Assessing South Indian Ocean tropical cyclone characteristics in HighResMIP simulations

IF 3.5 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES
Pardeep Pall, Alexandre S. Gagnon, Massimo A. Bollasina, Colin M. Zarzycki, Yuner Huang, Christopher T. S. Beckett, Harinaivo Ramanantoanina, Thomas P. S. Reynolds
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引用次数: 0

Abstract

Several damaging tropical cyclones (TCs) have occurred recently over the South Indian Ocean (SIO) region, causing enormous social and economic losses. Yet, while many studies have examined SIO TC characteristics using observations and reanalysis, only a few have assessed these characteristics specifically for this region in climate models, and fewer have investigated their projections under climate change. Here we do this for a historical (1980–2010) and future (2020–2050) period, using multimodel simulations from the High Resolution Model Intercomparison Project, as well as examine biases in the historical period relative to a reanalysis (ERA5). The models have horizontal resolutions of 25–50 km, which has enabled an improved ability to represent tropical cyclones globally in previous studies. TempestExtremes software is employed to detect tropical storm and cyclone tracks. In cases where TempestExtremes cannot be applied due to a lack of requisite variables in a dataset, we instead examine extreme wind speeds in that dataset. For the historical period, we find considerable variation in model biases compared to ERA5, which itself exhibits realistic spatial patterns of tracks and their monthly distribution. Models do at least agree on positive biases in track frequency east of Madagascar and somewhat in the Mozambique Channel. However, the models and ERA5 only produce Category 3 tropical cyclones at best. Wind speeds for 25 km resolution models have much larger positive biases than for 50 km ones, suggesting the former can simulate even higher-category tropical cyclones. Considerable intermodel variation is also found in track changes between the future and historical periods. No systematic intercategory pattern of change exists, and low signal-to-noise may obscure any such patterns in the limited timespan of available data. Thus, no meaningful conclusions can be drawn regarding changes in track intensity. Nevertheless, track frequency broadly decreases across models for the region, as does accumulated cyclone energy. An east-to-west shift in track location from east of Madagascar toward the Mozambique Channel is also implied by track frequency and wind speed changes. Our findings provide information to potentially improve storm resiliency in this vulnerable region.

Abstract Image

评估 HighResMIP 模拟中的南印度洋热带气旋特征
最近,南印度洋(SIO)地区发生了几次破坏性热带气旋(TC),造成了巨大的社会和经济损失。然而,虽然许多研究利用观测数据和再分析数据研究了南印度洋热带气旋的特征,但只有少数研究专门评估了该地区气候模式的这些特征,更少的研究调查了气候变化下的预测。在此,我们利用高分辨率模式相互比较项目的多模式模拟,对历史时期(1980-2010 年)和未来时期(2020-2050 年)进行了评估,并研究了历史时期相对于再分析(ERA5)的偏差。这些模式的水平分辨率为 25-50 公里,从而提高了以往研究中对全球热带气旋的表现能力。TempestExtremes 软件用于探测热带风暴和气旋的路径。如果由于数据集缺乏必要的变量而无法使用 TempestExtremes,我们则会研究该数据集中的极端风速。在历史时期,我们发现与ERA5相比,模式偏差有相当大的差异,而ERA5本身显示了真实的路径空间模式及其月度分布。模式至少在马达加斯加以东和莫桑比克海峡的路径频率正偏差上是一致的。然而,模式和ERA5 最多只能生成 3 级热带气旋。分辨率为 25 公里的模式的风速正偏差比分辨率为 50 公里的模式大得多,这表明前者甚至可以模拟更高级别的热带气旋。在未来和历史时期的路径变化中,也发现了相当大的模式间差异。在有限的可用数据时间跨度内,不存在系统的类别间变化模式,低信噪比可能会掩盖任何此类模式。因此,无法就航迹强度的变化得出有意义的结论。尽管如此,该地区各模式的路径频率普遍下降,气旋累积能量也是如此。路径频率和风速的变化也暗示了路径位置从马达加斯加以东向莫桑比克海峡由东向西的转移。我们的研究结果为改善这一脆弱地区的风暴恢复能力提供了潜在的信息。
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来源期刊
International Journal of Climatology
International Journal of Climatology 地学-气象与大气科学
CiteScore
7.50
自引率
7.70%
发文量
417
审稿时长
4 months
期刊介绍: The International Journal of Climatology aims to span the well established but rapidly growing field of climatology, through the publication of research papers, short communications, major reviews of progress and reviews of new books and reports in the area of climate science. The Journal’s main role is to stimulate and report research in climatology, from the expansive fields of the atmospheric, biophysical, engineering and social sciences. Coverage includes: Climate system science; Local to global scale climate observations and modelling; Seasonal to interannual climate prediction; Climatic variability and climate change; Synoptic, dynamic and urban climatology, hydroclimatology, human bioclimatology, ecoclimatology, dendroclimatology, palaeoclimatology, marine climatology and atmosphere-ocean interactions; Application of climatological knowledge to environmental assessment and management and economic production; Climate and society interactions
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