▲ 作者:MATTHEW W. JONES, SANDER VERAVERBEKE et al.
▲ 链接:
https://www.science.org/doi/10.1126/science.adl5889
▲ 摘要:
我们使用机器学习系统地将森林生态区分为12个全球森林群落,每个群落都显示出了对对气候、人类和植被控制不同的敏感性。这表明,在2001年至2023年期间,与气候变化有关的热带焦树中迅速增加的森林火灾排放抵消了热带焦树中下降的排放。
由于容易着火的天气及森林覆盖率和生产力的增加,一个温带地区的年排放量增加了两倍。这导致全球森林生态区的森林火灾碳排放量增加了60%。我们的研究结果强调了在气候变化的情况下,森林及其碳储量对火灾干扰的脆弱性日益增加。
▲ Abstract:
We use machine learning to systematically group forest ecoregions into 12 global forest pyromes, with each showing distinct sensitivities to climatic, human, and vegetation controls. This delineation revealed that rapidly increasing forest fire emissions in extratropical pyromes, linked to climate change, offset declining emissions in tropical pyromes during 2001 to 2023. Annual emissions tripled in one extratropical pyrome due to increases in fire-favorable weather, compounded by increased forest cover and productivity. This contributed to a 60% increase in forest fire carbon emissions from forest ecoregions globally. Our results highlight the increasing vulnerability of forests and their carbon stocks to fire disturbance under climate change.