Tundra Fire in Alaska:A Weather Perspective

By: James WhiteThe Ohio State University: Atmospheric and Earth Sciences

Mentor: Rick ThomanNational Weather Service Alaska Region: Climate Science and Services Manager

Project Background

Fire in the Tundra

- Over the past 50 years, more than 4.5 million acres of Alaska tundra have burned2

- Fires are very rapid but sporadic, leading to difficult forecasts

- Impacts vegetation, wildlife, permafrost, carbon cycling, air quality and local communities3

- May increase rapidly with climate change4

Photo: Jennifer Barnes, National Park Service

Photo: Robert Ziel, Alaska Fire Service

Fire Weather in the Tundra

- Ignition and fire growth centered around the summer solstice during peak solar drying and before late summer rains.

- Current models based off grass and shrub fuels7:

- Heavily weights wind as a critical spread variable

- Sensitive to short term, fine fuel moisture

- Long term build up important to burn severity, but not necessarily spread

Figure: Most common fuel models used in tundra fire, Adapted from (Scott, 2005)8

The connection between weather and tundra fire growth has not been studied

in detail!

Goal: Determine weather variables critical to forecasting tundra fire spread

Step 1: Identify Tundra Fire

Defining Tundra

- A vast, treeless arctic ecosystem marked by grasses, dwarf shrubs, mosses, and lichens and underlain with permafrost.

- We used vegetation maps from the NLCD5 and University of Alaska Anchorage6 to define tundra regions

- Focused on only 3 regions due to lack of data:

- Noatak River Basin

- Seward Peninsula and SelawikRiver Basin

- Yukon-Kuskokwim River Delta

Determining Fire Growth Days

- Used high resolution daily fire growth perimeters from the NASA ABoVE program which uses MODIS satellite data9

- Provides daily fire growth information for all large fires after 2001

- Worked with GaBriella to get yearly burned acreage since 1970

Determining Fire Growth Days

- Zeke identified initial growth events as the most important to forecast, used a formula to highlight these dates

- Identified several large fire growth days from each region:

- Noatak: 17 days, Seward: 21 days, YK Delta: 30 days

- All days fell between May 24th and August 20th

Some Tundra Fire Examples

- Noatak:

- 2005 Imelyak

- 2010 Sidik Lake

- 2010 Eli River

- 2010 Kaluktavik River

- 2012 Uvgoon Creek

- Seward Peninsula:

- 2003 Kerulu Creek

- 2004 Oregon Creek

- 2010 Kilovilik Creek

- 2011 Fish River

- 2015 Koyuk

- 2015 Mingvk Lake

- YK Delta:

- 2005 Talbiksok

- 2006 Negeethluk River

- 2009 Allman Creek

- 2013 Doestock Creek

- 2015 Whitefish Lake

- 2015 Fog River

Photo: Uvgoon Creek, Alaska Fire Service Photo: Whitefish Lake Fire, Alaska Forestry Service

Step 2: Obtaining Reliable Weather Data

Obtaining Reanalysis Data

- Previous analysis has shown that MERRA reanalysis performs best for Alaskan tundra locations10

- Retrieved weather information from MERRA for use in this analysis11

Photo: NASA Global Modeling and Assimilation Office

Obtaining Station Data

- Retrieved historical station data to validate MERRA data and check historic fire weather index calculations12

- Lack of historical network density meant only one record could be accurately created for each region

- Noatak: KTZA2

- Seward: HDOA2 and QRZA2

- YK Delta: PABE

Validating MERRA

- Using hourly station data and hourly data from MERRA, created a 15 year (2001-2015) summer (May 24th – Aug 20th) climatology of daily surface variables

- Compared MERRA grid cells with corresponding stations over climatology, showed good agreement for variables of interest.

- Used line of best fit to create a rough bias correction for some variables

Step 3: Determining Significant Variables

Significance Testing

- Performed significance tests on many surface variables for large fire spread days vs 15 year summer climatology

- Tests applied to MERRA data and surface stations independently reach the same conclusions.

