Chinese Journal of Oceanology and Limnology

http://dx.doi.org/10.1007/s00343-017-5330-3

A comparison of Argo nominal surface and near-surface temperature for validation of AMSR-E SST*

LIU Zenghong (刘增宏) 1, 2 , ** , CHEN Xingrong (陈幸荣) 3 , SUN Chaohui (孙朝辉) 2 , WU Xiaofen (吴晓芬) 2 , LU Shaolei (卢少磊) 2 1 State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration,

Hangzhou 310012, China 2 Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China 3 National Marine Environmental Forecasting Center, Beijing 100081, China

Received Nov. 18, 2015; accepted in principle Feb. 16, 2016; accepted for publication Apr. 11, 2016 © Chinese Society for Oceanology and Limnology, Science Press, and Springer-Verlag Berlin Heidelberg 2017

Abstract Satellite SST (sea surface temperature) from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) is compared with in situ temperature observations from Argo profi ling fl oats over the global oceans to evaluate the advantages of Argo NST (near-surface temperature: water temperature less than 1 m from the surface). By comparing Argo nominal surface temperature (~5 m) with its NST, a diurnal cycle caused by daytime warming and nighttime cooling was found, along with a maximum warming of 0.08±0.36°C during 14:00–15:00 local time. Further comparisons between Argo 5-m temperature/Argo NST and AMSR-E SST retrievals related to wind speed, columnar water vapor, and columnar cloud water indicate warming biases at low wind speed (<5 m/s) and columnar water vapor >28 mm during daytime. The warming tendency is more remarkable for AMSR-E SST/Argo 5-m temperature compared with AMSR-E SST/Argo NST, owing to the eff ect of diurnal warming. This eff ect of diurnal warming events should be excluded before validation for microwave SST retrievals. Both AMSR-E nighttime SST/Argo 5-m temperature and nighttime SST/Argo NST show generally good agreement, independent of wind speed and columnar water vapor. From our analysis, Argo NST data demonstrated their advantages for validation of satellite-retrieved SST.

Keyword : Argo; near-surface temperature (NST); Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E); sea surface temperature (SST)

1 INTRODUCTION

SST (sea surface temperature) is an important parameter for both ocean and climate studies. It is a key indicator of climate change and has been widely used in studies related to the El Niño Southern Oscillation (e.g., Suarez and Schopf, 1988; McPhaden, 1999; Wang and Weisberg, 2000; Chen and Cane, 2008). In the past few decades, satellite-measured SST has become a major data source because of global coverage and high temporal resolution. Infrared satellite sensors measure the “skin” of the ocean at depths on the order of 0.01 mm, whereas microwave sensors detect “subskin” temperatures corresponding to the top 1 mm of the ocean (Donlon et al., 2002; Donlon and GHRSST-PP Science Team, 2005).

Infrared SST is aff ected by cloudy conditions, and its biases are mainly attributable to diurnal variability, water vapor attenuation, atmospheric aerosols, and incomplete removal of cloud contamination (Brown et al., 1985; McClain, 1989; Emery et al., 1994; Gohil et al., 1994). Regional SST analyses indicate that diurnal SST warming can exceed 6°C (Flament et al., 1994; Gentemann and Minnett, 2008; Gentemann et al., 2008; Merchant et al., 2008). The magnitude of this warming depends on both solar insolation and wind

* Supported by the National Basic Research Program of China (973 Program) (No. 2013CB430301), the National Natural Science Foundation of China (Nos. 41321004, 41206022, 41406022), and the National Special Research Fund for Non-Profi t Marine Sector (No. 201305032) ** Corresponding author: liuzenghong@139.com

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speed (Gentemann et al., 2003). In contrast, microwave SST is unaff ected by aerosols and can be retrieved in the presence of clouds simultaneously with wind speed, columnar water vapor, cloud water content, and rain rate (Wentz, 1998; Wentz et al., 2000).

