PUBLICATIONS. Journal of Geophysical Research: Oceans. Sea surface salinity variability in the East China Sea observed by the Aquarius instrument

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1 PUBLICATIONS RESEARCH ARTICLE Special Section: Early scientific results from the salinity measuring satellites Aquarius/SAC-D and SMOS Key Points: The Aquarius monitors the ECS salinity variations with the open ocean precision RFI very weakly affects the retrieval of salinity variability during the study period The river discharge rate dominantly controls with the Aquarius salinity Correspondence to: S.-b. Kim, Citation: Kim, S.-b., J. H. Lee, P. de Matthaeis, S. Yueh, C.-S. Hong, J.-H. Lee, and G. Lagerloef (2014), Sea surface salinity variability in the East China Sea observed by the Aquarius instrument, J. Geophys. Res. Oceans, 119, , doi:. Received 20 MAR 2014 Accepted 13 SEP 2014 Accepted article online 18 SEP 2014 Published online 21 OCT 2014 Sea surface salinity variability in the East China Sea observed by the Aquarius instrument Seung-bum Kim 1, Jae Hak Lee 2, Paolo de Matthaeis 3, Simon Yueh 1, Chang-Su Hong 2, Joon-Ho Lee 4, and Gary Lagerloef 5 1 Department of Earth and Marine Sciences, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA, 2 Department of Earth and Marine Sciences, Korea Institute of Ocean Science and Technology, Ansan, South Korea, 3 Department of Earth and Marine Sciences, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA, 4 Department of Earth and Marine Sciences, Jeju National University, Jeju, South Korea, 5 Department of Earth and Marine Sciences, Earth and Space Research, Seattle, Washington, USA Abstract This study demonstrates that the spaceborne Aquarius instrument is able to monitor the sea surface salinity (SSS) variations in the East China Sea (ECS) with the spatial resolution of about 150 km at 7 day interval, where routine observations are difficult. The two geophysical contaminants enter the sidelobes of the Aquarius antenna and bias the coastal SSS low: the emission from the land surface and the radiofrequency interference (RFI). Away from about one Aquarius pixel (150 km) from the coastline, the Aquarius SSS is fairly insensitive (less than about 0.2 psu) to the radiometric details of the method to correct for the land emission. The ascending orbits appear to be affected by unfiltered RFI much less than the descending tracks. The Aquarius SSS along the ascending tracks is low over the ECS by psu (with respect to the in situ data during the two separate 7 day periods) and is biased low by psu (accuracy, or the time-mean of difference from the regional model along three Aquarius tracks over a 18 month period). The presence of the ascending and descending differences in the Aquarius SSS, and the spatially widespread bias suggest that the bias is attributed to the unfiltered RFI originating from strong point sources (rather than to the land contamination from weak distributed sources, or to other seasonally varying geophysical contaminants). Despite the bias, the Aquarius data describe well the temporal and spatial variability of the ECS SSS. The temporal trend and magnitude of salinity changes agree remarkably between Aquarius and a regional numerical model, during both the freshwater discharge season from the Yangtze river and the rest of the year. The precision of the Aquarius observation in the ECS is comparable with the Aquarius mission requirement (0.2 psu one-sigma for a monthly average over the open ocean). The river discharge rate correlates with the Aquarius SSS with the coefficient of 0.71 on a seasonal scale with the discharge leading the SSS changes. The Aquarius SSS increases away from the coast, in response to the river outflow. The interannual changes in the Aquarius SSS capture the effect of the regional drought in summer Introduction The East China Sea (ECS) is the marginal sea occupying an area of 800 km km and is surrounded by China, Korea, and Japan (Figure 1). During the summer monsoon, the Yangtze River, that has the world s fifth largest runoff, dilutes the fertile fishing spot. The surface salinity within 300 km from the coast typically drops by 3 4 psu and at times down to 25 psu [Lie et al., 2003], often devastating fishery. The freshwater plume may inhibit vertical mixing, as with the plumes from Amazon and Orinoco Rivers [Ffield, 2007]. Recent major engineering projects such as the Three Gorge Dam reduced the discharge rate and allowed stronger intrusion of the seawater into the river mouth [Dai et al., 2011]: how such project impacts the ECS salinity is not understood yet. The regional drought contributes to the reduction of the river discharge and impacts consequently the ECS salinity [Delcroix and Murtugudde, 2002]. These investigations used the data from ship cruise and buoys. Due to frequent typhoon passage in the warm season, moored buoy stations are often inoperable on a long-term basis. Seasonal severe weather conditions limit deployment of observation platforms. Although the Argo floats provide the global coverage at a regular interval, the ECS is one of several marginal seas not sampled by the Argo floats because of the shallow depth (less than 200 m deep). Spaceborne measurements are alternatives to provide KIM ET AL. VC American Geophysical Union. All Rights Reserved. 7016

