com/) were assimilated at a 5-daily interval into

60 the model using a simple optimum interpolation scheme described in details by Oey et al [2005],

61 Lin et al [2007], Yin and Oey [2007] and Oey et al [2013]. In order to obtain SST approximating

62 the observed conditions on August 6th, we also incorporated SST observations into the model.

63 After some experimentations, a simple and effective way was to nudge the model SST to

64 observed SST from satellite using a time constant of 1/3 day-1. The satellite data used was

65 AVHRR SST (Advanced Very High Resolution Radiometer, Multi-Channel Sea Surface P a g e | 4

66 Temperature; http://gcmd.nasa.gov/records/GCMD_NAVOCEANO_MCSST.html). The

67 assimilated and SST-nudging experiment was run for 6 months from February 6th through August

68 6th 2008 and was itself initialized from a 47-year non-assimilated run forced by the CCMP and

69 NCEP wind and other surface fluxes, as detailed in Oey et al [2013] and Xu and Oey [2014].

70 c. Ocean model validation:

71 As ocean dynamics play an important role in the evolution of Typhoon Nuri, it is

72 necessary to validate the modeled upper-ocean thermal structure. To do that, we compared the

73 POM temperature profiles against all available Argo data during Nuri period August 18th – 22nd

74 2008 and in the Nuri study region east of the and in (rectangle in

75 main text Fig.3, reproduced here as Fig.SM-1). Figure SM-2a-f compare the profiles at 6

76 example locations (“+” in Fig.SM-1): 3 in western Pacific east of the Philippines (d, e and f), 1

77 just east of the Strait (b) and 2 west of the Luzon Strait (a and c). Figure SM-2g compares

78 all the data in a scatter plot. The Argo data show deep Z26 150 m at the southern-most

79 location (f) and just east of the Luzon Island (e) where the SST reaches 29~30oC, and shallower

o 80 Z26 100 m at the northeast location (d) with cooler SST  29 C. The agreements between model

81 and Argo are good at these stations in the western Pacific. The Z26 decreases below 100 m for the

82 2 profiles near the Luzon Strait, where SST  28oC to the east (b) and  27oC to the west (a) of

o 83 the strait. For the profile in South China Sea (c), the SST is warmer ~29 C and Z26 thinner  70

84 m. The POM generally simulates these features well. However, the Z26 is shallower, and there

85 are larger discrepancies at 2 profiles near the Kuroshio (a and b) which are being compared P a g e | 5

86 during times when Typhoon Nuri has just passed, and the thermoclines were undergoing strong

87 inertial oscillations. In general, the scatter plot comparison shows that POM simulated

88 temperatures are slightly cooler near the surface and slightly warmer at subsurface; i.e. the

89 modeled profile is more diffusive. Otherwise the agreements are good: the slope of the

90 regression line is 1.03, bias is -0.1 oC (POM is cooler), and the scatter is generally small with a

91 high R2 value. We also repeated the regression analysis using only the upper 500 m Argo and

92 model data, and obtained very similar result; the regression formula becomes Argo SST = POM

93 SST*1.03 – 0.15. We emphasize that the comparison of the simulated SST’s with Argo’s is for

94 the period during the storm; the agreements are quite good considering that strong temperature

95 variations existed as the storm passed.

96 d. Ocean run summary:

97 Satellite altimetry and SST data were assimilated into POM to initialize the ocean state

98 on Aug/6th, 12 days prior to Nuri. The model was then allowed to run without data assimilation

99 from Aug/6th through Aug/25th to simulate the upper-ocean response, forced by the merged

100 IBTrACS-CCMP wind data. We therefore allowed 12 days (Aug/6-17) for the model to adjust

101 from its initial data-assimilated state before applying the merged wind data on Aug/18. The

102 resulting POM SST was then used in the WRF simulation (Exp#7 or pom_KS in main text Table

103 1). Additionally, by perturbing the various parameters in the ocean model simulation: IBTrACS

104 track, minimum SLP, and radius of maximum wind, as well as the SST relaxation time constant

105 and the interval of altimetry data assimilation, POM was repeated to produce a set of perturbed P a g e | 6

106 SSTs. These SSTs were then used to produce five additional WRF experiments (Exp#8-12 in

107 main text Table 1).