For validation of satellite-retrieved SSTs, many studies have used buoy-measured SST and XBTs (expendable bathythermographs), which gives subsurface “bulk” SST (Pandey and Kniff en, 1991; Bhat et al., 2004; Gentemann et al., 2004; Muraleedharan et al., 2004; Parekh et al., 2007) from depths 1 to 5 m. In situ sensors are typically at depth 1 m. Skin temperatures are not easily measured in situ, and few skin observations are available from only dedicated radiometers (e.g., Minnett, 2003; Gentemann and Minnett, 2008). Below this thin skin layer, during daytime, weak wind speed and increased solar insolation can cause thermal stratifi cation over several meters of the upper ocean (Price et al., 1986; Yokoyama et al., 1995). Udaya Bhaskar et al. (2009) compared the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and TRMM Microwave Imager (TMI) SSTs with Argo nominal surface temperatures for the fi rst time over the Indian Ocean. The results show a substantial spatial match and correlation between Argo foundation and satellite-retrieved SSTs. The former SST, used by Udaya Bhaskar TVS et al. (2009), is typically measured at depths 3–10 dbar, which might have a large diff erence relative to the skin or satellite-retrieved subskin SST. To avoid degrading salinity accuracy by ingesting sea surface contaminants, a CTD pump is turned off at ~5 dbar beneath the sea surface as the Argo fl oat ascends. To meet Argo requirements of near-surface temperature (NST hereafter) for validation of satellite SST retrievals, many fl oats are now designed to sample NST without an extra sensor. This type of fl oat can measure about 5~15 NSTs at depths 0–5 dbars without pumping water into the conductivity cell (this does not measure near-surface conductivity). In addition, Sea-Bird Electronics Inc. has developed a Surface Temperature Salinity (STS) sensor, which is a second, free-fl ushed, conductivity sensor used in conjunction with their main SBE-41 CP CTD installed on Argo fl oats (Larson et al., 2008). The STS sensor samples at 1 Hz concurrently with the SBE-41 CP CTD near the fl oat park depth (960–980 dbar) and again in the upper ocean (3–20 dbar), just before the SBE-41 CP is turned off . After that, the STS sensor continues sampling through the ocean surface for ~500 s as the fl oat prepares to transmit data.

In this paper, we compare AMSR-E retrieved SSTs with Argo nominal surface (~5 m) temperatures and non-pumped NSTs from Argo profi ling fl oats over the global oceans, to evaluate the advantages of Argo NST data for validation of satellite-retrieved SST. The dataset used is described in Section 2. In Section 3, we compare Argo NSTs with Argo 5-m temperatures over the global oceans, and then compare AMSR-E SST/Argo 5-m and AMSR-E SST/Argo NST related to wind speed and columnar water vapor. Finally, a summary and discussion are given in Section 4.

2 DATA AND METHOD

2.1 AMSR-E microwave SSTs

AMSR-E is a passive microwave sensor that was launched with the Aqua satellite in May 2002 (and failed in October 2011), with resolution of 5.4–56 km. The Aqua satellite had a sun synchronous orbit that crossed the equator at 13:30 local time on northward (ascending) passes and 01:30 on southward (descending) passes. The SST product from AMSR-E has been validated by in situ measurements from high-resolution XBTs, TSGs (hull-mounted thermosalinographs), and Argo profi ling fl oats (Dong et al., 2006; Emery et al., 2006; Uday Bhaskar et al., 2009).

AMSR-E retrieved SST data are provided by Remote Sensing Systems, Santa Rosa, CA, USA (Wentz, 1998; Wentz and Meissner, 2000). These data are released on a 0.25°×0.25° grid of global extent (±89.875°). We used a daily-pass SST product of version 7 in this study. This updated version of product has been validated by Gentemann (2014) using in situ measurements from ships, moored and drifting buoys, meanwhile a vapor correction is suggested for all users prior to performing any analysis. AMSR-E also provides data of wind speed (measured in the 10.7 and 18.7 GHz channels), columnar water vapor, columnar cloud water, and rain rate. AMSR-E has an onboard two-point calibration system that continuously compensates for variations in radiometer gain and noise temperature. The two vital components of this calibration system are the cold mirror and hot reference load. Wentz et al. (2003) used coincident measurements from collocated observations from other satellites to develop a system for predicting the temperature of the hot reference. One limitation in this procedure is that in situ data for calibration of satellite data are more concentrated in the tropics than extratropics.