2 Figure 1. Geographic extent and bathymetry of the East China Sea together with the domain of the numerical model (solid line box) and the locations of the in situ measurements of 2011 (circle) and 2012 (cross). routine monitoring of SSS over the wide regions of the ECS. The goal of this study is to examine the capability of the Aquarius instrument [Lagerloef et al., 2008] to provide routine observation of the SSS of the ECS. The ECS is adjacent to the landmass to the three sides of the sea, and is next to strong sources of radiofrequency interference (RFI) [Daganzo-Eusebio et al., 2013; Le Vine et al., 2014]. The emission from the land surface at L-band (emissivity ranging from 0.5 to 0.9 for the Aquarius imaging) is stronger than the ocean emission (emissivity of ). Even stronger emission from the manmade sources also adds to the L-band observation of the ocean. These two contaminants enter the Aquarius antenna and decrease salinity retrievals. Algorithms to remove these two contamination are implemented in the Aquarius product [Wentz and Le Vine, 2011, for the land emission; Le Vine et al., 2014, for the RFI] but the residual contamination may exist as will be presented in this paper. Section 2 discusses the properties of the two geophysical contaminants and how they appear in the Aquarius SSS over the ECS. The Aquarius SSS is validated using in situ measurements and numerical model outputs in section 3. Section 4 reports the spatial and temporal variability of the ECS SSS. Section 5 summarizes the paper. The Aquarius standard product version 2.0 was used throughout the paper. 2. Aquarius Salinity in the Presence of Land Emission and RFI 2.1. Effect of Land Emission There is a significant contrast in brightness temperature (T B ) between land and ocean surfaces, posing a difficulty in salinity retrieval over coastal seas. At L-band and 40 incidence angle, the v-polarized T B is typically around 115 K for ocean [e.g., Kim et al., 2011] and 280 K for land [e.g., Njoku et al., 2002] although these values vary with incidence angle, polarization, and surface properties. When the boresight of the Aquarius antenna is on the coastline, roughly 50% of the retrieved power originates from land and the other half from the ocean. The observed T B would be higher by 82 K (5( )/2), compared with T B of a footprint covered 100% by ocean. Further complicating, the dynamic range of the variation in T B of land is much larger than that of the ocean T B, because of the large emissivity of land and the large seasonal changes in land surface temperature. The contamination by the land T B reduces rapidly as the Aquarius beam moves away from the coastline. Simulation shows that the areal fraction of land surface weighted by the antenna gain pattern is around 0.2%, and 0.5% at 500 and 150 km from the coastline, respectively (the percentage varies somewhat depending on antenna gain pattern and coastline geometry). The corresponding perturbation to the ocean T B would be 0.3 K and 0.8 K, respectively, which would translate into the salinity error of about 0.6 and 1.6 psu based on the sensitivity curve [Yueh et al., 2001]. These errors are expected to reduce further with the algorithm to correct for the land emission, which is implemented in the Aquarius product. The algorithm to correct for the land emission employs the simulation of land emission (Appendix A for details). The sensitivity of the simulation to the details of land emission model and input data is assessed below. Two different approaches for simulating the land emission were compared: one implemented in the Aquarius standard product and the other for the Soil Moisture Active Passive mission (see Appendix A). The KIM ET AL. VC American Geophysical Union. All Rights Reserved. 7017

3 Figure 2. Sensitivity of the simulation of the land emission to the emission model and input data for (right) V-polarization and (left) H-polarization. The difference of simulated antenna temperature from land (T A,land of equation (A1) in Kelvin) between two approaches are shown for October 2011 on the global (top) and regional (bottom) scales. Gray dots represent no data, which is caused by the gridding of the simulation to difference in the antenna temperature simulated between the two modeling approaches is shown in Figure 2 for October Globally the two approaches may differ up to 20 K over land (e.g., rain forest and boreal forest regions). The 20 K difference is consistent with the state-of-the-art capability of the model before further tuning [De Lannoy et al., 2013]. However, over the coastal seas where the land fraction becomes 0.5% at the distance of about one Aquarius pixel away from the coastline, the modeling difference of 20 K is reduced to 0.1 K (Figure 2). The sensitivity of the correction algorithm to the other components of the simulation such as the geometric aspect (i.e., the choice of antenna gain pattern) will be conducted in the future Effect of Radio-Frequency Interference (RFI) The RFI corrupts the raw Aquarius observation of antenna temperature (T A ) made at a 10 ms interval. Sixty of the raw observations are averaged to produce one sample of Aquarius data at 1.44 s interval. The T B of RFI is often greater than 1000 K, appearing as a glitch in the time series of the 10 ms data. The glitches are filtered statistically [Le Vine et al., 2014]. After the removal, the clean measurement (T F, F denoting filtered) becomes lower than the original T A. The frequent occurrence of the positive difference of the original T A minus T F (Figure 3a) indicates that the RFI is heavily present over the ECS. Even after the removal, some residual RFI still remains, especially when the magnitude of the residual RFI is too small to be detected as a glitch. The residual RFI contaminates the Aquarius observations along the ascending tracks differently from those along the descending tracks. In Figure 4a, most of the ascending tracks show much higher SSS than the descending SSS. The difference between ascending and descending SSS is not limited to the ECS but found also over the northwest Pacific Ocean (Figure 5a). The similar pattern is found consistently in the other periods but the magnitude of the difference changes in time (Figures 4b and 5b). When flying on the ascending tracks, the Aquarius antenna points away from China, where the RFI sources are mainly located [e.g., Le Vine et al., 2014]. The RFI would enter the antenna sidelobes far away (from the main-lobe), where the antenna gain is small. On the descending tracks, the antenna points toward the source areas and the RFI would enter the sidelobes close to the main-lobe, where the gain is relatively high. Consequently, the residual RFI would impact SSS retrieval more severely. The contamination from the land emission is not likely to be a cause of the difference between the ascending and descending SSS. The spatial pattern of the land contamination does not resemble the ascending- KIM ET AL. VC American Geophysical Union. All Rights Reserved. 7018

4 Figure 3. (a) Magnitude of the RFI shown in terms of measured Aquarius v-pol antenna temperature before (T A ) minus after (T F ) the RFI filtering for 1 and 7 October b indicating the Aquarius beam number. bb1 denotes beam 1 of another pass. Inside the parallelogram the SSS on the three ascending-tracks were compared with the numerical model outputs. The ascending passes point to the northwest. (b) Fraction of land within the antenna field of view weighted by the antenna gain. The Aquarius data within the two boxes in Figure 3b were averaged for science analysis. descending difference in SSS: the land contamination is limited to near the coast (as shown by the land fraction in Figure 3b); its contamination does not show significant difference between ascending and descending tracks (Figure 3b). The land emission originates from distributed sources located all over the land surface. Therefore, as the land fraction decreases away from the coast, the land contamination becomes smaller. Further limiting the land contamination to near the coast is the relatively small size of the land emission (only about 200 K higher than the ocean s). In comparison, the RFI is caused by a strong point source. As long as the point source is within the sidelobes of the Aquarius field of view, the RFI will introduce the undetected residual RFI to the retrieved SSS. Since the Aquarius field of view is nearly limbto-limb of the Earth, the strong RFI point source would corrupt the SSS retrieval over a wide region. Further simulation is ongoing to confirm the different properties of land and RFI contamination in the ECS. In this section, we concluded that the widespread differences between ascending and descending SSS are caused by the undetected RFI, and that the ascending tracks are contaminated less severely than the descending tracks. The analyses in the next sections focus on the ascending tracks only. Other strong sources of contamination such as galaxy and wind may impact the retrieved SSS over a large area. These effects have been corrected in the Aquarius product using the model for galaxy scattering from the ocean surface and as a part of retrieval optimization. The remaining contamination originating from incomplete correction typically is expected to be visible on the basin and seasonal scales, while the ascending-descending difference is localized and temporally persistent. The residual errors due to these two sources are expected to be 0.20 and psu (current best estimate, Table 3 of Lagerloef et al. [2008]). In summary, these sources have the spatiotemporal patterns that are different from those of the ascending-descending differences; and the residual errors associated with these sources are expected to be smaller than the ascending-descending difference in Figure Validation of the Aquarius SSS 3.1. Validation With In Situ Data The Aquarius SSS is validated by comparing with the ship-board CTD (conductivity, temperature, depth) measurements. The CTD data were collected for 1 week (5 9 October) in 2011 and another week (23 28 September) in The locations of the CTD stations are shown in Figure 4. The unweighted spatial KIM ET AL. VC American Geophysical Union. All Rights Reserved. 7019

