1. NACLIM 20 year re-processed Sea and Ice Surface Temperatures data set Gorm Dybkjær, Jacob Høyer, Rasmus Tonboe and Steffen M Olsen Center for Ocean and Ice, DMI NACLIM - annual meeting, October 1-2 -Trieste 2013

2. Outline Arctic SST/IST from satellite – why IST/SST reprocessing data set –Status and a few technical issues IST/SST – existing level 2 and 3 data –Samples and comparison to other observation Your wishes or suggestions for the reprocessed temperature data set…

3. Why Arctic surface temperature climate record from satellite Arctic surface temperatures are important boundary condition for atmosphere, ocean and sea ice models. A consistent climate data set for trend analysis. Arctic surface temperatures are poorly represented by traditional observations. Traditional Arctic temperature observations are ambigeous observations.

4. Arctic Surface Temperature - Requested by the sea ice user community ESA CCI project on Sea Ice User survey 91 respondents IST ranking 4 out of 22 parameters.

5. Low density of traditional temperature observations in the Arctic Sparse network of observations for the Arctic Ocean and Greenland Ice sheet A challenge for both model and rs communities Arctic Temperatures available on the DMI GTS service, September 10, 12Z GL ice sheet weather stations: DMI summit and promice(.dk) NH sea ice concentration September 10.

6. Ambiguous surface temperature observations. Extreme vertical and diurnal temperature variation in sea ice (air- snow-ice ) Most Arctic Ocean temperature observations are from sea ice buoys, with limited knowledge of actual sensor position. No consistent relation between 2mT and Tsurf Only IR radiometry can determine surface temperatures adequately In situ temperatures at different depth – showing large vertical gradient even at thin snow layer.

7. Version 1: Periode: 1989-01-01 til 2009-12-31. Area: Global, above 40N and below 40S Data set: –Full globe swath level 2, with filled values at mid latitudes –Multiple daily coverage –Surface temperatures, uncertainties(stde, bias), cloud and land mask, (ice concentration) Arctic Surface Temperature data set (AST) - WP 3.2 deliverable D32.28 AVHRR GAC swath width is ~2000km, hence a high frequent daily coverage a high latitudes

8. Arctic Surface Temperature data set - WP 3.2 deliverable D32.28 -The AST will be based on the reprocessed NOAA-GAC data set created by Climate Monitoring SAF, Eumetsat. The NOAA-GAC data set provide: -Brightness temperatures -Cloud-mask data. -Sun, sat and view geometry -GAC data resolution is ~4km, based on NOAA AVHRR data. Temporal coverage of NOAA AVHRR

9. Integrated HL SST, IST and Marginal Ice Zone Temperature product, based on AVHRR data SST for HL –Split Window Algorithm –Regional calibration coefficients (separate day/night algorithms, as Le Bourgne, 2006) IST –Split Window algorithm (Key et al., 1992) MIZT –Scaled linearly between IST and SST (Vincent et al., 2008) Split window IST algorithm including view-angle and water vapour corretion terms: –AST = a + bT11 + d(T11-T12)sec  (Key and Haefliger, 1992) (SST is based on similar split window algorithm) AST Algorithm description

10. AST is beeing developed jointly between DMI and met.no Data set format will be similar to GHRSST SST data (The Group for High-Resolution Sea Surface Temperature ) Version 1: –Expected ready second quarter 2014 –Ice, Sea and Marginal Ice Zone Surface Temperatures, Distributed uncertainties, Cloud and land masks, Sun-Satellite and view geometry –Data volume ~17TB Version 2-> : –More work on distributed uncertainty and algorithm calibration (if nessesary) and cloud filtering. Arctic Surface Temperature data set - WP 3.2 deliverable D32.28

