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Cross-timescale Interference and Rainfall Extreme Events in South Eastern South America.

机译：南美东南部的跨时间尺度干扰和极端降雨事件。

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摘要

The physical mechanisms and predictability associated with extreme daily rainfall in South East South America (SESA) are investigated for the December-February season. Through a k-mean analysis, a robust set of daily circulation regimes is identified and then it is used to link the frequency of rainfall extreme events with large-scale potential predictors at subseasonal-to-seasonal scales. This basic set of daily circulation regimes is related to the continental and oceanic phases of the South Atlantic Convergence Zone (SACZ) and wave train patterns superimposed on the Southern Hemisphere Polar Jet. Some of these recurrent synoptic circulation types are conducive to extreme rainfall events in the region through synoptic control of different meso-scale physical features and, at the same time, are influenced by climate phenomena that could be used as sources of potential predictability. Extremely high rainfall (as measured by the 95th- and 99th-percentiles) is preferentially associated with two of these weather types, which are characterized by moisture advection intrusions from lower latitudes and the Pacific; another three weather types, characterized by above-normal moisture advection toward lower latitudes or the Andes, are preferentially associated with dry days (days with no rain). The analysis permits the identification of several subseasonal-to-seasonal scale potential predictors that modulate the occurrence of circulation regimes conducive to extreme rainfall events in SESA. It is conjectured that a cross-timescale interference between the different climate drivers improves the predictive skill of extreme precipitation in the region. The potential and real predictive skill of the frequency of extreme rainfall is then evaluated, finding evidence indicating that mechanisms of climate variability at one timescale contribute to the predictability at another scale, i.e., taking into account the interference of different potential sources of predictability at different timescales increases the predictive skill. This fact is in agreement with the Cross-timescale Interference Conjecture proposed in the first part of the thesis. At seasonal scale, a combination of those weather types tends to outperform all the other potential predictors explored, i.e., sea surface temperature patterns, phases of the Madden-Julian Oscillation, and combinations of both. Spatially averaged Kendall's ? improvements of 43% for the potential predictability and 23% for realtime predictions are attained with respect to standard models considering sea-surface temperature fields alone. A new subseasonal-to-seasonal predictive methodology for extreme rainfall events is proposed, based on probability forecasts of seasonal sequences of these weather types. The cross-validated realtime skill of the new probabilistic approach, as measured by the Hit Score and the Heidke Skill Score, is on the order of twice that associated with climatological values. The approach is designed to offer useful subseasonal-to-seasonal climate information to decision-makers interested not only in how many extreme events will happen in the season, but also in how, when and where those events will probably occur. In order to gain further understanding about how the cross-timescale interference occurs, an externally-forced Lorenz model is used to explore the impact of different kind of forcings, at inter-annual and decadal scales, in the establishment of constructive interactions associated with the simulated "extreme events". Using a wavelet analysis, it is shown that this simple model is capable of reproducing the same kind of cross-timescale structures observed in the wavelet power spectrum of the Nino3.4 index only when it is externally forced by both inter-annual and decadal signals: the annual cycle and a decadal forcing associated with the natural solar variability. The nature of this interaction is non-linear, and it impacts both mean and extreme values in the time series. No predictive power was found when using metrics like standard deviation and auto-correlation. Nonetheless, it was proposed that an early warning signal for occurrence of extreme rainfall in SESA may be possible via a continuous monitoring of relative phases between the cross-timescale leading components.
机译：在12月至2月的季节中，调查了南美东南部（SESA）每日极端降雨的物理机制和可预测性。通过k均值分析，确定了一套可靠的日常循环机制，然后将其用于将降雨极端事件的发生频率与亚季节间尺度的大规模潜在预报因子联系起来。这套基本的日常循环机制与南大西洋收敛带（SACZ）的大陆和海洋阶段以及叠加在南半球极地喷气机上的波列模式有关。这些周期性天气循环类型中的某些，通过对不同的中尺度物理特征进行天气控制，有利于该地区的极端降雨事件，同时受到气候现象的影响，这些气候现象可作为潜在可预测性的来源。这些天气类型中，有两种天气类型的降雨量最高（分别由95％和99％百分数测量），其特征是来自低纬度地区和太平洋的水汽平流入侵。另外三种天气类型的特征是对低纬度地区或安第斯山脉的水汽平流高于正常水平，通常与干旱天（无雨天）相关。通过分析，可以确定几个季节到季节尺度下潜在的预测因子，这些预测因子可以调节有利于SESA极端降雨事件的循环形式的发生。据推测，不同气候驱动因素之间的跨时标干扰会提高该地区极端降水的预报能力。然后评估了极端降雨频率的潜在和真正的预测技巧，发现证据表明一个时间尺度上的气候变化机制有助于另一尺度上的可预测性，即考虑到不同潜在的可预测性来源的干扰。时间尺度提高了预测能力。这个事实与论文第一部分提出的跨时标干扰猜想是一致的。在季节尺度上，这些天气类型的组合往往胜过所探索的所有其他潜在预测指标，即海表温度模式，Madden-Julian涛动期的相位以及两者的组合。空间平均Kendall的？对于仅考虑海面温度场的标准模型，潜在可预测性提高了43％，实时预测提高了23％。基于这些天气类型季节序列的概率预报，提出了一种针对极端降雨事件的新的季节到季节预测方法。通过命中得分和海德克技能得分测得的新概率方法的交叉验证实时技能约为与气候值相关的两倍。该方法旨在为决策者提供有用的季节到季节的气候信息，这些决策者不仅对本赛季将发生多少极端事件感兴趣，而且对那些事件可能发生的方式，时间和地点感兴趣。为了进一步了解跨时标干扰是如何发生的，在建立与年际和年代际尺度有关的建设性互动时，使用外力洛伦兹模型探索年际和年代际尺度上不同类型强迫的影响。模拟的“极端事件”。通过小波分析，表明该简单模型仅在受到年际和年代际信号的外力作用时，才能够再现在Nino3.4指数的小波功率谱中观察到的相同类型的跨时标结构。 ：与自然太阳变化有关的年度周期和十年强迫。这种交互的性质是非线性的，并且会影响时间序列中的均值和极值。使用标准偏差和自相关等指标时，没有发现预测力。尽管如此，有人提出，通过对跨时标前导分量之间的相对相位进行连续监测，可能会出现SESA中出现极端降雨的预警信号。

著录项

  • 作者

    Munoz, Angel G.;

  • 作者单位

    Columbia University.;

  • 授予单位 Columbia University.;
  • 学科 Environmental studies.;Physics.;Geophysics.
  • 学位 Ph.D.
  • 年度 2016
  • 页码 127 p.
  • 总页数 127
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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