5 Figure 4. The Aquarius SSS collected during (a) 1 and 7 October 2011 and (b) 23 and 30 September 2012 is plotted on the satellite tracks. The ascending passes point to the northwest. The in situ surface salinity values are shown in star and are color-coded by their salinity values. To compare with the in situ data, the Aquarius SSS is averaged within the rectangular boxes. averages within the validation domains defined in Figure 4 show that the Aquarius SSS is generally low compared with the in situ data (Table 1). The very small mean difference of the northern box of September 2012 cruise is not statistically significant because the spatial variability is too large. Below, we evaluate possible causes of the negative difference: 1. Sampling depth. During the comparison, the CTD measurements were selected from the depth of 0.5 m for the 2011 samples and from 2 to 5 m for the 2012 samples. Within the top 15 m, the salinity is vertically homogeneous with the standard deviation being smaller than 0.01 psu per each station. The well-mixed situation is generally the case for salinity: wind speed of larger than 5 m/s generates enough mixing and the stratification becomes weak enough to allow comparison between Argo buoy measurements at 5 m and Aquarius SSS for more than 83% of the world ocean [the remaining 17% is mostly the tropics, Drucker and Riser, 2014]. 2. Time gap between Aquarius individual observations and CTD measurements up to 6 days. Strong wind during severe weather events such as typhoons may degrade the correction of wind effect during salinity retrieval. Such events may not be sampled with sufficient frequency by the two observing platforms. There was no typhoon during the week in 2011; the typhoon Jelawat was approaching the study area ( but the winds in the area were not severe: 5 10 m/s according to the ship-board wind measurements during the cruise. The rain rate retrieved at the Remote Sensing Systems with the observations from the Aquarius/SAC-D platform shows that no rain during the week in During the week of 2011, no retrieval was available within the validation domain but 1 mm/h rainfall was found in the vicinity. Within 8 h, the freshwater input from rain mixes to 5 m or deeper [Drucker and Riser, 2014], and is not expected to introduce a significant bias to the comparison. 3. Some errors are expected due to the incomplete correction of the land contamination (especially considering that the residual land emission would bias the Aquarius SSS low). However, the southern box of 2012 has the lowest land fraction among the three validation areas (Figure 3b) while the bias is the most negative among the three boxes, which suggests no significant influence by the land contamination. Excluding the above three potential causes, the residual RFI is the most likely source of the difference considering that the RFI is present over the entire ECS (Figure 5). If the excluded causes introduce an error, their sizes would be smaller than 0.2 psu for the incomplete land correction (Figure 2) and smaller than 0.1 psu associated with the rain-induced vertical stratification [0.1 psu is for the tropics, Boutin et al., 2013; Cronin and McPhaden, 1999; psu globally, Drucker and Riser, 2014]. The attribution of the difference to the RFI is further supported by the comparison with numerical model fields in section 3.2. KIM ET AL. VC American Geophysical Union. All Rights Reserved. 7020

6 Figure 5. Difference in the Aquarius SSS (ascending minus descending) in psu for (a) 1 7 October 2011 and (b) September Validation With Numerical Model Output The Aquarius SSS is compared with numerical model outputs since the comparison is not subject to mismatch in spatial and temporal sampling. The model is the Regional Ocean Modeling System (ROMS) configured and run for the ECS at the Jeju National University, South Korea. The numerical model outputs have the following details: the spatial resolution of 0.05 (zonal) and 0.1 (meridional), the depth intervals of 1, 10, 20, 30, 50, 75 m, and the daily interval for the output with the domain defined in Figure 1. The contemporaneous Yangtze river flow and the climatological seasonal current through the Taiwan Strait are used as input. Model s surface temperature realistically described the spatial pattern of the satellite-observed sea surface temperature [Moon et al., 2010, Figure 11]. The full details of the model runs over the ECS are available elsewhere [Moon et al., 2010]. The model s 1 m depth fields were averaged using Gaussian weights with a halfpower point at 50 km (corresponding to the radius of the Aquarius footprint ranging from 50 to 75 km) for every Aquarius data. Within each Aquarius observation, there are about 50 model-grid points. The Aquarius SSS is too low (Figures 6 and 7) as was the case for the comparison with the CTD data. Spatial and temporal patterns of the difference between the Aquarius and model are analyzed below. 1. The mean difference between the Aquarius and model along the three ascending tracks ( b1, b2, b3 in Figure 3a) is 1.07, 0.41, and 0.70 psu, respectively, for beam 1, 2, and 3 (Figures 6 and 7). The mean difference along beam 1 of another pass ( bb1 in Figure 3a) is 0.76 psu, which is very close to that of beam3 and suggests the mean difference is not beam-dependent. There is no significant spatial dependence of the bias on the location of the tracks. The incomplete correction of the land contamination is ruled out as a cause of bias, because the difference would have reduced as a function of distance from the coast. The widespread bias between Aquarius and model is consistent with the widespread difference Table 1. Comparison of Aquarius SSS (Ascending Tracks Only) and In Situ Measurements at the Locations Shown in Figure 4 a (a) Aquarius (b) In Situ (a) Minus (b) Distance to Coast Oct 2011 (mean) km (stdev) Sept 2012 Area-north (mean) km Area-north (stdev) Area-south (mean) km Area-south (stdev) a Mean and stdev were derived from unweighted spatial averaging of the comparison. The distance for the north area of 2012 is to a small island: thus, the actual land contamination should be smaller than one expected from a coastline that is 100 km away. KIM ET AL. VC American Geophysical Union. All Rights Reserved. 7021

