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首页> 外文期刊>Journal of Geophysical Research. Biogeosciences >Predictability of carbon emissions from biomass burning in Indonesia from 1997 to 2006
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Predictability of carbon emissions from biomass burning in Indonesia from 1997 to 2006

机译：1997年至2006年印度尼西亚生物质燃烧产生的碳排放量的可预测性

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摘要

Drought and sea surface temperature were examined as the causes of severe biomass burning C emissions in Indonesia for 1997–2006, obtained from the Global Fire Emissions Database. Eighteen predictor variables were considered under log linear and piecewise regression models. The predictor variables considered were precipitation totals of up to 6 months, output from two soil moisture models, and sea surface temperature (SST) indicators reflecting El Ni?o and Indian Ocean Dipole strength. Nonparametric bootstrap techniques were used to estimate confidence intervals for predictability and thresholds below which severe C emissions are likely. Across equatorial Southeast Asia, the best predictor was 3-month total precipitation, which explained 79% of variance in C emissions. When considered individually, and with the incorporation of satellite precipitation estimates, predictability for southern Sumatra and southern Kalimantan improved to 97% and 92%, respectively, using 4-month total precipitation. There is a high risk of severe burning when 4-month precipitation falls below thresholds of 350 mm in southern Sumatra and 650 mm in southern Kalimantan and when 6-month precipitation falls below 900 mm in Papua. In general, simple precipitation totals outperformed more complicated soil moisture models and SST-based indices. Physically, seasonal precipitation controls fire emissions through its regulation of groundwater level and, hence, the amount of peat available for drying. Seasonal precipitation, in turn, is strongly influenced by SST patterns in the tropical Pacific and Indian oceans. The most severe drought and fire events appear equally influenced by Indian Ocean Dipole events and El Ni?o events.
机译：从全球火灾排放数据库获得的干旱和海面温度作为印度尼西亚1997–2006年严重生物质燃烧碳排放的原因进行了调查。在对数线性和分段回归模型下考虑了18个预测变量。所考虑的预测变量包括长达6个月的降水总量，两个土壤湿度模型的输出以及反映厄尔尼诺和印度洋偶极子强度的海面温度（SST）指标。非参数自举技术用于估计可预测性和阈值的置信区间，低于该阈值可能会严重释放C。在整个赤道东南亚，最好的预测指标是3个月的总降水量，这解释了79％的C排放量变化。如果单独考虑并结合卫星降水估计，使用4个月的总降水量，苏门答腊南部和加里曼丹南部的可预测性分别提高到97％和92％。当苏门答腊南部的4个月降水量低于350毫米，加里曼丹南部的650毫米以下时，巴布亚的6个月的降水低于900毫米时，存在严重燃烧的高风险。通常，简单的降水总量要优于更复杂的土壤水分模型和基于SST的指数。从物理上讲，季节性降水通过调节地下水位以及因此可干燥的泥炭量来控制着火的排放。反过来，季节性降水受到热带太平洋和印度洋海表温度的强烈影响。最严重的干旱和火灾事件似乎同样受到印度洋偶极子事件和厄尔尼诺事件的影响。

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