1. Development of a Tool for Downscaling of Operational Climate Forecasts to Regional and Local Fire Indices 1,2 Nicole Mölders & 1 Gerhard Kramm University of Alaska Fairbanks 1 Geophysical Institute 2 College of Natural Sciences, and Mathematics

2. Motivation Wildfires reduce visibility => affect land and air traffic Released aerosols and trace gases reduce air quality PM2.5 may affect health Destruction of properties Natural treat with high occurrence nearly worldwide … Agencies need data for planning fire management several months ahead a fire season A tool to provide suitable data for support in decision making and planning is required

3. Wildfires occur worldwide

4. Alaska has long wildfire history

5. High temporal variability in wildfire frequency and area burned Modified after Mölders and Kramm (2006)

6. Fire weather assessment difficult ahead of a season or when data are sparse Operational climate model predictions are too coarse Higher resolution non-linearly slows down the CPU and turnaround time Only 7 first class sites in Interior Alaska 21 additional sites of unknown data quality run by volunteers for limited amount of quantities Clouds or smoke from existing fire may affect remote sensing None of the above methods permits 3 months ahead assessment of fire risk as required by regional fire agencies for planning

7. Moisture, temperature and wind dependent Fosberg Fire Weather Index typically used for fire risk assessment Equilibrium moisture content Fosberg Fire Weather Index (FFWI) Wind factor Moisture damping coefficient Wind speed Temperature Relative humidity after Goodrick (2002)

8. Fosberg Fire Weather Index obtained from climate simulations not very helpful Arctic Ocean AlaskaCanada FFWI (-.-)

9. Modified Fosberg Fire Weather Index (mFFWI) by inclusion of Keetch-Byram Drought Index (KBDI) and fuel availability Spatially varying fuel availability factor FFWIModified FFWI observations model

10. Heterogeneous precipitation distribution requires spatial fuel availability factor 30-average observed precipitation according to the GPCC (color) and as simulated by CCSM (solid lines)

11. Dryness yields to high fuel availability Arctic Ocean

12. mFFWI obtained from climate simulations lacks regional details Improvement compared to FFWI, but too coarse modified FFWI (-.-) FFWI (-.-) Arctic Ocean

13. Schematic to bridge operational climate forecasts to regional and local mFFWI operational climate forecasts WRF nested runs in endangered areas weekly mFFWI maps for quick-looks 6-hourly forcing data data base of climate model data plus derived mFFWI, regional model data plus derived mFFWI, observations statistical analysis to derive relationships, evaluation, improvement, etc. observations daily mFFWI maps for analysis daily mFFWI local maps if required

14. Schematic of time staggering for mFFWI ensembles operational climate forecasts WRF simulations every x days for 5d time local mFFWI mFFWI form various WRF runs started at different times envelop of mFFWI start procedure 3 month ahead a fire season for guesstimates repeat procedure until end of the fire season if a “real” month is over

15. Application of WRF for 4 km x 4 km grid increments 6-27-2005 1400 AST

16. Great horizontal variability of fuel availability factor within the Interior

17. Importance of consideration of fuel availability within the region 6-27-2005 1400 AST

18. Modified FFWI derived from scare meteorological observations often shows lower fire risk than FFWI

19. Fires in June 2005 2005/06/14 2215 UT Fires in eastern Alaska Aqua http://rapidfire.sci.gsfc.nasa.gov/ gallery/?search=alaska&date 2005/06/26 2050 UT Fires in Alaska and Yukon Territory Terra http://rapidfire.sci.gsfc.nasa.gov/gallery/ ?search=alaska&date

20. Evaluation of simulated (m)FFWI difficult Not every area with high (m)FFWI burns Network density (4 sites in Interior Alaska) Complex terrain, low representativeness Random errors due to initial and boundary conditions or observations Systematic errors from consistent misrepresentation of geometrical, physical, or numerical factors Error propagation in measurements/simulations Actual fires and burned areas affect temperature, humidity, precipitation Overall evaluation recommended

21. Small uncertainty of (m)FFWI from propagation of measurement errors Where  stands for FFWI or mFFWI and  is RH, T, v, P Assume:  (RH)=  5%  (T)=  0.5K  (v)=  0.5m/s  (P)=  0.01inch Note that uncertainty of FFWI and mFFWI will only differ if precipitation occurs

22. Note that McGrath and Northway airports are outside the WRF model domain! WRF well provides modified FFWI

23. Forecast errors propagate in mFFWI Simulation started 6-11-2005 0600 UT valid for 6-11-2005 2300 UT (1400 AST) Simulation started 6-7-2005 0600 UT valid for 6-11-2005 2300 UT (1400 AST) Errors in P yield errors in soil moisture, FAF, KBDI, mFFWI Errors in T yield errors in RH, FFWI, mFFWI Errors in RH yield errors in FFWI, mFFWI Errors in v yield errors in FFWI, mFFWI => Estimate uncertainty of mFFWI with Gaussian Error Propagation (GEP) principles

24. Suitability of WRF for fire risk assessment Simulation length matters Mean FFWI and mFFWI obtained by WRF at the 4 observational sites are the same as for observations Errors between (m)FFWI derived from WRF and observations are within the range of observational uncertainty RMSE are lower for mFFWI than FFWI Determination of (m)FFWI more difficult in mountainous than relatively flat terrain Means of (m)FFWI derivded from WRF and observations do not differ significantly (95% confidence) according to a t-test, but variance does according to f-test WRF is suitable for fire weather forecast

25. Acknowledgements We thank Edward O'Lenic and the organizing team for inviting us Zhao Li, Debasish Pai Mazumder, Ted Fathauer for collaboration You for your attention

26. Released aerosols and trace gases reduce air quality Courtesy: J. Connor

27. Fires, smoke, and burn scars in Alaska and Yukon Territory in August 2005 2005/08/10 2210 UT (false color) Aqua http://rapidfire.sci.gsfc.nasa.gov/gallery/?search=alaska&date

28. Large fire scars increase of upward transport and development of a non-classical mesoscale circulation 0500 AST1100 AST 2100 AST 1400 AST From Mölders & Kramm 2006

29. Cloud formation changes due to wildfire scars 0500 AST1100 AST 2100 AST 1400 AST From Mölders & Kramm 2006