Will a Warmer World Mean a Wetter or Drier Australian Monsoon?

Brown, Josephine R.; Moise, Aurel; Colman, Robert; Zhang, Huqiang

doi:10.1175/jcli-d-15-0695.1

Cited by 51 publications

(69 citation statements)

References 49 publications

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“…when associated with 10 a decrease of the intensity of the moist flow during the monsoon period. The differences between our study and the one of Trenberth et al (2005) are consistent with the theory that precipitation over western and northern Australia (the part of Australia mostly influenced by the monsoon flow in DJF) are strongly sensitive to the SST over the western central Pacific Ocean (10°S-10°N; 150°-200°E) (Brown et al, 2016). In ERA-Interim and Wang et al (2016), during 1995-2010, the SST over this part of ocean has increased (indicated by a moistening, according to C-C law), and is associated with a drying over 15 Australia, while during 1988-2001, a strong drying is observed over the central western Pacific Ocean, associated with a moistening over Australia.…”

Section: Trends In Gps and Era-interim Iwv (1995-2010) 20supporting

confidence: 91%

Global IWV trends and variability in atmospheric reanalyses and GPS observations

Parracho¹,

Bock²

2018

Preprint

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Abstract. Water vapour plays a key role in the climate system. However, its short residence time in the atmosphere and its high variability in space and time make it challenging when it comes to study trends and variability. There are several sources of water vapour data. In this work we use Integrated Water Vapour (IWV) estimated from GPS observations and atmospheric 10 reanalyses. Monthly and seasonal means, interannual variability, and linear trends are analysed and compared for the period between 1995 and 2010. A general good agreement is found but this study highlights issues in both GPS and reanalysis data sets. In GPS, gaps and inhomogeneities in the time series are evidenced, which affect mainly variability and trend estimation.In ERA-Interim, too strong trends in certain regions (e.g. drying over northern Africa and Australia, and moistening over northern South America) were found. Representativeness differences in coastal areas and regions of complex topography 15 (mountain ranges, islands) are also evidenced as limitations to the intercomparison of the point observations and reanalysis data. A general good agreement is found for the means and variabilities, with the exception of a few stations where representativeness issues are suspected. Monthly IWV trends are also found to be in good sign agreement, with the exception of a handful of stations where, in addition to representativeness errors, there might be inhomogeneities in the GPS time series.Seasonal trends are found to be different and more intense than monthly trends, which emphasizes the influence of atmospheric 20 circulation on IWV trends. In order to assess strong trends over regions lacking GPS stations, a second reanalysis, MERRA-2, is introduced. The period of analysis is extended to 1980-2016 (the longest period the reanalyses have in common) and differences with the shorter period are found. This exemplifies how much IWV trends are dependent on the time period at study and must be interpreted carefully. Temperature trends are also computed for both reanalyses. The Clausius-Clapeyron scaling ratio is found to not be a good humidity proxy at seasonal and regional scales. Regions over northern Africa and 25 Australia, where ERA-Interim and MERRA-2 disagree, are investigated further. Dynamics at these regions is assessed by analyzing the wind fields at 925 hPa and is shown to be tightly linked with the trends and variability in IWV.

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Section: Trends In Gps and Era-interim Iwv (1995-2010) 20supporting

confidence: 91%

Global IWV trends and variability in atmospheric reanalyses and GPS observations

Parracho¹,

Bock²

2018

Preprint

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“…Several models project very large increases or decreases in total rainfall. Model 14 projects the largest decrease (−42%) which has been noted previously to be related to warming in the western equatorial Pacific where the model also has a large negative sea surface temperature bias (Brown et al ., ). The present analysis finds that this decrease is associated with a decrease in the percentage of rain that falls on burst days (−48%, Figure b), a decrease in burst days (−58%, Figure c) and a large decrease (−42%) in the number of burst events (Figure d).…”

