Regime transitions of Australian climate and climate extremes

Frederiksen, Jorgen Segerlund; Osbrough, Stacey Lee

doi:https://doi.org/10.5194/wcd-2021-72

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https://doi.org/10.5194/wcd-2021-72

Preprints

15 Nov 2021

| 15 Nov 2021

Status: this preprint was under review for the journal WCD but the revision was not accepted.

Regime transitions of Australian climate and climate extremes

Jorgen Segerlund Frederiksen and Stacey Lee Osbrough

Abstract. Systematic changes, since the beginning of the 20^th century, in average and extreme Australian rainfall and temperatures indicate that Southern Australian climate has undergone regime transitions into a drier and warmer state. South-west Western Australia (SWWA) experienced the most dramatic drying trend with average streamflow into Perth dams, in the last decade, just 20 % of that before the 1960s and extreme, decile 10, rainfall reduced to near zero. In south-eastern Australia (SEA) systematic decreases in average and extreme cool season rainfall became evident in the late 1990s with a halving of the area experiencing average decile 10 rainfall in the early 21^st century compared with that for the 20^th century. The shift in annual surface temperatures over SWWA and SEA, and indeed for Australia as a whole, has occurred primarily over the last 20 years with the percentage area experiencing extreme maximum temperatures in decile 10 increasing to an average of more than 45 % since the start of the 21^st century compared with less than 3 % for the 20^th century mean. Average maximum temperatures have also increased by circa 1 °C for SWWA and SEA over the last 20 years. The climate changes are associated with atmospheric circulation shifts and are indicative of second order regime transitions, apart from extreme temperatures for which the dramatic increases are suggestive of first order transitions.

Received: 20 Oct 2021 – Discussion started: 15 Nov 2021

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.

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Jorgen Segerlund Frederiksen and Stacey Lee Osbrough

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RC1:
'Comment on wcd-2021-72', Anonymous Referee #1, 17 Dec 2021

Review of “Regime transitions of Australian climate and climate extremes” by Frederiksen and Osbrough

This study attempts to explain changes in regional temperature, rainfall and streamflow in Australia by analysing the observational record and reanalysis. The authors suggest regime transitions are identifiable in the extreme timeseries.

I have a couple of major concerns with this study. The second major concern I think warrants rejection of this study in my opinion unfortunately.

Major concerns:

1. The actual purpose of the study is unclear to me. The paper, while written in a way that’s grammatically correct, doesn’t really flow and is missing important sections, such as a Methods section. The motivation and novelty of this work needs to come through much more clearly. In addition, the lack of a Methods section would make the paper very difficult to reproduce.

2. ’m afraid to say I think that the extremes-based results are largely artefacts of the use of decile-based indices that are defined over long seasons. Given these deciles are defined based on some climatological period (which I don’t see defined anywhere) and you’re looking at area coverage of top decile values, a small shift in the distribution of seasonal-average temperatures will result in a big increase in the incidence of top decile events. The relatively low interannual variability in seasonal temperatures (compared with daily extreme indices) and the spatial homogeneity of the temperatures mean that large jumps in the extreme index would be entirely expected.

A test to show that this isn’t an artefact of the use of area of top decile seasonal-average temperatures would be to use an inherently noisier index, such as a daily extreme index like aggregated area exceeding a high percentile.

Other comments:

L16-17: I’d suggest not talking about first and second order transitions in the Abstract as at this stage in reading the paper it’s not clearly what this means.

L86: “Pert” should be “Perth”

Section 2.2. This is a bit brief- perhaps a summary of what variables were used and why NCEP and these variables were chosen would be helpful. NCEP1 exhibits quite different trends in extremes to other reanalyses in this region (Donat et al., 2014) so some comparison with another dataset would be helpful.

L95-97: Another relevant paper is Delworth & Zeng, (2014).

L124-125: The focus on a quadratic fit suggests there is a physical reason why this relationship would be quadratic, but I suspect there isn’t. It might be more useful just to focus on the rainfall-streamflow relationship without this fit applied.

L140: The definitions used here for Southern Wet Season and Southern Cool Season are so similar that I think it’s redundant to say the results are similar for these two.

L208-209: I think you mean the timeseries are synchronous rather than the variability is the same.

L234: “average” should be “on average”

Section 5 without any further analysis is unnecessary as it doesn’t add to the paper.

L251: Comma needed after “variability”

References

Delworth, T. L., & Zeng, F. (2014). Regional rainfall decline in Australia attributed to anthropogenic greenhouse gases and ozone levels. , (8), 583–587. https://doi.org/10.1038/ngeo2201

Donat, M. G., Sillmann, J., Wild, S., Alexander, L. V., Lippmann, T., & Zwiers, F. W. (2014). Consistency of Temperature and Precipitation Extremes across Various Global Gridded In Situ and Reanalysis Datasets*. , (13), 5019–5035. https://doi.org/10.1175/JCLI-D-13-00405.1

Citation: https://doi.org/10.5194/wcd-2021-72-RC1
- AC1: 'Reply on RC1', Jorgen Frederiksen, 04 Jan 2022
  
