Comparison between the Pathfinder Versions 5.0 and 4.1 Sea Surface Temperature Datasets: A Case Study for High Resolution

Transcription

1 1MARCH 2010 V Á ZQUEZ-CUERVO ET AL Comparison between the Pathfinder Versions 5.0 and 4.1 Sea Surface Temperature Datasets: A Case Study for High Resolution JORGE VÁZQUEZ-CUERVO AND EDWARD M. ARMSTRONG Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California KENNETH S. CASEY NOAA/National Oceanographic Data Center, Silver Spring, Maryland ROBERT EVANS AND KATHERINE KILPATRICK Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida (Manuscript received 12 September 2008, in final form 14 September 2009) ABSTRACT Two Pathfinder sea surface temperature (SST) datasets version 5.0 (V50) and version 4.1 (V41) were compared in two test areas: 1) the Gulf Stream (GS) between 358 and 438N, 758 and 608W and 2) the California coast (CC) between 308 and 458N, 1308 and 1208W. Using a nearest-neighbor approach, V50 data were regridded to the lower resolution V41 9-km data. The V50 and V41 versions were also independently compared with data from the World Ocean Database (WOD). Climatological monthly rms differences between V50 and V41 were calculated as well as seasonal differences between V50, V41, and the WOD. Maximum rms differences of 0.88C between the V50 and V41 were seen in June for the GS. In the CC maximum differences of 0.48C were seen in July. Significant seasonal trends were evident in rms differences between V41 and the WOD, with a maximum of 1.58C occurring in the GS in June and in the CC in July. No seasonal peaks occurred in the rms differences between V50 and the WOD. SST gradients were calculated using both V50 and V41 datasets. Maximum climatological SST gradients were seen in the June time frame for the GS and July for the CC, consistent with the largest rms differences compared to the WOD. Results indicate the importance of projects such as the Group for High-Resolution Sea Surface Temperature (GHRSST) and the creation of high-resolution SST datasets for resolving air sea interactions, specifically in areas of strong SST gradients. 1. Introduction Recent results have clearly shown the importance of developing high-resolution sea surface temperature (SST) datasets for coastal applications and modeling. In general, coupling between the oceans and atmospheres has been closely linked to SST gradients and fronts, indicating a need for high-resolution SSTs, specifically in the areas of large gradients associated with coastal regions. Thus, an accurate determination of SST gradients has become critical for determining the appropriate air sea coupling and the influence on ocean modeling (Samelson et al. 2006). Corresponding author address: Dr. Jorge Vázquez-Cuervo, Jet Propulsion Laboratory, M/S 300/323, Pasadena, CA jorge.vazquez@jpl.nasa.gov SST gradients induce changes in near-surface wind speed. Two mechanisms appear plausible for the intensification: 1) a change in the boundary layer physics and mixing of momentum within the boundary layer and 2) horizontal pressure gradients that are set up due to the temperature gradients across the fronts. Chelton et al. (2007) showed that, off the California coast (CC), the wind stress curl and divergence are linearly related to the crosswind and downwind components of the local SST gradient, respectively. Additionally, they showed that the summertime coupling was not well represented in the NOAA North American Mesoscale Model, most likely owing to the poor resolution of the model. Samelson et al. (2006) found a linear relationship between wind stress and the boundary layer depth. Song et al. (2006) found a relationship between surface wind and oceanic fronts, specifically in the area of the Gulf Stream (GS). DOI: /2009JCLI Ó 2010 American Meteorological Society

2 1048 J O U R N A L O F C L I M A T E VOLUME 23 Thus, a proper representation of SST gradients is critical for understanding the coupling of the ocean and atmosphere at high resolution on coastal and regional scales. An opportunity for comparing how SST gradients are improved with higher resolution can be determined by examining differences between the Pathfinder version 5.0 (V50) and version 4.1 (V41) datasets. These two versions provide a unique opportunity for comparing datasets where the primary difference is their spatial resolution. The Pathfinder program was jointly created by the National Aeronautics and Space Administration and the National Oceanic and Atmospheric Administration through the Earth Observing System (EOS) program office. The focus of the Pathfinder program was to determine how existing