LIU et al.: Validation of AMSR-E SST based on Argo temperature

2.2 Argo near-surface temperatures

The Argo NST data used are non-pumped temperatures collected from the APEX, PROVOR, and ARVOR profi ling fl oats over the global oceans from October 2008 through March 2015. The data are assembled and available on the National Oceanography Centre (UK) ftp site (ftp://ftp.pol.ac.uk/pub/bodc/argo/NST/). These fl oats with the capacity to sample non-pumped NSTs are deployed or processed by the British Oceanographic Data Centre, Indian National Centre for Ocean Information Services, Japan Meteorological Agency, Scripps Institution of Oceanography, and University of Washington. We used a total of 497 such fl oats and 36 740 NSTs (Fig.1). Of these fl oats, 69 fl oats are equipped with a STS sensor, which can sample through the ocean surface for ~500 s as the fl oat prepares to transmit data. As a result, 10 024 NSTs are observed by fl oats with a STS sensor. Each non-pumped temperature profi le usually contains several measurements at depths 0–5 dbars. In our analysis, the uppermost temperature above 1 dbar in each profi le was selected for comparison with satellite-retrieved SST.

Each Argo NST was matched to AMSR-E measurements collected on the same day, 1 day prior, and 1 day after. A spatial window of 0.8°×0.5° was chosen to ensure more than one satellite SST retrieval to match a corresponding Argo profi le location for each satellite pass. The selected AMSR-E observations

measured by ascending and descending passes on each day were then linearly interpolated to the Argo NST location. To reduce temperature diff erence between Argo NST and AMSR-E SST, a temporal window of ±3 h was selected to pair Argo and AMSR-E observations following the study done by Gentemann (2014). If more than one interpolated AMSR-E retrieval fell into the temporal window, an average AMSR-E observation was computed; otherwise, the nearest SST was selected. As a result, 1 473 pairs (786 pairs for ascending passes and 687 for descending) were selected for our analysis. Figure 2 shows the geographic distribution of the AMSR-E/Argo data pairs used. Most of these pairs were (1) between 10°S and 45°S in the southern Pacifi c Ocean, (2) in the southern Indian Ocean, and (3) in the South Atlantic.

3 RESULT 3.1 Diff erence between Argo NST and nominal Argo surface temperature

To evaluate the advantage of Argo NST against nominal Argo surface temperature (~5 m), the near-surface and 5-m temperatures (all Argo pressure data have been converted to depth) were compared for each Argo temperature profi le over the global oceans from October 2008 through March 2015. Figure 3 shows the temperature diff erence between Argo near-surface and 5-m temperatures (interpolated from the corresponding non-pumped temperature profi le), bin-

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Fig.1 Locations of Argo NSTs over the global oceans from October 2008 through March 2015

CHIN. J. OCEANOL. LIMNOL.,

averaged per local time. A pronounced warming of average 0.04±0.26°C was observed between 10:00 and 18:00 local time, with maximum warming ~0.08±0.36°C during 14:00–15:00 local time. This is clearly attributable to diurnal surface warming eff ects. A slight cooling before 08:00 can also be observed, by an average ~0.04±0.21°C. By comparison between Argo NST and 5-m temperature, remarkable diff erences and a diurnal cycle are evidenced in Fig.3, owing to solar radiation. A diurnal SST cycle is induced by the formation and destruction of near-surface warm layers (Fairall et al., 1996; Kawai and

Wada, 2007), and the reported amplitude of this cycle is typically as much as 4°C (e.g., Stramma et al., 1986). Our analysis indicates depth-dependent diurnal stratifi cations even in the upper 5 m of the ocean, and the maximum warming and cooling can be as much as 4.5 and 4.3°C, respectively.