  Interactive comment on “Regime transitions of Australian climate and climate extremes” by Jorgen S. Frederiksen and Stacey L. Osbrough
  Jorgen S. Frederiksen^1,2, Stacey L. Osbrough^1,2
  ¹CSIRO Oceans and Atmosphere, Aspendale, 3195, Australia
  ²Monash University, Clayton, 3800, Australia
  Correspondence to: Jorgen S. Frederiksen (jorgen.frederiksen@csiro.au)
  Reply to RC1 - doi:10.5194/bgd-12-15087-2015
  1 Methods section
  We will follow the reviewer’s suggestion and include a Methods section in the revision of the paper. This will include a pedagogical summary of the method of determining rainfall and temperature deciles as described by the Bureau of Meteorology (2022b), some of the issues in point 2 below, and discussion of regression methods and averaging methods. Much of the discussion on anthropogenic climate change tipping points has focussed on major tipping points of global extent that may be exceeded in the future. For example, Lenton et al. (2019) discuss tipping points such as the possible acceleration of the melting of the Greenland ice sheet that could occur with a 1.5^o C warming. As noted, our particular interest in this article is whether the changes that have already occurred in Australian climate and climate extremes over the last seventy years are indicative of regime transitions in a noisy environment. We also discuss the relationship between these changes and the large-scale circulation and in the revision will further expand on additional implications and connections.
  2 Extended seasonal and annual deciles
  Perhaps to provide background to our response it is useful to first summarize the development and uses of rainfall and temperature deciles over the last 55 years. As noted by Keyantash (2021) “An established quantile methodology is the usage of ten quantiles, or deciles. A decile-based system for monitoring meteorological drought in Australia was proposed by Gibbs and Maher (1967) and adopted by the Australian Bureau of Meteorology (BoM) to monitor drought conditions in that nation.” Gibbs and Mayer (1967) presented Australian maps of the distribution of decile ranges of annual rainfall for the years 1885 to 1965 in a study of drought. The Bureau of Meteorology (2022c) has continued the publication of Australian maps of annual rainfall deciles from 1900 to the present and has presented them for extended seasons (e.g., Bureau of Meteorology and CSIRO 2020) as well as for seasons and years in numerous reports. Keyantash (2021) further notes “The rainfall decile methodology begins by assembling three-month (or longer) precipitation totals … as drought is not validly recognized for briefer periods in Australia…”. The caption in Fig. 11.1 of Keyantash (2021) states “Decile map of 12-month precipitation totals in Australia, through April 2020. Meteorological drought in Australia may be assessed across a variety of timescales, but the duration must be a minimum of three months.” Keyantash (2021) further notes “It is interesting that BoM also examines monthly rainfall totals from the decile perspective, even when drought characterization is not the objective”.
  Keyantash and Dracup (2002) have made similar determinations to those above and note that, in the USA context, for Meteorological Drought the rainfall decile index as used at BoM is the superior index overall and particularly in terms of robustness, transparency and extendability (their Table 3). In the Handbook of Drought Indicators and Indices by the World Meteorological Organization and Global Water Partnership (2016) some of the properties of deciles are noted. In particular: deciles are “easy to calculate” and “examples from Australia are useful”. “Daily, weekly, monthly, seasonal and annual values can all be considered in the methodology, as it is flexible when current data are compared to the historical record for any given period.”. “Applications: With the ability to look at different timescales and time steps, deciles can be used in meteorological, agricultural and hydrological drought situations.” Table 2 of the Handbook also lists some of the Meteorological Institutions, in addition to those in Australia and USA, that use deciles.
  Deciles of rainfall and temperatures have also been used in the horticultural and agricultural industries on a variety of time scales including the extended seasonal time scale. For example: Cool season – April to October – rainfall deciles were used in the South Australian Government study of climate change, wheat production and erosion risk by Sweeney and Liddicoat (2012). In a study of “The Riverland Climate for Almond Production” Thomas and Hayman (2019) examine September – April deciles of temperature. Hayman and Hudson (2021) explore the value of recent new BoM forecast products of weekly, monthly, and seasonal rainfall and temperature deciles for grain production.
  The Bureau of Meteorology (2022a) publishes monthly, seasonal, extended seasonal and annual deciles of rainfall and temperatures and has published numerous reports in which they are employed for various purposes.
  In view of the above background, we do not understand why the reviewer thinks that extremes-based results using deciles for long seasons should be artifacts. There is no essential difference in studying time span averaged data in decile 5 – the median for general distributions and the mean for symmetric distributions – and any other decile. As noted by Keyantash (2021) the decile approach is nonparametric so there are no fitted distributions or assumptions. The approach involves a simple time span averaging, ranking, ordering, and binning of the observed results and the values, in a given decile, at a particular time, and time scale, represent just the reality of the observations. If the regime transitions are more obvious for decile 10 than decile 5 then that is the reality of the meteorology, the climatology, the physics and the chemistry just as they are for streamflow compared with rainfall. This is the case whether the resulting distribution is Gaussian, Poisson or fat-tailed and whether the future is just a shift in the mean or a significant change in the tail. The frequency of bushfires and the failure of crops are the reality of phenomena that depend more on the extremes than the mean or median.
  The broad conclusions of our study are borne out by monthly, seasonal, extended seasonal and annual data. Monthly data are, of course, noisier which is why for many purposes seasonal, extended seasonal or annual deciles are preferred as generally presented by the Australian Bureau of Meteorology.
  