satellite-based datasets could be processed and used to study global change. The datasets were designed to be long time series data processed with stable calibration and community consensus algorithms to better assist the research community. The NOAA NASA Pathfinder SST program (PFSST) has been extremely successful in reprocessing efforts from the original version 1.0 to version 5.0 (Kilpatrick et al. 2001). Each reprocessing effort has led to higher quality SST. Improvements include the calculation of monthly versus yearly coefficients, as well as two sets of coefficients covering different water vapor regimes. Additionally, the PFSST V41 and V50 datasets represent the longest satellite-derived climate data record (CDR), currently spanning This 22-yr record presents a unique opportunity for comparing climatological means across a variety of parameters, including SST gradients (Mesias et al. 2007). Other than spatial resolution, differences between V50 and V41 include an improved land mask and reference field and the enhanced use of sea ice information in quality flag determination. Motivation for comparison of SST gradients using V50 and V41 datasets are summarized below. Essentially V50 and V41 datasets were processed using the same algorithm, thus differences could only be attributable to the differing land mask, reference field, quality flag assignment based on sea ice information, or higher resolution. Since the test areas defined in this study, the Gulf Stream and off the California coast, were essentially far enough offshore to not be affected by the land mask and in midlatitudes, where sea ice is not an issue, differences could only be attributable to quality flag assignment, reference field, or higher resolution. An example of SSTs using V50, V41, and the corresponding difference map are shown in Figs. 1a 1c for an area off the U. S. East Coast. Over an area this large only subtle differences can be seen for the month of June However, the map in Fig. 1c clearly shows large differences up to 28C that are aligned with frontal boundaries associated with the Gulf Stream region. Figures 2a and 2b show a smaller area for the region off the Florida Keys. Figure 2a shows the SSTs for V50 for June 2000, while Fig. 2b shows the SSTs for V41. Clearly visible on these scales are differences associated with both higher resolution of the SST map and the improved land mask of V50. Specific areas of colder water are completely missing in the V41 SSTs. In this example, it is easy to see that the differences are due to both resolution and the improved landmask. The focus of the paper will be to determine specifically whether the difference in resolution between V50 and V41 leads to quantitative improvements in the determination of SSTs in coastal areas and whether those improvements are associated with changes in SST gradients. The paper is divided into six sections, including the introduction. Section 2 of the paper gives an overview of the Pathfinder algorithm. Section 3 will focus on the seasonal differences between V50 and V41, specifically along the western and eastern coasts of the United States. Section 4 presents the results from a comparison of V50 and V41 with data from the World Ocean Database (WOD) (available online at gov/oc5/select/dbsearch/dbsearch.html). Section 5 presents the results comparing SST gradients calculated from V50 and V41 and their consistency with results presented in sections 3 and 4. Section 6 summarizes and concludes the paper. An appendix is added with a simple test that further confirms that the observed rms seasonal trends are due to resolution and are associated with areas of large SST gradients. 2. Pathfinder algorithm development For a complete detailed description on the processing of the PFSST V41 time series, see Kilpatrick et al. (2001). Briefly, the PFSST V41 SST retrieval algorithm is SST 5 a 1 1 a 2 T 4 1 a 3 (T 4 T 5 )T surf 1 a 4 ( secu 1)(T 4 T 5 ), where a 1, a 2, a 3, and a 4 are coefficients based on a least squares fit to in situ data andt 4 and T 5 are the brightness temperatures in channels 4 and 5, corresponding to center wavelengths of ;11 and ;12 mm, respectively; u is the satellite scan angle and T surf is a first-guess sea surface temperature field, in this case supplied from the Reynolds and Smith (1994) Optimally Interpolated Sea Surface Temperature version 1 (OISSTv1) analysis. In the PFSST V41, coefficients are derived on a monthly basis by fitting a running mean to five months of

3 1MARCH 2010 V Á ZQUEZ-CUERVO ET AL FIG. 1. June 2000 temperatures for (a) V50 and (b) V41 and (c) difference.

