基于扩散模型的概率降尺度方法用于180年东亚气候重建
Diffusion Model-based Probabilistic Downscaling for 180-year East Asian Climate Reconstruction
AI总结：
本文提出扩散概率降尺度模型DPDM，将气象数据从1°降至0.1°，通过概率采样生成集合成员以评估不确定性，并重建180年东亚月尺度地表气候数据集。
摘要：
随着地球正进入“全球沸腾”时代，理解区域气候变化变得势在必行。能够提供局地化洞察的有效降尺度方法对于实现这一目标至关重要。传统方法包括计算需求高昂的区域动力模型或统计降尺度框架，往往容易受到降尺度不确定性的影响。为此，我们通过将扩散概率降尺度模型（DPDM）引入气象领域来解决这些局限。该模型能够高效地将数据从1°分辨率转换为0.1°分辨率。与确定性降尺度方案相比，它不仅具有更准确的局地细节，还能够基于概率分布采样生成大量集合成员，以评估降尺度的不确定性。此外，我们应用该模型生成了东亚地区180年的月尺度地表变量数据集，为理解过去几个世纪局地尺度的气候变化提供了更细致的视角。
英文摘要：
As our planet is entering into the "global boiling" era, understanding regional climate change becomes imperative. Effective downscaling methods that provide localized insights are crucial for this target. Traditional approaches, including computationally-demanding regional dynamical models or statistical downscaling frameworks, are often susceptible to the influence of downscaling uncertainty. Here, we address these limitations by introducing a diffusion probabilistic downscaling model (DPDM) into the meteorological field. This model can efficiently transform data from 1° to 0.1° resolution. Compared with deterministic downscaling schemes, it not only has more accurate local details, but also can generate a large number of ensemble members based on probability distribution sampling to evaluate the uncertainty of downscaling. Additionally, we apply the model to generate a 180-year dataset of monthly surface variables in East Asia, offering a more detailed perspective for understanding local scale climate change over the past centuries.
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