108 2. Change in SST due to vertical mixing

109 To better understand why the SST cooling is stronger in South China Sea, we plot in

110 Fig.SM-3 the along-track vertical section contours of potential temperature T and meridional

111 velocity v. The “T” and “v” are shown at the initial time August 18 00:00, and represent the

112 background fields before the passage of typhoon Nuri. Figure SM-3a shows that in the

113 near-surface 100 m layer, the isotherms are approximately 2 times deeper east of Luzon in the

114 western Pacific than to the west in South China Sea. The transition occurs at the Luzon Strait

115 (121~122oE) across the cyclonic, western side of the Kuroshio, as can be seen from the

116 v-contours in Fig.SM-3b.1 Price [1981] has shown that the SST response tends to be largest

117 where cold water is near the sea surface, i.e., where the initial mixed layer is thin and the upper

118 thermocline temperature gradient is sharp. In other words, when the initial mixed layer is thin, it

119 would take less energy to lower its temperature through mixing with the cooler subsurface water

120 [see equation 2 in Oey et al 2006]. This is the case for South China Sea, and the mechanism may

121 therefore explain part of the simulated strong cooling. To estimate the drop in temperature due to

122 this mechanism, let the surface layer depth h1 be much thinner than the subsurface layer depth h2

123 so that the surface-layer temperature after mixing is approximately the temperature of the

1 Subsurface isotherms (down to 300~400m below the surface) slope upward from east to west in approximate geostrophic balance with the strong vertical shear of the Kuroshio at the entrance of the Luzon Strait (Fig.SM-3b). P a g e | 7

124 subsurface layer. We assume that the temperature in the surface layer linearly decreases from Ts

125 at the surface z = 0 to Tb at the base of the surface layer z = -h1; we simplify further and set the

126 subsurface-layer temperature equal to Tb (Fig.SM-4a). Now let h1SCS be the thickness of the

127 surface layer in South China Sea that is mixed by the Nuri wind. Then the drop in temperature in

128 South China Sea is:

129 TSCS  (Tb – Ts). (SM2.1a)

130 Now, typhoon Nuri reached a maximum intensity in Luzon Strait, and its intensity and duration

131 before and after entering South China Sea are approximately the same, as seen in main text

132 Fig.1b. The wind power is therefore also approximately equal in both basins, and it mixes

133 approximately to the same depth h1SCS in western Pacific. However, in the western Pacific, the Tb

134 is at a deeper level z = -h1WP (< -h1SCS) below the surface (Fig.SM-4b). Therefore, the

135 corresponding drop in temperature after mixing in western Pacific is:

136 TWP  (Tb – Ts)(h1SCS/h1WP) (SM2.1b)

137 where we have used the fact that the SST (i.e. Ts) in the two basins is initially approximately the

138 same (Fig.SM-1). Then,

139 TSCS/TWP  h1WP/h1SCS  2. (SM2.2)

140 An estimate of TWP may be obtained from the red contours of Fig.7c of the main text, which

o 141 yield TWP  -1 to -2 C. However, that estimate includes cooling processes by horizontal

142 processes such as upwelling and near-inertial internal waves [Oey et al 2008; Chiang et al 2011].

143 A more accurate estimate is obtained by re-running the POM experiment with the same initial P a g e | 8

144 conditions but in the vertical z-direction only at each grid point of the same model domain. This

145 is called the “vertical mixing experiment or POMZMix; it most closely corresponds to the simple

146 situation described above. The result is shown in Fig.SM-5b which is also compared with the

o 147 result of full POM experiment (Fig.SM-5a); the TWP is approximately -1 C (red contour in

148 Fig.SM-5b). From (SM2.2), we estimate therefore that the cooling in South China Sea as a result

o 149 of its thinner surface layer is TSCS  -2 C, as stated in the main text.