4 1050 J O U R N A L O F C L I M A T E VOLUME 23 FIG. 2. As in Figs. 1a,b but for a smaller area, the Straits of Florida. satellite in situ matchup data centered on the month in question. Separate coefficients are derived for low and intermediate to high water vapor burdens using the split-window channel difference as a proxy. Earlier forms of the algorithm used yearly coefficients. The transition to monthly coefficients took place to reduce biases associated with seasonal trends. Results still indicate that biases remain owing to volcanic eruptions, such as Mt. Pinatubo in 1986, and in areas of high aerosol concentrations, such as off the African coast where Saharan dust storms are prevalent (Vázquez- Cuervo et al. 2004). Because of the use of in situ buoy data to determine regression forms the PFSST calculates a satellite skin temperature that is referenced to a bulk temperature. The PFSST V50 differs from V41 in several significant ways. In addition to the increased spatial resolution of 4 versus 9 km, enhanced ice and land masks have been applied. An improved version of the first-guess and reference fields is also used, moving from OISSTv1 to OISSTv2 (Reynolds et al. 2002). The new land mask is based on a 1-km resolution dataset derived from the U.S. Geological Survey (USGS) Land Processes Distributed Active Archive Center (visit lpdaac/products/modis_product_table/land_cover/yearly_ l3_global_1km/v5/terra for more information). Motivation for the land mask improvement was based on the study of near-coast ecosystems such as coral reefs. Information on sea ice is derived from a weekly composite at one-quarter degree of daily Special Sensor Microwave Imager (SSM/I) ice fields and is used in V50 to help exclude ice-impacted pixels from the three-week internal reference check. However, differences between V50 and V41 in areas, apart from the high latitudes and within a few kilometers from land, should essentially be due to resolution. This can best be summarized by the following: d d The first-guess temperature estimate has an effect on the final temperature but a weak one (for more information, visit rrsl/pathfinder/processing/proc_index.html#). The percentage effect will be larger at low temperatures, but the absolute impact is relatively small. The largest effect of the first guess is at high temperatures. Thus, the first-guess field would have minimal, although not necessarily zero, impacts in such areas as the Gulf Stream and off the California coast. In both V50 and V41, the cloud-clearing algorithm was applied in a similar manner. For cloud clearing purposes, the assignment of quality flags and implementation of the tree algorithm are essentially the same

5 1MARCH 2010 V Á ZQUEZ-CUERVO ET AL (a more detailed description of these procedures is available online at rrsl/pathfinder/processing/proc_index.html#). The application of the tree algorithm is a quality level of 0 7, where 7 is the best. All level 2 (swath) pixels that fall onto the fixed latitude longitude output grid are ordered by quality value. The pixels with the largest available quality level are then averaged and the remaining pixels are discarded. The approach for assigning quality flags is the same for both V50 and V41. The only difference (because of resolution) is the number of pixels available that fall into the output bin. Since the global area coverage (GAC) pixel is constructed on board the Advanced Very High Resolution Radiometer (AVHRR) from four adjacent samples (a fifth is discarded and the following two lines are discarded), a 4-km GAC sample is really a43 1 km (at nadir) average from a 5 pixel 3 3 scan line box. The output grid is a predefined equal-area matrix at 4.63 or 9.28 km. Pixel replication is used to translate from an equal-area matrix to the final equalangle grid. The end result is that a 9-km grid cell is more likely to contain an AVHRR observation and consists of an average versus the nominal 4 km, which likely contains one sample. Thus, a 9.28-km bin is more likely to consist of averages of an average than the 4.62-km bin. The importance of this procedure in going from V41 to V50 is clearly demonstrated in the appendix. On an equal-angle grid, the V41 contains gridded files of ( ) pixels, whereas V50 contains ( ) pixels. Several other differences exist in the file structure. In V50 the parameters, SST, quality flags, and number of observations per bin are contained in separate files. Additionally, V50 comes in the hierarchical data format scientific data set (HDF SDS) format, whereas in V41 the data was in the HDF raster format (more information on the NOAA/NODC PFSST V50 dataset can be found online at noaa.gov/). 