Arctic waters and marginal ice zones: A composite Arctic sea surface temperature algorithm using satellite thermal data

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 113,, doi: /2007jc004353, 2008 Arctic waters and marginal ice zones: A composite Arctic sea surface temperature algorithm using satellite thermal data R. F. Vincent, 1 R. F. Marsden, 2 P. J. Minnett, 3 K. A. M. Creber, 4 and J. R. Buckley 1 Received 18 May 2007; revised 9 November 2007; accepted 8 January 2008; published 17 April [1] The retrieval of Arctic sea surface temperatures (SSTs) using satellite radiometric imagery has not been well documented owing to the paucity of match-ups with in situ data. SST algorithms developed in temperate regions lead to positive biases in high latitudes due to an overestimation of atmospheric IR absorption. The composite arctic sea surface temperature algorithm (CASSTA) presented in this paper was developed from concurrent satellite and shipborne radiometric data collected in the North Water Polynya between April and July This algorithm considers three temperature regimes: seawater above freezing, the transition zones of water and ice, and primarily ice. These regimes, which are determined by advanced very high resolution radiometer (AVHRR) calibrated brightness temperatures, require different calculations for temperature estimates. For seawater above freezing, a specific Arctic SST algorithm was produced through a linear regression of AVHRR against in situ data. Areas consisting mainly of ice use an established ice surface temperature (IST) algorithm. The transition zone uses a combination of the Arctic SST and IST algorithms. CASSTA determines the Channel 4 brightness temperature for each pixel in a calibrated AVHRR image and then applies the appropriate algorithm to create a thermal image. The mean deviation of CASSTA compared to in situ data was 0.17 K with a standard deviation of 0.21 K. This represents a significant improvement over SST values using McClain coefficients for temperate waters, which overestimate the same data set by an average of 2.40 K. Application of CASSTA to the North Water imagery gives superior results compared to existing SST or IST algorithms. Citation: Vincent, R. F., R. F. Marsden, P. J. Minnett, K. A. M. Creber, and J. R. Buckley (2008), Arctic waters and marginal ice zones: A composite Arctic sea surface temperature algorithm using satellite thermal data, J. Geophys. Res., 113,, doi: / 2007JC Introduction [2] The remote sensing of sea surface temperature (SST) from space is well established in temperate latitudes. Algorithms developed for oceans between latitudes of 60 N and 60 S have validated accuracies better than 0.5 K in cloud free environments [Kearns et al., 2000]. However, these algorithms perform poorly when applied to Arctic regions, typically overestimating the SST by 2 to 3 K owing to unrealistic corrections for atmospheric absorption due to water vapor. The annual mean distribution of specific humidity near the surface is approximately 18 g kg 1 in equatorial latitudes and decreases to 1 g kg 1 or less over 1 Department of Physics, Royal Military College of Canada, Kingston, Ontario, Canada. 2 Royal Military College of Canada, Kingston, Ontario, Canada. 3 Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida, USA. 4 Department of Chemistry and Chemical Engineering, Royal Military College of Canada, Kingston, Ontario, Canada. Copyright 2008 by the American Geophysical Union /08/2007JC Polar Regions [Kumar et al., 2003]. The relative dryness of the Arctic renders temperate SST algorithms ineffective in the region. Satellite thermal data has been used in previous studies to determine the surface temperature in Polar Regions [Comiso, 2003; Wang and Key, 2005], but a specific SST algorithm has not been established for Arctic waters to date owing to a paucity of match-ups between satellite radiometric imagery and ground truth data. The development of an algorithm is further complicated by the combination of seawater and ice in the transition zones. [3] The composite Arctic SST algorithm (CASSTA) presented in this paper addresses the requirement for accurate satellite-derived surface temperatures for Arctic waters and marginal ice zones. The algorithm was developed from coincidental satellite and shipborne data collected in the North Open Water polynya, or NOW. A polynya is a region of the polar oceans that remains relatively ice-free under climatic conditions that would normally dictate thick ice cover. Situated in northern Baffin Bay between Ellesmere Island and Greenland, the NOW is the largest polynya in the Canadian Arctic. During the early winter, pack ice carried south through Kane Basin becomes congested and forms a 1of13

2 Figure 1. Points of interest are shown on a satellite image of the NOW, May A blockage across Smith Sound, referred to as an ice bridge, sharply defines the northern limit of the polynya. The southern boundary, which is characterized by pack ice in Baffin Bay, is more diffuse and depends on weather conditions and time of year. During late March and April the winter pack ice begins to dissipate and the polynya expands southward from Smith Sound, reaching a maximum water area of 80,000 km 2 in July. blockage, or ice bridge, across the narrow head of Smith Sound. Newly formed ice is then swept southward by currents and prevailing winds [Nutt, 1969; Barber et al., 2001]. Figure 1 is a satellite image of the NOW. [4] Between April and July 1998, concurrent advanced very high resolution radiometer (AVHRR) imagery and in situ skin temperatures from the Marine-Atmosphere Emitted Radiance Interferometer (M-AERI) were collected in the NOW [Kearns et al., 2000]. These data were assessed for concurrency and validity to form a database of 252 points representing 17 days and 41 satellite passes. Three temperature regimes were determined: seawater above freezing, transition zones of water and ice, and primarily sea ice. These regimes, which are determined by AVHRR Channel 4 criteria, require different algorithms for temperature estimates. For seawater above freezing, a specific Arctic SST algorithm was developed through a linear regression of AVHRR data against M-AERI temperatures. Areas of primarily sea ice use an established ice surface temperature (IST) algorithm [Key et al., 1997]. The transition waters, representative of marginal ice zones, use a linear relationship between the Arctic SST and IST algorithms. CASSTA processing involves an evaluation of the original Channel 4 value for each pixel in an image to determine which algorithm to use for that point. This is carried out for the entire scene to create a composite thermal image of the area. [5] This paper is presented in the following manner. Section 2 discusses the theory of retrieving surface temperatures through satellite thermal imagery and demonstrates the problem associated with applying existing SST algorithms to northerly latitudes. Section 3 describes the collection and preparation of the data used in this study. Section 4 presents the methodology employed in the development of CASSTA, while section 5 discusses the application of the algorithm to the NOW. And, finally, section 6 summarizes the results. 