3.2 Argo NST/5-m temperature versus AMSR-E SST

Scatter plots between AMSR-E ascending (daytime) and descending (nighttime) pass SSTs and Argo NSTs/5-m temperatures are shown in Fig.4. Overall, AMSR-E SSTs observed by ascending and descending passes show good agreement with Argo NST over the global oceans, with correlation coeffi cients 0.994 and 0.993, respectively. The root mean square error (RMSE) for AMSR-E daytime SST/Argo NST is 0.569°C, approximately 0.005°C smaller than the nighttime SST. Both RMSEs are comparable to the results of other studies (Wentz et al., 2000; Uday Bhaskar et al., 2009) in which TAO/TRITON, moored buoy, drifting buoy, and XBT data were used for validation. The RMSE for AMSR-E daytime SST/Argo 5-m temperature is 0.711°C (~0.14°C higher than that for daytime SST/Argo

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Fig.2 Matchups between ascending/descending passes of AMSR-E and Argo NSTs Times of passes are indicated by color swaths. Locations of data pairs for ascending passes and descending passes are marked by blank triangles and fi lled dots, respectively.

LIU et al.: Validation of AMSR-E SST based on Argo temperature

NST). The RMSE for nighttime SST/Argo 5-m temperature is very comparable to that of nighttime SST/Argo NST. It is evident that daytime warming can increase the temperature diff erence between Argo NST and Argo nominal surface temperature, and Argo NST is expected to be superior to that temperature for validation of satellite-retrieved SST.

3.3 Temperature diff erences related to wind speed, water vapor, and cloud water

To evaluate improvement of Argo NST vs. Argo nominal surface temperature for validation of satellite-retrieved SST, temperature diff erences between AMSR-E SST and Argo 5-m/near-surface temperatures were bin-averaged for a range of physical parameters, including not only satellite-retrieved wind speed and columnar water vapor but also columnar cloud water. It is seen in Figure 5 that the greatest temperature diff erences were for weak wind speeds (10.7 GHz channel was used here) during ascending passes (daytime). For wind speeds <5 m/s, AMSR-E SST is warmer than either the Argo 5-m temperature or NST, with greater warming when

compared with the 5-m temperature. This warming bias increases with decreasing wind speed, reaching a maximum of about 2.5 and 1.1°C at 1 m/s for AMSR-E SST/Argo 5-m and AMSR-E SST/Argo NST, respectively. Mean warming is 0.896±0.735°C and 0.324±0.632°C, respectively (Table 1), for low wind speeds during daytime. In contrast, temperature diff erences for descending passes (nighttime) do not reveal a warming tendency at weak wind speeds. The temperature diff erences during nighttime are much smaller than during daytime for wind speeds <5 m/s, with means about 0.003±0.455°C and -0.070±0.393°C, respectively. Previous studies (Donlon et al., 2002; Gentemann et al., 2004; Dong et al., 2006) found that the temperature diff erence between skin and bulk had large variations at low wind speed. Under weak wind, especially during daytime with solar insolation, warming in the top 1–2 m of the ocean increases (Soloviev and Lukas, 1997; Gentemann et al., 2009). When winds are stronger than 5 m/s, both the AMSR-E SST/Argo 5-m temperature and AMSR-E SST/Argo NST diff erences decrease or become invisible, because wind-induced mixing homogenizes

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Fig.4 Scatterplots of Argo NSTs against AMSR-E (a) ascending and (b) descending pass SSTs, and Argo 5-m temperatures against AMSR-E (c) ascending and (d) descending pass SSTs, during October 2008–October 2011 Dashed line shows linear regression.