3. Seasonal rms differences between V50 and V41 To initially examine the differences between V50 and V41 root-mean-square differences between the two datasets were calculated for the east and west Coasts of North America. Based on the results, two test areas were defined to identify possible reasons for the seasonal trends. The Gulf Stream was further specified by a latitude, longitude box defined as N, W and the California Coast (CC) was defined in a similar manner as N, W. In section 4 of this paper, the area of the GS and the CC are used as test beds for comparisons of SST gradients from V50 and V41. Data for V41 and V50 were extracted from 1985 to 1999 to calculate the rms climatological differences and gradients. Only the highest quality flag of 7 was used for both versions. A simple nearest-neighbor pixel approach, because of two reasons, was used to change from a V50 grid to the lower resolution V41 grid. First, missing data owing to cloud cover makes bilinear interpolation problematic and, second, the nearest-neighbor approach preserves the original values associated with the V50 dataset. For datasets that are equal multiples of each other (V50 is a grid and V41 is a grid), the algorithm simply preserves the oddnumbered row and column starting with column 1 and row 1 and then column 3 and row 3 and so forth. Thus, the middle row and column of the 9-km grid is preserved in the 4-km grid, starting with the first row and column. Thus, differences between V50 and V41 on identical grids, in areas such as the Gulf Stream and the California coast, must be due to resolution and the observation of associated dynamics, different reference fields, or cloud masking. Base on application of similar cloud masking, along with sensitivity tests using different reference fields, significant differences are primarily due to resolution with only minor differences due to possible cloud masking and/or the reference field. However, it is important to acknowledge that issues of resolution, cloud masking, and reference fields are interrelated and not easily decoupled. For example, the higher resolution of the V50 data increases the probability that a particular bin will be flagged as cloud. This is simply due to fewer SST values found in a 4-km bin than a 9-km bin. The chances of there being a cloud-free pixel increases with bin size. Because the results are focused on seasonal differences, V50 and V41 monthly data were used. Monthly maps also mitigated the problem of missing data due to cloud cover. Both spatial and temporal climatological averages in the GS and CC were calculated, with seasonal maps of rms differences of V50 2 V41 shown in Fig. 3 and monthly climatological rms differences shown in Fig. 4. The spatial maps of the rms differences for the four seasons are shown in Figs. 3a 3d. The mean global rms difference was 0.028C. This strong similarity is expected over most of the ocean where gradients are weak. However, much larger rms differences (.0.58C) are seen off both the western and eastern coasts of the United States, including the areas of the GS and the CC. Further inspection showed that seasonal differences are evident for both the GS and the CC. In the GS the largest rms differences are clearly seen in the spring to summer time frame, whereas for the CC the largest differences are seen in the summer. In both the GS and the CC minimum differences are seen in both fall and winter. Although

6 1052 J O U R N A L O F C L I M A T E VOLUME 23 FIG. 3. Rms differences as defined by V50 2 V41 for (a) winter (Jan Mar), (b) spring (Apr Jun), (c) summer (Jul Sep), and (d) fall (Oct Dec). Color scale ranges from 08C (purple) to 1.58C (red). other areas of large rms differences exist, the GS and the CC are representative of western boundary currents and currents along the eastern boundaries that are associated with seasonal upwelling events. Additionally, stripes are clearly seen in the areas of high rms differences. This striping is due to an artifact of the 4-km gridding, whereby the underlying GAC data does not map identically onto the grid. Further complicating this is that the use of the km grid scale has an unfortunate effect of introducing a discontinuity at 18 km. This will be fixed in the new 6.0 version of Pathfinder by going to a grid of , which will allow for an exact mapping of the GAC data. Various alternative interpolation techniques were investigated but only served to move the problem, not fix it. Figures 4a and 4b show the rms climatological monthly differences for both the GS and CC. In the GS (Fig. 4a) largest differences are seen in the May June time frame, while in the CC (Fig. 4b) largest rms differences are seen in July. Biases between V50 and V41 are not plotted because they were essentially zero. This is expected since any systematic biases in V50 and V41 would be common to both datasets and thus removed in calculating the differences. The next step was to compare V50 and V41 separately against an in situ SST database. 4. Comparisons with the World Ocean Database Although Figs. 3 and 4 show the temporal and spatial differences between V50 and V41, they do not give any independent assessment of which dataset is performing better. To perform this assessment, in situ SST data from the World Ocean Database version 2005 FIG. 4. Rms monthly climatological differences V50 2 V41 for both GS (triangles) and CC (diamonds) areas.

