{"title":"Technical note: An improved methodology for calculating the Southern Annular Mode index to aid consistency between climate studies","authors":"Laura Velasquez-Jimenez, Nerilie J. Abram","doi":"10.5194/cp-20-1125-2024","DOIUrl":null,"url":null,"abstract":"Abstract. The Southern Annular Mode (SAM) strongly influences climate variability in the Southern Hemisphere. The SAM index describes the phase and magnitude of the SAM and can be calculated by measuring the difference in mean sea level pressure (MSLP) between middle and high latitudes. This study investigates the effects of calculation methods and data resolution on the SAM index, and subsequent interpretations of SAM impacts and trends. We show that the normalisation step that is traditionally used in calculating the SAM index leads to substantial differences in the magnitude of the SAM index calculated at different temporal resolutions. Additionally, the equal weighting that the normalisation approach gives to MSLP variability at the middle and high southern latitudes artificially alters temperature and precipitation correlations and the interpretation of climate change trends in the SAM. These issues can be overcome by instead using a natural SAM index based on MSLP anomalies, resulting in consistent scaling and variability in the SAM index calculated at daily, monthly and annual data resolutions. The natural SAM index has improved representation of SAM impacts in the high southern latitudes, including the asymmetric (zonal wave-3) component of MSLP variability, whereas the increased weighting given to mid-latitude MSLP variability in the normalised SAM index incorporates a stronger component of tropical climate variability that is not directly associated with SAM variability. We conclude that an improved approach of calculating the SAM index from MSLP anomalies without normalisation would aid consistency across climate studies and avoid potential ambiguity in the SAM index, including SAM index reconstructions from palaeoclimate data, and thus enable more consistent interpretations of SAM trends and impacts.","PeriodicalId":10332,"journal":{"name":"Climate of The Past","volume":"30 1","pages":""},"PeriodicalIF":3.8000,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Climate of The Past","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.5194/cp-20-1125-2024","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract. The Southern Annular Mode (SAM) strongly influences climate variability in the Southern Hemisphere. The SAM index describes the phase and magnitude of the SAM and can be calculated by measuring the difference in mean sea level pressure (MSLP) between middle and high latitudes. This study investigates the effects of calculation methods and data resolution on the SAM index, and subsequent interpretations of SAM impacts and trends. We show that the normalisation step that is traditionally used in calculating the SAM index leads to substantial differences in the magnitude of the SAM index calculated at different temporal resolutions. Additionally, the equal weighting that the normalisation approach gives to MSLP variability at the middle and high southern latitudes artificially alters temperature and precipitation correlations and the interpretation of climate change trends in the SAM. These issues can be overcome by instead using a natural SAM index based on MSLP anomalies, resulting in consistent scaling and variability in the SAM index calculated at daily, monthly and annual data resolutions. The natural SAM index has improved representation of SAM impacts in the high southern latitudes, including the asymmetric (zonal wave-3) component of MSLP variability, whereas the increased weighting given to mid-latitude MSLP variability in the normalised SAM index incorporates a stronger component of tropical climate variability that is not directly associated with SAM variability. We conclude that an improved approach of calculating the SAM index from MSLP anomalies without normalisation would aid consistency across climate studies and avoid potential ambiguity in the SAM index, including SAM index reconstructions from palaeoclimate data, and thus enable more consistent interpretations of SAM trends and impacts.
摘要南环流模式(SAM)强烈影响着南半球的气候变率。南环流模式指数描述了南环流模式的相位和幅度,可通过测量中纬度和高纬度之间的平均海平面气压(MSLP)差来计算。本研究调查了计算方法和数据分辨率对萨姆指数的影响,以及随后对萨姆影响和趋势的解释。我们发现,传统上用于计算 SAM 指数的归一化步骤会导致在不同时间分辨率下计算出的 SAM 指数大小存在巨大差异。此外,归一化方法对中纬度和南纬高纬度的 MSLP 变率给予同等权重,人为地改变了温度和降水的相关性以及对 SAM 中气候变化趋势的解释。这些问题可以通过使用基于 MSLP 异常的自然 SAM 指数来解决,从而使按日、月和年数据分辨率计算的 SAM 指数具有一致的比例和变异性。自然 SAM 指数能更好地反映 SAM 对南部高纬度地区的影响,包括 MSLP 变率中的非对称(带状波-3)成分,而在归一化 SAM 指数中,中纬度 MSLP 变率的权重增加,纳入了热带气候变率中与 SAM 变率无直接关联的更强的成分。我们的结论是,通过 MSLP 异常值计算 SAM 指数而不进行归一化的改进方法将有助于气候研究的一致性,并避免 SAM 指数(包括根据古气候数据重建的 SAM 指数)中潜在的模糊性,从而使对 SAM 趋势和影响的解释更加一致。
期刊介绍:
Climate of the Past (CP) is a not-for-profit international scientific journal dedicated to the publication and discussion of research articles, short communications, and review papers on the climate history of the Earth. CP covers all temporal scales of climate change and variability, from geological time through to multidecadal studies of the last century. Studies focusing mainly on present and future climate are not within scope.
The main subject areas are the following:
reconstructions of past climate based on instrumental and historical data as well as proxy data from marine and terrestrial (including ice) archives;
development and validation of new proxies, improvements of the precision and accuracy of proxy data;
theoretical and empirical studies of processes in and feedback mechanisms between all climate system components in relation to past climate change on all space scales and timescales;
simulation of past climate and model-based interpretation of palaeoclimate data for a better understanding of present and future climate variability and climate change.