150 P a g e | 9

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205

206 207 Fig.SM-1 The North Pacific Ocean, POM model domain showing the simulated SST (shading in 208 oC) on Aug 16th 2008. The blue rectangle is WRF fine-grid model domain used for Nuri 209 simulation. The pluses “+” inside the WRF domain show the positions of example Argo 210 [free-drifting profiling floats that measure the temperature and salinity of the upper 2000 m of 211 the ocean; http://www-argo.ucsd.edu/] stations where detailed profiles are compared against the 212 model in Fig.SM-2a-f. They and all Argo data during Nuri in the WRF domain are used in the 213 regression plot in Fig.SM-2g. This figure was reproduced from Fig.3 of the main text. 214 P a g e | 13

215 (a) (b)

216 (c) (d)

217 (e) (f)

218 219 220 221 P a g e | 14

222 (g)

223 224 Fig.SM-2 (a-f): comparison of POM temperature profiles with Argo at the six locations shown as 225 “+” in Fig.SM-1 inside the Nuri study domain: (a), (b) & (c) in South China Sea and Luzon 226 Strait; (d), (e) & (f) in western North Pacific. (g) scatter plot comparison of all POM and Argo 227 profiles from 0-1000m deep within the study domain (rectangle in Fig.SM-1) for the Nuri period 228 from Aug 18th – 22nd 2008. The red line is the regression line and black line is the reference 229 perfect match line. Additional profile comparisons are given in Fig.SM-2 (i-n). 230 P a g e | 15

(i) (j)

231

(k) (l)

232

(m) (n)

233 234 Fig.SM-2 (i-n). As in (a-f) at six other indicated locations. 235 P a g e | 16

(a) 236

(b) 237 238 Fig.SM-3 Along-track (Nuri track; see main text Fig.1) and vertical section contours of (a) the 239 potential temperature T (oC; contour interval is 1oC above 26oC (thick contour) and is 3oC 240 below), and (b) the meridional velocity v (m s-1; contours are -0.1, +0.1, 0.3, .., white is negative 241 and zero is omitted). Both for Aug/18, 2008, the initial time of typhoon Nuri. Abscissa is 242 longitude (oE) and ordinate is z (m). Nuri track is approximated by a straight line passing through 243 its positions on Aug/18/09:00 and Aug/21/21:00. 244 P a g e | 17

245

246

247 Fig.SM-4 A schematic of temperature profiles with T = Ts at surface and T = Tb at the base of a

248 thin surface layer of thickness h1 overlying a much thicker h2 (>> h1) deep layer with uniform

249 temperature Tb. (a) is for South China sea with a thin h1 and (b) is for the western Pacific with a

250 thicker h1 (but still << h2). 251 P a g e | 18

252

253 254 Fig.SM-5 SST change (shading) defined as the SST on Aug/22/12:00 minus SST on 255 Aug/18/00:00 for (a) POM and (b) POMZMix The observed typhoon Nuri track from IBTrACS 256 is plotted with daily positions shown as dots. Red contours (-1 and -2 oC) are SST changes before 257 Nuri entered South China Sea: i.e. SST on Aug/20/12:00 minus SST on Aug/18/00:00. P a g e | 19

(A)

258

(B)

259 260 Fig.SM-6 (a) Typhoon Nuri minimum SLP from observation (IBTrACS data ; black line) and 261 from experiments using the same NCEP SST but different microphysics schemes indicated 262 (various colored lines) (see Table 1), from Aug/18-23, 2008. Vertical bars show standard errors 263 of the minimum center pressure from the IBTrACS data. (b) RMS errors in minimum SLP 264 plotted as a function of microphysics schemes for NCEP & POM SST experiments. While the 265 observation showed a monotonic weakening of the storm after Aug/20, the simulated typhoons 266 with different microphysics all incorrectly re-intensified and achieved lower SLP’s in South 267 China Sea, suggesting that other factors controlled the intensity change. 268