7 1MARCH 2010 V Á ZQUEZ-CUERVO ET AL FIG. 5. Rms differences for (a) V41 2 WOD and (b) V50 2 WOD. White indicates 18 bins where no WOD existed. (WOD2005; available online at gov/oc5/wod05/pr_wod05.html) was used to calculate rms differences between the two satellite products and the WOD2005. Data were available from 1985 through the end of 2003, although only data from 1985 to 1999 was used because of the time period of V41. The WOD2005 contains a depth flag that allows one to compare V50 and V41 data with data closest to the surface and minimize any potential differences between skin and bulk temperatures. Thus, in the WOD comparisons with V50 and V41, the most conservative estimates of the WOD at depth 0 m (surface) and with the highest quality flag were chosen. Data were binned within a 18 box, which allows for reasonable coverage in such areas as the CC and GS. This was done by first collocating the closest SST pixel to the in situ data and then averaging all collocated pixels in the 18 box. Since V50 and V41 have identical algorithms and near-identical processing chains, one can assume that any skin 2 bulk temperature differences are inherent to both products. Therefore, any differences between V50 and V41 and WOD must be due to resolution (collocation based on nearest satellite SST pixel to WOD in situ data) and not because of measurements of skin versus bulk temperatures. Additionally, because the PFSST is based on a skin measurement tuned toward a bulk temperature (Kilpatrick et al. 2001), comparisons with in situ measurements at the surface should closely reflect the depth of both PFSST and the in situ measurement. The analysis was also recalculated without the use of XBT data, which could bias the statistics (R. Reynolds 2008, personal communication). Results without the XBT data showed very little difference in the CC, indicating the stability of the rms and bias calculations in the CC. However, in the GS the recalculation without the XBT data left insufficient collocations to get meaningful or statistically significant results, indicative that a dominant source of surface temperature data in the GS is from XBT data. The stability of the results in the CC, though, gives confidence that inclusion of the XBT data in the GS does not change the conclusions. Additionally, to avoid possible effects owing to diurnal warming, only nighttime data for V50 and V41 were used. Collocation was based on a nearest-neighbor approach in space and within the 24-h time period of the day defined by the descending pass. Thus daily, instead of monthly, nighttime fields were used for the WOD2005 comparisons. However, only cloud-free pixels contained within the V41 or V50 data were used. If the closest SST pixel to the WOD2005 observation was flagged as cloud, it was not used. The next V50 V41 nighttime map was then examined for possible collocation with the WOD2005. Thus, no collocations with the WOD2005, beyond the 24-h period for a given V50 map, also V41, were allowed. The space window for defining collocation in the V41 data (9 versus 4 km) was twice as large as for V50. It is clear that in major parts of the oceans, based on Fig. 5, no data are available within the 18 bin.