CHIN. J. OCEANOL. LIMNOL.,

the upper few meters of ocean. Solar heating is greatest near the ocean surface, favoring near-surface thermal stratifi cation, whereas wind-driven mixing erodes diurnal stratifi cation (Merchant et al., 2008). When wind speeds are between 11 and 17 m/s, AMSR-E nighttime SST tends to be lower than Argo 5-m temperatures and NSTs by about 0.165±0.692°C and 0.144±0.698°C on average. Such a cool bias has

also been revealed in the TMI SSTs at wind speed above 12 m/s (Wentz, 1996). In Southern Ocean, a cool bias was also found in the AMSR-E SSTs at higher wind speed (Dong et al., 2006). The cool bias could be explained by errors in the calibration at high latitudes (most high wind speed occurs at high latitudes). High wind speed is likely to aff ect surface roughness, and particularly to favor the formation of

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Fig.5 AMSR-E (a) ascending and (b) descending SSTs minus Argo NSTs, and AMSR-E (c) ascending and (d) descending SSTs minus Argo 5-m temperatures, versus wind speed measured by AMSR-E (measured in 10.7 GHz channel) Bin-averaged temperature diff erences based on wind speed are marked by dashed lines. Error bars are twice the standard error for each bin.

Table 1 Mean temperature diff erence between AMSR-E SST and Argo NST/5-m temperature and the corresponding standard error against diff erent parameters

Parameter AMSR-E - NST (°C) AMSR-E - Argo 5m (°C)

Wind speed (ascending) <5 m/s

>11 & <17 m/s Wind speed (descending)

<5 m/s >11 & <17 m/s

0.101±0.619 0.324±0.632 -0.018±0.754 -0.045±0.571 -0.070±0.393 -0.144±0.698

0.249±0.647 0.896±0.735 -0.036±0.750 -0.042±0.584 0.003±0.455 -0.165±0.692

Water vapor * (ascending) >28 mm

Water vapor * (descending) >28 mm

0.044±0.528 0.111±0.479 -0.015±0.460 -0.015±0.354

0.185±0.656 0.352±0.702 -0.024±0.472 -0.024±0.374

Cloud water (ascending) Cloud water (descending)

-0.058±0.537 -0.019±0.586

-0.026±0.591 -0.032±0.591

* A water vapor correction suggested by Gentemann (2014) has been applied to AMSR-E v7 SST.

LIU et al.: Validation of AMSR-E SST based on Argo temperature

foam and spray, which, in turn, leads to high sea surface emissivity.

To minimize the eff ect of diurnal warming, we exclude matchups during 10:00–16:00 following Gentemann (2014). Figure 6 shows the temperature diff erences (daytime and nighttime SSTs are combined) after excluding the observations aff ected by diurnal warming events. The warming biases are invisible when wind speeds are <5 m/s, furthermore, the temperature biases for both AMSR-E/Argo 5-m and AMSR-E/Argo NST are nearly centered on 0°C

varying with wind speed except for wind speeds between 12 and 16 m/s where a slightly cool bias is still visible.

Columnar water vapor is the next greatest eff ect on temperature diff erences, as found in some studies (Dong et al., 2006; Castro et al., 2008; Gille, 2012). Following the result from Gentemann (2014), a water vapor correction has been applied to all AMSR-E SSTs before validation. The correction has only a slight impact on the data at low water vapors. Figure 7 indicates that AMSR-E daytime SST is warmer than

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Fig.6 AMSR-E SST minus (a) Argo NST and (b) Argo 5-m temperature, versus wind speed after excluding the matchups during 10:00–16:00 Bin-averaged temperature diff erences based on wind speed are marked by dashed lines. Error bars are twice the standard error for each bin.