7 between ascending and descending Aquarius SSS (as discussed in section 2.2 and attributed to the residual RFI). 2. Figure 8 shows that the occurrence rate of the major RFI is fairly stable in time. The occurrence rate corresponds to the presence of the RFI sources in this region. We use the occurrence rate as a proxy information on the number and magnitude of the RFI sources in the absence of accurate information. The temporally stable presence of the RFI sources would result in the bias in the Aquarius SSS, which were shown in Figures 6 and 7. Furthermore, the temporal stability of T A minus T F was examined along the four tracks for the same period as shown in Figure 8: their size is temporally stable. 3. The spatial variability of the Aquarius SSS along the track on the intramonthly scale is larger than that of the model SSS (difference of stdev in Figures 6b or 7b). This contrast may result from the inherent smoothness of the model simulation. The temporal variations in the surface salinity agree very well between Aquarius and model: the correlation is 0.76, 0.64, and 0.75 over one and a half year period along beam 1, 2, and 3, respectively (Figures 6a and 7a), after attributing the mean difference to the residual RFI based on the above discussions. The temporal trend and magnitude of the salinity changes during the river discharge in summer agree remarkably between Aquarius and model along the three tracks. The salinity changes of up to 3 psu during the river discharge season are detected successfully in both data in terms of magnitude and timing. The gradual rise of salinity from September 2012 to March 2013 is described consistently between Aquarius and model. In summer, the discrepancy is larger than the rest of the period (difference of mean in Figures 6b or 7b): any error in model s prediction of the location of the river plume could contribute to the discrepancy. 4. Variability Analysis The freshwater input from the Yangtze river discharge is one of the primary causes of the SSS variation in the ECS, modified by wind-driven advection of the discharge [Lie et al., 2003], tidal mixing of the discharge [Moon et al., 2010], and severe storms. The river discharge rate is compared with the Aquarius SSS in Figure 9. The discharge rate shows a clear seasonal signal in response to the spring/summer monsoon rainfall over China. The Aquarius SSS along the ascending tracks was averaged over two regions: in the far west near the estuary and the entire ECS (both are defined in Figure 3b). The ECS-average is saltier than the average near the estuary (Figure 9), reflecting the longitudinal salinity gradient established by the discharge. In winter month (e.g., October 2012; Figure 10 and also the monthly climatology) [Lie et al., 2003] the spatial salinity gradient is established as the river discharge, though small, is confined near the Chinese coast. The confinement occurs as the discharge is advected by the northwesterly wind [Lie et al., 2003]. In summer month (e.g., July 2012; Figure 10 and the Lie et al. s monthly climatology), the discharge propagates east, by being advected by the Figure 6. Comparison of Aquarius and regional numerical model salinity along the ascending tracks b1 shown in Figure 3a. bias is the mean difference (model minus Aquarius). stdev is the standard deviation along the Aquarius track. southerly monsoon wind [Lie et al., 2003], creating the longitudinal salinity gradient. In summary, we KIM ET AL. VC American Geophysical Union. All Rights Reserved. 7022

8 Figure 7. Same as Figure 6 but for the different ascending tracks (left) b2 and (right) b3. found that the Aquarius SSS along the ascending tracks reasonably describes the spatial variability of the ECS SSS. To understand the temporal variability of the ECS SSS, the Aquarius SSS along the ascending tracks is compared with the river discharge rate (Figure 9a). Since the discharge rate has a clear seasonal component, the comparison is made also on a seasonal scale. The correlation of the seasonal signals after grouping the daily discharge data to the Aquarius observation date is when the discharge rate leads by days (Figures 9b and 11, the sign is negative because the freshwater input lowers salinity). The Datong station, where the river level is observed, is about 400kminlandfromtheestuary. It takes 2 4 days for the river level signal to travel from Datong to the estuary [Wang et al., 2013], which is not significant on the seasonal scale and therefore is not considered during the lead-lag correlation analysis. The correlation is high even if it does not incorporate the contributions by other sources of SSS variability, which suggests that the river discharge is one of the dominant parameters determining the SSS seasonal cycle. One of the largest SSS variability in the open