8 1054 J O U R N A L O F C L I M A T E VOLUME 23 FIG. 6. Rms monthly climatological differences in the GS for V41 2 WOD (triangles) and V50 2 WOD (diamonds). Figures 5a and 5b show the mean rms differences for V41 2 WOD and V50 2 WOD. Plots were generated by binning all differences in monthly boxes. Regions designated as white simply indicate areas where no matchups were defined. Based on Fig. 5 the WOD does not have the inherent coverage of either the V50 or V41 datasets. In some areas away from the coast no collocations occurred within the 18 bin. However, the purpose was not to calculate gradients but to determine statistically, based on a nearest-neighbor collocation to the WOD, which of the datasets, V50 or V41, was performing better. These results would then be examined for consistency with the V50 2 V41 differences. Clearly visible in Fig. 5a in the V41 2 WOD differences are rms values of greater than one degree in both the GS and CC. In contrast, Fig. 5b shows that rms differences as defined by V50 2 WOD are significantly smaller in both the GS and the CC; V50 2 WOD differences in the GS and CC are almost entirely in the 0.58C range, consistent with the historical analysis of Pathfinder data (Kilpatrick et al. 2001). To examine the temporal differences, monthly bias and rms differences were calculated in the GS and CC. Seasonal averages were then calculated based on the monthly maps. Figures 6 and 7 show the time series for the rms climatological differences as defined by V41 2 WOD and V50 2 WOD for regions of the GS and the CC. Figure 6 clearly shows a peak of 1.58C in the V41 2 WOD rms differences for June that is not visible in the V50 data. The June maximum is consistent with the maximum differences seen in V50 2 V41 (see Fig. 4a). Rms values for V50 2 WOD (Fig. 6) are consistently between 0.58 and 0.68C for the entire year, and there is no peak in the V50 rms values (Fig. 6) in this June time frame. Figure 7 is the same as Fig. 6 except in the CC; a maximum is seen in July in the V41 and V50 but with a value of approximately 1.58C for V41 and 1.08C for V50. These results are also consistent with the largest rms values found for V41 2 V50 in the CC (see Fig. 4). Figure 8 shows the bias in the CC. It must be pointed out that biases as defined by V41 2 WOD and V50 2 WOD, unlike V50 2 V41, were not identically equal to zero. Warm biases as defined by V41 2 WOD in the CC are greater than 0.58C. This is consistent with the lower resolution V41 dataset not fully resolving the cold SSTs associated with the CC summertime upwelling. The large RMS differences in V50 and V41 seen in areas such as the CC and GS are indicative of a possible relationship to the SST gradients found in both regions. The GS, as a western boundary current, is associated with large gradients due to changes in the intensity of the Gulf Stream, whereas the CC is dominated by seasonal changes in coastal upwelling. 5. Gradient calculation Although the differences between V50 and V41 are consistent with those seen in V50 2 WOD and V41 2 WOD, the question remains as to what is the primary cause of the spatial and temporal variability associated with the V50 and V41 differences. Because both the CC and GS are known regions of significant SST variability due to upwelling and the dynamics of western boundary currents, further investigation of the possible effects of strong and weak SST gradients (on monthly scales) was performed. Because gaps remain in the data, even at monthly time scales, a very simple finite difference scheme was applied to determine the SST gradients. For a given SST pixel (i, j) the gradient was calculated such that SST X (i, j) 5 FIG. 7. As in Fig. 6 but in the CC. [SST(i 1 1, j) SST(i 1, j)] 2D X

9 1MARCH 2010 V Á ZQUEZ-CUERVO ET AL FIG. 8. Monthly-mean climatological biases in the CC between V41 2 WOD (triangles) and V50 2 WOD (diamonds). FIG. 9. Monthly climatological gradients in the GS for V41 (triangles) and V50 (diamonds). [SST(i, j 1 1) SST(i, j 1)] SST Y (i, j) 5, 2D Y where SST X and SST Y are the gradients in the x and y directions as defined by longitude and latitude coordinates, respectively, and D X and D Y are the pixel resolutions in kilometers. The gradient was calculated only if all neighboring pixels were cloud free. For V41 D X and D Y are equivalent to 9.28 km, while for V50 they are equivalent to 4.63 km, SST AMP 5 SQRT(SST 2 X 1 SST2 Y ) for V41 and V50. Figure 9 shows the amplitude of the gradients in the GS region as calculated using V41 and V50. Maxima are seen in the June frame, consistent with maximum differences V41 V50, also V41 2 WOD and V50 2 WOD. However, maximum gradients in June are almost twice as large for V50 as those using V41. Figures 10a and 10b show the spatial patterns of the SST gradients for June for V50 and V41 in the GS. In the CC (Fig. 11) maximum gradients are seen