A REPROCESSING FOR CLIMATE OF SEA SURFACE TEMPERATURE FROM THE ALONG-TRACK SCANNING RADIOMETERS

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1 A REPROCESSING FOR CLIMATE OF SEA SURFACE TEMPERATURE FROM THE ALONG-TRACK SCANNING RADIOMETERS Owen Embury 1, Chris Merchant 1, David Berry 2, Elizabeth Kent 2 1) University of Edinburgh, West Mains Road, Edinburgh, UK 2) National Oceanography Centre, European Way, Southampton, UK Abstract An 18 year dataset of climate-quality Sea Surface Temperature (SST) has been produced from Along Track Scanning Radiometer (ATSR) data by the ATSR Reprocessing for Climate (ARC) project. The ATSR series of instruments were specifically designed to make high-quality (errors of ~.3 K or better) retrievals of SST. Compared to operational ATSR SSTs the ARC data use improved retrievals techniques, cloud detection, and inter-satellite homogenisation resulting in regional biases reduced from ~.3 K to <.1 K; inter-satellite differences reduced from ~.2 K to <.5 K while maintaining maximum stability and independence from the in situ record. INTRODUCTION The (A)ATSR Reprocessing for Climate (ARC) project has produced a new, high quality record of sea surface temperature (SST) from the Along Track Scanning Radiometer (ATSR) series of instruments intended for climate change research. In order for the SST record to be suitable for climate the ARC project aims were (Merchant et al. 28): Independence from other records At least 15 years global coverage Regional biases <.1 K Stability of.5 K per decade Both skin and bulk SSTs Comprehensive error characterization ARC SSTs have been generated by reprocessing the ATSR Level 1b dataset through to end 29 from the (A)ATSR multi-mission archive held at NEODC. This paper presents a brief overview of the ARC data set. Beginning with a brief description of the processing methods used, then the available data, and finally a summary of the comparison with in situ measurements. ALGORITHMS Cloud Detection The cloud detection algorithm used for the ARC processing is the Bayesian method described in Merchant et al. (25), updated to use visible and near infrared reflectances during the day (Mackie et al. 21) and the dual-view geometry of the ATSR instruments. Overall this approach appears more effective that the threshold-based SADIST method (Závody et al. 2) used for operational ATSR data. Table 1 shows a comparison of satellite to in situ drifters using the two cloud detection methods. Statistics shown are the standard deviation (SD) and robust standard deviation (RSD) of the satellite in situ difference. For a Gaussian distribution the SD and RSD would be the same, but outliers such

2 as those caused by cloud detection failures will increase the SD. The improvement from using the Bayesian cloud detection is greatest during the day where it gives nearly 4% more clear-sky matches and a lower SD indicating that there is less cloud contamination in the matches found. Number SD RSD Day SADIST Bayesian Night SADIST Bayesian Table 1: Impact of different cloud detection methods on ARC in situ drifter comparison for AATSR data using the dual-view two-channel (D2) retrieval. SD is the standard deviation of the satellite-in situ difference. RSD is an outliertolerant robust estimate of standard deviation (1.48 times the median absolute deviation from the median). Saharan Dust Detection Dust aerosol can be a problem for some satellite SST products as infrared SST retrievals are biased cold in the presence of dust. While very high dust loadings are often (incorrectly) flagged as cloud, more moderate amounts are passed as clear and impact the resulting SST fields. This is a particular problem over the Atlantic in the summer months when desert dust is lifted from the Saharan desert and transported west over the ocean. Desert dust is detected using the ATSR Saharan Dust Index (ASDI) method described in Good et al. 211). This functions as a dual-view retrieval of dust index using the 11 and 12 micron channels which can therefore be used both day and night. SST Retrieval ARC SSTs are estimated using a coefficient-based retrieval scheme (Embury and Merchant 211) which is robust to the presence of stratospheric aerosol from the Mount Pinatubo eruption in The coefficients are banded by: total column water vapour (TCWV) to reduce the effects of atmospheric variability on the nadir and day-time retrievals; satellite zenith angle to reduce viewing angle dependent biases from the dual-view geometry; and year to account for changes in trace gas concentrations during the ATSR missions. Skin to Depth Adjustment Infrared radiometers, such as the ATSR instruments, are sensitive to radiation emitted by the upper few 1s of microns of the sea surface. These measurements, known as skin SSTs, are generally ~.2 K cooler than the SST measured at depths of millimetres through meters due to evaporative cooling of the skin layer. Furthermore, under conditions of sufficiently low wind-driven mixing the upper ocean can become thermally stratified during the day as solar heating warms the upper ocean. In order to produce a depth-sst product which can be compared to in situ measurements, ARC data includes estimates of SST.2m and SST1.m which are estimated using the Fairall et al. (1996), and Kantha and Clayson (1994) models to account for skin and thermal stratification effects respectively (Embury et al. 211). Inter-satellite Adjustment The accurate calibration and characterisation of the ATSR instruments (for most channels) mean it is possible to derive the retrieval coefficients from radiative transfer (RT) model outputs without use of in situ SST measurements (Embury et al. 211). However, small uncertainties in the characterisation of the instruments result in ~.1 K differences in the retrieved SSTs. These have been eliminated by cross-calibrating between the three ATSR instruments. The homogenised SSTs therefore remain independent of the in situ record. The cross-calibration process compares the observed AATSR-ATSR2 differences against the differences predicted by simulation. These inter-satellite differences are used to adjust the RT