ocean is found during the El Ni~no Southern Oscillation but, in the absence of river discharge, the variation is smaller than 0.5 psu (Figure 7) [Vialard et al., 2002]. In comparison, the size of the ECS SSS seasonality of about 2 psu in Figure 9b is consistent with the simulated impact of the Yangze river on the seasonal scale [Delcroix and Murtugudde, 2002]. These further support the important role of the river discharge. Examination of the SSS balance equation may help understand the remaining processes controlling the SSS temporal variability, although the comprehensive analysis of the balance is beyond the scope of the current paper. The SSS balance may be formulated by adapting the mixed layer temperature balance [Kim et al., 2007] KIM ET AL. VC American Geophysical Union. All Rights Reserved. 7023

9 Figure 8. The percentage out of 60 raw Aquarius samples that were removed by the RFI detection algorithm along the three ascending tracks marked by b1, b2, and b3 in Figure 3a (beam 1, 2, and 3, respectively). The percentage is interpretated as a measure of the number of the RFI sources contaminating the Aquarius data. Highlighted are the percentages for the two periods analyzed in Figure 4. ds=dt 3 1=S5ðE-P-RÞ=H1ðu; vþ rs 1subsurface1mixing ðu; vþ rs5ðu 1 u 0 ; v 1 v 0 Þ rðs 1 S 0 Þ ðu; vþ5ðu E 1 u g ; v E 1 v g Þ In the equation, S is the upper ocean salinity; E, P, and R denotes evaporation, precipitation, and river discharge (after normalized by surface area), respectively; and H is the mixed layer depth. R may be treated as a part of surface flux or advection. (u,v) denotes horizontal current velocity, consisting of Ekman and geostrophic parts. The overbar and prime denote seasonal mean and nonseasonal anomaly, respectively. Subsurface includes entrainment and advection through sloping mixed layer base. Mixing includes horizontal and vertical components. As described earlier in this section with Figure 10, the Ekman transport assist the river discharge into the ECS in summer and vice versa in winter (u ES ), and thereby would lower SSS in summer and increase SSS in winter). The mean geostrophic currents flow northeastward and transport salinity spatial gradient ðu; vþ rðs1s Þ. They are expected to further contribute to the dilution of the ECS in summer. At the same time, the distribution of saline water that originates from the Kuroshio shows seasonal variation in the pattern of advancing (retreat) in the northwestward (southwestward) in cold (warm) season. The anticipated effect is to lower the ECS SSS in summer and vice versa in winter. Deepening mixed layer in fall and winter would entrain saline halocline water into the fresher mixed layer, which would add to the saltening tendency in fall and winter. The tidal mixing was found important for the migration of the river plume [Moon et al., 2010], however, the role of mixing in the SSS budget requires further studies. Inclusion of all the components of the SSS budget may revise the lead time: we were expecting the lead time of 90 days based on the quadrature relationship between freshwater input and SSS response (2R/H versus ds/dt), whereas the analysis of the data suggests days (Figure 11) due possibly to the unaccounted processes. The MicroWave Radiometer (MWR) onboard the Aquarius/SAC-D spacecraft offers concurrent observation of brightness temperature. The rain rate was retrieved within the MWR beam (30 km 3 40 km size) and weighted averaged to the Aquarius footprints by the Remote Sensing Systems. To exploit the availability of KIM ET AL. VC American Geophysical Union. All Rights Reserved. 7024

10 Figure 9. Comparison of weekly Aquarius salinity (green and black) with daily river discharge rate from Datong station (red): (a) raw signal and (b) mean seasonal cycle compiled over The MWR rain rate retrievals were averaged over 28 N 32 N, 123 E 128 E (blue). The ascending-track Aquarius salinity was averaged over the two regions defined in Figure 3b. The vertical bars are the spatial standard deviation of SSS. The seasonal curve of the river discharge was shifted forward by 30 days according to Figure 11 (but the raw discharge data in Figure 9a was not shifted). the concurrent and collocated rain observations, we have performed the scaling analysis of the precipitation in the SSS budget. The rain rate retrievals for 2011 are missing in many locations and not as reliable due to the lack of instrument stability. At present, the retrievals are available up to August The rain rate is compared with the Aquarius SSS in Figure 9a after averaging over the ECS. Rainfall events match the opposing intramonthly trends between SSS and river discharge in some cases (early March 2012, April 2012, and April 2013) but not in other cases (October 2011, January 2012, and late July 2012). Below, a scaling analysis is performed on the significance of the rainfall. During these periods, the peak-rain rate is about 1 mm/h. If the peak rate is sustained over the ECS and the 7 day Aquarius period, it will be equivalent to the freshwater input of m 3 /s. The Yangtze river plume outputs an additional m 3 /s of freshwater to the ECS (Figure 9a) and extends to the sea floor at the depth of 60 m in the ECS [e.g., Moon et al., 2010]. The corresponding drop in SSS is about 2 3 psu (Figure 9a). Supposing the rain input of m 3 /s