A critical examination of a newly proposed interhemispheric teleconnection to Southwestern US winter precipitation
A critical examination of a newly proposed interhemispheric teleconnection to Southwestern US winter precipitation
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对新提出的美国西南部冬季降水半球间遥相关的严格检查
DOI：
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发表时间：
2019
影响因子：
16.6
通讯作者：
M. DeFlorio
中科院分区：
文献类型：
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作者：
P. Gibson;D. Waliser;M. DeFlorio
在他们最近的研究1中，(以下简称M18)提出了一种新的半球间遥相关，将新西兰地区晚冬海温(SST)异常与美国西南部冬季降水联系起来。他们认为，暖的海温异常通过大气桥梁机制从新西兰地区传播到北半球(大约从7月到10月)，在随后的几个月(从11月到3月)，这种机制改变了影响美国西南部冬季降水变率的急流的性质。然而，在这一对应中，在考虑了非平稳性、记忆性和与其他众所周知的内部变率模式的共享变率后，我们显示出对所提出的机制至关重要的区域的SST异常之间的关联强度显著降低。M18中关于所涉及的物理机制的其他重要主张，即新西兰地区的海温异常加剧了远距离绝热变暖和减少了关键区域的云量，在再分析和卫星数据中得到了不充分的支持。新提出的M18远程联系的一个关键方面取决于新西兰(NZI)地区的海温，以及作为菲律宾以东地区(EPH)西热带太平洋海温的可预报性来源。为了减少在寻找新的遥相关时出现虚假相关的可能性，必须认真考虑任何众所周知的混杂因素(即共同影响两个区域SST的其他变量或过程)。首先，众所周知，在M18所考虑的时间段内，这两个地区都存在相当大的SST趋势(例如，见参考文献)。2)(另见补充图1)。其次，众所周知，厄尔尼诺-南方涛动(ENSO)和相关的振荡，包括南方涛动指数(SOI)，都会影响这两个区域的海温异常和大气环流3，4(另见补充图1)。在评估可预测性时，另一个重要的考虑因素在于格兰杰因果关系范式5，6)：因果关系取决于自变量(这里是NZI中的SSTS)的预测强度，大大超过仅有因变量(这里是EPH中的SSTS)的过去值(即记忆)的预测强度。最后，众所周知，同时进行多个假设检验确保了拒绝局部零假设的较高标准(即较低的p值)7。下面将详细分析每个因素(非平稳性、记忆性、其他混杂因素和显著性检验)的影响。图1显示了1982-2015年间NZI和EPH中的SST与NZI提前期(以月为单位)之间的关联强度(图1相当于M18中的图7)。如M18中所讨论的，这种相关性最强(r=0.85)出现在NZI 9月SST异常先于EPH 3-4个月，其他月份/滞后也表现出与M18中提出的大气搭桥机制(即NZI 7-10月SST异常先于EPH 1-5个月)有关的高度相关(r>；0.7)。当两个时间序列的异常在计算相关性之前被线性去趋势时(图1B)，这些重要月份/滞后期间的相关性强度在一定程度上被降低到r=0.52-0.76的范围，但在统计上仍然具有很强的意义。然而，EPH SST异常(在去趋势之后)也显示出相当大的记忆力(图1C)，并且在许多月/滞后中显示出比NZI更强的关联强度作为预测(比较图1C和图1B)。此外，SOI与EPH在相同的月份/滞后期间被发现有很好的相关性(比较图1d和图1b)，突出了EPH SST异常的一个重要的可预测性来源，该来源也与NZI地区的SST异常很好地相关(补充图1)。为了更直接地诊断SOI和EPH记忆在这些关联中的相对影响，我们提供了偏相关分析的结果(例如，参考文献)。8，9)。当通过偏相关(图1E)考虑来自EPH记忆的影响时，在https://doi.org/10.1038/s41467-019-10528-y开放中，NZI领先EPH之间的关联显著减少
In their recent study 1, (hereafter M18) propose a new interhemispheric teleconnection linking late austral winter sea surface temperature (SST) anomalies in the New Zealand region to Southwestern US winter precipitation. They propose that warm SST anomalies propagate from the New Zealand region into the Northern Hemisphere through an atmospheric bridge mechanism (from approximately July to October) which in subsequent months (from November to March) acts to alter properties of the jet stream that influence Southwestern US winter precipitation variability. However, in this correspondence, after accounting for non-stationarity, memory and shared variability with other well-known modes of internal variability, we show a large reduction in correlation strength between SST anomalies in regions key to the proposed mechanism. Other important claims in M18 regarding the physical mechanisms involved, whereby SST anomalies in the New Zealand region enhance remote adiabatic warming and reduce cloud cover in key regions, are not robustly supported when examined in reanalysis and satellite data. A critical aspect of the newly proposed teleconnection in M18 depends on SSTs in the New Zealand (NZI) region leading, and being a source of predictability for, western tropical Pacific SSTs in a region east of the Philippines (EPH). To reduce the likelihood of spurious correlation when searching for new teleconnections, it is crucial that any well-known confounders (i.e. other variables or processes that jointly influence SSTs in both regions) are given careful consideration. First, reasonably large trends in SSTs are known to exist in both of these regions over the time period considered in M18 (e.g. see ref. 2) (see also Supplementary Fig. 1). Second, the El Niño-Southern Oscillation (ENSO) and related oscillations, including the Southern Oscillation Index (SOI), are well known to influence SST anomalies and atmospheric circulation in both of these regions3,4 (see also Supplementary Fig. 1). Another important consideration when assessing predictability lies within the Granger causality paradigm5,6): that causation depends on the predictive strength of the independent variable (here SSTs in NZI) significantly exceeding the predictive strength of past values (i.e. memory) of the dependent variable alone (here SSTs in EPH). Lastly, it is well established that simultaneously conducting multiple hypothesis tests ensures a higher standard (i.e. lower p-values) for rejecting local null hypotheses7. The influence of each of these considerations (non-stationarity, memory, other confounders and significance testing) is examined in detail below. The correlation strength between SSTs in NZI and EPH as a function of NZI lead time in months over the period 1982–2015 is shown in Fig. 1 (Fig. 1a is equivalent to Fig. 7 in M18). As discussed in M18, the strongest of these correlations (r= 0.85) occurs for NZI September SST anomalies leading EPH by 3–4 months and with other months/lags also displaying high correlations (r > 0.7) related to the atmospheric bridging mechanism proposed in M18 (i.e. NZI July to October SST anomalies leading EPH by 1–5 months). When the anomalies from both timeseries are linearly detrended before computing the correlation (Fig. 1b) the correlation strength during these important months/lags is reduced somewhat to the range of r= 0.52–0.76 but remains strongly statistically significant. However, EPH SST anomalies (after detrending) also display considerable memory (Fig. 1c) and in many months/lags display greater correlation strength than NZI as a predictor (comparing Fig. 1c with 1b). Furthermore, SOI is found to be very well correlated with EPH over the same months/lags of interest (comparing Fig. 1d with 1b) highlighting an important source of predictability for EPH SST anomalies that is also well correlated with SST anomalies in the NZI region (Supplementary Fig. 1). To more directly diagnose the relative influence of SOI and EPH memory in these associations, we present results of partial correlation analysis (e.g. ref. 8,9). When the influence from EPH memory is accounted for through partial correlation (Fig. 1e), the association between NZI leading EPH is substantially reduced in https://doi.org/10.1038/s41467-019-10528-y OPEN