  3 Other comments
  L16-17: Yes, agreed – thank you.
  L86: Yes, agreed – thank you.
  Section 2.2: Yes, we will expand on this section in the revision and point out that the NCEP data is not used for the extremes but only the mean circulation and reference comparisons.
  L95-97: Thank you – will be referenced in the revision.
  L124-125: The purpose of the quadratic fit is just to indicate why the streamflow is more sensitive to changes in rainfall than mean rainfall itself. In that sense the streamflow acts like the decile 10 rainfall. We will rephrase this to make it clearer in the revision.
  L140: SWWA rainfall has often historically been considered for the SWS season of April to November and for the winter season of June to August and more recently also for the CS season of April to October. We feel that we should point out that our conclusions are broadly the same for all these periods.
  L208-209: Yes, the time series are essentially synchronous – thank you.
  L234: Yes, agreed – thank you.
  Section 5; We feel that it is important to point out that the rainfall changes in Northern Australia have been largely opposite to those in Southern Australia. We will consider this point further in the revision.
  L251: Yes, agreed – thank you.
  
  Acknowledgements. We wish to thank David Jones and Blair Trewin of the Australian Bureau of Meteorology for informative discussions on the rigour, robustness and value of the BoM decile temperature and rainfall data for months, seasons, extended seasons, and years.
  References
  
  Bureau of Meteorology: Australian climate variability and change - Time series graphs 2022a: http://www.bom.gov.au/climate/change/index.shtml#tabs=Tracker&tracker=timeseries, last access: 04/01/2022.
  Bureau of Meteorology: Rainfall Map Information 2022b: http://www.bom.gov.au/climate/austmaps/about-rain-maps.shtml#deciles, last access: 04012022.
  Bureau of Meteorology: 122 years of Australian rainfall 2022c: http://www.bom.gov.au/climate/history/rainfall/, last access: 04/01/2022.
  Bureau of Meteorology and CSIRO: State of the Climate 2020: www.csiro.au/state-of-the-climate, last access: 04/01/2022.
  Gibbs, W.J. and Maher, J.V.: Rainfall deciles as drought indicators, Australia Bureau of Meteorology, Melbourne, Bull 48, 1-85, 1967.
  Hayman, P. and Hudson, D.: Forewarned Is Forearmed – Exploring The Value Of New Forecast Products From The BOM To Enable More Informed Decisions On Profit And Risk On Grain Farms 1-11, 2021: https://grdc.com.au/__data/assets/pdf_file/0024/445902/Paper-Hayman-and-Hudson-May-2021.pdf, last access: 04/01/2022.
  Keyantask, J.: Indices for Meteorological and Hydrological Drought, Hydrological Aspects of Climate Change, 11, 215-236, 10.1007/978-981-16-0394-5, 2021.
  Keyantask, J. and Dracup, J.A.: The Quantification of Drought: An Evaluation of Drought Indices, Bulletin of the American Meteorological Society, 83, 1167-1180, 0.1175/1520-0477-83.8.1167, 2002.
  Lenton, T. M. R., J.; Gaffney, O.; Rahmstorf, S.; Richardson, K.; Steffen, W.; Schellnhuber, H. J. : Climate tipping points — too risky to bet against, Nature, 575, 592 - 595, 2019.
  Sweeney, S. and Liddicoat. C.: Climate change, wheat production and erosion risk in South Australia’s cropping zone:
  Linking crop simulation modelling to soil landscape mapping, Department of Environment, Water and Natural Resources, Government of South Australia, Technical Report 2012/05, 1-151, 2012: https://cdn.environment.sa.gov.au/environment/docs/kb-gen-climate-change-wheat-production-and-erosion-risk.pdf, last access: 04/01/2022.
  Thomas, D. and Hayman, P.: The Riverland Climate for Almond production: Analysis of strengths and challenges, South Australian Research & Development Institute, 1-44, 2019: https://www.horticulture.com.au/globalassets/hort-innovation/resource-assets/al14006-climate-strengths-and-challenges---riverland.pdf, last access: 04/01/2022.
  World Meteorological Organization and Global Water Partnership: Handbook of Drought Indicators and Indices, 1-52, 2016: https://www.droughtmanagement.info/literature/GWP_Handbook_of_Drought_Indicators_and_Indices_2016.pdf, last access: 04/01/2022.
  
  Citation: https://doi.org/10.5194/wcd-2021-72-AC1
RC2:
'Comment on wcd-2021-72', Anonymous Referee #2, 27 Jan 2022

The main idea of the paper is interesting and deserves to be published, but I have some concern about the shape of the paper and the way is it presented. The paper doesn't really flow and it would benefit from presenting the results in a way such as the reader know what question is to be addressed in each section and how it is related to the rest of the paper. Also, the main question(s) of the paper should be clarified. Does it focus on transition periods? or regional differences?.....

For most of the sections, there is a lack of conclusions. Results are presented in details but the key message of a section should be summarized so the reader can have a clear idea about what he got from the analyses.

Part 3.1: I didn't really understand the way authors defined the periods to average results (Fig.1 / Table 1). It seems a bit random. Also, authors refer to some particular signal for the last of the periods (2014-2018) but it is not surprising as it only concerns 5 years whereas most of the other periods refer to multi-decade averages. This whole paragraph should be written in a more consistent and justified way. Also, instead of Table 1, period-averaging results could be displayed directly in Fig.1 (as horizontal bars for example).