3 simulations for the earlier satellite bringing the retrieved SSTs into alignment with the later instrument. The same process is then repeated for the ATSR2-ATSR1 overlap accounting for the increased detector temperature at the end of the ATSR1 mission. As the calibration of the ATSR1 instrument is known to have varied with the 12 micron detector temperature, the adjustment applied to ATSR1 is interpolated from zero at start-of-mission to that found from the overlap analysis at the end-of-mission. DATA AVAILABLE Due to the various channel and view combinations available with the ATSR instrument, there are several different retrieval algorithms possible. Firstly, there are dual-view retrievals (indicated by the letter D) which use both the nadir and forward views from the ATSR instrument and nadir-only retrievals (indicated by the letter N) which only use the nadir view. The dual-view retrievals are the recommended SSTs as they are much more robust to atmospheric variability. The nadir-only retrievals, for instance, are not robust to stratospheric aerosol and will be negatively biased during the years following the Mount Pinatubo eruption in Secondly there are both three-channel retrievals (indicated by the number 3) using the 3.7, 11, and 12 micron channels and two-channel retrievals (indicated by the number 2) which only use the 11 and 12 micron channels. The three-channel retrievals are more accurate than the two-channel retrievals, but they are only valid at night as the 3.7 micron channel is strongly affected by reflected solar radiance. From the above, the recommended algorithm is the D3 retrieval at night, and the D2 for day. However, there are cases where users may wish to use the others. For instance, when the consistency of the retrieval method throughout the complete time period, both day and night, is the primary requirement then the D2 SSTs should be used. Alternatively, if very low-noise retrievals are required at the expense of aerosol robustness and day-time capability then the N3 SSTs could be considered. All SSTs are available as skin estimates (this is the SST which the satellite observes), and depth and time adjusted SSTs for 2cm and 1.m below the sea surface. The depth SSTs have been adjusted to a common Local Equatorial Crossing Time (LECT) of 1:3 to account for the change in orbit between the ATSR1/2 (1:3 LECT) and the AATSR instrument (1: LECT). COMPARISON WITH IN SITU DATA Accuracy Assessment The target accuracy for the ARC project is regional biases less than.1 K. This is assessed by calculating the average (median) ARC in situ drifter SST on a global 15x5 degree grid. Results for the AATSR instrument are shown in Figure 1, where the majority of cells show difference <.1 K for all retrievals except N2 which shows significant differences from drifters of order a couple of tenths of Kelvin. The N3 retrieval shows some significant cold biases between.1 and.2 K in regions commonly affected by dust aerosol, this reflects the fact that the nadir-only retrievals are more sensitive to aerosol (the Saharan dust detection algorithm requires both view and therefore is not applied in the case of nadir-only retrievals). The two dual-view retrievals show differences <.1 K over most of the globe. Although there are a few cells around Indonesia with positive differences >.1 K. However, this region has very poor coverage by drifting buoys and as a result the satellite-drifter differences are not significant at a 9% confidence level. Figure 2 shows the ARC drifter comparison for the ATSR-2 instrument. There are more cells with differences greater than.1 K, but very few of them are statistically significant once the number of drifting buoys is accounted for. Finally, Figure 3 shows the results for the ATSR-1 instrument (here the N3 and D3 results are missing as the 3.7 micron channel failed early during the instrument s life). Large numbers of cells now show differences >.1 K, but due to the low number of drifters which were active in the early 9s there are still relatively few which are statistically significant.