2. Theory 2.1. SST Calculation [6] The ocean emits radiation almost as a blackbody between 7 and 13 mm with emissivities ranging from 0.96 to 0.99 [Steffen, 1985; Masuda et al., 1988; Hanafin and Minnett, 2005]. Atmospheric constituents such as water vapor, CO 2,NO 2,CH 4, and aerosols subsequently absorb a portion of this energy before it can reach a spaceborne radiometer. In order to estimate the SST accurately from satellite information, a corrective algorithm is necessary to account for this absorption. Water vapor causes the most absorption of infrared (IR) energy [Barton, 1995] and is subsequently the main focus of adjustments in SST computations. However, the method of correcting for water vapor absorption in temperate latitudes is not applicable for northerly environments. [7] Satellite derived atmospheric correction algorithms generally take a multichannel approach in the form, SST ¼ at i þ b T i T j þ Constant; ð1þ 2of13

3 Table 1. Daytime McClain Coefficients for NOAA-12 SST Algorithm a Coefficient Value a b c d a T4 and T5 temperatures are in Kelvin, but the final SST estimate is in degrees Celsius by virtue of a conversion embedded in the a coefficient. where a and b are coefficients and T i and T j represent the brightness temperatures of different IR bands. In this case T i is used as the main estimator for temperature, while T j is used in conjunction with T i to determine the absorptive correction factor. The coefficients can be obtained in three ways: (1) the use of atmospheric profiles to determine and model the effects of atmospheric absorption of IR energy [Barton, 1985; Maul, 1983; Llewellyn-Jones et al., 1984]; (2) regression analysis of concurrent satellite and in situ data [McMillan and Crosby, 1984]; and (3) a combination where a regression analysis is used to refine models for atmospheric absorption [McClain, 1981; McClain et al., 1985; Kilpatrick et al., 2001]. Most algorithms also introduce an additional absorption factor to account for the increased path length that occurs with increasing sensor zenith angle. In the case of thermal data derived from AVHRR satellites, the correction algorithm is generally written as, SST ¼ a þ bt 4 þ cðt 4 T 5 Þþ d½ðt 4 T 5 Þðsec q 1ÞŠ; ð2þ where a, b, c and d are the coefficients, T 4 and T 5 represent the brightness temperatures for Channel 4 (11 mm) and Channel 5 (12 mm) respectively and q is the sensor zenith angle. AVHRR SST algorithms use the physical property that energy in the 12 mm regime undergoes more absorption due to atmospheric water vapor than the 11 mm regime. The temperature difference between these two wavelengths when measured at the satellite sensor is then used to calculate the IR absorption due to water vapor in the intervening atmosphere. This difference (AVHRR Channel 4 Channel 5) is indicated as T 4 T 5 in SST algorithms and may be shortened to T45. [8] McClain [McClain, 1981; McClain et al., 1985] is generally recognized as the pioneer of satellite-derived SST estimates, and the coefficients generated from his work are still used today. McClain empirically determined SST algorithm coefficients that differed from satellite to satellite and from day to night [McClain et al., 1985]. Although McClain s algorithm was developed for open ocean environments in temperate latitudes, it was initially utilized for radiometric images of the NOW in this study owing to the lack of an existing Arctic SST algorithm. National Oceanic and Atmosphere Administration Satellite 12 (NOAA-12) daytime McClain coefficients, as shown in Table 1, were used for this study in conjunction with equation (2). As illustrated in Figure 2, the McClain algorithm produced an average overestimate of 2.40 K with a standard deviation of 0.95 K when compared to concurrent M-AERI in situ data, which has an accuracy better than 0.1 K [Minnett et al., 2001]. [9] Algorithms developed for temperate waters overestimate SST values in Arctic waters. A robust SST retrieval system is the Pathfinder program, developed at the University of Miami [Kilpatrick et al., 2001]. Pathfinder is based on the nonlinear SST, or NLSST [Walton et al., 1998] where, SST ¼ a þ bt 4 þ cðt 4 T 5 ÞSST Guess þ d½ðt 4 T 5 Þðsec q 1ÞŠ; ð3þ in which a regressive estimate of the SST, or SST Guess,is used to minimize errors or biases in the absorption term. Pathfinder coefficients are determined through a regression analysis of an extensive database of coincidental buoy data and AVHRR imagery. Coefficients are available for NOAA-7, 9, 11, 14, 16 and 17 polar orbiting satellites for latitudes ranging from 40 S to 60 N. There are no coefficients outside these latitudes owing to a lack of match-ups between satellite radiometric imagery and surface truth data. [10] The Pathfinder algorithm differs from the NLSST in that the T45 term (T 4 -T 5 ) is divided into specific regimes. Diagnostics on earlier Pathfinder versions suggested that the atmospheric correction was significantly different for dry Figure 2. Satellite SST estimates calculated from a temperate ocean algorithm [McClain et al., 1985] are plotted against concurrent in situ data from the North Water Polynya. The resulting residuals show a significant positive bias with an average of 2.40 K. 3of13

4 Figure 3. Typical AVHRR NOAA-12 T45 values are shown for the NOW from March to July Land areas have been masked out for this sequence of images. By the standards of temperate SST algorithms these values indicate a moist tropical environment, which is clearly not the case for the dry Arctic climate. This highlights the reason why SST algorithms developed for temperate regions are not applicable in high latitudes. and moist atmospheres [Kilpatrick et al., 2001]. As a result of these observations, separate coefficients are estimated for dry regions (T45 < 0.7 K) and moist regions (T K). While the segregation of dry and moist atmospheres has improved the fidelity of the Pathfinder SST between 40 S and 60 N, these parameters do not apply to arctic regions. In contrast to the tropics, the mid and high latitudes show a progressively weaker association between T45 and absorption due to water vapor [Kumar et al., 2003]. Furthermore, analyses conducted by Kumar et al. [2003], Emery et al. [1994] and Minnett [1986] show that the T45 temperature is not a strong function of water vapor in high latitudes. This result was confirmed by the findings of previous studies in the retrieval of Arctic surface temperatures [Lindsay and Rothrock, 1993; Key and Haefliger, 1992] in which high T45 values were not indicative of atmospheric moisture. [11] High T45 values were observed in the NOW during this study. Figure 3 shows T45 in the NOW from March to July computed from NOAA-12 AVHRR data. Between March and May the T45 values generally fall between 1 and 2 K. As the ambient temperature