This comment is also valid for all other tables and results showing period averaging.

In a same way, figures showing 10-year running means could simply be plotted on the top of annual results (with a curve or something similar).

Also, a lot of litterature review is merged with the results and many references are quite old. Although I totally agree that it's important to refer to previous studies, in this paper there's sometime a mixed approach between a review paper and a new study paper. I'd suggest to limit more the review part to the introduction, to focus more on the most recent studies, and only refer to previous papers in a limited way in method and result sections. But in general I appreciate that authors did an extensive review work, especially for people (like myself) who are not familiar with Australian climate.

Variable and indices used should be described to the methodology instead of being presented in the main text (deciles, percentage area coverage.....). Especially, % area coverage should be clearly defined. Does it use a single threshold for whole period/season? In that case, it may explain the dramatic change for extreme temperatures (bascially it's just a shift in seasonal temperatures). It would be more interesting to use calendar day thresholds for example. But I'm not sure about which methodology has been used here....

Some other comments:

Fig.1(a): why is it displayed as centered values around the mean whereas all other similar plots are displayed as absolute values?

Fig,4: When plotting differences between 2 periods it's good to show where it is significant or not (with a t-test for exemple).

Fig5: although the decrease then flattening is quite clear, I'm wondering how reliable are reanalysis before 1970s, as there weren't satellite observation at that time?

L109-110: Figure description should be in the figure caption, not in the text.

Section 5 is not really useful...

Citation: https://doi.org/10.5194/wcd-2021-72-RC2
- AC2: 'Reply on RC2', Jorgen Frederiksen, 01 Mar 2022
  
  Interactive comment on “Regime transitions of Australian climate and climate extremes” by Jorgen S. Frederiksen and Stacey L. Osbrough
  Jorgen S. Frederiksen^1,2, Stacey L. Osbrough^1,2
  ¹CSIRO Oceans and Atmosphere, Aspendale, 3195, Australia
  ²Monash University, Clayton, 3800, Australia
  Correspondence to: Jorgen S. Frederiksen (jorgen.frederiksen@csiro.au)
  Reply to RC2 - doi.org/10.5194/wcd-2021-72-RC2
  
  We are pleased to learn that the Referee thinks that the main idea in the paper is interesting and deserves to be published. Here we present a summary of how we have addressed the Reviewer’s comments with more details in Response to RC2 (Part 2) accompanying the revised paper.
  1 Motivation and summary of findings for major sections
  We have added questions to be answered at the start of the major sections and a summary of findings at the end. The conclusions provide further summaries of our findings. We are interested in both the timings of the transition periods and how this varies regionally or not. Much of the discussion on anthropogenic climate change tipping points has focussed on major tipping points of global extent that may be exceeded in the future. For example, Lenton et al. (2019) discuss tipping points such as the possible acceleration of the melting of the Greenland ice sheet that could occur with a 1.5^o C warming. As noted, our particular interest in this article is whether the changes that have already occurred in Australian climate and climate extremes over the last seventy years are indicative of regime transitions in a noisy environment. Further discussion on the aims and purpose, and main findings, is given in the main sections of the paper that have been updated. We also discuss the relationship between these changes and the large-scale circulation and in the revision further expand on additional implications and connections.
  2 Data and Methods 2.1 Rainfall, temperature and streamflow data sets
  The average and extreme – decile 10 – rainfall and temperature data used in this paper have been obtained from the Bureau of Meteorology (2022a) website. In this study we focus on various regions such as SWWA, SEA, Northern Australia, and Australian states, shown in Fig. 1. These Bureau of Meteorology (BoM) data sets are based on averages of station data and are of higher quality than earlier BoM data sets (Jones et al., 2009; Trewin 2013). Gridded data sets based on observed station data are also generally more consistent than reanalysis data sets particularly in the pre-satellite era (e.g., Donat et al., 2014). The data for streamflow into Perth dams has been obtained from the Water Corporation (2020) of Western Australia.
  2.2 Reanalysis data sets
  The investigation of the changes in atmospheric circulation in our study uses the reanalysis data set of the National Centers for Environmental Prediction (NCEP) and the National Centre for Atmospheric Research (NCAR), (Kalnay et al., 1996). It will be referred to as the NNR data set. The NNR data set is available for the whole period from 1948 to the present and has been one of the most studied reanalysis data set incorporating the pre-satellite period. We focus here on changes in the Southern Hemisphere zonal jet in the Australian region and their strong relationships to rainfall changes in Southern Australia. In Supplement S1, we summarize the results of studies of the comparison of the NNR data set in the Australian region with other reanalysis data sets and with observations.
  2.3 Methods
  Next, we summarize the method of determining decile data from the BoM station data and our method for establishing the changing trends and critical points in data sets.
  2.3.1 Determination of decile data
  Gibbs and Maher (1967) developed a decile-based methodology for characterizing meteorological drought and presented Australian maps of the distribution of decile ranges of annual rainfall for the years 1885 to 1965. This method of determining decile data from station rainfall data (Jones et al., 2009) and temperature data (Trewin 2013) is detailed on the websites of the Bureau of Meteorology (2022b, 2022c). Briefly, deciles are a convenient way of coarse-graining the frequency distribution of a variable into ten bands each with 10% of the values. Decile 1 corresponds to the lowest 10%, decile 5 gives the median and decile 10 the highest 10% of the data which is generally monthly, seasonal, multi-month or annual data. The method makes no assumption about the distribution – it is nonparametric – and is based on all the data for a given time span. In practice, for a given time span, gridded data in each grid box, are sorted from lowest value to highest value and placed into ten equal bands, labelled decile 1 to 10, so that any value in a lower decile is smaller than those in the next decile. The percentage of grid boxes (percentage area) with values in a given decile and year and in a particular geographical region are then calculated based on all the grid boxes, which may be as small as circa 5 km by 5 km (0.05 degrees by 0.05 degrees) for regional rainfall.
  The utility and applications of decile data is further described in Supplement S2.
  