spreads over the mixed layer (as happened over the upper m layer of the Bay of Bengal), the impact of rain on SSS is expected to be 20.2 to 20.3 psu that is comparable with psu for 1 mm/h rain, derived empirically for the tropical ocean [Boutin et al., 2013; Drucker and Riser, 2014]. These scaling analyses indicate that the impact of rain is an order of magnitude smaller than that of the river discharge at the time scale from intraseasonal to seasonal. Evaporation has the same order of magnitude as precipitation (e.g., and therefore is expected to be minor too. The interannual signal is apparent in the Aquarius SSS and river discharge when comparing the two July 2012 and 2013 (Figures 9 and 10). The interannual changes well match the drought event over China in 2013 summer, which is the worst in decade ( shtml). This finding is consistent with the drought impact on the ECS SSS reported in the past [Delcroix and Murtugudde, 2002] based on multiyear ship-cruise observations. These analyses of the temporal variability of the Aquarius ECS demonstrate that the Aquarius SSS along ascending tracks is capable of monitoring the ECS SSS. 5. Summary and Discussions This study examined the feasibility of the spaceborne Aquarius instrument to monitor the sea surface salinity (SSS) variation in the East China Sea (ECS). Despite the economic (fishery) and climatic (typhoon passage) interest, the routine monitoring of the ECS SSS is a challenge due to the shallow depth of the marginal sea and severe weather condition (typhoons). The spaceborne remote sensing of the ECS SSS is complicated by the geophysical contamination originating from the nearby land and the radiofrequency interference (RFI). KIM ET AL. VC American Geophysical Union. All Rights Reserved. 7025

11 The Aquarius SSS along the satellite s ascending tracks is higher than along the descending tracks by 1 psu or lager over the entire ECS and surroundings (Figure 5 and section 2.2). The difference between the ascending and descending SSS is not due to the land contamination. Because the land contamination has distributed sources with small magnitude, its contamination is limited near the coast. Instead, we attribute the difference to strong RFI sources entering the sidelobes of the Aquarius antenna (section 2.2). As a result of the combined effect of the location of the point sources of the RFI and the Aquarius viewing geometry, the descending SSS is expected to suffer more severely (section 2.2). We recommend that only the ascending track s SSS be used for the validation of the Aquarius SSS and science analysis. Figure 10. Monthly spatial pattern of the Aquarius SSS along ascending orbits. The dense contours near Japan land boundaries are due to land contamination. Figure 11. Lead-lag correlation analysis between seasonal discharge rate and SSS averaged over 28 N 32 N, 123 E 125 E. The land contamination is limited to about one Aquarius footprint from the coast. About one footprint away from the coast (150 km), the fraction of land within the antenna field of view becomes smaller than about 0.5% and the effect of the land emission on SSS is sufficiently diminished. Without the algorithm to correct for the land emission, the contamination may reach 0.8 K or 1.6 psu at about 150 km from the coast. With the correction for the land emission, the land contamination would diminish substantially. The sensitivity of the correction method to the details of the radiometric simulation of the land emission is estimated to be smaller than 0.1 K (equivalently 0.2 psu) away from the coast by 1 pixel (section 2.1). The Aquarius standard salinity product was compared with the shipboard in situ data in the fall of 2011 and 2012 sufficiently away from the coast. This study used the level-2 data along satellite tracks, which offer the spatial resolution of about 150 km and the temporal revisit at every 7 days. The Aquarius SSS along the ascending tracks was too fresh by 0.40 and 0.93 psu in the two regions (Table 1 and section 3.1). When compared over the 18 month period KIM ET AL. VC American Geophysical Union. All Rights Reserved. 7026

12 with the regional numerical model outputs, the Aquarius SSS along the ascending tracks was too low (bias or accuracy) by 1.07, 0.41, and 0.70 psu, respectively, for beam 1, 2, and 3 over the ECS with the temporal standard deviation (precision) ranging from 0.48 to 0.62 psu for the three beams (Figures 6, 7 and section 3.2). These precision values in the coastal ECS are comparable with the Aquarius mission requirement (0.2 psu one-sigma for a monthly average over the open ocean) after divided by a factor of 2 (5square-root of the number of the seven daily observations per month). These bias differences do not resemble the spatial pattern of the land contamination that decreases away from the coast. Furthermore, the validation with the model field does not exhibit the temporal trend expected from the other geophysical contaminants such as galaxy radiation or wind. Therefore, we deduce that the RFI is the most likely cause of the difference found in the validation results. The occurrence rate of the detected RFIs, which indicates the presence and strength of the RFI sources in this region, is temporally stable (Figure 8 and section 3.2): this finding further supports that the difference between the Aquarius