increases above zero through June and into July, the average T45 decreases to approximately 0.7 K. These T45 values are typical for a moist equatorial region whose warmth and humidity are clearly not representative of dry polar conditions. Using equation (2) with McClain coefficients (Table 1) at a zenith angle of 0, the T45 absorption term adds 1.8 to 3.9 K to SST estimates of the NOW for T45 values between 0.7 and 1.5 K. These errors are amplified with increasing sensor zenith angle. [12] As the ambient temperature increases in the NOW during spring and summer, so does the amount of atmospheric water vapor [Key et al., 1994]. In temperate latitudes these increases would lead to an increase in T45, contrary to the measurements in this study that show a T45 decrease during this time period. The relationship between the Arctic environment and T45 is examined by Vincent [2006] and R. F. Vincent et al. (Arctic waters and marginal ice zones: 2. An investigation of Arctic atmospheric infrared absorption for AVHRR sea surface temperature estimates, submitted to Journal of Geophysical Research, 2008) (hereinafter Vincent et al., submitted manuscript, 2008). To summarize the work by Vincent [2006] and Vincent et al. (submitted manuscript, 2008), an analysis of IR absorption during the development of CASSTA demonstrated that there are two issues that must be addressed in the retrieval of surface temperatures. First, during the colder months areas of open water may be cloaked by ice fog (characterized by T45 > 2.0 K), which is difficult to detect by single channel analysis and renders the thermal picture deficient if not taken into consideration. The second issue is the clear sky absorption of wavelengths in the 12 mm (T5) regime, which is significantly higher in the Arctic than temperate regions. Observations suggest that ice crystals suspended in the atmosphere induce maximum absorption in the T5 regime, which is a function of ice crystal size shape and orientation, while T4 remains relatively unimpeded. Regardless of the cause, there is unexpected absorption of IR energy in the Arctic maritime environment Ice Surface Temperature (IST) Calculation [13] Since Arctic waters may also contain a mixture of ice and snow, any satellite temperature retrieval system must take this into consideration. One method of estimating surface temperature is to model satellite sensor brightness temperatures through a radiative transfer model [Key and Haefliger, 1992]. This technique has been used successfully for SST calculations [Minnett, 1990; Barton, 1985; Llewellyn-Jones et al., 1984] and is a common approach for IST retrieval. If sufficient in situ data are available, another approach is to relate satellite data to surface temperature observations through a regression model. [14] Key and Haefliger [1992] presented an IST algorithm that was applicable to the central Arctic ice pack using modeled AVHRR thermal data for NOAA-7, 8 and 9. The algorithm was based on a radiative transfer model of the atmosphere that utilized temperature and humidity profiles collected by a Russian ice camp in 1986 and Lindsay and Rothrock [1993] used this algorithm to perform a comprehensive analysis of the spatial and temporal variability of surface temperatures across the Arctic basin. Further substantiation and validation was provided by 4of13

5 Table 2. Key IST Coefficients for NOAA-12 a Estimated Temperature Coefficients (NOAA-12 AVHRR T4) a b c d <240 K to 260 K >260 K a Coefficients used are based on the initial T4 value. Conversion to Celsius is accomplished by subtracting from the calculated IST estimate. subsequent studies [Key et al., 1994; Yu et al., 1995; Lindsay and Rothrock, 1994; Massom and Comiso, 1994]. Subsequently, Key et al. [1997] published a refinement and extension of the original IST, including specific temperature regimes and NOAA-12 coefficients. The results of that study were incorporated into the Extended AVHRR Polar Pathfinder project, which produced surface temperature estimates for the Arctic [Wang and Key, 2005]. An extensive validation analysis of the algorithm using an annual cycle of surface measurements gave accuracies in the range of 0.3 to 2.1 K, with larger values attributable to the surface variability of the validation area [Key et al., 1997]. Using the standard satellite temperature retrieval format (equation (2)), the modeled coefficients are dependent on the satellite type and the estimated temperature of the ice/snow surface. The IST coefficients for NOAA-12 data are shown in Table 2. In the case of ice/snow temperature retrieval, factors other than water vapor absorption are taken into consideration with the T45 term. These factors include modeled directional snow emissivities and the spectral response functions of the satellite thermal channels [Key and Haefliger, 1992]. 3. Data Collection and Preparation 3.1. Data Collection [15] In 1998 the NOW Research Network brought together researchers from six countries to study and model the climatic and oceanographic mechanisms involved in the formation of the NOW. As part of the NOW project, the Royal Military College of Canada installed and operated a ground station at Canadian Forces Station Alert on the northern tip of Ellesmere Island that was capable of receiving AVHRR data from NOAA polar orbiting satellites. [16] NOAA AVHRR satellites continuously broadcast High Resolution Picture Transmission (HRPT) data that can be captured by receiving stations within line-of-sight. The NOAA satellites are in 830 to 870 km, circular, nearpolar, sun synchronous orbits. The orbital period is approximately 102 minutes, giving 14.1 orbits per day. Between 10 March and 1 August 1998, 1440 NOAA-12 images were downloaded, 317 of which were retained for subsequent analysis. Images were kept if the NOW was in the field-ofview and contained less than 20% cloud cover. Typically, the NOW was visible for seven consecutive orbits, before disappearing for the next seven orbits as the satellite tracked across Siberia. The close proximity of the region to the North Pole allowed for a unique data set. [17] The AVHRR has a cross-track scanning system designed with a mirror rotating at 360 revolutions per minute. For each scan line, the AVHRR takes 2048 samples per channel, spanning a viewing angle of ± 55 off nadir. The channels are digitized 10 bits deep and the HRPT data is transmitted to the ground station. The instantaneous fieldof-view of the sensor is 1.4 mrad, resulting in a spatial resolution of 1.1 km at nadir and degrading to approximately 8 km at the edge of the satellite swath. The NOAA-12 satellite, which was used for this study, measures emitted and reflected radiance in five spectral bands as indicated in Table 3. [18] The M-AERI is a shipborne IR radiometric interferometer that is capable of accurately measuring in situ sea surface skin temperatures. Mounted topside of the ship approximately 10 m from the surface, the emitted sea radiance enters the M-AERI aperture and is directed into a