  2.3.2 Determination of changes in trends and critical points
  The critical times of large and sustained changes in the trends of the rainfall, streamflow and temperature data considered in this study have been determined as follows. The data have been low-pass filtered by applying a 10 year running mean to reduce noise due to the interannual variability. Graphs of the filtered data indicate time periods when these trends change significantly. Regression of the filtered data against time over each time periods incorporating these trend changes are then used to focus in on the critical times. Firstly, regression is applied against a quadratic function of time which highlights the critical time of gradient change. Then regressions against linear functions of time are performed between the beginning and first critical time, between the last critical time and the end of the timeseries and between any two adjacent intermediary critical points. This then determines the large changes in linear trends we find that are sustained for 15 to 20 years or longer. Averages of the unfiltered data are calculated for the associated time periods.
  S1 Reanalysis data sets and observations
  We have noted the consistency between aspects of the large-scale circulation in the Australian region as characterized by the NCEP-NCAR reanalysis data set (Kalnay et al., 1996) (hereafter NNR) and Southern Australian rainfall in this study and in our earlier works discussed in the Introduction. In our earlier works (e.g., Frederiksen et al., 2017) we also compared results based on NNR reanalyses with those for European Centre for Medium Range Weather Forecasting (ECMWF) Reanalysis (ERA-40) project (Kållberg et al., 2007) and Twentieth Century Reanalysis (20CR v2) project (Compo et al., 2011) that include the pre-satellite era. Here we consider the relationships between NNR reanalyses and other reanalyses and observations in the Australian region including during the pre-satellite era.
  Hertzog et al. (2006) compared the results of the August 1971 to December 1972 EOLE (from the Greek God of the Winds) experiment, involving flights of super-pressure balloons in the Southern Hemisphere upper troposphere, largely between 230 hPa and 190 hPa and 20S-70S, with NNR and ERA-40 data. They argued that their findings are representative of reanalysis accuracy for the pre-satellite era between 1957 and 1979. They noted that their analysis of the zonal wind structure in their Fig. 6 shows that both NNR and ERA-40 largely capture the meridional structure of the mean upper tropospheric jet although ERA-40 has a spurious double-jet peak structure. As well, ERA-40 has much larger errors in capturing upper tropospheric synoptic-scale variability. ERA-40 also has larger errors than NNR in representing mean sea level pressure and 500 hPa geopotential heights in the mid to high latitudes of the Southern Hemisphere in the pre-satellite era as shown in Figs. 3 and 6 of Bromwich and Foght (2004).
  Frederiksen and Frederiksen (2005, 2007; hereafter FF05, FF07) found broad consistency between changes in the Southern Hemisphere July large-scale circulation determined by NNR data, and the results of instability calculations of synoptic disturbances based on this data, and changes in rainfall over south-west Western Australia before and after the mid-1970s. FF07 (Table 7) also compared results for ERA-40 data with those for NNR and found very close agreement for leading synoptic scale modes with growth rates differing by less than 4% and pattern correlations between 0.94 and 0.99. The similar weakening of the mid-winter Southern Hemisphere subtropical jet at 200 hPa around the Australian region, in both NNR and ERA-40 data, in the 1990s compared with the 1950s and 1960s, was also noted by Joseph and Sabin (2008; Fig. 4).
  Frederiksen et al. (2017; Figs. 1 and 2) compared the Southern Hemisphere linear trend in Phillips (1954) criterion (discussed in our Section 3.2), in each of the four seasons, for NNR and 20CR v2 over the period 1950-1999 and for ERA-40 over 1958-1999. Of particular interest here are the consistent negative trends in the criterion upstream of Australia in NNR and ERA-40 with generally poorer agreement with 20CR. Rikus (2018), in a study of mid-latitude jetstreams in 9 reanalyses, including NNR, ERA-40 and 20CR, over the period 1979-2009, noted that 20CR had some systematic biases in upper-level winds compared with the the other reanalyses. Nevertheless, as shown in Fig. 1 of Freitas et al. (2015), the mid-winter reduction in the Southern Hemisphere subtropical jet that occurred in NNR data between the periods (1949-1968) and (1975-1994), and the increase further south, is evident in 20CR v2 data but with peak values at slightly lower levels, consistent with the findings of Rikus (2018).
