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Characterizing and Predicting Surface Melt in Antarctica

Characterizing and Predicting Surface Melt in Antarctica. David B. Reusch (New Mexico Tech) Derrick Lampkin (Penn State) David Schneider (NCAR). Funded by the Office of Polar Programs, National Science Foundation.

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Characterizing and Predicting Surface Melt in Antarctica

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  1. Characterizing and Predicting Surface Melt in Antarctica David B. Reusch (New Mexico Tech) Derrick Lampkin (Penn State) David Schneider (NCAR) Funded by the Office of Polar Programs, National Science Foundation

  2. Collaborative Research: Decoding & Predicting Antarctic Surface Melt Dynamics with Observations, Regional Atmospheric Modeling and GCMs Outline • Overview of the project ideas • Science questions • Methods (modeling, analysis) • Case study plan

  3. The Premise: Looking Backward • Satellite-based passive microwave data tell us about surface melting on polar ice sheets • Meteorological models provide gridded temperatures, winds, energy balance components, etc. • We know they’re imperfect and can be expensive • But still invaluable as an information source • We ought to be able to create a useful calibration between the satellite and model datasets

  4. The Premise: Looking Forward • Assume that the recent past and future share the same physical principles… • Then calibration of satellite->atm model should be usable for predicting future surface melt from climate projections

  5. Objectives • Compare atmospheric model-based estimates of surface melt occurrence to satellite-based melt records to better understand this aspect of model skill for the Antarctic and to build confidence in our model-based predictions of the future. • Diagnose the synoptic factors, present and future, controlling Antarctic surface melt through objective classifications and analysis of synoptic-scale meteorology (from global and dynamically downscaled datasets based on regional models) and sea ice conditions. • Evaluate modern climates and surface melt estimates of CMIP5 general circulation models (GCMs) to identify best candidates for future prediction of surface melt occurrence.

  6. Significance • Retrospective studies help climate scientists and glaciologists better understand the relationship between surface melt and synoptic meteorology/climate, aid investigations of melt intensity and amount, and contribute to improved paleoclimate interpretations from ice core melt layers. • Prognostic results inform the climate change community about possible future changes in surface melt and ice shelf behavior. Surface melt is a significant factor in ice dynamics, a key to future ice-sheet behavior and critical for predicting the future of global sea level. • Results will both leverage and complement ongoing community evaluations of Polar WRF and CMIP5 GCMs in Antarctica.

  7. Example Science Questions • What are the variability characteristics (existence, frequency, spatial patterns) of the satellite data? • How do our melt estimates (GCMs, PWRF) compare to the satellite data? To each other? • How dependent are PWRF results on the source of boundary condition data? • What are the synoptic controls on melt occurrence and how do they vary in space and time? • How does future compare with the recent past?

  8. Methods: satellite observations • XPGR algorithm (Abdalatiand Steffen, 1995) uses passive microwave data to detect changes in emissivity associated with melt • Well-established technique • Only detects melt presence, not magnitude • Processing at Penn State for Nov-Feb, 1987-2008 • 25 km pixels, daily

  9. Example Record from Greenland Threshold (Abdalati and Steffen, 1995)

  10. Three Days in January 1988 Missing Data Melt Jan 13 Jan 14 Jan 15

  11. Outline of PWRF modeling • Periods: Recent & Future • Boundary conditions: Reanalyses& GCMs • Resolutions/domains (tentative) • Continental (45 km) • Interior West Antarctica (15 km) • Selected ice shelves (5 km) • Configuration • Use community-identified “best practices” • “Climate” so keeping options fixed CCSM3 MM5 60 km WRF 20 km

  12. SOMs (Self-organizing maps) • Summarize complex datasets as generalized patterns • PMM5 December 1990-1992 • 6-hourly 2-m temperature anomalies Ross sea is up Red line is coast

  13. Case studies • Look at dates with distinct melt in West Antarctica • Compare PWRF and other datasets to try to identify synoptic conditions, etc. • Evaluate results • More at Fall AGU poster sessions • Thu a.m. 12/8: C41E-0482. Characterizing and Predicting Surface Melt in Antarctica, Reusch et al • Tues p.m. 12/6: C23C-0513. Synoptic scale atmospheric forcings on surface melt occurrence on West Antarctic ice shelves, Karmosky et al

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