Fourier transform IR interferometer [Griffiths and de Haseth, 1986] where it is divided into two paths by a beam splitter. Oscillating mirrors reflect the light back to the beam splitter and give the two paths different lengths so that on recombination they generate time varying interference signals. The recombined rays are sent to dual detectors of indium antimonide (InSb) and mercury cadmium telluride (HgCdTe) that span the wavelength range from 3.3 to 20 mm. The time-dependent interference signal measured at the detectors is the Fourier transform of the spectrum of the incoming radiation [Griffiths and de Haseth, 1986; Minnett et al., 2001]. An average of each view is taken over 40 to 90 pairs of interferograms, corresponding to approximately 1.5 to 3 minutes [Kearns et al., 2000]. For a ship traveling at 10 kts this corresponds to an along-track integration of about 0.5 km. [19] The M-AERI has been used on research cruises in a wide range of conditions. The device has taken measurements at sea in temperature ranges from -20 to 35 C, and in wind speeds up to 17 m s 1. At high winds the risk of spray contamination on the mirrors causes the instrument to enter a safe mode, which is also the case in conditions of heavy rainfall [Minnett et al., 2001]. Calibration is maintained in the field using two internal blackbody calibration targets. The skin temperatures derived from the M-AERI spectra have residual uncertainties less than 0.1 K, and have been used to validate satellite-derived SSTs [Kearns et al. 2000; Noyes et al., 2006]. The comparison of M-AERI values and AVHRR-derived SSTs yields discrepancies of approximately 0.3 K [Minnett et al., 2001]. [20] The M-AERI was deployed in the NOW onboard the Canadian Coast Guard Ship Pierre Radisson, an icebreaker, from April to July Skin temperatures were recorded at approximately six-minute intervals when the device was operating. The M-AERI readings were subjected to quality Table 3. NOAA-12 Spectral Response Indicated by Channel and Wavelength a Channel Region Wavelength (mm) 1 Visible Visible/Reflected IR Emitted Near IR Emitted Far IR Emitted Far IR a Traditionally, Channel 4 is the main estimator for SST, while Channel 5 is used in conjunction with Channel 4 to determine IR losses due to water vapor in the atmosphere. Channel 3 may be useful for cloud determination owing to reflected IR energy, but this regime may be noisy and unreliable. 5of13

6 control protocols to delete spurious SST readings due to contaminants such as ship exhaust, sea spray and sunlight entering the detector. The ambient air temperature and the ship s GPS position were recorded concurrently with the surface temperature readings. [21] For the NOW data, the skin temperature, T o, was calculated from the 7.7 mm radiance, R o, at a fixed angle of the ocean and the atmosphere, R a, using the relationship, BT ð o Þ ¼ R o ½ð1 eþr a Š R h ; ð4þ e where B(T o ) refers to the Planck radiance emitted by a body at the temperature of the sea surface, e represents the surface emissivity and R h is the radiance emitted by the atmosphere between the instrument and the surface [Smith et al., 1996]. Here a value empirically derived from the M-AERI spectra of is used for e [Kearns et al., 2000]. In this formulation the skin temperature is relatively insensitive to variations in surface emissivity since any change in surface emission is partially compensated by the reflected sky radiance term, (1-e)R a in equation (4) [Kearns et al., 2000]. Such changes, resulting mainly from variations in the wind roughening of the sea surface, and changing emission angle caused by the ship s roll, are minimal [Hanafin and Minnett, 2005]. Since the M-AERI is close to the surface, R h is small and is estimated by a simple parameterization from radiative transfer simulations [Smith et al., 1996]. [22] The use of 7.7 mm for calculating the skin temperature provides a basis for acceptably accurate measurements of seawater that is mixed with ice in the marginal zones. At 7.7 mm the difference in the vertically pointed emissivity between water and ice is approximately (University of California, Santa Barbara, Emissivity Library, Moderate Resolution Imaging Spectrometer, modis/emis/html/water.html). This difference remains relatively constant with increasing viewing angle [Kislovskii, 1959]. The small emissive difference between water and ice is compensated by the reflected sky radiance term, (1-e)R a in equation (4) Data Preparation [23] The first step was to identify coincident satellite and in situ measurements. To reduce errors introduced by timespace variability [Minnett, 1991] data points were subjected to tight constraints. M-AERI and AVHRR readings were considered concurrent if taken within ±30 minutes and ±0.1 of latitude and longitude of one another [Schluessel et al., 1990]. The AVHRR images meeting the concurrency requirements were converted to calibrated brightness temperature and geocorrected using ENVI 4.0 remote sensing software. Geolocation was performed automatically using ground control points in the level 1b AVHRR file and validated against known geographic points. [24] The images were then examined for cloud using all five channels to identify acceptably clear areas in the vicinity of the M-AERI readings. Pixels closest to the GPS location of the recorded M-AERI temperatures were chosen as the data points. These initial criteria produced 585 data points; however, further restrictions were necessary owing to anomalous readings and the potential variability of the surface type. The following are the constraints that were subsequently applied to the data. [25] 1. Since the M-AERI measurements were taken in six-minute intervals, up to 10 readings could meet the concurrency requirements per satellite point. In order to capture the representative in situ skin temperature, the M- AERI measurements used for a ±30-minute time interval were required to fall within a 0.5 K range. This constraint restricted M-AERI readings to a homogeneous surface type during a satellite pass so that a meaningful comparison could be made with AVHRR radiometric data. [26] 2. To ensure that AVHRR brightness temperatures were representative of the surface temperature, a pixel had to be within 0.7 K of adjacent pixels to be considered valid. This is similar to the uniformity test conducted in the processing of Pathfinder global SST fields [Kilpatrick et al., 2001]. [27] 3. Satellite sensor zenith angles were limited to a maximum of ±45. At angles greater than this, the increased path length between atmosphere and sensor, in concert with a degradation of resolution for high zenith angles, could lead to anomalous readings. The emissivity of water is relatively insensitive to inclination until 45, at which point there is a sharp decrease in the value [Steffen, 1985]. This limitation is similar to the zenith angle test conducted in the processing of Pathfinder global SST fields. [28] 4. AVHRR points were excluded in which the T4 value was greater than the corresponding