  The study of Osbrough and Frederiksen (2021), based on six hourly NNR 850 hPa data, found there was good correspondence between the reduction in fast growing storms in the Australian region since the late 1960s and the reduction in Southern Australian rainfall providing further evidence of the general consistency of NNR data with rainfall variability.
  S2 Utility and applications of decile data
  Since the first introduction by Gibbs and Maher (1967), the decile representation of meteorological data has been widely used in the study of droughts and rainfall and temperature variability. As noted in Section 2, the decile data is available from the website of Bureau of Meteorology (2022a) and the decile method is described on the websites of Bureau of Meteorology (2022b, 2022c). Decile data for seasons, extended seasons and years have been presented in many reports (e.g., Bureau of Meteorology and CSIRO 2020) and BoM publishes Australian maps of annual rainfall deciles from 1900 to the present (Bureau of Meteorology 2022d). Deciles of rainfall and temperatures have also been used in the horticultural and agricultural industries. For example, cool season – April to October – rainfall deciles were used in the South Australian Government study of climate change, wheat production and erosion risk by Sweeney and Liddicoat (2012). In a study of “The Riverland Climate for Almond Production” Thomas and Hayman (2019) examined September – April deciles of temperature. Hayman and Hudson (2021) explored the value of recent new BoM forecast products of weekly, monthly, and seasonal rainfall and temperature deciles for grain production.
  Keyantash (2021) reviewed indices of meteorological and hydrological drought and compared the established quantile methodology of deciles with other approaches. He noted the simplicity and nonparametric nature of deciles that make no assumption about the distribution function but determines a coarse-grained version directly from the total available data. Keyantash and Dracup (2002) noted that, in the USA context, for Meteorological Drought the rainfall decile index as used at BoM is the superior index overall and particularly in terms of robustness, transparency and extendability (their Table 3). In the Handbook of Drought Indicators and Indices by the World Meteorological Organization and Global Water Partnership (2016) some of the properties of deciles are noted. In particular: deciles are “easy to calculate” and “examples from Australia are useful”. “Daily, weekly, monthly, seasonal and annual values can all be considered in the methodology, as it is flexible when current data are compared to the historical record for any given period.”. “Applications: With the ability to look at different timescales and time steps, deciles can be used in meteorological, agricultural and hydrological drought situations.” Table 2 of the Handbook also lists some of the Meteorological Institutions, in addition to those in Australia and USA, that use deciles.
  3 Presentational and minor comments
  The Referee’s presentational and minor comments will also be answered in Response to RC2 (Part 2) accompanying the revised paper.
  References
  Bureau of Meteorology: Australian climate variability and change - Time series graphs 2022a: http://www.bom.gov.au/climate/change/index.shtml#tabs=Tracker&tracker=timeseries, last access: 27/02/2022.
  Bureau of Meteorology: Rainfall Map Information 2022b: http://www.bom.gov.au/climate/austmaps/about-rain-maps.shtml#deciles, last access: 27/02/2022.
  Bureau of Meteorology: About the deciles timeseries graphs 2022c: http://www.bom.gov.au/climate/change/about/deciles_timeseries.shtml , last access 27/02/2022.
  Bureau of Meteorology: 122 years of Australian rainfall 2022d: http://www.bom.gov.au/climate/history/rainfall/, last access: 27/02/2022.
  Bureau of Meteorology and CSIRO: State of the Climate 2020: www.csiro.au/state-of-the-climate, last access: 27/02/2022.
  Bromwich, D. H., and Fogt, R. L.: Strong trends in the skill of the ERA-40 and NCEP-NCAR reanalyses in the high and midlatitudes of the southern hemisphere, 1958–2001, J. Climate, 17, 4603-4619, 2004.
  Compo, G.P., Whitaker, J.S., Sardeshmukh, P.D., Matsui, N., Allan, R.J., Yin, X., Gleason, Jr., B.E., Vose, R.S., Rutledge, G., Bessemoulin, P., Bronnimann, S., Brunet, M., Crouthamel, R.I., Grant, A.N., Groisman, P.Y., Jones, P.D., Kruk, M.C., Kruger, A.C., Marshall, G.J., Maugeri, M., Mok, H.Y., Nordli, Ø., Ross, T.F., Trigo, R.M., Wang, X.L., Woodruff, S.D., and Worleyu, S.J.: The twentieth century reanalysis project, Q J R Meteorol Soc 137,1-28, 10.1002/qj.776, 2011.