4 N2 D2 9N 6N 3N 3S 6S 9S N 6N 3N 3S 6S 9S Bias / K N3 D3 9N 6N 3N 3S 6S 9S N 6N 3N 3S 6S 9S Figure 1: Median of difference between AATSR-estimated SST.2m and drifting buoy SST for (a) N2, (b) N3, (c) D2, and (d) D3 retrievals. X symbols indicate the difference exceeds.1 K with a significance of 9%, * symbols indicate a significance of 99%. Significance is calculated with a Student s t-test using the number of unique drifters in each cell. Figure 2: As Figure 1, but for ATSR-2.

5 N2 D2 9N 6N 3N 3S 6S 9S N 6N 3N 3S 6S 9S Bias / K N3 D3 9N 6N 3N 3S 6S 9S N 6N 3N 3S 6S 9S Figure 3: As Figure 1, but for ATSR-1. Stability Assessment The target stability of the ARC data is to have trend artefacts less than.5 K per decade i.e. for the difference between any observed trend and the true trend in the SST to be less than.5 K decade -1 in magnitude. This level of stability is required so that the ARC data can be used to quantify actual trends of order.2 K decade -1 and have the error in the trend smaller than the trend itself. In order to assess the stability of the ARC SSTs and if they meet the target, it is necessary to identify a set of buoys which are themselves sufficiently stable. In order to be suitable an in situ buoy must meet two criteria. Firstly they must have at least 12 months of data available, boys with shorter records will typically only cover one or two of the ATSR instruments and are unsuitable for analysing the long term stability. Secondly, the buoy itself must not show evidence of in-homogeneity or step-changes. Stepchanges in an individual buoy record are detected using a Penalised Maximal t Test (PMT; Wang et al. 27) on the deseasonalised ARC-buoy differences. By applying the PMT to the ARC-buoy time series like this we are assuming that artefacts due to the satellites are smaller than artefacts due to individual buoys. A trend of order.5 K decade -1, which we are trying to detect in the satellite data, is unlikely to be detectable in an individual buoy time series due to the noise in the data. There are 36 buoys which pass the requirements for length of record and stability, 15 in the tropical pacific and 21 in United States coastal waters. Time series for these two regions were generated by averaging deseasonalised time series from the individual buoys and are shown in Figures 4 and 5. Step-changes in the combined time series are detected using PMT and shown as dashed lines in the figures, in this case where the step-change is detectable in the combined series we assume it reflects an artefact in the satellite data. For the tropical pacific region just one step change is detected, this is in 1993 and is consistent with a residual trend in ATSR-1 data likely due to the effects of stratospheric aerosol from the Mount Pinatubo eruption in In the US coastal region several more stepchanges were detected. The first at the end of 1995 corresponds to the ATSR-2 scan mirror failure and a 6 month gap in ATSR-2 data. The remaining US coastal step-changes correspond to changes in the ECMWF assimilation system which may be affecting the Bayesian cloud screening. If the stepchanges are related to cloud detection then they may be expected to be different for the East and West coasts. When the PMT is performed for the two coasts separately, the step changes are found to only affect the Atlantic coast. This suggests that there are problems with cloud screening off the US Atlantic coast; however, this was based on just 5 buoys so further investigation will be required.

6 Figure 4: Time series of the ARC buoy SSTs for the tropical Pacific. The top panel shows the daytime values and the bottom the nighttime. The dashed lines indicate the identified break points and mean values for each segment Figure 5: As Figure 4, but for US Coastal region. ATSR-2/AATSR only data shown in black; data from all three sensors in grey. Trend estimates for the tropical pacific region are shown in Table 2. When considering the complete ARC period the fitted trend is within the.5 K decade -1 target; however, the 95% confidence interval exceeds the limit for the nighttime analysis. Limiting the comparison to data after the indentified stepchange, or to excluding all the ATSR-1 data reduces the confidence interval and the data meets the stability target. Table 3 shows the trend estimates for the US coastal, US Pacific coastal, and US Atlantic costal regions all cases excluding ATSR-1 data. The combined US coastal region does not meet the stability target, but this is primarily due to the impact from the US Atlantic coastal data which

