Ludescher Josef, Bunde Armin, Schellnhuber Hans Joachim
Institut für Theoretische Physik, Justus-Liebig-Universität Giessen, D-35392 Giessen, Germany.
Institute for Climate Impact Research, D-14412 Potsdam, Germany;
Proc Natl Acad Sci U S A. 2017 Apr 11;114(15):E2998-E3003. doi: 10.1073/pnas.1700838114. Epub 2017 Mar 27.
The question whether a seasonal climate trend (e.g., the increase of summer temperatures in Antarctica in the last decades) is of anthropogenic or natural origin is of great importance for mitigation and adaption measures alike. The conventional significance analysis assumes that () the seasonal climate trends can be quantified by linear regression, () the different seasonal records can be treated as independent records, and () the persistence in each of these seasonal records can be characterized by short-term memory described by an autoregressive process of first order. Here we show that assumption is not valid, due to strong intraannual correlations by which different seasons are correlated. We also show that, even in the absence of correlations, for Gaussian white noise, the conventional analysis leads to a strong overestimation of the significance of the seasonal trends, because multiple testing has not been taken into account. In addition, when the data exhibit long-term memory (which is the case in most climate records), assumption leads to a further overestimation of the trend significance. Combining Monte Carlo simulations with the Holm-Bonferroni method, we demonstrate how to obtain reliable estimates of the significance of the seasonal climate trends in long-term correlated records. For an illustration, we apply our method to representative temperature records from West Antarctica, which is one of the fastest-warming places on Earth and belongs to the crucial tipping elements in the Earth system.
季节性气候趋势（例如，过去几十年南极洲夏季气温上升）是人为起源还是自然起源的问题，对于缓解措施和适应措施都至关重要。传统的显著性分析假定：（1）季节性气候趋势可以通过线性回归进行量化；（2）不同季节的记录可以视为独立记录；（3）这些季节性记录中的每一个的持续性可以通过一阶自回归过程描述的短期记忆来表征。在此我们表明，由于不同季节之间存在强烈的年内相关性，假设（2）是无效的。我们还表明，即使在没有相关性的情况下，对于高斯白噪声，传统分析也会导致对季节性趋势显著性的严重高估，因为未考虑多重检验。此外，当数据表现出长期记忆时（大多数气候记录都是这种情况），假设（3）会导致对趋势显著性的进一步高估。结合蒙特卡罗模拟和霍尔姆 - 邦费罗尼方法，我们展示了如何在长期相关记录中获得季节性气候趋势显著性的可靠估计。为作说明，我们将我们的方法应用于南极西部具有代表性的温度记录，南极西部是地球上变暖最快的地区之一，属于地球系统中的关键临界点。
