1. A comparison of AMSR-E and AATSR SST time-series A preliminary investigation into the effects of using cloud-cleared SST data as opposed to all-sky SST on global mean SST time-series.

2. Motivation SST is designated an Essential Climate Variable by IPCC. The analysis of global mean SST changes is useful in climate change monitoring. Now, we have satellite records that are long enough to be of use in climate change monitoring. Most satellite SST records are derived from IR data and so are a record of clear-sky SST. MW instruments can observe the surface through clouds (tho’ not in the presence of precipitation). Does using clear-sky-only SST in calculating global mean SST reduce or enhance any trend in global mean SST? A comparison of MW and IR SST should enable us to assess the impact of using clear-sky SST to estimate global mean SST.

3. Data AATSR is the most precise SST but it is an IR radiometer and so cannot observe the ocean surface through clouds. Also, AATSR has a relatively narrow swath. AMSR-E, a MW sensor, can observe the surface through clouds but not in the presence of precipitation. Its wider swath permits global coverage in 2 days but it is less precise than AATSR. *Reference: O’Carroll et al., Journal of Atmospheric and Oceanic Technology, 25, 1197-1207, 2008.

4. Methods AMSR-E – 0.25 ° resolution, daily files. – Night-time data (solar elevation angle < 0°) binned to 0.5 ° resolution monthly averages. Only GHRSST L2P proximity quality flag ≥ 4 used. – Averaged to 5.0 ° grid AATSR – Level 3 monthly average night-time SST at a resolution of 30 arcmin – SSES applied (adapted from L2P SSES) – Averaged to 5.0 ° grid Use night-time data to minimize influence of diurnal cycle. Mask formed for time-series work: include only locations where there is a complete time-series of data for both datasets SST anomalies are formed by subtracting the Reynolds O.I. climatology for 1971-2000.

5. DIFFERENCES IN DAILY NIGHT-TIME SST OBSERVED BY AMSR-E AND AATSR

6. Daily differences between AMSR-E SST and AATSR SST An investigation into the effect of using clear-sky SST as an estimate for global mean SST first requires we characterize and quantify the differences between AMSR-E SST and AATSR SST. Look at daily differences in night-time SST for the two datasets, but noting that: – AMSR-E ~3.5 hours later than AATSR so at night we might expect AMSR-E SST to be 0.1­0.3 K colder than AATSR SST. – AMSR-E (sub-skin) expected to be 0.1­1.0 K warmer than AATSR (skin). AATSR Level 2 data used to construct differences

8. AMSRE minus AATSR : latitude bands Northern HemisphereSouthern Hemisphere January July Polar Tropics Mid-latitudes

9. Characteristics of the daily differences between AMSR­E SST and AATSR SST Maps show the monthly-averaged daily difference in night-time SST. Bias is generally small, between +/- 1.0 K. Bias has latitude band pattern: 0­30°N and 0­30°S have opposite bias, with the winter hemisphere showing a warm bias at these latitudes. Larger biases, both warm and cold, are present in areas known to have a high frequency of cloud cover. Possible causes of these large biases include: – AATSR cloud contamination – AATSR high gradients or cloud in one view – AMSR-E precipitation contamination – AMSRE wind-affected retrieval Histograms of daily differences by latitude band for January and July confirm a warm bias in the tropics of the winter hemisphere.

10. TIME-SERIES

11. Time-series Time-series of monthly-averaged global mean SST anomaly. Anomalies calculated using Reynolds climatology 1971­2000. Use a mask to restrict the analysis to locations where there is a complete time-series of SST for both datasets.

12. Combined mask for AATSR and AMSR-E 0.5° grid5.0° grid AATSR AMSR-E COMBINED

13. Time series of global mean SST anomaly AMSR-E minus AATSR very small and negative. Differences range +/- 0.1 K Causes of differences: —Time difference in observations —Retrieval of sub-skin AMSR-E compared with AATSR skin SST —Inclusion of some cloudy AMSR-E data —Spatial sampling —Cloud contamination of AATSR pixels

14. Monthly average, global mean night-time SST anomaly : AATSR and AMSR-E : Common mask 5.0 degree resolution 0.5 degree resolution AMSR-E AATSR AMSR-E minus AATSR

15. AMSR-E ‘cloud-cleared’ and all-sky AMSR-E, 5.0° all data and cloud-cleared using AATSR data at 0.5°. Time-series very similar: all-sky is ~0.02K less than cloud-cleared. But difficult to separate the spatial sampling effects from the cloud- clearing effects.

16. COMPARISON WITH AVHRR AND HADSST2 SST ANOMALIES

17. Comparison with other datasets Datasets – AVHRR pathfinder v5.0 night-time, bulk SST. – HadSST2 (buoy and ship) day and night-time combined, bulk SST. – AMSR-E night-time, sub-skin (bulk at night) SST. – AATSR night-time, skin SST. Expect: – AATSR to be ~ 0.1-0.17 K colder than in situ due to skin effect. – AMSR-E to be similar to bulk SST – AVHRR to be similar to bulk SST – Inclusion of day-time data in HadSST2 may result in a warm bias when compared with the night-time only data.

18. Common mask : night-time except HadSST2 which is day and night combined.

19. Conclusions AATSR SST and AMSR-E SST are generally in good agreement, remembering the observations are 3.5 hours apart and the MW instrument measures the sub-skin SST and the IR instrument measures the skin SST. There are some larger differences in the daily night-time SSTs and possible causes of these have been mentioned. A small seasonal bias has been observed; AMSR-E has a small warm bias cf. AATSR in the winter hemisphere. The AMSR-E and AATSR time-series of monthly-average global mean SST show good agreement. On a 5 degree grid agreement is good whether or not cloudy MW data is included in the global mean SST which allows us confidence in continuing to calculate time- series of AATSR global mean SST on a 5 degree grid. When calculating global mean SST time-series the swath width of the satellite instrument as well as its accuracy are important. Analysis of the effect of cloud would be aided by the inclusion of a cloud proximity flag in the AMSR-E data.

20. Future work Look at regional differences Look at using cloud liquid water as a proxy for cloud cover (RSS dataset) in AMSR-E data. Compare AMSR-E further with AVHRR pathfinder as AVHRR has wider swath than AATSR. Use AMSR-E data to estimate sampling error in calculating AATSR global mean SST from 5.0° gridded data.

21. Acknowledgements This work is funded by The UK Government’s Department of Energy and Climate Change through a Space Connexions contract. Data: – AATSR ATS_AR_2P ESA – AMSR-E – GHRSST L2P AMSR-E data are produced by Remote Sensing Systems and sponsored by the NASA Earth Science MEaSUREs DISCOVER Project and the AMSR-E Science Team. Data are available at www.remss.com. – AVHRR Pathfinder version 5 GCOS dataset supplied by The GHRSST Long Term Stewardship and Reanalysis Facility, NODC. – HadSST2 UK Met Office Hadley Centre – Rayner, N.A., P.Brohan, D.E.Parker, C.K.Folland, J.J.Kennedy, M.Vanicek, T.Ansell and S.F.B.Tett 2006: Improved analyses of changes and uncertainties in sea surface temperature measured in situ since the mid-nineteenth century: the HadSST2 data set. Journal of Climate. 19(3) pp. 446- 469.

22. THE END

29. 0.5 degree Northern Hemisphere Southern Hemisphere Global 5.0 degree

30. Individual mask AMSR-E AATSR