in the July time frame, consistent with earlier results showing when the largest differences are seen along the CC. Clearly visible in the spatial patterns (Figs. 12a and 12b) are the increased magnitudes of the gradients for the V50 as well as gradients in the CC in the V50 not identifiable in the V Discussion and summary As stated previously, motivation for this work comes from earlier studies that have identified a clear relationship between SST gradients and the coupling between the ocean and the atmosphere. Thus, high-resolution SST datasets in coastal and/or regional areas that appropriately define these gradients are necessary. Results indicate that near areas of high SST variability, specifically in areas of western boundary currents and eastern boundary upwelling regions, significant differences can occur between SST datasets depending on spatial resolutions. Two test areas were chosen, the GS and the CC, to examine possible relationships between two AVHRR Pathfinder SST datasets when large variations occur, defined by V50 2 V41 rms differences and large values in SST gradients. Although there are also large rms differences in the high polar latitudes, a complete analysis of those regions is beyond the scope of this paper and needs to be the focus of future research. Seasonal differences found between V50 and V41 are consistent with those defined by V50 2 WOD and V41 2 WOD. In the CC region largest rms values are seen in the mid-late summertime, consistent with periods of maximum upwelling along the California coast (Chelton et al. 2007). In both V50 and V41 datasets maxima in SST gradients in the CC are observed in the July time frame; however, V50 gradients are larger than for V41. In the GS, maximum gradients, as defined by seasonal means, are found in June. However, similar to the CC comparison, gradients calculated using V50 are almost twice as large as those calculated using V41. Lower rms values between V50 and WOD as compared to V41 2 WOD indicate that the larger V50 gradient values are dominated by the gradient signal and not additional noise in the V50 dataset. Furthermore, in the CC region biases between V50 and WOD are also close to zero but V41 2 WOD are warmer during the summer coastal upwelling season. The warmer bias in the V41 2 WOD is indicative that V41 is not resolving the cooler SST pixels associated with the seasonal upwelling events. To simulate the possible effect that lower resolution datasets can have on the calculation of SST gradients, data from the ship-based Marine-Atmospheric Emitted Radiance Interferometer (M-AERI) were used to model a SST front in very high spatial resolution across the

10 1056 J O U R N A L O F C L I M A T E VOLUME 23 FIG. 10. Climatological gradients in the GS for June: (a) V41 and (b) V50. Gulf Stream. In this case, data from the June 2003 Brown_C1 cruise were extracted. Figure 13 shows the ship track overlaid on a Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua SST image for the same period of time with the north wall of the Gulf Stream clearly visible. Figure 14 shows the commensurate SST taken from the M-AERI instrument identifying the jump and drop of the SST as the ship crosses the north wall of the Gulf Stream. These data were used as a realistic profile of an actual crossing of the north wall of the Gulf Stream. Figure 15, using different colors for different spatial resolutions, shows the significant drop in the magnitude of SST gradients as the spatial resolution is decreased. Gradients were calculated using the finite difference approach with increasing window size (resolution). Not only does the magnitude of the gradient decrease significantly, but also the width over which the gradient occurs increases. The decrease of maximum gradients by 50% for each color is consistent with the incremental reduction in resolution by successive subsampling. This characteristic is also clearly seen in Fig. 9, which shows a 50% decrease in the gradients between the V50 and V41. Gradients of less than 0.18C/km are typically reported for the Gulf Stream from satellite data, yet the M-AERI data, at the original resolutions, show gradients equivalent to more than 0.48C/km. These results clearly indicate that, to properly detect strong gradients, high-resolution SST datasets are necessary. In the case of V50 and V41, significant differences between the two are seen in areas of large SST gradients. Seasonal difference maps, as well as comparisons with data from the WOD, indicate that these differences are strongly associated with periods of large SST gradients. The investigation has shown the effects of high-resolution SST datasets on improving the determination of strong SST gradients in dynamic coastal ocean and western boundary current regions. The V50 gradients are typically 50% greater. Comparisons with FIG. 11. As in Fig. 9 but in the CC.