M-AERI measurement. Such an occurrence suggested an error in the measurements or anomalous atmospheric conditions that could not be resolved [Kumar et al., 2003; Key et al., 1997]. In the development of CASSTA, this generally occurred when the ship was stationary and could not take a representative sample of the AVHRR pixel. [29] 5. Data points in which T45 exceeded 2 K were discarded. A study of very high T45 values within the NOW, which persisted in the vicinity of apparent open water between March and May, indicated that these areas were regions of ice fog [Vincent, 2006; Vincent et al., submitted manuscript, 2008]. [30] Once the above constraints were applied to the 585 initial cloud-free samples, 252 points remained, representing 17 days and 41 satellite passes from 17 April to 21 July Methodology [31] Initially, the M-AERI skin temperatures were plotted against the concurrent calibrated AVHRR Channel 4 brightness temperatures. Three regions were identified as follows. [32] 1. The first region is seawater. Data above the freezing point of seawater at approximately 1.8 C appeared highly correlated, indicating a continuity of surface type. [33] 2. The second region is transition waters. In the transition zone between seawater and sea ice the points were less correlated owing to a mixture of different surface types. [34] 3. The final region is primarily ice. As the surface temperature decreased below 2 C, the data tended toward a higher correlation once again as the surface type became more homogeneous. [35] Initially identified in terms of the M-AERI temperatures, these regimes were subsequently classified by the 6of13

7 Table 4. Regression Results Using 190 Coincidental M-AERI and AVHRR Data Points for Areas of Seawater a Regression Variables Coefficients a b c d Standard Error (K) T4, T45, Zenith T4, T N/A T4, Zenith N/A T4 Only N/A N/A a Since the standard error was essentially the same for all regressions, the simplest form was used (T4 only). This data indicates that T45 is not a strong measure of IR absorption in the NOW. AVHRR Channel 4 (T4) values. This allowed the specific regimes to be categorized by the calibrated T4 brightness temperatures Seawater [36] For M-AERI determined SSTs greater than 1.8 C for a satellite pass, the corresponding AVHRR T4 values appeared well correlated with respect to the M-AERI data. Between 26 May and 21 July 1998 there were 190 data points in this regime, which was sufficient to conduct a regression analysis. The form of the regression equation was that of the SST algorithm used in temperate waters (equation (2)). The regression merely estimated the four constants in that equation. Results of the multiple linear regression analysis demonstrated that there was no significant relationship between SST and the T45 term for this data set. The coefficients for c and d were and respectively, yielding maximum corrections to SST no larger than 0.04 C, far smaller than the minimum resolvable change in temperature due to the digitization increment of Channel 4. Mathematically, the lack of correlation between T45 and SST is due to the large variation in T45 for the data set, which ranged from 0.22 to 1.74 K, with a mean of 1.07 K and a standard deviation of 0.37 K. The possible reasons for this large variation in T45 may be the result of the spectral properties of ice crystals suspended in the atmosphere [Arnott et al., 1995; Huang et al., 2004] and is discussed in detail by Vincent [2006] and Vincent et al. (submitted manuscript, 2008). [37] Multiple regressions that were carried out excluding the zenith angle term (d) and the basic T45 value (c) yielded results that were similar to the full multiple regression. Table 4 gives the regression analysis results for the SST. Owing to the trivial contribution of the c and d coefficients, the standard error was virtually identical for all regressions, so the simplest form was chosen. The regression using only the single variable, T4, produced a specific Arctic SST algorithm, SST Arctic ¼ 4: þ 1: T 4 273:15: where T 4 is the NOAA-12 Channel 4 brightness temperature in Kelvin and represents the conversion factor to degrees Celsius. [38] The Arctic SST is a linear equation that adds larger corrections to higher T4 values. For example, a T4 value of 2.2 C is corrected to 1.8 C (0.4 C increase) while a T4 value of 5 C is corrected to 5.5 C (0.5 C increase). The increase in the algorithm correction with rising SST may be a function of increasing atmospheric water vapor, and subsequent absorption of Channel 4 energy, through spring ð5þ and summer in the region [Key et al., 1994]. The Arctic SST implies that the lower SST limit of 1.8 C corresponds to a T4 value of K, or 2.20 C. In the Arctic SST regime (T4 > 2.20 C) there were 192 points, resulting in a mean deviation of 0.19 K from the M-AERI values and a standard deviation of 0.22 K of the residuals. [39] It should be noted that the a and b coefficients are similar for both the Arctic SST and Key IST. The main difference between the two algorithms is the inclusion of a T45 function in the Key algorithm primarily to account for the emissive properties of ice. The Key IST also contains a zenith angle term to account for increased path length and subsequent elevated IR absorption, whereas the Arctic SST uses the a and b coefficients to correct for absorption based on the T4 value alone Transition Waters [40] The transition waters consisted of T4 temperatures below 2.20 C as established by the lower limit of the Arctic SST. This resulted in 60 data points with T4 values ranging between 2.21 and 4.62 C. Standard regression analysis for determining an algorithm in this regime was not successful. This result was expected owing to the different physical properties of water and ice that make up the transition zone. Consequently, the algorithm for transition waters was developed through the combination of the Arctic SST and the Key IST algorithms. [41] The Key IST, Arctic SST and M-AERI values were plotted against AVHRR T4 temperatures for the seawatersea ice transition. Trend lines drawn through the data points showed a linear progression from the Arctic SST to the Key IST in the transition zone. It was determined that a Transition SST algorithm combining appropriately weighted Arctic SST and Key IST values was most suitable for this temperature regime. The decision criterion for this application was necessarily contingent on the T4 value since this is the only variable in the Arctic SST algorithm. The data indicated that a linear transition from the Arctic SST to Key IST over a T4 range of 2.2 to 4.2 C most aptly described the M-AERI data, where: SST Trans ¼ ðt 4 270:95Þð 0:5ÞIST Key þ ½ðT 4 268:95Þð0:5 Š: ð6þ ÞSST Arctic This algorithm, which will be referred to as the Transition SST, gives a linear progression of 0/100 to 100/0 percent mixture of Key IST/Arctic SST for T4 values between 2.2 C ( K) and 4.2 C ( K). For the 50 measurements in this temperature range the Transition SST resulted in a mean deviation of 0.14 K from the MAER-I values and a standard deviation of 0.16 K of the residuals. 7of13