11. MO/OSI-SAF Metop ST product (MST) - The basis for the AST reprocessing data set. 3 minute granules of 1 km AVHRR data from the NWC SAF PPS software. Integrated HL SST, IST and Marginal Ice Zone Temperature product, based on Metop AVHRR MSST for HL –Split Window Algorithm –Regional algorithm coefficients (separate day/night ) MIST –Split Window algorithm, as Key et al., 1992 MMIZ –Scaled linearly between MIST and MSST –Vincent et al., 2008 Above: a 6- day mean L3 MIST product. Left: A day and a night L2 MIST product from Ingle field Bredning, by Qaanaaq NE Greenland

12. Does it work? Comparison to: Traditional ice buoys Ice Mass Balance Buoys Radiometer surface temperatures Weather Station – surface and 2m temperatures Modelled temperatures Field work, Oden, Arctic Ocean 2012 Field work, Qaanaaq, 2011-onwards 2mT – NWP (ecmwf) Surface temperature, Barrow WS. Multisensor intercomparison project 2m temperature, Summit AWS Temperature, drifters, Arctic Ocean Ice Mass Balance Buoys, Field Work, Oden, Arctic Ocean Greeland ice sheet monitoring weather stations

13. MST vs ground observations MST vs Arctic Buoy observations. Red points are removed by filter MST vs Barrow Surface T. Multisensor intercomparison project MST vs Summit 2mT. Red points are removed by filter MST vs DMI-ISAR radiometer. Qaanaaq field work

14. Selected Comparisons Satellite MIST vs in situ VALIDATION SITESSTDE (K) BIAS (K) Pop.PERIOD/TIME/AREACOMMENT MST – Air/SnowTEMP (DRIFTERS) 3.69-2.7678092011Metop PPS cloud mask NWPsurf – Air/SnowTEMP (DRIFTERS) 4.580.6many2011--- MST – SurfaceTEMP (FIELDWORKS, radiometer) 1.02-1.81284 days in April, 2011Manual cloud mask MST – SurfaceTEMP (BARROW) 0.66-0.3232Jan-Feb-Mar, 2012Metop PPS cloud mask MST – AirTEMP (SUMMIT) 3.14-3.225902011Metop PPS cloud mask MST – SurfaceTEMP (PROMICE, GL) 3.060.748062011Metop PPS cloud mask MST – SurfaceTEMP, Sea ice (NWP) 3.92-3.5Many2011Metop PPS cloud mask The SST algorithm has proven better with STD of errors of less than 1K and very small bias.

15. Main issues in this task Cal/Val and Uncertainty estm. – temporally and spatially distributed. SST matchup data will be based on ESA CCI’s Training, Test and Validation data sets (since 1991, noaa 10 -> metop) -> Regional and seasonal calibration coefficients. IST matchup data will be based on Arctic drifters and other long term data sets -> Regional and seasonal calibration coefficients. Cloud issues Setting up proper quality filters and still retaining a reasonable amount of data. Simple data handling Final data set will add up to ~17TB in compressed format

16. You suggestions Now is a good time to make wishes or come up with suggestions for the Arctic Surface Temperature data set - Version 1 e.g.: Level 3? Weekly/Monthly means? Additional parameters in data set? Less parameters…? …

17. Summary We are building a 20 year reprocessed Arctic ocean and ice temperature data set The data set concept will be based on existing and operational level 2 Arctic ocean and ice temperature data processing system Validation and uncertainty estimation will be done against a wide range of in situ observations Major tasks are: Uncertainties Cloud issues Simple data handling

18. Some Arctic Ocean temperature data sets Modis Aqua and Terra (since 2000) AVHRR Polar Pathfinder dataset (reprocessing data set, since ) International Arctic Buoy Programme… Metop_A (near real time, since 2011) ATSR AMSR-E VIIRS Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) IASI Enhanced Thematic Mapper Plus (ETM+)

19. The research leading to these results has received funding from the European Union 7th Framework Programme (FP7 2007-2013), under grant agreement n.308299 NACLIM www.naclim.euwww.naclim.eu