On using dynamical seasonal forecasts to develop management-driven wildland fire outlooks in Alaska
As wildland fires in Alaska and its boreal forest become more extreme, preparing for the upcoming wildfire season has become increasingly challenging for fire managers. This study was developed in close collaboration with fire managers to address their need for advanced summer fire outlooks issued i...
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Elsevier
2025-08-01
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| Series: | Climate Services |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405880725000536 |
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| author | Cecilia Borries-Strigle Uma S. Bhatt Peter A. Bieniek Mitchell Burgard Eric Stevens Heidi Strader Richard L. Thoman Alison York Robert H. Ziel |
| author_facet | Cecilia Borries-Strigle Uma S. Bhatt Peter A. Bieniek Mitchell Burgard Eric Stevens Heidi Strader Richard L. Thoman Alison York Robert H. Ziel |
| author_sort | Cecilia Borries-Strigle |
| collection | DOAJ |
| description | As wildland fires in Alaska and its boreal forest become more extreme, preparing for the upcoming wildfire season has become increasingly challenging for fire managers. This study was developed in close collaboration with fire managers to address their need for advanced summer fire outlooks issued in March and May. Three seasonal forecast models are used to create summer fire outlooks: NOAA CFSv2, ECMWF SEAS5, and Météo-France System8. Variables from these forecasts are used to calculate Buildup Index (BUI), an operationally used fire weather index from the Canadian Forest Fire Danger Rating System. The BUI outlooks are evaluated based on Alaska wildfire subseason, BUI tercile, and predictive service area subregion with the area under the ROC curve (AUROC), Heidke, and mean squared error (MSE) skill scores. Skill is greatest for the wind (April 1–June 10) and drought (July 21–August 9) subseasons and in the Western Boreal subregion of Alaska. Combining the models into a multimodel ensemble increases forecast skill by an average of 11% (19%) for the March (May) forecast AUROC score and an average of 87% (92%) for the March (May) forecast Heidke skill score. May forecasts typically have equal or greater skill than March forecasts, with the greatest increases in skill seen during the wind subseason. However, instances of higher Heidke and MSE skill scores for March forecasts, especially in later subseasons and during large fires years, could be explained by the seasonally decreased predictability. Practical Implications: Alaska’s wildfire season has changed over the past 30 years. The season has lengthened by about a month, and extreme fire events have become more frequent. Fire managers begin preparing for the upcoming fire season in March, several weeks before the administrative start of the fire season (April 1) and about three months before the typical peak in late June to early July. With the increasing availability of dynamical seasonal forecasts, the Alaska fire management community has expressed growing interest in using these tools for operational planning.In this study, we used March-initialized seasonal forecasts to generate early-season outlooks of the Buildup Index (BUI), a key fire weather variable. These outlooks align with the timing of critical early-season decision-making by fire managers, including resource allocation and national coordination. After several years of providing these outlooks, fire managers requested additional outlooks initialized in May to support decisions after the season has begun but before its peak. Although May-initialized forecasts are typically more skillful, our early focus on the more challenging March forecasts reflects our commitment to meeting fire managers’ needs. This long-term collaboration, including presentations at spring meetings and sustained engagement through biweekly calls, has helped refine our scientific focus—e.g., by emphasizing the duff-burning subseason and the timing of season-ending rains. Throughout this work, we have taken an operational perspective, aiming to keep methods computationally efficient to accommodate the large data volumes and time-sensitive decision-making. As such, the current study establishes a baseline of forecast skill using relatively streamlined methods. This foundation allows the fire management community to explore ways to tailor and enhance forecast products, such as applying more advanced bias correction techniques for high-latitude models or refining skill assessments by subregion or subseason. It also creates a platform for optimizing the multimodel ensemble approach, either by adjusting model weights or expanding the ensemble membership.This work represents one component of a broader effort to improve seasonal fire weather prediction in Alaska. As collaboration continues, these BUI outlooks can be integrated with emerging long-range forecasting products for fuels and lightning to build a more comprehensive picture of the upcoming fire season. We remain actively engaged with fire managers, sharing updates during spring and fall operational meetings and incorporating their feedback in ongoing research and tool development. |
| format | Article |
| id | doaj-art-a533c4dd888d4b98a604f0ecef4a1861 |
| institution | DOAJ |
| issn | 2405-8807 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Climate Services |