8 Table 5. Mean Deviation From the MAER-I Data and the Standard Deviation of the Residuals for CASSTA, McClain SST, and Key IST for the Three Different Regimes a REGIME 1 Seawater (192 Points) REGIME 2 Transitional (50 Points) REGIME 3 Primarily Ice (10 Points) Overall (252 Points) Algorithm Mean Dev Std Dev Mean Dev Std Dev Mean Dev Std Dev Mean Dev Std Dev Mean of Residuals CASSTA McClain Key IST a Values are in K. CASSTA demonstrated a significant improvement, particularly in the transitional zone of ice and water. The mean of the residuals are also shown, which indicate CASSTA to be an unbiased estimator for the data set, while McClain and Key have significant positive biases Primarily Ice [43] In accordance with the Transition SST, the Key IST algorithm took full effect for T4 values less than 4.2 C. Although there were only 10 values in this regime, these showed good agreement with a mean deviation of 0.14 K from the MAER-I values and a standard deviation of 0.18 K of the residuals Composite Arctic Sea Surface Temperature Algorithm (CASSTA) [44] CASSTA utilizes the three temperature regimes to determine skin temperatures from NOAA-12 radiometric imagery. Using T4 as the decision criterion, CASSTA is based on the following conditions: (1) seawater (T4 > 2.2 C): arctic SST; (2) transition waters ( 2.2 T4 4.2 C): transition SST; and (3) primarily ice (T4< 4.2 C): key IST. CASSTA determines the Channel 4 value for each pixel in a calibrated AVHRR image and then applies the appropriate algorithm to create a composite thermal image. [45] A comparison of CASSTA and M-AERI values resulted in a standard deviation of 0.21 K. For the 252 M-AERI temperatures, which ranged from 2.44 to 4.41 C, the maximum magnitude of the difference was 0.49 K. The mean of the residuals is K, indicating that CASSTA is an unbiased estimator. 5. Discussion 5.1. Algorithm Comparison [46] Table 5 shows the statistical analysis for CASSTA, the McClain SST and the Key IST for each regime. Figure 4 is a graphic representation of the three different algorithms compared to M-AERI for the data set. Statistically, CASSTA outperformed the McClain SST and Key IST for the data set, which had mean deviations of 2.40 K and 1.30 K respectively, with corresponding standard deviations of 0.95 K and 0.68 K of the residuals. The McClain SST had a maximum residual of 4.67 K, compared to 2.96 K for the Key IST. Interestingly, the Key Figure 4. M-AERI ground truth data are compared to both CASSTA and McClain SST estimates. A significant gain in accuracy is evident with CASSTA, which closely follows the 1:1 line. It should be noted that the McClain and Key algorithms were not designed for the unique geophysical properties of the NOW and are only shown here to demonstrate the necessity of a different approach in determining surface temperatures in the region. 8of13

9 Figure 5. Three different algorithms are shown for the NOW, 10 May CASSTA indicates that the open water is approximately at the freezing point of seawater ( 1.8 C), which is a reasonable estimate for this time of year. In contrast, the Key IST and McClain SST give estimates of approximately 1.0 and 0.5 C, respectively. The warm area south of the ice bridge is an artifact of the algorithms caused by very high T45 values associated with ice fog. IST outperformed the McClain SST for the 192 data points in the seawater regime. This is due to the fact that the T45 value in the Key IST, which primarily accounts for emissivity differences between the channels for ice, leads to less of an overestimate than the McClain T45 value, which is used to account for atmospheric absorption. The mean of the residuals is 2.37 K for the McClain SST and 1.30 K for the Key IST, indicating that these algorithms are positively biased for this data set. While Table 5 and Figure 4 statistically demonstrate the superiority of CASSTA for the data set, a comparison of thermal images supplies a dramatic testament to the fidelity gained by the algorithm. Figure 5 shows three images of the NOW based on the McClain SST, Key IST and CASSTA. Only CASSTA demonstrates a realistic thermal picture of the region, which is a combination of ice and near freezing seawater at 1.8 C. The application of CASSTA to 181 images between 11 March and 27 July consistently produced superior results when compared to the original application of McClain coefficients to the available imagery Ice Fog [47] Analysis of March to mid May imagery revealed sections of apparently open water with T4 values of approximately 5 C, which were too cold to be seawater. For the most part, these sections coincided with T45 values in excess of 2 K. These regions were found to be patches of ice fog that were not indicative of the surface temperature [Vincent, 2006; Vincent et al., submitted manuscript, 2008]. In these areas of very high T45, the CASSTA algorithm uses the Key IST in accordance with the T4 value. Owing to the large T45 differential, the Key IST applies a positive adjustment of approximately 4 K, resulting in apparent warm surface temperatures. The addition of a mask to block out areas where T45 exceeds 2 K on CASSTA images is essential to accurately portray valid surface temperatures. Figure 5 illustrates the problem associated with ice fog and the consequence of not accounting for the phenomenon. All three thermal images show a warm area just south of the ice bridge. This is a region of T45 > 2 K, where T4 and T5 are approximately 9of13

10 Figure 6. The NOW is shown for 11 May A composite of AVHRR Channels 1, 2 and 3 is shown to the left for visual reference, while the CASSTA representation is to the right. Open water, marginal ice zones, and areas of mainly ice are clearly shown on the CASSTA image. A group of ice floes appear near Ellesmere Island south of the ice bridge. In fact, ice floes probably extend all the way to the ice bridge, but are not detected on the thermal or visual image owing to ice fog. A T45 mask (T45 > 2 K) has been applied to the CASSTA image as well as a land mask. 5 and 7 C respectively. The high temperatures observed on the thermal mappings are artifacts of the algorithms, with CASSTA using the Key IST exclusively for T4< 4.2 C. This is a result of the T45 term adding an excessive corrective value to the base T4 value associated with the ice fog. This phenomenon is studied in detail by Vincent [2006] and Vincent et al. (submitted manuscript, 2008) Marginal Ice Zones [48] A unique feature of CASSTA is the ability to bridge the gap between seawater and ice by showing marginal ice zones in which there is a combination of these constituents. The marginal ice zones in the CASSTA imagery for March through May appeared in logical and consistent locations, usually bordering areas of open water or showing up in leads. Figure 6 shows a visible image of the NOW for 11 May and compares it to the corresponding CASSTA representation. Areas of open water, marginal