  Frederiksen, C. S., Frederiksen, J. S., Sisson, J. M., and Osbrough, S. L.: Trends and projections of Southern Hemisphere baroclinicity: the role of external forcing and impact on Australian rainfall, Clim Dynam, 48, 3261-3282, 10.1007/s00382-016-3263-8, 2017.
  Frederiksen, J. S. and Frederiksen, C. S.: Decadal changes in Southern Hemisphere winter cyclogenesis, CSIRO Marine and Atmospheric research paper, 002, 29, 2005: http://www.cmar.csiro.au/e-print/open/frederiksenjs_2005b.pdf, last access 27/02/2022
  Frederiksen, J. S. and Frederiksen, C. S.: Interdecadal changes in southern hemisphere winter storm track modes, Tellus A, 59, 599-617, 10.1111/j.1600-0870.2007.00264.x, 2007.
  Freitas, A. C. V., Frederiksen, J. S., Whelan, J., O’Kane, T. J., and Ambrizzi, T.: Observed and simulated inter-decadal changes in the structure of Southern Hemisphere large-scale circulation, Clim Dynam, 45, 2993-3017, 10.1007/s00382-015-2519-z, 2015.
  Gibbs, W.J. and Maher, J.V.: Rainfall deciles as drought indicators. Bull 48, Australia Bureau of Meteorology, Melbourne, 1-85, 1967.
  Hayman, P. and Hudson, D.: Forewarned Is Forearmed – Exploring The Value Of New Forecast Products From The BOM To Enable More Informed Decisions On Profit And Risk On Grain Farms 1-11, 2021: https://grdc.com.au/__data/assets/pdf_file/0024/445902/Paper-Hayman-and-Hudson-May-2021.pdf, last access: 27/02/2022.
  Joseph, V.L., and Sabin, T.P.: 2008: Trends in SST and reanalysis 850 and 200 hPa wind data of Asian summer monsoon season during the recent six decades. Proc. Third WCRP Int. Conf. on Reanalysis, Tokyo, Japan, 1-6, 2008.: http://wcrp.ipsl.jussieu.fr/Workshops/Reanalysis2008/abstract.html , last access: 27/02/2022.
  Hertzog, A., Basdevant, C., and Vial, F.: An Assessment of ECMWF and NCEP/NCAR Reanalyses in the Southern Hemisphere at the End of the Pre-Satellite Era : Results from the EOLE Experiment (1971-1972), 133, 1-16, 2006.
  Kållberg, P., Simmons A., Uppala S., and Fuentes M.: The ERA-40 Archive. ERA-40 project report series no. 17, ECMWF, 1-36, 2007: http://www.emcc.mgm.gov.tr/FILES/model-data/ERA40_PRS17_rev1.pdf , last access: 27/02/2022.
  Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang, J., Leetmaa, A., Reynolds, R., Jenne, R., and Joseph, D.: The NCEP/NCAR 40-year reanalysis project, Bulletin of the American Meteorological Society, 77, 437-471, 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2, 1996.
  Keyantask, J.: Indices for Meteorological and Hydrological Drought, Hydrological Aspects of Climate Change, 11, 215-236, 10.1007/978-981-16-0394-5, 2021.
  Keyantask, J. and Dracup, J.A.: The Quantification of Drought: An Evaluation of Drought Indices, Bulletin of the American Meteorological Society, 83, 1167-1180, 0.1175/1520-0477-83.8.1167, 2002.
  Lenton, T. M. R., J., Gaffney, O., Rahmstorf, S., Richardson, K., Steffen, W., and Schellnhuber, H. J. : Climate tipping points — too risky to bet against, Nature, 575, 592-595, 2019.
  Osbrough, S. L. and Frederiksen, J. S.: Interdecadal changes in Southern Hemisphere winter explosive storms and Southern Australian rainfall, Clim Dynam, 56, 3103-3130, 10.1007/s00382-021-05633-y, 2021.
  Phillips. N.A.: Energy Transformations and Meridional Circulations associated with simple Baroclinic Waves in a two-level, Quasi-geostrophic Model Tellus 6:273-286, 10.1111/j.2153-3490.1954.tb01123.x, 1954.
  Rikus, L.: A simple climatology of westerly jet streams in global reanalysis datasets part 1: mid-latitude upper tropospheric jets, Clim Dynam, 50, 2285–2310, 10.1007/s00382-015-2560-y, 2018.
  Sweeney, S and Liddicoat. C.: Climate change, wheat production and erosion risk in South Australia’s cropping zone:
  Linking crop simulation modelling to soil landscape mapping, Department of Environment, Water and Natural Resources, Government of South Australia, Technical Report 2012/05, 1-151, 2012: https://cdn.environment.sa.gov.au/environment/docs/kb-gen-climate-change-wheat-production-and-erosion-risk.pdf, last access: 27/02/2022.
  Thomas, D. and Hayman, P.: The Riverland Climate for Almond production: Analysis of strengths and challenges, South Australian Research & Development Institute, 1-44, 2019: https://www.horticulture.com.au/globalassets/hort-innovation/resource-assets/al14006-climate-strengths-and-challenges---riverland.pdf, last access: 27/02/2022.
  World Meteorological Organization and Global Water Partnership: Handbook of Drought Indicators and Indices, 1-52, 2016: https://www.droughtmanagement.info/literature/GWP_Handbook_of_Drought_Indicators_and_Indices_2016.pdf, last access: 27/02/2022.
  
