Global Fire Forecasts Using Both Large‐Scale Climate Indices and Local Meteorological Parameters. Issue 8 (31st August 2019)

• Record Type: Journal Article
• Authors: Shen, Huizhong; Tao, Shu; Chen, Yilin; Odman, Mehmet Talât; Zou, Yufei; Huang, Ye; Chen, Han; Zhong, Qirui; Zhang, Yanyan; Chen, Yuanchen; Su, Shu; Lin, Nan; Zhuo, Shaojie; Li, Bengang; Wang, Xilong; Liu, Wenxin; Liu, Junfeng; Pavur, Gertrude K.; Russell, Armistead G.
• Is Part Of: Global biogeochemical cycles. Volume 33:Issue 8(2019:Aug.)
• Page Start: 1129
• Page End: 1145
• Publication Date: 2019-08-31
• Subjects: global fire forecasts -- climate indices -- meteorological conditions -- climate change; Biogeochemical cycles -- Periodicals; Electronic journals; 577.1405
• DOI: 10.1029/2019GB006180
• Languages: English
• ISSNs: 0886-6236

Abstract

Abstract: Fire forecasts that predict dry‐season fire activities several months in advance are beneficial for fire management. On a global scale, however, the predictability of fires is limited because fires depend on multiple factors and lack a single dominant predictor to describe diverse fire characteristics across regions. Here, based on 33 local meteorological parameters (MPs) and 37 large‐scale climate indices (CIs), we establish four empirical model clusters to predict global interannual fire variability. We show that across various geographic locations, the models provide reliable fire forecasts at least three months prior to the peak fire months. Compared to MPs, CIs such as the Oceanic Niño Index are comparable or even superior predictors. Globally, as well as in most continents, the El Niño–Southern Oscillation is the major driving force, explaining 17% of interannual fire variability, with strong implications for fire carbon emissions and the global carbon cycle. Other important predictors include the Northern Atlantic sea surface temperature (9%), the Southern Atlantic sea surface temperature (5%), and the Pacific/North American Pattern (3%). The predictive models reveal a strong interaction between MPs and CIs, indicating potential climate‐induced modification of fire responses to meteorological conditions. We show that the newly developed predictive models can benefit future fire management in response to climate change. Plain Language Summary: Integration of climate indices and meteorological parameters enables more accurate fire forecasts globally with a longer forecast length. Key Points: We developed empirical models to provide global fire forecasts 3 months prior to the peak fire months Combining meteorological and climate variables enhances fire predictability The El Niño–Southern Oscillation is a driving factor that affects global interannual fire variability