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Quantifying the deep convective temperature signal within the tropical tropopause layer (TTL)

机译:量化热带对流层层(TTL)内的深度对流温度信号

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摘要

Dynamics on a vast range of spatial and temporal scales, from individual convective plumes to planetary-scale circulations, play a role in driving the temperature variability in the tropical tropopause layer (TTL). Here, we aim to better quantify the deep convective temperature signal within the TTL using multiple datasets. First, we investigate the link between ozone and temperature in the TTL using the Southern Hemisphere Additional Ozonesondes (SHADOZ) dataset. Low ozone concentrations in the TTL are indicative of deep convective transport from the boundary layer. We confirm the usefulness of ozone as an indicator of deep convection by identifying a typical temperature signal associated with reduced ozone events: an anomalously warm mid to upper troposphere and an anomalously cold upper TTL. We quantify these temperature signals using two diagnostics: (1) the "ozone minimum" diagnostic, which has been used in previous studies and identifies the upper tropospheric minimum ozone concentration as a proxy for the level of main convective outflow; and (2) the "ozone mixing height", which we introduce in order to identify the maximum altitude in a vertical ozone profile up to which reduced ozone concentrations, typical of transport from the boundary layer are observed. Results indicate that the ozone mixing height diagnostic better separates profiles with convective influence than the ozone minimum diagnostic. Next, we collocate deep convective clouds identified by CloudSat 2B-CLDCLASS with temperature profiles based on Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) Global Position System (GPS) radio occultations. We find a robust large-scale deep convective TTL temperature signal, that is persistent in time. However, it is only the convective events that penetrate into the upper half of the TTL that have a significant impact on TTL temperature. A distinct seasonal difference in the spatial scale and the persistence of the temperature signal is identified. Deep-convective cloud top heights are on average found to be well described by the level of neutral buoyancy.
机译:在各个空间和时间尺度上的动力学,从个体对流羽毛到行星级循环,在驱动热带对象层(TTL)中的温度变异方面发挥作用。这里,我们的目标是使用多个数据集更好地量化TTL内的深度对流温度信号。首先,我们使用南半球其他臭氧(ShadoZ)数据集来研究TTL中臭氧和温度之间的链接。 TTL中的低臭氧浓度表示从边界层深度对流传输。我们通过鉴定与减少臭氧事件相关的典型温度信号,确认臭氧作为深度对流的指标:致正对流层的异常温暖的中间TTL。我们使用两种诊断量化这些温度信号:(1)“臭氧最小”诊断,其已在先前的研究中使用,并将上部对流层最小臭氧浓度识别为主要对流流出水平的代理; (2)我们介绍的“臭氧混合高度”,以便识别垂直臭氧谱中的最大高度,从而减少臭氧浓度,典型的来自边界层的典型的运输。结果表明,臭氧混合高度诊断更好地将曲线与对流影响分开,而不是臭氧最小诊断。接下来,我们将Cloudsat 2b-cldclass识别的深入对流云与基于气象,电离层和气候(宇宙)全球位置系统(GPS)无线电掩星的星座观察系统进行温度型材。我们发现一个强大的大型深度对流TTL温度信号,其持久性。然而,只有渗透到TTL的上半部分对TTL温度产生显着影响的对流事件。鉴定了空间尺度的不同季节性和温度信号的持久性。深入对比的云顶部高度平均地发现是通过中性浮力水平良好的描述。

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