- Hypothesis:

- Synoptic, wind driven spread

- Requires low humidity (~30%)

- Needs dry fine fuels

Photo: KNOM Radio Mission

Significance Conclusions

- Synoptic wind speed is a poor fire forecast variable

- This does not mean local, plume associated winds are unimportant!13

- Note that tundra has climatologically high winds

- High maximum temperatures (~70° F) and low minimum relative humidity (< 45%) associated with strong diurnal solar heatingappear critical to fire spread

- In sunlight, tundra surfaces can heat significantly above 2m air temperature14

- Cloud cover and hence diurnal solar radiation is likely the single most important variable to forecast

Step 4: Synoptic Analysis

Synoptic Analysis

- Used MERRA and 30 year climatology to map variables

- Conducted a brief synoptic analysis of each identified fire event

- Created composite maps for the previously identified fire days

- Found clear synoptic patterns

2m Temperature Anomaly

Noatak Seward YK Delta

Temperature Anomaly (F)

Sea Level Pressure

Noatak Seward YK Delta

Pressure (mb)

500mb Height

Noatak Seward YK Delta

Height (m)

500mb Wind Speed

Noatak Seward YK Delta

Wind Speed (mph)

Synoptic Conclusions

- Large synoptic patterns:

- Well above average 2m temperatures (+4° F or more)

- Broad surface low pressure pattern

- Weak, localized 500mb ridge often with a moderate Aleutian low

- Local 500mb wind speed minimum with a jet streak in the Gulf of Alaska

- This pattern is consistent with solar heating being more important than synoptic winds!

A Note on Mixing

- Many extreme fire spread days associated with low level (850mb) dry air

- There seems to be a weak but independent relationship between humidity minimum and boundary layer depth

- These may indicate low level dry air mixing as a fire growth driver in some events

Step 5: Fire Weather Index Performance

Fire Weather Index System

- Looked at 3 main indices: FFMC, ISI, and BUI

- Used historic calculated daily variables, followed the same overall methodology as the variable significance testing

- Included all spread days

- Hypothesis:

- FFMC and ISI will both perform well

- BUI will perform poorly

Figure: Fire Weather Index System description, Mesowest Alaska Fire and Fuels

Fire Weather Index Conclusions

- FFMC had high skill at identifying spread days and often even distinguishes between small and large spread days

- General threshold of ~85 FFMC for large spread

- ISI had little skill at distinguishing spread days

- BUI had some skill at generally identifying spread

- General spread values between BUI = 30 and 80

- BUI may assist in predicting burn severity which is not addressed here

Step 6: Weather Model Performance

Checking the GFS

- Checked GFS performance of some key forecast variables

- Pulled the GFS forecasts of 12 large growth days (4 from each region) and analyzed model skill up to 7 days from the event over the Alaska Region

- Used a normalized measure of skill

Figure: An example GFS output retrieved from Tropical Tidbits

Weather Model Conclusions

- Synoptic variables had high skill by day 5

- Temperature had high skill by day 3

- Humidity had moderate skill by day 3

- Unfortunately, cloud cover data could not be retrieved from NCEP

- This analysis was very provisional with a very coarse resolution; GFS performance should be explored in significantly more depth!

- Special thanks to Brian Brettschneider for assisting in GFS data processing!

Conclusion

Improving Fire Weather Forecasts

- Cloud cover and solar heating are the most critical forecast variables

- Weak upper-level dynamics often promote growth

- FFMC is the single best fire weather index parameter

- GFS can have high skill for many fire variables within 3 days

Current tundra fire models often overweight wind as a spread variable.

Future Research

- Consider seasonal climate parameters.

- Use information and data from this research to analyze and improve individual fire model performance in the tundra

- Broaden analysis to other tundra regions (Canada, North Slope, Alpine)

- Further explore short term variables such as dry air mixing and GFS performance

Figure: Most common fuel models used in tundra fire, Adapted from (Scott, 2005)8

Acknowledgments

- Thank you NOAA Hollings for making this research possible

- Thank you Rick Thoman, John Walsh, Alison York, and Tina Buxbaumfor all your wonderful mentorship and support

- Thank you Robert Ziel, GaBriella Branson, Sharon Alden, Heidi Strader and everyone at AFS and AICC for you advice and hard work

- Thank you Vladimir Alexeev, Celia Fisher, and all the IARC REU students for the interesting lectures, conversations, and adventures

Thank you for an unforgettable summer!