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Fig.7 Same as Fig.5 but against columnar water vapor measured by AMSR-E

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Argo 5-m temperature and NST when columnar water vapor is >28 mm, whereas nighttime SST is nearly consistent with both Argo 5-m and NST, regardless of change in columnar water vapor. The temperature diff erence for AMSR-E/Argo 5-m shows a greater warm bias than that of AMSR-E/Argo NST when columnar water vapor is >28 mm (with means about 0.352±0.702°C and 0.111±0.479°C, respectively). This warm bias between AMSR-E SST and Argo 5-m temperature increases with increasing columnar water vapor at high atmospheric water vapor. Radiation is aff ected by the absorption and emission of water vapor and cloud liquid water in the atmosphere, so brightness temperatures measured by satellites diff er from surface-measured brightness temperatures (Wentz and Meissner, 1999). With the increase of water vapor during daytime, especially at low latitudes, columnar water vapor is expected to have a greater impact on the calibration of AMSR-E SST retrievals. However, our analysis does not show a warming tendency for nighttime AMSR-E retrievals, as indicated by Gille (2012) using Argo 5-m temperatures under low columnar water vapor. This is clear evidence of the diurnal warming eff ect on the satellite observations. After excluding the matchups between 10:00 and 16:00, the temperature diff erences both for AMSR-E/Argo 5-m and AMSR-E/Argo NST are close to 0°C (Fig.8). It indicates that the diurnal warming events should be masked before validation of satellite SST retrievals.

Finally, temperature diff erences between AMSR-E SST and Argo 5-m temperature/Argo NST and their relationships to columnar cloud water are analyzed after excluding the diurnal warming events. There are no obvious SST/Argo temperature diff erences across the entire range of cloud water (Fig.9).

4 SUMMARY AND CONCLUSION

In this study, a fi rst-time comparison of Argo nominal surface (5 m) and near-surface temperatures for validation of AMSR-E SST over the global oceans was made. Diff erence between Argo NST and 5-m temperature was examined before validation for AMSR-E SST retrievals. The diff erence between Argo NST and 5-m temperature indicates a diurnal cycle caused by solar radiation, with maximum warming occurred between 14:00 and 15:00. Comparisons between Argo NST/5-m temperature and AMSR-E SST indicate that the satellite-retrieved SSTs and in situ observations agree fairly well. However, the RMSE of temperature diff erence of AMSR-E SST/Argo 5-m temperature can be ~0.14°C greater than that of AMSR-E SST/Argo NST owing to the eff ect of diurnal warming.

Subsequently, we compared Argo NST/Argo 5-m temperature and AMSR-E SST as related to wind speed, columnar water vapor, and columnar cloud water. An obvious warm bias owing to diurnal warming was observed only between AMSR-E daytime SST and Argo observations at low wind

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LIU et al.: Validation of AMSR-E SST based on Argo temperature

speed. This warm bias is more signifi cant for AMSR-E SST/Argo 5-m matchups. For wind speeds between 11 and 17 m/s, a cool bias has been revealed from AMSR-E nighttime SST and Argo observations, which is related to surface roughness and the formation of foam and spray. For columnar water vapors are >28 mm, a warm bias was also observed only for daytime AMSR-E SST/Argo observations, with a greater bias between AMSR-E SST and Argo 5-m temperature. This warm bias is also caused by eff ect of diurnal warming. It is suggested that the eff ect of diurnal warming should be excluded before validation of satellite SST retrievals.

Our analysis implies that Argo NST data are superior to nominal Argo surface temperature for validation of satellite-retrieved SST, so the former might be used to calibrate microwave or infrared SST more eff ectively over the global oceans.

Data matchups were very sparse and not well distributed (most matchups were in the Southern Hemisphere), either because only a small proportion of the Argo fl oats have the capacity to sample NST data or because there were no NST data before October 2008 over the global oceans (the Aqua satellite was launched in May 2002 and failed in October 2011). Nevertheless, the advantages of Argo NST over Argo nominal surface temperature for validation of satellite SST retrievals have been demonstrated. The Argo real-time ocean observing array is expected to be maintained for a long period. Therefore, it should provide more accurate NST data for validation and calibration of satellite retrievals over the global oceans, if more fl oats with the capacity for near-surface sampling are deployed.

5 ACKNOWLEDGMENT

The Argo near-surface temperature data were collected and made freely available by the National Oceanography Centre. AMSR-E data were produced by Remote Sensing Systems and sponsored by the NASA Earth Science REASoN DISCOVER Project and AMSR-E Science Team. Data are available at www.remss.com.

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