11 1MARCH 2010 V Á ZQUEZ-CUERVO ET AL FIG. 12. As in Fig. 10 but in the CC for July. data from the World Ocean Database indicate the larger gradients cannot be attributed to more noise in the V50 data. This result confirms that high-resolution SST products, including level-4 blended products, derived from projects such as the Group for High-Resolution Sea Surface Temperature (GHRSST) project (Donlon et al. 2007) are needed if these satellite-derived SSTs are to be applied successfully in coastal and regional studies, numerical weather forecasting, and data assimilation. Furthermore, accurate, comprehensive (long time series), cloud-free, and high-resolution SST products will represent a significant step toward improving the FIG. 13. Ship track for the June 2003 Brown_C1 cruise. FIG. 14. M-AERI SST data from the June 2003 Brown_C1 cruise.

12 1058 J O U R N A L O F C L I M A T E VOLUME 23 FIG. 15. M-AERI SST-derived gradients from the June 2003 Brown_C1 cruise. Colors indicate gradients calculated at different resolutions. Black line and triangles are gradients at the original resolution of the data. Purple, blue, and yellow lines are gradients calculated at incrementally (½) lower resolution. modeling of interactions between the oceans and atmosphere. Acknowledgments. The work was carried out under contract with the National Aeronautics and Space Administration at the Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California. Data was provided through the Physical Oceanography Distributed Active Archive Center (PO.DAAC). Pathfinder data can be retrieved online (available at ftp://data. nodc.noaa.gov/pub/data.nodc/pathfinder/version5.0/); data is available online from 1985 to the present through the ESIP POET interface (at poet or through the FTP site at ftp://podaac.jpl.nasa. gov). The authors thank members of the Oceanography Group at the Jet Propulsion Laboratory for their advice on the manuscript. We thank Peter Minnett, at the University of Miami, for providing the M-AERI data. The authors would also like to thank two reviewers for their very helpful comments and significantly improving the manuscript. APPENDIX V50 Rms Differences To further confirm that the seasonal rms differences seen in the GS and CC between V50 and V41 can be explained by resolution, two simple tests were performed. The first test used only the V50 data, whereas the second test applied different quality flags to examine the possibility of possible cloud masking as a source of the rms differences. The V50 4-km data (only pixels with a quality flag 7) were averaged into 9-km bins that replicated the V41 9-km grids. The averaged V50 9 km grid was then expanded to the V50 4-km grid by applying a simple pixel FIG. A1. Climatological rms differences between V50 at 9 and 4 km in the GS (triangles) and the CC (diamonds). replication. Thus, the only difference between the two 4-km datasets was the averaging of pixels performed in changing from the V50 4-km grid to the 9-km grid. Any differences must, by default, be due solely to this averaging. The purpose was to determine whether seasonal trends rms differences V50 2 V41 could be replicated by using only V50 data. If the trends could be replicated, then the dominant cause of the differences between V50 and V41 must be the resolution and inherent averaging used in the V41 data. Rms seasonal differences were calculated for the GS and CC regions. Figures A1a,b show the results for both the GS and the CC. In both cases the maxima and patterns are consistent with those seen in Fig. 4. The actual values are different, but this is expected since averaging, binning, and quality checks in the PFSST occur at the level of the GAC data. The conclusion is simply that the averaging, regardless of landmask and first-guess field, in going from the V50 to the V41 grid is sufficient to reproduce the seasonal rms trends seen in Fig. 4. A second test was performed to determine how the usage of quality flags in the V50 and V41 was affecting the rms differences. The V50 2 V41 rms differences were recalculated, but after applying a quality flag of 4 (instead of 7) and higher. The results did not change the month of the maxima of the rms differences in the CC and GS. Rms seasonal differences increased uniformly by about 0.18C, consistent with an increase of noise (cloudy pixels) in the data due to the use of lower quality flags. However, the lower quality flags did not change the overall seasonal trends, indicating that, at least for the higher quality flags, the trends were robust. Both these tests confirm that the seasonal rms differences observed in the CC and GS, and their respective maxima in June and July, are driven primarily by changes in resolution and thus consistent with the larger SST gradients observed during those periods of time.

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