| spelling | doaj-art-a533c4dd888d4b98a604f0ecef4a18612025-08-20T03:04:50ZengElsevierClimate Services2405-88072025-08-013910059210.1016/j.cliser.2025.100592On using dynamical seasonal forecasts to develop management-driven wildland fire outlooks in AlaskaCecilia Borries-Strigle0Uma S. Bhatt1Peter A. Bieniek2Mitchell Burgard3Eric Stevens4Heidi Strader5Richard L. Thoman6Alison York7Robert H. Ziel8Department of Atmospheric Sciences, University of Alaska Fairbanks, Fairbanks, AK, USA; Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK, USA; Corresponding author.Department of Atmospheric Sciences, University of Alaska Fairbanks, Fairbanks, AK, USA; Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK, USAInternational Arctic Research Center, University of Alaska Fairbanks, Fairbanks, AK, USAInternational Arctic Research Center, University of Alaska Fairbanks, Fairbanks, AK, USA; Alaska Fire Science Consortium, Fairbanks, AK, USAAlaska Interagency Coordination Center, Alaska Fire Services, Fairbanks, AK, USAAlaska Interagency Coordination Center, Alaska Fire Services, Fairbanks, AK, USAInternational Arctic Research Center, University of Alaska Fairbanks, Fairbanks, AK, USA; Alaska Center for Climate Assessment and Policy, USAInternational Arctic Research Center, University of Alaska Fairbanks, Fairbanks, AK, USA; Alaska Fire Science Consortium, Fairbanks, AK, USAInternational Arctic Research Center, University of Alaska Fairbanks, Fairbanks, AK, USAAs wildland fires in Alaska and its boreal forest become more extreme, preparing for the upcoming wildfire season has become increasingly challenging for fire managers. This study was developed in close collaboration with fire managers to address their need for advanced summer fire outlooks issued in March and May. Three seasonal forecast models are used to create summer fire outlooks: NOAA CFSv2, ECMWF SEAS5, and Météo-France System8. Variables from these forecasts are used to calculate Buildup Index (BUI), an operationally used fire weather index from the Canadian Forest Fire Danger Rating System. The BUI outlooks are evaluated based on Alaska wildfire subseason, BUI tercile, and predictive service area subregion with the area under the ROC curve (AUROC), Heidke, and mean squared error (MSE) skill scores. Skill is greatest for the wind (April 1–June 10) and drought (July 21–August 9) subseasons and in the Western Boreal subregion of Alaska. Combining the models into a multimodel ensemble increases forecast skill by an average of 11% (19%) for the March (May) forecast AUROC score and an average of 87% (92%) for the March (May) forecast Heidke skill score. May forecasts typically have equal or greater skill than March forecasts, with the greatest increases in skill seen during the wind subseason. However, instances of higher Heidke and MSE skill scores for March forecasts, especially in later subseasons and during large fires years, could be explained by the seasonally decreased predictability. Practical Implications: Alaska’s wildfire season has changed over the past 30 years. The season has lengthened by about a month, and extreme fire events have become more frequent. Fire managers begin preparing for the upcoming fire season in March, several weeks before the administrative start of the fire season (April 1) and about three months before the typical peak in late June to early July. With the increasing availability of dynamical seasonal forecasts, the Alaska fire management community has expressed growing interest in using these tools for operational planning.In this study, we used March-initialized seasonal forecasts to generate early-season outlooks of the Buildup Index (BUI), a key fire weather variable. These outlooks align with the timing of critical early-season decision-making by fire managers, including resource allocation and national coordination. After several years of providing these outlooks, fire managers requested additional outlooks initialized in May to support decisions after the season has begun but before its peak. Although May-initialized forecasts are typically more skillful, our early focus on the more challenging March forecasts reflects our commitment to meeting fire managers’ needs. This long-term collaboration, including presentations at spring meetings and sustained engagement through biweekly calls, has helped refine our scientific focus—e.g., by emphasizing the duff-burning subseason and the timing of season-ending rains. Throughout this work, we have taken an operational perspective, aiming to keep methods computationally efficient to accommodate the large data volumes and time-sensitive decision-making. As such, the current study establishes a baseline of forecast skill using relatively streamlined methods. This foundation allows the fire management community to explore ways to tailor and enhance forecast products, such as applying more advanced bias correction techniques for high-latitude models or refining skill assessments by subregion or subseason. It also creates a platform for optimizing the multimodel ensemble approach, either by adjusting model weights or expanding the ensemble membership.This work represents one component of a broader effort to improve seasonal fire weather prediction in Alaska. As collaboration continues, these BUI outlooks can be integrated with emerging long-range forecasting products for fuels and lightning to build a more comprehensive picture of the upcoming fire season. We remain actively engaged with fire managers, sharing updates during spring and fall operational meetings and incorporating their feedback in ongoing research and tool development.http://www.sciencedirect.com/science/article/pii/S2405880725000536Seasonal forecastingBoreal fireWildfire managementCo-production |
| spellingShingle | Cecilia Borries-Strigle Uma S. Bhatt Peter A. Bieniek Mitchell Burgard Eric Stevens Heidi Strader Richard L. Thoman Alison York Robert H. Ziel On using dynamical seasonal forecasts to develop management-driven wildland fire outlooks in Alaska Climate Services Seasonal forecasting Boreal fire Wildfire management Co-production |
| title | On using dynamical seasonal forecasts to develop management-driven wildland fire outlooks in Alaska |
| title_full | On using dynamical seasonal forecasts to develop management-driven wildland fire outlooks in Alaska |
| title_fullStr | On using dynamical seasonal forecasts to develop management-driven wildland fire outlooks in Alaska |
| title_full_unstemmed | On using dynamical seasonal forecasts to develop management-driven wildland fire outlooks in Alaska |
| title_short | On using dynamical seasonal forecasts to develop management-driven wildland fire outlooks in Alaska |
| title_sort | on using dynamical seasonal forecasts to develop management driven wildland fire outlooks in alaska |
| topic | Seasonal forecasting Boreal fire Wildfire management Co-production |
| url | http://www.sciencedirect.com/science/article/pii/S2405880725000536 |
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