Australian Rainfall Anomalies in 2018–2019 Linked to Indo‐Pacific Driver Indices Using ERA5 Reanalyses

Watterson, I. G.

doi:10.1029/2020jd033041

Cited by 9 publications

(13 citation statements)

References 40 publications

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“…A combination of extreme values in remote drivers of variability, including extreme positive IOD, central-Pacific El Niño, and sustained negative SAM associated with very strong sudden stratospheric warming, exacerbated already dry and hot conditions in spring 2019 to promote one of Australia's deadliest fire seasons (Watterson, 2020;Lim et al, 2021a;Abram et al, 2021, Marshall et al 2021. Further, projected trends toward positive IOD (Cai et al, 2014;Abram et al, 2020) or toward negative TPI (Timbal and Hendon, 2011) may contribute to higher maximum temperatures in the future, particularly in later spring when the tropics exert greater influence on Australia's dynamical heat mechanisms.…”

Section: Discussionmentioning

confidence: 99%

“…In the tropics, the positive phases of El Niño Southern Oscillation (ENSO) in the tropical Pacific and the Indian Ocean Dipole (IOD) in the tropical Indian Ocean are the strongest drivers of high maximum temperatures in Australia in spring, particularly in the south and east (Power et al, 1998;Jones and Trewin, 2000;Saji et al, 2005;Min et al, 2013;White et al, 2014). Many more studies focus on the ENSO and IOD relationships to drier spring conditions (Nicholls et al, 1989;Meyers et al, 2007;Ummenhofer et al, 2009;Risbey et al, 2009a;Watterson, 2010;Cai et al, 2011;Min et al, 2013;Pepler et al, 2014;McIntosh and Hendon, 2018;Watterson, 2020) and to more extreme spring fire weather (Harris and Lucas 2019;Marshall et al 2021). While ENSO and the IOD co-vary significantly in austral spring (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Strengthening tropical influence on heat generating circulation over Australia through spring

McKay

Arblaster

Hope

2021

Preprint

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Abstract. Extreme maximum temperatures during Australian spring can have deleterious impacts on a range of sectors from health to wine grapes to planning for wildfires, but are relatively understudied compared to spring rainfall. Spring maximum temperatures in Australia have been rising over recent decades, and, as such, it is important to understand how Australian spring maximum temperatures develop. Australia’s climate is influenced by variability in the tropics and extratropics, but some of this influence impacts Australia differently from winter to summer, and, consequently, may have different impacts on Australia as spring evolves. Using linear regression analysis, this paper explores the atmospheric dynamics and remote drivers of high maximum temperatures over the individual months of spring. We find that the drivers of early spring maximum temperatures in Australia are more closely related to low-level wind changes, which in turn are more related to the Southern Annular Mode than variability in the tropics. By late spring, Australia’s maximum temperatures are proportionally more related to warming through subsidence than low-level wind changes, and more closely related to tropical variability. This increased relationship with the tropical variability is linked with the breakdown of the subtropical jet through spring and an associated change in tropically-forced Rossby wave teleconnections. However, much of the maximum temperature variability cannot be explained by either tropical or extratropical variability. An improved understanding of how the extratropics and tropics projects onto the mechanisms that drive high maximum temperatures through spring may lead to improved sub-seasonal prediction of high temperatures in the future.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Strengthening tropical influence on heat generating circulation over Australia through spring

McKay

Arblaster

Hope

2021

Preprint

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“…Smith 2004, Power et al 2006. Watterson (2020) found that the spatial average rainfall over Australia (Fig. 1), All-Australia precipitation (AApr), related particularly well with the PID.…”

Section: Introductionmentioning

confidence: 93%

“…The effects of the atmospheric transport of moisture on rainfall have been studied by Taminiau and Haarsma (2007), Gu and Adler (2019), Marshall (2019), Hauser et al (2020), Holgate et al (2020), Ye et al (2020), and others. Watterson (2020) presented regression relationships with various driver indices, including the 'Pacific-Indian Dipole' (PID), which combines the west Pacific and the east Indian Ocean anomalies, as represented in the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis 5 (ERA5) data (Hersbach et al 2020). The quantities considered included the vertically integrated (from the surface to top of atmosphere) horizontal moisture transport or 'flux' vector.…”