zones, ice and very cold ice are distinguished on the CASSTA image. Regions in which ice is mixed with seawater are shown as marginal ice on the temperature scale. The ice becomes colder in a coherent fashion outward from these marginal zones. Very cold ice is clearly illustrated north of the ice bridge, in Jones Sound and in the vicinity of Makinson Inlet, which is an ice plug that persists into July. This type of thermal mapping may be of assistance to higher-resolution remote sensing systems, such as RADARSAT, in determining the presence and type of ice. Figure 6 also illustrates the difficulty of detecting ice fog on visible imagery and the subsequent T45 mask applied in the CASSTA processing Solar Heating [49] In the late spring and during the summer, continuous daylight in the region leads to solar heating, particularly of stationary ice masses in the region. The CASSTA imagery for early June showed a diurnal cycle of solar heating north of the ice bridge, while July imagery demonstrated the same process for the Baffin Bay ice pack to the south. Solar heating is also apparent in the ice masses that surround the perimeter of the NOW. By June, CASSTA uses only the Arctic SST portion of the algorithm since the overall T4 value of the polynya is greater than 2.2 C. This means that T4 is the only variable in the temperature calculation. Analysis of the NOW imagery indicated that the Arctic SST gave a more reasonable estimate of ice temperature than the Key IST during the summer months since the absence of a T45 term helped to mitigate temperature inflation Summer Ice Detection [50] CASSTA allows good differentiation of ice and seawater during the warmer months since it uses the Arctic SST exclusively, which relies solely on the T4 value. As a consequence, ice floes are readily detected on the summer CASSTA images. If a standard IST algorithm is applied to the region, the same results are not achieved. Since T45 is higher for ice than seawater, the IST algorithm inflates the cooler ice signature so that it becomes difficult to differentiate from the surrounding seawater. Figure 7 illustrates 10 of 13

11 Figure 7. A Channel 1 depiction of the NOW and the corresponding CASSTA and Key IST images is shown for 01 July. The thermal images have been divided into three regimes: ice, marginal zones, and seawater. CASSTA shows a clear differentiation between these regimes. The Arctic SST portion of CASSTA is used exclusively in this scene since the T4 threshold is above 2.2 C. This allows the algorithm to differentiate more easily between surface types since there is no T45 correction to artificially inflate the thermal signature of ice, which has a higher T45 value than water. CASSTA ice differentiation compared to the Key IST algorithm for 01 July Cloud Detection [51] The presence of clouds is problematic for any space-based temperature retrieval system. This is of special concern in Arctic environments where ice and cloud signatures may be thermally and texturally similar. In this study, much of the NOW imagery was obscured by clouds. In many cases the clouds proved easy to detect, but there were cases of low-lying and thin cloud that evaded visual detection and corrupted the thermal image. The occurrence of ice fog was observed through T45 mapping, and is a type of surface cloud. In the summer months, thin haze can be difficult to detect and may subsequently lead to a signature that is either cooler, warmer or approximately the same as the seawater. In this study CASSTA had an advantage over other temperature algorithms for detecting clouds, particularly in the summer months. The same principle that allows ice differentiation applies for most cloud formations. The T45 signature of clouds is generally higher than the water surface depending on the concentration of water droplets and ice crystals within the structure. Since there is no T45 term used in CASSTA during the warmer months, only the T4 component of the cloud is used to determine the temperature. In most instances, the T4 value of cloud was colder than the surface and subsequently appeared on the CASSTA image as a dark region. When McClain coefficients were applied to the imagery, in which T45 is incorporated, areas of thin cloud commonly went undetected in the thermal mappings Arctic Water Temperature Retrieval Issues [52] While CASSTA gave good results for this data set, the Arctic maritime environment presents a number of obstacles in extracting surface temperatures from satellite thermal data. The presence of ice fog is persistent in the winter months and can be difficult to detect. During summer, the extended hours of daylight can lead to high temperatures in the top few millimeters of both ice and seawater that are not indicative of the bulk temperature. Additionally, the NOW was a cloudy region. Out of approximately 400 satellite passes that coincided with M-AERI data between 17 April and 21 July, only 41 were assessed as suitable for temperature retrieval. The detection of clouds proved difficult in some cases owing to the similarity of ice and cloud signatures. The reflected IR component offered by Channel 3 was helpful at times in the identification of cloud formations, but it often proved to be a noisy and unreliable source. These observations underline some of the issues that must be considered when thermally mapping areas of polar oceans. With respect to CASSTA, it should be noted that the algorithm was developed from data obtained in the relatively small confines of the NOW over a short time period and it is unknown if it will be applicable over a wider area. 6. Summary [53] The retrieval of surface temperature estimates in arctic waters and marginal ice zones using spaceborne radiometry has not been well documented to date owing to the paucity of match-ups of in situ and satellite data. Correction algorithms developed in temperate latitudes lead to positive biases in high latitudes due to an overestimation of IR absorption by water vapor in the atmosphere. This underscores the requirement for a specific SST algorithm for Arctic waters, particularly in light of the future dissolution of ice in the polar seas due to global warming. The algorithm presented in this paper accounts for the atmo- 11 of 13

