Abstract Models of atmospheric composition rely on fire emissions inventories to reconstruct and project impacts of biomass burning on air quality, public health, climate, ecosystem dynamics, and land-atmosphere exchanges. Many such global inventories use satellite measurements of active fires and/or burned area from the Moderate Resolution Imaging Spectroradiometer (MODIS). However, differences across inventories in the interpretation of satellite imagery, the emissions factors assumed for different components of smoke, and the adjustments made for small and obscured fires can result in large regional differences in fire emissions estimates across inventories. Using Google Earth Engine, we leverage 15 years (2003–2017) of MODIS observations and 6 years (2012–2017) of observations from the higher spatial resolution Visible Imaging Infrared Radiometer Suite (VIIRS) sensor to develop metrics to quantify five major sources of spatial bias or uncertainty in the inventories: (1) primary reliance on active fires versus burned area, (2) cloud/haze burden on the ability of satellites to “see” fires, (3) fragmentation of burned area, (4) roughness in topography, and (5) small fires, which are challenging to detect. Based on all these uncertainties, we devise comprehensive “relative fire confidence scores,” mapped globally at 0.25° × 0.25° spatial resolution over 2003–2017. We then focus on fire activity in Indonesia as a case study to analyze how the choice of a fire emissions inventory affects model estimates of smoke-induced health impacts across Equatorial Asia. We use the adjoint of the GEOS-Chem chemical transport model and apply emissions of particulate organic carbon and black carbon (OC + BC smoke) from five global inventories: Global Fire Emissions Database (GFEDv4s), Fire Inventory from NCAR (FINNv1.5), Global Fire Assimilation System (GFASv1.2), Quick Fire Emissions Dataset (QFEDv2.5r1), and Fire Energetics and Emissions Research (FEERv1.0-G1.2). We find that modeled monthly smoke PM2.5 in Singapore from 2003 to 2016 correlates with observed smoke PM2.5, with r ranging from 0.64–0.84 depending on the inventory. However, during the burning season (July to October) of high fire intensity years (e.g., 2006 and 2015), the magnitude of mean Jul-Oct modeled smoke PM2.5 can differ across inventories by >20 μg m−3 (>500%). Using the relative fire confidence metrics, we deduce that uncertainties in this region arise primarily from the small, fragmented fire landscape and very poor satellite observing conditions due to clouds and thick haze at this time of year. Indeed, we find that modeled smoke PM2.5 using GFASv1.2, which adjusts for fires obscured by clouds and thick haze and accounts for peatland emissions, is most consistent with observations in Singapore, as well as in Malaysia and Indonesia. Finally, we develop an online app called FIRECAM for end-users of global fire emissions inventories. The app diagnoses differences in emissions among the five inventories and gauges the relative uncertainty associated with satellite-observed fires on a regional basis.

中文翻译：

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诊断全球火灾排放清单的空间偏差和不确定性：印度尼西亚作为区域案例研究

摘要 大气成分模型依赖于火灾排放清单来重建和预测生物质燃烧对空气质量、公共卫生、气候、生态系统动态和陆地-大气交换的影响。许多此类全球清单使用中分辨率成像光谱仪 (MODIS) 对活跃火灾和/或燃烧区域的卫星测量。然而，不同清单在卫星图像解释方面的差异、针对不同烟雾成分假设的排放因子以及针对小型和隐蔽火灾所做的调整可能导致清单之间火灾排放估计的巨大区域差异。使用谷歌地球引擎，我们利用来自更高空间分辨率可见光成像红外辐射计套件 (VIIRS) 传感器的 15 年（2003-2017）MODIS 观测和 6 年（2012-2017）观测来开发指标来量化空间偏差或不确定性的五个主要来源清单：（1）主要依赖于活跃的火灾与燃烧区域，（2）云/雾对卫星“看到”火灾的能力造成的负担，（3）燃烧区域的碎片化，（4）地形的粗糙度，以及（ 5) 难以检测的小火。基于所有这些不确定性，我们设计了全面的“相对火灾置信度分数”，在 2003-2017 年以 0.25° × 0.25° 的空间分辨率进行全球映射。然后，我们将印度尼西亚的火灾活动作为案例研究，以分析火灾排放清单的选择如何影响对整个赤道亚洲烟雾引起的健康影响的模型估计。我们使用 GEOS-Chem 化学传输模型的伴随模型，并应用来自五个全球清单的颗粒有机碳和黑碳（OC + BC 烟雾）的排放：全球火灾排放数据库 (GFEDv4s)、来自 NCAR 的火灾清单 (FINNv1.5) 、全球火灾同化系统 (GFASv1.2)、快速火灾排放数据集 (QFEDv2.5r1) 和火灾能量学和排放研究 (FEERv1.0-G1.2)。我们发现，新加坡 2003 年至 2016 年模拟的每月烟雾 PM2.5 与观察到的烟雾 PM2.5 相关，r 范围为 0.64–0.84，具体取决于库存。然而，在高火强度年份（例如，2006 年和 2015 年），7 月至 10 月模拟的平均烟雾 PM2.5 的大小可能因清单而异 >20 μg m-3 (>500%)。使用相对火灾置信度指标，我们推断出该地区的不确定性主要来自小而分散的火灾景观，以及由于每年这个时候的云层和浓雾造成的非常差的卫星观测条件。事实上，我们发现使用 GFASv1.2 模拟烟雾 PM2.5，它调整了被云层和浓雾遮蔽的火灾并考虑了泥炭地排放，与新加坡以及马来西亚和印度尼西亚的观察结果最一致。最后，我们为全球火灾排放清单的最终用户开发了一个名为 FIRECAM 的在线应用程序。