Section: Introductionmentioning

confidence: 99%

Australian rainfall anomalies and Indo-Pacific driver indices: links and skill in 2-year-long forecasts

Watterson

O’Kane

Kitsios

et al. 2021

J. South. Hemisph. Earth Syst. Sci.

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Two-year-long simulations of the atmosphere and ocean by the Commonwealth Scientific and Industrial Research Organisation's (CSIRO) Climate Analysis Forecast Ensemble (CAFE) modelling system are analysed, with a focus on Indo-Pacific sea surface temperature (SST) climate drivers and their teleconnection to Australian rainfall. The simulations are 11-member ensemble forecasts (strictly, hindcasts) initiated each month from 2002 to 2015, supplemented by a 100-year-long control simulation. Using correlations r between seasonal and annual means, it is shown that the links between the interannual variations of All-Australia precipitation (AApr) and the standard driver indices, together with the Pacific-Indian Dipole (PID), are mostly similar to those derived from observational data. The vertically integrated meridional flux of moisture towards northern Australia is linked to both the SSTs and AApr. Correlations between ensemble averages and observations are used as a measure of forecast skill, calculated for each start month and for lead time after start. Positive correlations hold over the first year for much of the low-latitude Pacific and for the drivers. The forecasts become more skillful than persistence, with r for PID averaging 0.3 higher over lead times of 7–13 months. The forecast of seasonal AApr has moderate to good correlations (r 0.4–0.8) for seasons centred on September–February. This is largely consistent with skill in both the flux and in the SST drivers. Correlations are also good for 1-year and 2-year means. This apparent skill is currently being explored using a new larger suite of CAFE forecasts.

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“…He et al [67], Xue et al [68], Li et al [69], and Wang et al [70] verified the feasibility of the ERA5 dataset in analyzing climate change on TP. In addition, Josey et al [71], Watterson [72], Tarek et al [73], and Tang et al [74] also used ERA5 temperature and precipitation datasets for hydrology modeling, climate change, and other studies.…”

Section: Climate Datamentioning

confidence: 99%

Spatio-Temporal Variation Characteristics of Snow Depth and Snow Cover Days over the Tibetan Plateau

Zhang

Mou

Niu

et al. 2021

Water

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Changes in snow cover over the Tibetan Plateau (TP) have a significant impact on agriculture, hydrology, and ecological environment of surrounding areas. This study investigates the spatio-temporal pattern of snow depth (SD) and snow cover days (SCD), as well as the impact of temperature and precipitation on snow cover over TP from 1979 to 2018 by using the ERA5 reanalysis dataset, and uses the Mann–Kendall test for significance. The results indicate that (1) the average annual SD and SCD in the southern and western edge areas of TP are relatively high, reaching 10 cm and 120 d or more, respectively. (2) In the past 40 years, SD (s = 0.04 cm decade−1, p = 0.81) and SCD (s = −2.3 d decade−1, p = 0.10) over TP did not change significantly. (3) The positive feedback effect of precipitation is the main factor affecting SD, while the negative feedback effect of temperature is the main factor affecting SCD. This study improves the understanding of snow cover change and is conducive to the further study of climate change on TP.

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Australian Rainfall Anomalies in 2018–2019 Linked to Indo‐Pacific Driver Indices Using ERA5 Reanalyses

Cited by 9 publications

References 40 publications

Strengthening tropical influence on heat generating circulation over Australia through spring

Strengthening tropical influence on heat generating circulation over Australia through spring

Australian rainfall anomalies and Indo-Pacific driver indices: links and skill in 2-year-long forecasts

Spatio-Temporal Variation Characteristics of Snow Depth and Snow Cover Days over the Tibetan Plateau

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