1. Meteorologisk institutt met.no

2. SPAR : Rasmus E. Benestad, Yvan Orsolini, Ina T. Kindem, Arne Melsom Seasonal Predictability over the Arctic Region – exploring the role of boundary conditions Norwegian Institute for Air Research

3. Motivation

5. SPAR

6. Motivation

8. SPAR

9. Motivation

10. SPAR Courtesy of ClimateExplorer, Geert Jan van Oldenborgh, KNMI Ensemble mean correlation http://climexp.knmi.nl/forecast_verification.cgi

11. SPAR Courtesy of ClimateExplorer, Geert Jan van Oldenborgh, KNMI Brier Skill Score w.r.t. climatology http://climexp.knmi.nl/forecast_verification.cgi

12. Seasonal predictability  'high' in the Tropics (ENSO).  Notoriously low in higher-latitudes Forecast models  Initial conditions in the Tropics  Sea-ice, snow, SST, role of stratosphere not well represented Climate change & media pressure International Polar Year  New observations Fundamental science  How does predictability vary with latitude?

13. Objectives Identify signals in the Arctic. Examining predictability in northern Europe SPAR

14. Means Numerical experiments  ECMWF IFS model Analysis  Model data  Observations (IPY?) SPAR

15. Seasonal forecast models apply prescribed sea ice, alternatively, initial fields are strongly relaxed towards climatology. …but sea ice undergo interannual fluctuations, e.g. Deser et al., 2000 SPAR WP1: Sea-ice Is the sea-ice in seasonal forecast models a limiting factor for the quality of these forecasts at high latitudes? WP1.1: Winter sea ice patterns and summer ice extent extremes WP1.2: A summertime “blue Arctic” WP1.3: Limits of seasonal predictions with “perfect” sea ice data N.H. September ice extent, CCSM/SRES-A1B (Holland et al., GRL ’06) NOAA ocean explorer

16. SPAR Eurasian snow cover has been linked to the forcing of large-scale NH teleconnections. Extensive fall snow cover has been linked to a negative NAO phase in the following winter. (e.g. Cohen et al., 2004; Saito et al., 2001) SPAR WP2: The role of snow cover Recent 20-year ensemble simulations with an AGCM forced by global satellite snow cover observations indicates a better representation of the year-to-year variability in the Icelandic and Aleutian Lows, and their out-of-phase coupling, compared to control simulations. (Orsolini, Kvamstø and Sorteberg, to be submitted, 2007) We aim at improving the use of snow variables in initialisation of seasonal forecasting, and assess the impact of snow cover inter-annual variability onto the NH high latitude circulation.

17. SPAR Impact on 500mb wind speed : note Negative NAO-like signal SPAR WP3: The stratosphere SSW composite We aim at examining three issues: 1: SSWs and Arctic storm track changes: While stratospheric extreme events influence mid-latitude storm tracks (see figure to the left), do they specifically influence the path of cyclones into the Arctic ? 2: Predictability of the spring onset: Can the occurrence of the stratospheric final warming lead to improved predictability in the troposphere (like SSWs in mid winter) ? 3: Predictability of SSWs: we will carry out ensemble medium- time-scale forecasts during the IPY to better understand precursors of SSWs, and synoptic conditions during their downward influence. Simulations with Arpege GCM (T106- 60lev) Kindem, Orsolini and Kwamstø, to be submitted, 2007.

18. SST patterns are associated with the large-scale atmospheric circulation, not only in the tropical region (El Niño), but also at mid-latitudes (e.g. the NAO/SST tri-pole pattern.) (Rodwell et al., 1999; Benestad & Melsom, 2002) SPAR WP4: SST Examine how seasonal forecasts are affected by perturbations of anomalies on a large scale, with an emphasis on SST and sea ice consistency WP4.1: The NAO-tripolar SSTA pattern WP4.2: Arctic SSTA patterns WP4.3: Simulations with different polar SSTAs N. Norway Svalbard archipelago Barents Sea 75N 30E SST from IR: + Data coverage + Front resolved - Resolution - Coastal data SST from IR: SST from microwave: Winter data

19. SPAR Stratosphere SST Sea-ice Snow cover Picture from ozonewatch.gsfc.nasa.gov

20. Meteorologisk institutt met.no

21. Extra slides

22. SPAR Figure copyright: David W. J. Thompson The troposphere responds to changes in the stratospheric state. (e.g. Wallace and Thompson, 1998, Baldwin and Dunkerton, 2001) SPAR WP3: The stratosphere NH winter While numerical simulations with troposphere- stratosphere AGCM indicates downward influence of extreme stratospheric events (such strong vortex episodes, or stratospheric sudden warmings), there is mild impact on winter seasonal forecasts. Rather the influence is on the medium time scale. We aim at improving practical use of the S-T coupling in sub-seasonal and seasonal forecasts

23. SPAR SST data in SPAR: SST from existing seasonal forecasts (control experiment) SST from IR measurements (sensivity to ocean model trends & errors) SST from microwave data (spatio-temporal data coverage and instrumentation)

24. Life-cycle of stratospheric sudden warmings: geopotential composites Winter Results Precursory High anomalies over northern Europe, akin to Scandinavian blocking Lingering negative NAO anomalies near surface (as shown by Baldwin et al., or Limpasuvan et al) ONSET GROWTH MATURE DECLINE DECAY 100mb1000mb Nao negative 30mb500mb1000mb Precursory Blocking Lingering NAO- Simulations with Arpege GCM (T106- 60lev) Kindem, Orsolini and Kwamstø, to be submitted, 2007.

25. SPAR SST data in SPAR: SST from existing seasonal forecasts (control experiment) SST from IR measurements (sensivity to ocean model trends & errors) SST from microwave data (spatio-temporal data coverage and instrumentation)

26. SPAR SST from IR measurements (traditional method) vs. SST from microwave data (experimental method); winter data N. Norway Svalbard archipelago Barents Sea 75N 30E C + data coverage + front resolved - resolution - coastal data K

27. IR measurements vs. microwave data; summer data SPAR CK similar features microwave data are warmer in the southern Barents Sea

28. IR measurements vs. microwave data; summer data trends over an 8 day period SPAR obvious differences, e.g. in the central Barents Sea ‘noisy’ trends in the high-resolution IR measurements

29. Collaboration with EC-Earth Complimentary studies  Use experimental set-up for comparisons  Share data  Use EC-Earth data as input?  Different focus but overlapping interests Share experience  Modelling  Data processing SPAR

30. Time schedule: July 2007:Hire postdoc. Start setting up model Analysing input data 2008-2009: experimental runs 2010:analysis and reporting.

31. SPAR