12 spheric conditions of northerly latitudes and is adaptable to marginal ice zones in which a mixture of seawater and ice adds to the complexity of the thermal picture. [54] CASSTA was developed from coincident satellite and M-AERI shipborne data collected in the NOW between April and July of The algorithm considers three temperature regimes: seawater above freezing, transition zones of water and ice, and primarily ice. These regimes, which are determined by AVHRR Channel 4 calibrated brightness temperatures, require different calculations for temperature estimates. For seawater above freezing, a specific Arctic SST algorithm was produced through a linear regression of AVHRR against in situ data. The transition waters, representative of marginal ice zones, use a linear relationship between the Arctic SST and an established IST algorithm [Key et al., 1997]. Areas consisting mainly of ice use the IST algorithm. CASSTA determines the Channel 4 brightness temperature for each pixel in a calibrated AVHRR image and then applies the appropriate algorithm to create a composite thermal image. [55] The mean deviation between CASSTA and the in situ data, consisting of 252 coincidental points, was 0.17 K with a standard deviation of 0.21 K of the residuals. Additionally, the mean of the residuals was K, indicating that the algorithm is an unbiased estimator. This proved to be a significant improvement over surface temperature values using an established temperate SST Algorithm [McClain, 1981], which overestimated the same data set by an average of 2.40 K. [56] Application of CASSTA to the NOW imagery produced superior results when compared to existing SST algorithms for this region. CASSTA values for open water in March, April and early May generally varied between 2.5 and 1.5 C, which is a reasonable estimate since the open section of the polynya is a complex mixture of nearfreezing seawater and newly formed ice during this time of the year. Considerations when using CASSTA include ice fog, cloud identification and solar heating, which are factors that would be problematic for any space-based Arctic temperature retrieval system. Although preliminary studies indicate that the algorithm is applicable outside the polynya (Baffin Bay, Nares Strait, Lincoln Sea), these results have yet to be validated. [57] The Arctic is viewed as an environmentally sensitive region where the effects of changing global climate are first manifested. Polar orbiting satellites equipped with radiometers present ample opportunity to monitor the thermal characteristics of the region. Provided that accurate temperature retrieval algorithms are implemented, this type of observation may provide valuable information in understanding the complex interplay of ocean, ice and atmosphere. This paper adds to the ongoing research in surface temperature retrieval in Polar Regions [Comiso, 2003; Wang and Key, 2005], and specifically illustrates the problem with temperate SST estimates when applied to the Arctic. The CASSTA algorithm presents a solution based on in situ radiometric data. Continued investigation in this area of study should lead to algorithm refinement, CASSTA validation for areas outside the NOW, as well as coefficients for other satellite systems detecting IR energy in the 10 to 13 mm regime. [58] Acknowledgments. E. L. Key and J. A. Hanafin are thanked for their at-sea support during Legs 2 and 4 of the NOW cruise. PJM acknowledges support from National Science Foundation Office of Polar Programs (OPP ) and the National Aeronautics and Space Administration (NAG56577). References Arnott, W. P., Y. Y. Dong, and J. Hallett (1995), Extinction efficiency in the infrared (2 18 mm) of laboratory ice clouds: Observations of scattering minima in the Christiansen bands of ice, Appl. Optic., 34(3), Barber, D. G., R. F. Marsden, P. J. Minnett, G. Ingram, and L. Fortier (2001), Physical processes within the North Water (NOW) Polynya, Atmos. Ocean, 39, Barton, I. J. (1985), Transmission model and ground-truth investigation of satellite-derived sea surface temperatures, J. Clim. Appl. Meteorol., 24, Barton, I. J. (1995), Satellite-derived sea surface temperatures: Current status, J. Geophys. Res., 100, Comiso, J. C. (2003), Warming trends in the Arctic from clear sky satellite observations, J. Clim., 16(21), Emery, W. J., Y. Yu, G. A. Wick, P. Schluessel, and R. W. Reynolds (1994), Correcting infrared satellite estimates of sea surface temperature for atmospheric water vapor attenuation, J. Geophys. Res., 99, Griffiths, P. R., and J. A. de Haseth (1986), Fourier Transform Infrared Spectrometry, Chem. Anal., vol. 83, John Wiley, Hoboken, N.J. Hanafin, J. A., and P. J. Minnett (2005), Infrared-emissivity measurements of a wind-roughened sea surface, Appl. Opt., 44, Huang, H., P. Yang, H. Wei, B. A. Baum, Y. Hu, P. Antonelli, and S. A. Ackerman (2004), Inference of ice cloud properties from high spectral resolution infrared observations, IEEE Trans. Geosci. Remote Sens., 42(4), Kearns, E. J., J. A. Hanafin, R. H. Evans, P. J. Minnett, and O. B. Brown (2000), An independent assessment of Pathfinder AVHRR sea surface temperature accuracy using the Marine Atmosphere Emitted Radiance Interferometer (M-AERI), Bull. Am. Meteorol. Soc., 81, Key, J., and M. Haefliger (1992), Arctic ice surface temperature retrieval from AVHRR thermal channels, J. Geophys. Res., 97, Key, J., T. Maslanik, T. Papakytiakou, M. C. Serreze, and A. J. Schweiger (1994), On the validation of satellite-derived sea ice surface temperature, Arctic, 47(3), Key, J., J. B. Collins, C. Fowler, and R. S. Stone (1997), High-latitude surface temperature estimates from thermal satellite data, Remote Sens. Environ., 61, Kilpatrick, K. A., G. P. Podestá, and R. Evans (2001), Overview of the NOAA/NASA advanced very high resolution radiometer Pathfinder algorithm for sea surface temperature and associated matchup database, J. Geophys. Res., 106, Kislovskii, L. D. (1959), Optical characteristics of water and ice in the infrared and radiowave regions of the spectrum, Opt. Spectrosc. Engl. Transl., 8(3), Kumar, A., P. J. Minnett, G. Podestá, and R. H. Evans (2003), Error characteristics of the atmosphere correction algorithms used in retrieval of sea surface temperatures from infrared satellite measurements: Global and regional aspects, J. Atmos. Sci., 60, Lindsay, R. W., and D. A. Rothrock (1993), The calculation of surface temperature and albedo of Arctic sea ice from AVHRR, Ann. Glaciol., 17, Lindsay, R. W., and D. A. Rothrock (1994), Arctic sea ice surface temperature from AVHRR, J. Clim., 7(1), Llewellyn-Jones, D. T., P. J. Minnett, R. W. Saunders, and A. M. Zavody (1984), Satellite multichannel infrared measurements of sea surface temperatures of the northeast Atlantic Ocean using AVHRR/2, Q. J. R. Meteorol. Soc., 110, Massom, R., and J. C. Comiso (1994), The classification of Arctic sea ice types and the determination of surface temperatures using advanced very high resolution radiometer data, J. Geophys. Res., 99, Masuda, K., T. Takashima, and Y. Takayama (1988), Emissivity of pure and sea waters for the model sea surface in the infrared window region, Remote Sens. Environ., 24, Maul, G. A. (1983), Zenith angle effects in multichannel infrared sea surface remote sensing, Remote Sens. Environ., 13, McClain, E. P. (1981), Multiple atmospheric-window techniques for satellite derived sea-surface temperatures, in Oceanography from Space, edited by J. F. R. Gower, pp , Plenum, New York. McClain, E., W. Pichel, and C. Walton (1985), Comparative performance of AVHRR-based multichannel sea surface temperatures, J. Geophys. Res., 90, 11,587 11,601. McMillan, L. M., and D. S. Crosby (1984), Theory and validation of multiple window sea surface temperature technique, J. Geophys. Res., 89, of 13