1. 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. 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. 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. 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. 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. 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. XPGR algorithm ( Abdalati and 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. Threshold Example Record from Greenland (Abdalati and Steffen, 1995)

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

11. 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 CCSM3MM5 60 km WRF 20 km

13. Summarize complex datasets as generalized patterns PMM5 December 1990-1992 6-hourly 2-m temperature anomalies Ross sea is up Red line is coast

14. 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