7 has a very large trend due to the step-changes identified. Considering just the US Pacific coastal moorings the trends are much smaller and the confidence intervals are just within the target stability. Region Period Time of day Trend (K decade -1 ) 95% confidence interval Tropics All ( ) Day.26.6 < trend <.45 Tropics All ( ) Night.44.2 < trend <.69 Tropics > 1993 Day < trend <.15 Tropics > 1993 Night < trend <.34 Tropics ATSR-2/AATSR Day < trend <.9 Tropics ATSR-2/AATSR Night < trend <.16 Table 2: Trend estimates and 95% confidence intervals for the combined tropical satellite buoy SST differences. Region Period Time of day Trend (K decade -1 ) 95% confidence interval US Coast ATSR-2/AATSR Day < trend < -.7 US Coast ATSR-2/AATSR Night < trend < -. Atlantic ATSR-2/AATSR Day < trend < -.19 Atlantic ATSR-2/AATSR Night < trend < -.16 Pacific ATSR-2/AATSR Day < trend <.43 Pacific ATSR-2/AATSR Night < trend <.43 Table 2: Trend estimates and 95% confidence intervals for the combined satellite buoy SST differences in the US Coastal regions. SUMMARY The ARC SST products described here represent a consistent reprocessing of the (A)ATSR multimission archive to produce data suitable for climate applications. The SSTs are generated independently of in situ measurements using retrieval coefficients based on radiative transfer simulations. The initial data release comprises 18 years of data from the start of ATSR-1 data in August 1991 through to the end of 29, and includes both skin SSTs and estimates of SST at depths of.2m and 1.m comparable to in situ measurement depths. The recommended dual-view retrievals meet the target of regional biases <.1 K compared to drifting buoys over the majority of the global oceans. Of the regions with average ARC-drifter differences >.1 K the majority have insufficient drifting buoys for the differences to be statistically significant. An analysis of the product stability indicates that the target of <.5 K decade -1 has been met in tropical regions for data after 1993 and may have been met by US Pacific Coastal data for ATSR-2 and AATSR data. Data for the US Atlantic Coastal region is outside the target stability, but this region contained just 5 in situ buoys suitable for the comparison. The high quality of ATSR data make them a suitable choice as an independent climate quality record of SST. As such the ARC record can contribute to refining out knowledge of recent marine climate change and understanding biases inherent in the in situ record. The ARC SST products are available from the NERC Earth Observation Data Centre (NEODC) at: REFERENCES Embury, O., Merchant, C.J., Filipiak, M.J., (211) A reprocessing for climate of sea surface temperature from the along-track scanning radiometers: Basis in radiative transfer, Rem. Sens. Env., In Press. Embury, O., Merchant, C.J., (211) A reprocessing for climate of sea surface temperature from the along-track scanning radiometers: A New Retrieval Scheme, Rem. Sens. Env., In Press. Embury, O., Merchant, C.J., Corlett, G.K., (211) A reprocessing for climate of sea surface temperature from the along-track scanning radiometers: Preliminary validation, accounting for skin and diurnal variability, Rem. Sens. Env., In Press.

8 Fairall, C.W., Bradley, E.F., Godfrey, J.S., Wick, G.A., Edson, J.B., Young, G.S., (1996) Cool-skin and warm-layer effects on sea surface temperature, J. Geophys. Res., 11, C1, pp Good, E.J., Kong, X., Embury, O., Merchant, C.J., Remedios, J.J., (211) An infrared desert dust index for Along-Track Scanning Radiometers. Rem. Sens. Env., In Press. Kantha, L.H., Clayson, C.A., (1994), An improved mixed layer model for geophysical applications. J. Geophys. Res., 99, C12, pp 25,235 25,266 Mackie, S., Merchant, C.J., Embury, O., Francis, P., (21) Generalized Bayesian cloud detection for satellite imagery. Part 2: Technique and validation for daytime imagery. Int. J. Rem. Sens, 31, 1, pp Merchant, C.J., Harris, A.R., Maturi, E., Maccallum, S., (25) Probabilistic physically based cloud screening of satellite infrared imagery for operational sea surface temperature retrieval. Quart. J. Royal Met. Soc., 131, 611, pp Merchant, C.J., Llewellyn-Jones, D., Saunders, R.W., Rayner, N.A., Kent, E.C., Old, C.P., Berry, D., Birks, A.R., Blackmore, T., Corlett, G.K., Embury, O., Jay, V.L., Kennedy, J., Mutlow, C.T., Nightingale, T.J., O'Carroll, A.G., Pritchard, M.J., Remedios, J.J., Tett, S., (28) Deriving a sea surface temperature record suitable for climate change research from the along-track scanning radiometers. Adv. Sp. Res., 41, 1, pp 1-11 Wang, X.L., Wen, Q.H., Wu, Y., (27) Penalized Maximal t Test for detecting undocumented mean change in climate data series. Journal of Applied Meteorology and Climatology, 46, pp Závody, A.M., Mutlow, C.T., Llewellyn-Jones, D.T., (2) Cloud clearing over the ocean in the processing of data from the Along-Track scanning radiometer (ATSR). J. Atmos. and Oceanic Tech., 17, 5, pp