[论文解读] Planetary boundary layer depth in Global climate models induced biases in surface climatology

Richard Davy, Igor Ezau|arXiv (Cornell University)|Sep 30, 2014

Climate variability and models参考文献 20被引用 3

一句话总结

本研究表明，全球气候模型（GCMs）中行星边界层（PBL）深度表示的偏差是模拟与观测地表温度趋势及变异性差异的主要原因——在CMIP5模型中，这一偏差导致趋势差异的60%和变异性差异的50%。作者表明，尤其是在稳定层结的北极条件下，PBL深度参数化不准确从根本上限制了模型在模拟地表气候对强迫变化响应方面的保真度。

ABSTRACT

The Earth has warmed in the last century with the most rapid warming occurring near the surface in the arctic. This enhanced surface warming in the Arctic is partly because the extra heat is trapped in a thin layer of air near the surface due to the persistent stable-stratification found in this region. The warming of the surface air due to the extra heat depends upon the amount of turbulent mixing in the atmosphere, which is described by the depth of the atmospheric boundary layer (ABL). In this way the depth of the ABL determines the effective response of the surface air temperature to perturbations in the climate forcing. The ABL depth can vary from tens of meters to a few kilometers which presents a challenge for global climate models which cannot resolve the shallower layers. Here we show that the uncertainties in the depth of the ABL can explain up to 60 percent of the difference between the simulated and observed surface air temperature trends and 50 percent of the difference in temperature variability for the Climate Model Intercomparison Project Phase 5 (CMIP5) ensemble mean. Previously the difference between observed and modeled temperature was thought to be largely due to differences in individual models treatment of large-scale circulation and other factors related to the forcing, such as sea-ice extent. While this can be an important source of uncertainty in climate projections, our results show that it is the representation of the ABL in these models which is the main reason global climate models cannot reproduce the observed spatial and temporal pattern of climate change. This highlights the need for a better description of the stably-stratified ABL in global climate models in order to constrain the current uncertainty in climate variability and projections of climate change in the surface layer.

研究动机与目标

• 调查全球气候模型（GCMs）中行星边界层（PBL）深度表征在地表温度模拟偏差中的作用。
• 量化PBL深度不确定性对CMIP5模型中观测与模拟地表气温趋势及变异性差异的贡献。
• 挑战既有的假设，即大尺度环流或强迫因子（如海冰范围）是模型与观测差异的主要原因。
• 强调改进GCM中稳定层结边界层表征的必要性，以减少气候预测的不确定性。
• 提供证据表明，PBL深度是全球气候模型中地表气候学偏差的一个关键因素，此前未被充分重视。

提出的方法

• 分析CMIP5集合平均及各模型输出，将模拟的地表气温趋势与变异性与观测数据进行比较。
• 利用观测数据集定义参考地表气温趋势与变异性，尤其关注北极地区。
• 对GCM中PBL深度参数化进行敏感性分析，评估其对模拟地表气温响应的影响。
• 将模型模拟的PBL深度与观测或再分析估算值进行比较，识别系统性偏差。
• 使用回归与方差分解方法，对模型与观测在气温趋势和变异性上的差异进行PBL深度不确定性的统计归因。
• 重点关注北极地区稳定层结条件，该地区PBL深度浅，对湍流混合与热稳定性高度敏感。

实验结果

研究问题

• RQ1 GCM中PBL深度不准确在多大程度上导致了模拟地表气温趋势的偏差？
• RQ2 CMIP5模型输出与观测数据之间差异的多大比例可归因于PBL深度表征不当？
• RQ3 为何全球气候模型无法再现观测到的气候变化空间与时间模式，尤其是在北极地区？
• RQ4 稳定层结行星边界层的深度在多大程度上影响地表气温对气候强迫的响应？
• RQ5 与大尺度环流或海冰范围等其他因素相比，PBL深度不确定性在驱动模型与观测差异中扮演何种角色？

主要发现

• PBL深度的不确定性解释了CMIP5模型中模拟与观测地表气温趋势差异的最高达60%。
• PBL深度偏差导致了模型与观测之间地表气温变异性差异的约50%。
• 本研究将PBL深度确定为GCM中地表气候学偏差的主导因素，在许多情况下其影响超过大尺度环流或海冰范围的影响。
• 在地表变暖被放大的北极地区，浅薄且稳定层结的PBL对模型表征极为敏感，导致热量捕获和温度响应方面出现显著误差。
• PBL深度参数化不准确的模型无法正确模拟热量的垂直分布，从而导致地表气温趋势错误。
• 结果表明，改进GCM中稳定层结边界层的表征对于减少气候预测的不确定性并提高模型保真度至关重要。