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Understanding and Improving Marine Air Temperatures

Understanding and Improving Marine Air Temperatures. David I. Berry and Elizabeth C. Kent National Oceanography Centre, Southampton dyb@noc.soton.ac.uk. MARCDAT II, Exeter, 17th - 20th October 2005. Outline. Introduction Why Marine Air Temperature (MAT) Sources and current status

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Understanding and Improving Marine Air Temperatures

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  1. Understanding and Improving Marine Air Temperatures David I. Berry and Elizabeth C. Kent National Oceanography Centre, Southampton dyb@noc.soton.ac.uk MARCDAT II, Exeter, 17th - 20th October 2005

  2. Outline • Introduction • Why Marine Air Temperature (MAT) • Sources and current status • Recent developments • Individual observations • Gridded dataset • Results • Summary and future work

  3. Intro - Why marine air temperature ? • MAT observations give an independent indicator of climate change and can be used to confirm the trends seen in SST • We also need observation of the MAT to understand air - sea interaction and to calculate the turbulent heat fluxes • We rely on in-situ data for observations of the MAT

  4. Intro - Sources of in-situ MAT observations • Three different observing platforms • Moored buoys • Drifting buoys • Voluntary Observing Ships • Problems with all platform types • Limited geographic coverage for moored buoys • Uncertain reliability and error characteristic of drifting buoys • Inhomogeneous distribution in time and space • Heating errors, biasing observations by up to 2 °C

  5. Intro - Gridded products - current status • Despite these problems the in-situ sources are the only source of air temperature information over the oceans • Hence they need to be handled with care • This has been done in a number of recent datasets, e.g. HadMAT • Night only analysis, excludes observations with daytime heating errors • Bulk height correction • Uncertainty estimates • However there is still room for improvement

  6. Recent developments - Individual ship observations • Platform heights from merged WMO Pub. 47 / ICOADS dataset (Kent et al., 2005) • Allows height correction to standard height (10m) • Without height correction artificial trend introduced • Uncertainty estimates for individual VOS observations (Kent and Berry, 2005, CLIMAR-II IJC special issue) • Allows uncertainty estimates to be made for gridded products • Correction for heating errors in VOS observations (Berry et al., 2004, Presented at CLIMAR-II) • Allows use of day time MAT observations

  7. Berry et al. (2004) - Summary • Heat budget solved analytically to give correction • Heating errors estimated as day - night or ship - model difference • Correction fitted to small subset of estimated errors Berry, D. I., E. C. Kent and P. K. Taylor, 2004: An analytical model of heating errors in marine air temperatures in ships. Journal of Atmos. Oceanic Technol., 21(8), 1198 - 1215.

  8. Recent developments – Gridded fields • These developments have been combined in OI scheme to give daily 1˚ MAT fields (see poster by Kent and Berry for further details on scheme) • Only VOS observations used (no buoy observations) • Daily analysis (heating error and sampling error estimates) • Individual corrections (height and heating errors) • Uncertainty estimates (natural variability, random errors and sampling) • OI scheme still under development but promising initial results

  9. Results - Overview • Mean fields • Realistic monthly mean fields and variability • Reasonable uncertainty estimates • Ship - buoy comparisons • Good agreement between daily MATs from buoys and OI • Monthly mean MAT values from OI and buoy observations within a few tenths °C • Air temperature correction • Effect of using uncorrected MAT observations

  10. Results – Average daily MAT during June 1991 (°C)

  11. Results – Variability (MAT standard deviation 1990 - 1999, °C)

  12. Results – Uncertainty in daily MAT field averaged over June 1991 (˚C)

  13. Results – Comparison buoys – Subduction array (07/91 – 03/93) Data from Woods Hole Upper Ocean Mooring Data Archive at http://uop.whoi.edu/uopdata/

  14. Results – Good agreement in daily MAT between OI (red) and observations from NW buoy (black)

  15. Results – Monthly means from buoy (black) and OI (red) within a few tenths ˚C

  16. Results – Similar results at all 4 buoysBlack = buoy, Red = Ship OI

  17. Results – Comparison of daily MAT observations from NW buoy (black) with daily OI MAT using uncorrected (green) and corrected (red) ship observations

  18. Summary and Further work • Improved MAT product under development at NOC • Daytime observations recovered with removal of heating errors • Trends due to changing platform heights removed • Uncertainty estimates • Initial comparison to buoy observations promising but further validation needed • Improvements to OI scheme possible • Refinement of spatial scales • Improved estimates of natural variability • Improved observational error estimates (random errors and bias)

  19. References • Berry, D. I., E. C. Kent and P. K. Taylor, 2004: An analytical model of heating errors in marine air temperatures in ships. Journal of Atmos. Oceanic Technol., 21(8), 1198 - 1215. • Kent, E. C. and D. I. Berry, 2005: Quantifying random measurement errors in Voluntary Observing Ships’ meteorological observations. Int. J. Climatol., 25, 843 - 856. • Kent, E. C., Woodruff, S. D., and D. I. Berry, 2005: WMO Publication No. 47 metadata and an assessment of observation heights in ICOADS. Submitted to Journal of Atmos. Oceanic Technol. Acknowledgements • This work has been funded under NOC core funding CSP1 and MoD/NERC Joint Grant Funding Scheme • ICOADS data has been provided by Steve Worley • The plots shown in this presentation have been created using Ferret available from NOAA’s Pacific Marine Environmental Laboratory • Original OI code used to develop scheme supplied by Dick Reynolds and Diane Stokes

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