Improving Australian Rainfall Prediction Using Sea Surface Salinity
Improving Australian Rainfall Prediction Using Sea Surface Salinity
复制标题
DOI：
10.1175/jcli-d-20-0625.1
复制
发表时间：
2021-04
影响因子：
4.9
通讯作者：
S. Rathore;N. Bindoff;C. Ummenhofer;H. Phillips;M. Feng;Mayank D Mishra
中科院分区：
文献类型：
--
作者：
S. Rathore;N. Bindoff;C. Ummenhofer;H. Phillips;M. Feng;Mayank D Mishra
本研究使用海面盐度（SSS）作为改进澳大利亚东北部夏季（12月至2月，DJF）降雨预测的附加前兆。从以往季节SSS与DJF降雨量之间的奇异值分解中，我们注意到印度-太平洋暖池地区的SSS [SSSP （150oE-165oW和10oS-10oN）和SSSI (50oE-95oE和10oS-10oN)]与澳大利亚降雨量共变，特别是在东北地区。基于SSSP区和SSSI区高（低）SSS事件的复合分析，了解SSS与源自异常高（低）SSS区大气水分和澳大利亚降水之间的物理联系。复合数据显示，拉尼娜和印度洋负偶极子同时发生（厄尔尼诺和印度洋正偶极子同时发生），澳大利亚上空异常湿（干）。在SSSP和SSSI区域的高（低）SSS事件期间，来水通量的辐合（辐散）导致澳大利亚出现异常的湿（干）状况，土壤湿度出现正（负）异常。此外，我们从随机森林回归分析中发现，厄尔尼诺南方涛动是澳大利亚降雨的最重要前兆，其次是西太平洋暖池（SSSP）的SSS。随机森林回归也可以预测澳大利亚的降雨，并且通过包括前一季节的SSS来改进这种预测。这一证据表明，对SSS的持续观测可以改善对澳大利亚区域水文循环的监测。
This study uses sea surface salinity (SSS) as an additional precursor for improving the prediction of summer (December-February, DJF) rainfall over northeast Australia. From a singular value decomposition between SSS of prior seasons and DJF rainfall, we note that SSS of the Indo-Pacific warm pool region [SSSP (150oE-165oW and 10oS-10oN), and SSSI (50oE-95oE and 10oS-10oN)] co-vary with Australian rainfall, particularly over the Northeast. Composite analysis based on high (low) SSS events in SSSP and SSSI region is performed to understand the physical links between the SSS and the atmospheric moisture originating from the regions of anomalously high (low) SSS and precipitation over Australia. The composites show the signature of co-occurring La Nina and negative Indian Ocean dipole (co-occurring El Nino and positive Indian Ocean dipole) with anomalously wet (dry) conditions over Australia. During the high (low) SSS events of SSSP and SSSI regions, the convergence (divergence) of incoming moisture flux results in anomalously wet (dry) conditions over Australia with a positive (negative) soil moisture anomaly. Furthermore, we show from the random forest regression analysis that the El Nino Southern Oscillation is the most important precursor for the Australian rainfall, followed by the SSS of the western Pacific warm pool (SSSP). The random forest regression also predicts Australian rainfall, and this prediction is improved by including SSS from the prior season. This evidence suggests that sustained observations of SSS can improve the monitoring of the Australian regional hydrological cycle.