Vegetation is an important component in the Earth system, providing a direct link between the biosphere and atmosphere. As such, a representative vegetation pattern is needed to accurately simulate climate. We attempt to model global vegetation (biomes) with a data‐driven approach, to test if this allows us to create robust global and regional vegetation patterns. This not only provides quantitative reconstructions of past vegetation cover as a climate forcing, but also improves our understanding of past land cover‐climate interactions which have important implications for the future. By using a Random Forest (RF) machine learning tool, we train the vegetation reconstruction with available biomized pollen data of present and past conditions to produce broad‐scale vegetation patterns for the preindustrial (PI), the mid‐Holocene (MH, ∼6,000 years ago), and the Last Glacial Maximum (LGM, ∼21,000 years ago). We test the method's robustness by introducing a systematic temperature bias based on existing climate model spread and compare the result with that of LPJ‐GUESS, an individual‐based dynamic global vegetation model. The results show that the RF approach is able to produce robust patterns for periods and regions well constrained by evidence (the PI and the MH), but fails when evidence is scarce (the LGM). The apparent robustness of this method is achieved at the cost of sacrificing the ability to model dynamic vegetation response to a changing climate.

中文翻译：

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利用随机森林机器学习重建过去的全球植被，牺牲动态响应以获得可靠的结果

植被是地球系统中的重要组成部分，提供了生物圈与大气之间的直接联系。因此，需要有代表性的植被格局来精确模拟气候。我们尝试使用数据驱动的方法对全球植被（生物群落）进行建模，以测试这是否使我们能够创建强大的全球和区域植被格局。这不仅提供了对过去植被覆盖的定量重建，从而促进了气候变化，而且还增进了我们对过去土地覆盖－气候相互作用的理解，这对未来具有重要意义。通过使用随机森林（RF）机器学习工具，我们利用当前和过去条件的可用生物化花粉数据训练植被重建，以产生工业化前（PI），中全新世（MH，〜6 ，000年前）和最后的冰川最大值（LGM，约21,000年前）。我们通过引入基于现有气候模型分布的系统温度偏差来测试该方法的鲁棒性，并将结果与​​基于个体的动态全球植被模型LPJ‐GUESS的结果进行比较。结果表明，RF方法能够针对受证据（PI和MH）严格约束的时间段和区域产生鲁棒的模式，但在证据稀缺时（LGM）则无法使用。该方法的明显健壮性是以牺牲对动态植被对气候变化的响应进行建模的能力为代价的。基于个人的动态全球植被模型。结果表明，RF方法能够针对受证据（PI和MH）严格约束的时间段和区域产生鲁棒的模式，但在证据稀缺时（LGM）则无法使用。该方法的明显健壮性是以牺牲对动态植被对气候变化的响应进行建模的能力为代价的。基于个人的动态全球植被模型。结果表明，RF方法能够针对受证据（PI和MH）严格约束的时间段和区域生成稳健的模式，但在证据稀缺时（LGM）却无法使用。该方法的明显健壮性是以牺牲对动态植被对气候变化的响应进行建模的能力为代价的。