Citations

1. Ziel, R. (2015). Modeling Fire Growth Potential by Emphasizing Significant Growth Events: Characterizing a Climatology of Fire Growth Days in Alaska’s Boreal Forest. Paper presented at 11th Symposium on Fire and Forest Meteorology, Minneapolis, United States. Retrieved from https://ams.confex.com/ams/11FIRE/webprogram/Paper272864.html

2. Alaska Interagency Coordination Center. (2018). Alaska Fire History [Dataset]. Retrieved May 30, 2018, from https://fire.ak.blm.gov/predsvcs/maps.php

3. Higuera, P. E., Chipman, M. L., Barnes, J. L., Urban, M. A., & Hu, F. S. (2011). Variability of tundra fire regimes in Arctic Alaska: millennial‐scale patterns and ecological implications. Ecological Applications, 21(8), 3211-3226. doi:https://doi.org/10.1890/11-0387.1

4. Young, A. M., Higuera, P. E., Duffy, P. A., & Hu, F. S. (2016). Climatic thresholds shape northern high‐latitude fire regimes and imply vulnerability to future climate change. Ecography, 40(5), 606-617. doi:https://doi.org/10.1111/ecog.02205

5. Multi-Resolution Land Characteristics Consortium. (2014, October 10). National Land Cover Database 2001 [Dataset]. Retrieved June 2, 2018, from https://www.mrlc.gov/nlcd01_data.php

6. Alaska Natural Heritage Program, Boggs, K., Flagstad, L., Boucher, T., Kuo, T., Fehringer, D., . . . Aisu, M. (2016, July 20). Vegetation Map and Classification: Northern, Western, and Interior Alaska Second Edition [Dataset]. Retrieved June 5, 2018, from http://accs.uaa.alaska.edu/vegetation-ecology/vegetation-map-northern-western-and-interior-alaska/

7. Alaska Wildland Fire Coordinating Group. (2018). Fuel Model Guide to Alaska Vegetation. Retrieved from https://www.frames.gov/files/9515/2887/5259/AK_Revised_FuelModelGuide_FINAL_May2018.pdf

8. Scott, J. H., & Burgan, R. E. (2005). Standard Fire Behavior Fuel Models: A Comprehensive Set for Use with Rothermel’s Surface Fire Spread Model. Retrieved from https://www.fs.fed.us/rm/pubs/rmrs_gtr153.pdf

9. Loboda, T. V., & Hall, J. V. (2017, December 27). ABoVE: Wildfire Date of Burning within Fire Scars across Alaska and Canada, 2001-2015 [Dataset]. Retrieved May 29, 2018, from https://daac.ornl.gov/ABOVE/guides/Wildfires_Date_of_Burning.html

10. Lader, R., Bhatt, U. S., Walsh, J. E., Rupp, S. T., & Bieniek, P. A. (2015). Two-Meter Temperature and Precipitation from Atmospheric Reanalysis Evaluated for Alaska. Journal of Applied Meteorology and Climatology, 55, 901-922. doi:10.1175/JAMC-D-15-0162.1

11. Global Modeling and Assimilation Office. (2015). MERRA-2 tavg1_2d_slv_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Single-Level Diagnostics V5.12.4 [Dataset]. Retrieved July 13, 2018, from https://disc.gsfc.nasa.gov/datasets/M2T1NXSLV_V5.12.4/summary?keywords=single-level%20diagnostics

12. Mesowest. (2018). [Alaska Fire and Fuels Download] [Dataset]. Retrieved July 3, 2018, from https://akff.mesowest.org/download/

13. Clements, C. B., Zhong, S., Bian, X., Heilman, W. E., & Byun, D. W. (2008). First observations of turbulence generated by grass fires. Journal of Geophysical Research: Atmospheres, 113(D22), 1-13. doi:https://doi.org/10.1029/2008JD010014

14. Lund, M., Steigler, C., Abermann, J., Citterio, M., Hansen, B. U., & As, D. (2017). Spatiotemporal variability in surface energy balance across tundra, snow and ice in Greenland. Ambio, 46, 81-93. doi:10.1007/s13280-016-0867-5

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