and model is a temporal quasi-bias in nature. The understanding that the RFI contamination appears in the SSS as a quasi-bias allows the study of SSS variability. The Aquarius data describe the spatial and temporal variability of the ECS SSS (the bias due to the undetected RFI remains in the data but does not significantly impact the study of the variability). The temporal trend and magnitude of the salinity changes during the summer events of river discharge agree remarkably between Aquarius and model. The salinity changes of up to 3 psu during the river discharge season are detected successfully in both data in terms of magnitude and timing. The gradual rise of salinity from September 2012 to March 2013 is described consistently between Aquarius and model (Figure 6a and section 3.2). The Aquarius SSS shows the decrease of SSS toward the Yangtze river estuary (Figure 10 and section 4) reflecting the river discharge as the primary source of the SSS variability in the ECS. The river discharge is confined to the estuary in winter but propagates into the sea in summer as expected by the seasonality of discharge and wind pattern (Figure 10). The discharge from the Yangtze river correlates with the Aquarius SSS with the coefficient of on a seasonal scale with the discharge rate leading the SSS by days. The Aquarius SSS captures that the regional drought in summer 2013 impacted the ECS SSS. Finally, the procedures to examine the land and RFI contamination presented in this paper may apply usefully to the studies of the other coastal seas of the world ocean. Analyses at the finer and more frequent resolution than performed here may be achieved through coastal data assimilation [e.g., Chao et al., 2009]. Appendix A: Forward Model for Emission From the Land Surface The algorithm to correct the Aquarius observation for the emission from the land surface simulates the antenna temperature (T A,land ) of the land surface and removes it from the Aquarius measurement of T A [Wentz and Le Vine, 2011]: T A;land 5 1 ð GðbÞWð/ÞT B;toa dx 4p land The integral applies to the top-of-the-atmosphere T B (T B,toa ) over the land surfaces of the Earth, where dx is the differential solid angle. The dielectric model [Dobson et al., 1985], soil surface rough model [Wang et al., 1983], and vegetation model [Jackson and Schmugge, 1991] simulates the emission from the land surface. Radiative transfer from the surface through the atmosphere provides T B,toa [Wentz and Le Vine, 2011]. The land coverage is defined by 1 km resolution landmask with all the known islands. The matrix G at a boresight vector b is a matrix of the antenna gain. W(/) is a rotation matrix and the rotation angle / accounts for the Faraday rotation in the ionosphere and the difference angle between the antenna polarization and the Earth polarization vectors. Further details of the simulation approach are available in Kim et al. [2011] and Wentz and Le Vine [2011]. We first define the regions where the land contamination is not significant. The gain-weighted fraction is shown in Figure 3b. On most of the tracks the fraction is smaller than 1% away from about 1 from the coastline. Interestingly, the land fraction is larger along the descending track that crosses 27 N, E than on the track to its west, while the former is farther from the coast. These two tracks are imaged by the outer and inner beams of the Aquarius instrument. The outer beam has a larger incidence angle than the inner beam. The footprints of main-lobes and side-lobes will be larger for the outer beam than the inner (A1) KIM ET AL. VC American Geophysical Union. All Rights Reserved. 7027

13 Table 2. Comparison of the Simulation of the Land Emission Between Aquarius Processor and Soil Moisture Active Passive (SMAP) Mission a Land Model Elements SMAP Processor Aquarius Processor Soil texture (static) Global map Constant Vegetation amount (dynamic) MODIS 10 daily Monthly climatology Constant forest Surface roughness (static) Global map Constant Vegetation albedo (static) Global map Constant Dielectric model Mironov Approximated Dobson a See the ancillary data set section in O Neill et al. [2012] for references. beam. Consequently, the outer beam becomes more susceptible to the land emission. The regions where the land fraction is smaller than 1% are focused in the following analyses. The fidelity of the simulated T A,land depends on those of the simulation of Aquarius observation, antenna gain pattern, land emission model, and input data. Here the sensitivity of the simulation is assessed to the land emission model and input data using the observation simulator developed at Jet Propulsion Laboratory. Table 2 summarizes the key differences in the land model and input data between the Aquarius processor and the forward model implemented for the Soil Moisture Active Passive (SMAP) mission. The SMAP model employs more realistic ancillary data that vary spatially and temporally. Acknowledgments The comments by two reviewers were very helpful to significantly improve the manuscript. We thank Steven Riser for valuable discussions. The MWR rain rate retrieval data were provided by J. Scott, Remote Sensing Systems, USA. This research was carried out in part at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. 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