Section: Results From Cmip5 Modelsmentioning

confidence: 98%

“…The station data analysed here are for Darwin Airport (WMO Station 94120) while the regional average values (that encompass Darwin) refer to the “Australian monsoon domain” defined here by (land areas within) 10–20 ° S, 110–150 ° E for both observations and models, consistent with previous studies, for example, Brown et al . (). The Australian monsoon domain and Darwin locations are shown in Figure .…”

Section: Methodsmentioning

confidence: 99%

Observed and projected intra‐seasonal variability of Australian monsoon rainfall

Moise

Smith

Brown

et al. 2019

Intl Journal of Climatology

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Indices derived from daily rainfall time series are used to measure “burst” features of the northern Australia monsoon, corresponding to one or more days of heavy rainfall. These indices include number of burst days, numbers and durations of burst events, and average intensity. The results using observational data show how these features can vary from one year to the next, and how they can vary from the station scale (Darwin) to the regional scale (northern Australia). The results from Coupled Model Intercomparison Project Phase 5 (CMIP5) climate model simulations under both historical and future greenhouse gas conditions have also been analysed and indicate how well models can capture these features and how they might change by the end of the 21st century under a high emissions scenario. While most models provide a reasonable simulation of present‐day burst features, there is little consensus for a significant change to seasonal rainfall totals when looking at the full CMIP5 ensemble. A subset of models with detectable skills with respect to the Madden‐Julian Oscillation shows evidence for an increase in the seasonal total rainfall amount and most other monsoon metrics, except a slight decrease in the number of burst events. This is consistent with a basic thermodynamic response to warming and consistent with findings elsewhere. However, the Australian monsoon is strongly influenced by the large‐scale circulation and there remains some doubt about whether we can confidently diagnose all the changes to monsoon bursts that could occur given the limited ability of many of the current generation of models to simulate tropical cyclones, the Madden‐Julian Oscillation and other relevant features.

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“…Therefore, this Tripole index is a relevant tool to monitor future SST warming in the near future, and in future projection work and how it may impact on future SEA rainfall trends. The spatial pattern of SST warming in the tropical Pacific was also found to be an important influence on northern Australian rainfall projections (Brown et al ., ). It confirms the importance of patterns of tropical warming in driving range of rainfall projections across Australia from climate models as was noted by Watterson () for the CMIP3 generation of models.…”

Section: Discussionmentioning

confidence: 97%

Understanding south‐east Australian rainfall projection uncertainties: the influence of patterns of projected tropical warming

Timbal

Fiddes

Brown

2017

Intl Journal of Climatology

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South‐Eastern Australia and, in particular the state of Victoria, has experienced record deficits of rainfall over the last 20 years, in which the cool part of the year from April to October has been most affected. This situation has created difficulties for water managers, farmers and fire services, with the need to provide more certainty about future climate trends becoming clear. The latest climate projections for South‐Eastern Australia project an overall drying in the cool part of the year with little change in the rest of the year. Although this is in line with current trends, very large uncertainties are associated with these projections. In this study, this range of projections has been investigated, first by assessing how the current suite of climate models simulate the regional rainfall as well as the tropical variability, known to be a key driver of south‐eastern Australian climate. The models were found to be reasonable overall, although a number overestimate Victorian summer rainfall. Model rainfall projections are found to be related to the models' projected patterns of tropical warming, where 60% of the range in cool season rainfall projections can be explained by the range of pattern of tropical changes. In addition, the projected drying tends to be more intense in the models best able to simulate summer rainfall, thus suggesting that the upper end of the uncertainty range is less likely to be realized as it may reflect inherent model biases, rather than physical changes.

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Will a Warmer World Mean a Wetter or Drier Australian Monsoon?

Cited by 51 publications

References 49 publications

Global IWV trends and variability in atmospheric reanalyses and GPS observations

Global IWV trends and variability in atmospheric reanalyses and GPS observations

Observed and projected intra‐seasonal variability of Australian monsoon rainfall

Understanding south‐east Australian rainfall projection uncertainties: the influence of patterns of projected tropical warming

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