Tropical climate–vegetation–fire relationships: multivariate evaluation of the land surface model JSBACH
Tropical climate–vegetation–fire relationships: multivariate evaluation of the land surface model JSBACH
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DOI：
10.5194/bg-15-5969-2018
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发表时间：
2018-10
期刊：
影响因子：
4.9
通讯作者：
G. Lasslop;T. Moeller;D. D’Onofrio;S. Hantson;S. Kloster
中科院分区：
文献类型：
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作者：
G. Lasslop;T. Moeller;D. D’Onofrio;S. Hantson;S. Kloster
抽象的。气候、植被和火灾之间的相互作用可以强烈影响地球系统模型中植被的未来轨迹。我们使用简单的火灾方案和复杂的火灾模型 SPITFIRE 评估全球植被模型 JSBACH 中的热带气候、植被和火灾之间的关系，旨在确定模型改进的潜力。我们使用两种不同分辨率的遥感产品（基于 MODIS 和 Landsat）来评估所获得的观测关系的稳健性。我们使用多变量比较来评估模型，这使我们能够关注气候、植被和火灾之间的相互作用，并测试土地利用变化对建模模式的影响。众所周知，气候-植被-火灾关系在各大洲之间存在差异。因此，我们分别对每个大陆进行分析。两个卫星数据集中观察到的关系相似，但在较高的降水量和较粗的分辨率下达到最大树木覆盖率。这表明模型和数据的空间尺度需要一致才能进行有意义的比较。该模型捕捉了具有区域差异的广泛空间模式，这部分是由于地球系统模型得出的气候强迫造成的。与简单的火灾方案相比，SPITFIRE极大地改善了燃烧区域的空间格局以及燃烧区域沿降水量增加的分布。观测中降水量与树木覆盖率之间的相关性高于主要由气候驱动的植被模型（包括两种火灾模型）。多变量比较确定了低降水量地区的树木覆盖率过高，以及复杂火灾模型中火灾发生率高与树木覆盖率低之间的关系过强。因此，我们建议可以改善干旱对树木覆盖的影响以及烧毁面积对树木覆盖或树木对火灾的适应的影响。观测到的大陆间降水量与最大树木覆盖率之间关系的变化高于模拟的变化。土地利用导致了 SPITFIRE 火灾状况的洲际差异，并强烈地印记了 SPITFIRE 模拟的树木覆盖多模态。这里使用的多变量模型-数据比较有几个优点：它改善了模型-数据不匹配对模型过程的归因，减少了气象强迫偏差对评估的影响，使我们不仅可以评估特定的目标变量，还可以评估相互作用。
Abstract. The interactions between climate, vegetation and fire can strongly influence the future trajectories of vegetation in Earth system models. We evaluate the relationships between tropical climate, vegetation and fire in the global vegetation model JSBACH, using a simple fire scheme and the complex fire model SPITFIRE with the aim to identify potential for model improvement. We use two remote-sensing products (based on MODIS and Landsat) in different resolutions to assess the robustness of the obtained observed relationships. We evaluate the model using a multivariate comparison that allows us to focus on the interactions between climate, vegetation and fire and test the influence of land use change on the modelled patterns. Climate–vegetation–fire relationships are known to differ between continents; we therefore perform the analysis for each continent separately. The observed relationships are similar in the two satellite data sets, but maximum tree cover is reached at higher precipitation values for coarser resolution. This shows that the spatial scale of models and data needs to be consistent for meaningful comparisons. The model captures the broad spatial patterns with regional differences, which are partly due to the climate forcing derived from an Earth system model. Compared to the simple fire scheme, SPITFIRE strongly improves the spatial pattern of burned area and the distribution of burned area along increasing precipitation. The correlation between precipitation and tree cover is higher in the observations than in the largely climate-driven vegetation model, with both fire models. The multivariate comparison identifies excessive tree cover in low-precipitation areas and a too-strong relationship between high fire occurrence and low tree cover for the complex fire model. We therefore suggest that drought effects on tree cover and the impact of burned area on tree cover or the adaptation of trees to fire can be improved. The observed variation in the relationship between precipitation and maximum tree cover between continents is higher than the simulated one. Land use contributes to the intercontinental differences in fire regimes with SPITFIRE and strongly overprints the modelled multimodality of tree cover with SPITFIRE. The multivariate model–data comparison used here has several advantages: it improves the attribution of model–data mismatches to model processes, it reduces the impact of biases in the meteorological forcing on the evaluation and it allows us to evaluate not only a specific target variable but also the interactions.