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NACLIM 20 year re-processed S ea and I ce S urface T emperatures data set

NACLIM 20 year re-processed S ea and I ce S urface T emperatures 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. Outline. Arctic SST/IST from satellite – why

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NACLIM 20 year re-processed S ea and I ce S urface T emperatures data set

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  1. NACLIM 20 year re-processedSea 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 NH sea ice concentration September 10. GL ice sheet weather stations: DMI summit and promice(.dk) Arctic Temperatures available on the DMI GTS service, September 10, 12Z

  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. Arctic Surface Temperature data set (AST)- WP 3.2 deliverable D32.28 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) 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. AST Algorithm description Integrated HL SST, IST and Marginal Ice Zone Temperature product, based on AVHRR data • SST for HL • Split WindowAlgorithm • Regional calibrationcoefficients(separate day/nightalgorithms, as Le Bourgne, 2006) • IST • Split Windowalgorithm(Key et al., 1992) • MIZT • Scaledlinearlybetween IST and SST (Vincent et al., 2008) • Split window IST algorithmincludingview-angle and watervapourcorretion terms: • AST = a + bT11 + d(T11-T12)sec(q)(Key and Haefliger, 1992) (SST is basedonsimilar split windowalgorithm)

  10. Arctic Surface Temperature data set- WP 3.2 deliverable D32.28 • 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.

  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 buoysIce Mass Balance BuoysRadiometer surface temperaturesWeather Station – surface and 2m temperaturesModelled temperatures Field work, Oden, Arctic Ocean 2012 Ice Mass Balance Buoys, Field Work, Oden, Arctic Ocean Field work, Qaanaaq, 2011-onwards Temperature, drifters, Arctic Ocean Greeland ice sheet monitoring weather stations Surface temperature, Barrow WS . Multisensor intercomparison project 2mT – NWP (ecmwf) 2m temperature, Summit AWS

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

  14. Selected ComparisonsSatellite MIST vs in situ 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.eu

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