Suppr超能文献

对流组织程度的变化作为极端降水动态变化的一种机制

Changing Degree of Convective Organization as a Mechanism for Dynamic Changes in Extreme Precipitation.

作者信息

Pendergrass Angeline G

机构信息

Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, PO Box 3000, Boulder, CO 80303 USA.

Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland.

出版信息

Curr Clim Change Rep. 2020;6(2):47-54. doi: 10.1007/s40641-020-00157-9. Epub 2020 May 4.

Abstract

PURPOSE OF REVIEW

What does recent work say about how changes in convective organization could lead to changes in extreme precipitation?

RECENT FINDINGS

Changing convective organization is one mechanism that could explain variation in extreme precipitation increase through dynamics. In models, the effects of convective self-aggregation on extreme precipitation are sensitive to parameterization, among other factors. In both models and observations, whether or not convective organization influences extreme precipitation is sensitive to the time and space scales analyzed, affecting extreme precipitation on some scales but not others. While trends in observations in convective organization associated with mean precipitation have been identified, it has not yet been established whether these trends are robust or relevant for events associated with extreme precipitation.

SUMMARY

Recent work has documented a somewhat view of how changes in convective organization could affect extreme precipitation with warming, and it remains unclear whether or not they do.

摘要

综述目的

近期研究对于对流组织的变化如何导致极端降水的变化有何看法?

近期发现

对流组织的变化是一种可以通过动力学解释极端降水增加变化的机制。在模型中,对流自聚集对极端降水的影响对参数化等因素敏感。在模型和观测中,对流组织是否影响极端降水对所分析的时间和空间尺度敏感,在某些尺度上影响极端降水,而在其他尺度上则不然。虽然已经确定了与平均降水相关的对流组织观测趋势,但这些趋势是否稳健或与极端降水相关事件相关尚待确定。

总结

近期研究记录了对流组织变化如何在变暖情况下影响极端降水的一些观点,而它们是否真的有影响仍不清楚。

相似文献

1
Changing Degree of Convective Organization as a Mechanism for Dynamic Changes in Extreme Precipitation.
Curr Clim Change Rep. 2020;6(2):47-54. doi: 10.1007/s40641-020-00157-9. Epub 2020 May 4.
2
Significant Amplification of Instantaneous Extreme Precipitation With Convective Self-Aggregation.
J Adv Model Earth Syst. 2021 Nov;13(11):e2021MS002607. doi: 10.1029/2021MS002607. Epub 2021 Nov 18.
3
Response of extreme precipitation to uniform surface warming in quasi-global aquaplanet simulations at high resolution.
Philos Trans A Math Phys Eng Sci. 2021 Apr 19;379(2195):20190543. doi: 10.1098/rsta.2019.0543. Epub 2021 Mar 1.
4
Implicit learning of convective organization explains precipitation stochasticity.
Proc Natl Acad Sci U S A. 2023 May 16;120(20):e2216158120. doi: 10.1073/pnas.2216158120. Epub 2023 May 8.
5
Rapid decadal convective precipitation increase over Eurasia during the last three decades of the 20th century.
Sci Adv. 2017 Jan 25;3(1):e1600944. doi: 10.1126/sciadv.1600944. eCollection 2017 Jan.
6
Relationship Between Precipitation Extremes and Convective Organization Inferred From Satellite Observations.
Geophys Res Lett. 2020 May 16;47(9):e2019GL086927. doi: 10.1029/2019GL086927. Epub 2020 May 6.
8
Cold Pool Dynamics Shape the Response of Extreme Rainfall Events to Climate Change.
J Adv Model Earth Syst. 2021 Feb;13(2):e2020MS002306. doi: 10.1029/2020MS002306. Epub 2021 Feb 23.
10
Exploring changes of precipitation extremes under climate change through global variable-resolution modeling.
Sci Bull (Beijing). 2024 Jan 30;69(2):237-247. doi: 10.1016/j.scib.2023.11.013. Epub 2023 Nov 7.

引用本文的文献

1
NPCC4: Tail risk, climate drivers of extreme heat, and new methods for extreme event projections.
Ann N Y Acad Sci. 2024 Sep;1539(1):49-76. doi: 10.1111/nyas.15180. Epub 2024 Aug 19.
2
Mesoscale convective clustering enhances tropical precipitation.
Sci Adv. 2023 Jan 13;9(2):eabo5317. doi: 10.1126/sciadv.abo5317. Epub 2023 Jan 11.
3
The Relationship Between Convective Clustering and Mean Tropical Climate in Aquaplanet Simulations.
J Adv Model Earth Syst. 2020 Aug;12(8):e2020MS002070. doi: 10.1029/2020MS002070. Epub 2020 Aug 8.

本文引用的文献

1
Relationship Between Precipitation Extremes and Convective Organization Inferred From Satellite Observations.
Geophys Res Lett. 2020 May 16;47(9):e2019GL086927. doi: 10.1029/2019GL086927. Epub 2020 May 6.
2
Observing Convective Aggregation.
Surv Geophys. 2017;38(6):1199-1236. doi: 10.1007/s10712-017-9419-1. Epub 2017 Jun 28.
3
Anthropogenic influences on major tropical cyclone events.
Nature. 2018 Nov;563(7731):339-346. doi: 10.1038/s41586-018-0673-2. Epub 2018 Nov 14.
4
Thermodynamic control of anvil cloud amount.
Proc Natl Acad Sci U S A. 2016 Aug 9;113(32):8927-32. doi: 10.1073/pnas.1601472113. Epub 2016 Jul 13.
5
Increases in tropical rainfall driven by changes in frequency of organized deep convection.
Nature. 2015 Mar 26;519(7544):451-4. doi: 10.1038/nature14339.
6
Weak linkage between the heaviest rainfall and tallest storms.
Nat Commun. 2015 Feb 24;6:6213. doi: 10.1038/ncomms7213.
7
Human contribution to more-intense precipitation extremes.
Nature. 2011 Feb 17;470(7334):378-81. doi: 10.1038/nature09763.
8
The physical basis for increases in precipitation extremes in simulations of 21st-century climate change.
Proc Natl Acad Sci U S A. 2009 Sep 1;106(35):14773-7. doi: 10.1073/pnas.0907610106. Epub 2009 Aug 19.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验