  Citation: https://doi.org/10.5194/wcd-2021-72-AC2

Status: closed

RC1:
'Comment on wcd-2021-72', Anonymous Referee #1, 17 Dec 2021

Review of “Regime transitions of Australian climate and climate extremes” by Frederiksen and Osbrough

This study attempts to explain changes in regional temperature, rainfall and streamflow in Australia by analysing the observational record and reanalysis. The authors suggest regime transitions are identifiable in the extreme timeseries.

I have a couple of major concerns with this study. The second major concern I think warrants rejection of this study in my opinion unfortunately.

Major concerns:

1. The actual purpose of the study is unclear to me. The paper, while written in a way that’s grammatically correct, doesn’t really flow and is missing important sections, such as a Methods section. The motivation and novelty of this work needs to come through much more clearly. In addition, the lack of a Methods section would make the paper very difficult to reproduce.

2. ’m afraid to say I think that the extremes-based results are largely artefacts of the use of decile-based indices that are defined over long seasons. Given these deciles are defined based on some climatological period (which I don’t see defined anywhere) and you’re looking at area coverage of top decile values, a small shift in the distribution of seasonal-average temperatures will result in a big increase in the incidence of top decile events. The relatively low interannual variability in seasonal temperatures (compared with daily extreme indices) and the spatial homogeneity of the temperatures mean that large jumps in the extreme index would be entirely expected.

A test to show that this isn’t an artefact of the use of area of top decile seasonal-average temperatures would be to use an inherently noisier index, such as a daily extreme index like aggregated area exceeding a high percentile.

Other comments:

L16-17: I’d suggest not talking about first and second order transitions in the Abstract as at this stage in reading the paper it’s not clearly what this means.

L86: “Pert” should be “Perth”

Section 2.2. This is a bit brief- perhaps a summary of what variables were used and why NCEP and these variables were chosen would be helpful. NCEP1 exhibits quite different trends in extremes to other reanalyses in this region (Donat et al., 2014) so some comparison with another dataset would be helpful.

L95-97: Another relevant paper is Delworth & Zeng, (2014).

L124-125: The focus on a quadratic fit suggests there is a physical reason why this relationship would be quadratic, but I suspect there isn’t. It might be more useful just to focus on the rainfall-streamflow relationship without this fit applied.

L140: The definitions used here for Southern Wet Season and Southern Cool Season are so similar that I think it’s redundant to say the results are similar for these two.

L208-209: I think you mean the timeseries are synchronous rather than the variability is the same.

L234: “average” should be “on average”

Section 5 without any further analysis is unnecessary as it doesn’t add to the paper.

L251: Comma needed after “variability”

References

Delworth, T. L., & Zeng, F. (2014). Regional rainfall decline in Australia attributed to anthropogenic greenhouse gases and ozone levels. , (8), 583–587. https://doi.org/10.1038/ngeo2201

Donat, M. G., Sillmann, J., Wild, S., Alexander, L. V., Lippmann, T., & Zwiers, F. W. (2014). Consistency of Temperature and Precipitation Extremes across Various Global Gridded In Situ and Reanalysis Datasets*. , (13), 5019–5035. https://doi.org/10.1175/JCLI-D-13-00405.1

Citation: https://doi.org/10.5194/wcd-2021-72-RC1
- AC1: 'Reply on RC1', Jorgen Frederiksen, 04 Jan 2022
  
  Interactive comment on “Regime transitions of Australian climate and climate extremes” by Jorgen S. Frederiksen and Stacey L. Osbrough
  Jorgen S. Frederiksen^1,2, Stacey L. Osbrough^1,2
  ¹CSIRO Oceans and Atmosphere, Aspendale, 3195, Australia
  ²Monash University, Clayton, 3800, Australia
  Correspondence to: Jorgen S. Frederiksen (jorgen.frederiksen@csiro.au)
  Reply to RC1 - doi:10.5194/bgd-12-15087-2015
  1 Methods section
  We will follow the reviewer’s suggestion and include a Methods section in the revision of the paper. This will include a pedagogical summary of the method of determining rainfall and temperature deciles as described by the Bureau of Meteorology (2022b), some of the issues in point 2 below, and discussion of regression methods and averaging methods. Much of the discussion on anthropogenic climate change tipping points has focussed on major tipping points of global extent that may be exceeded in the future. For example, Lenton et al. (2019) discuss tipping points such as the possible acceleration of the melting of the Greenland ice sheet that could occur with a 1.5^o C warming. As noted, our particular interest in this article is whether the changes that have already occurred in Australian climate and climate extremes over the last seventy years are indicative of regime transitions in a noisy environment. We also discuss the relationship between these changes and the large-scale circulation and in the revision will further expand on additional implications and connections.
  2 Extended seasonal and annual deciles
  Perhaps to provide background to our response it is useful to first summarize the development and uses of rainfall and temperature deciles over the last 55 years. As noted by Keyantash (2021) “An established quantile methodology is the usage of ten quantiles, or deciles. A decile-based system for monitoring meteorological drought in Australia was proposed by Gibbs and Maher (1967) and adopted by the Australian Bureau of Meteorology (BoM) to monitor drought conditions in that nation.” Gibbs and Mayer (1967) presented Australian maps of the distribution of decile ranges of annual rainfall for the years 1885 to 1965 in a study of drought. The Bureau of Meteorology (2022c) has continued the publication of Australian maps of annual rainfall deciles from 1900 to the present and has presented them for extended seasons (e.g., Bureau of Meteorology and CSIRO 2020) as well as for seasons and years in numerous reports. Keyantash (2021) further notes “The rainfall decile methodology begins by assembling three-month (or longer) precipitation totals … as drought is not validly recognized for briefer periods in Australia…”. The caption in Fig. 11.1 of Keyantash (2021) states “Decile map of 12-month precipitation totals in Australia, through April 2020. Meteorological drought in Australia may be assessed across a variety of timescales, but the duration must be a minimum of three months.” Keyantash (2021) further notes “It is interesting that BoM also examines monthly rainfall totals from the decile perspective, even when drought characterization is not the objective”.
  Keyantash and Dracup (2002) have made similar determinations to those above and note that, in the USA context, for Meteorological Drought the rainfall decile index as used at BoM is the superior index overall and particularly in terms of robustness, transparency and extendability (their Table 3). In the Handbook of Drought Indicators and Indices by the World Meteorological Organization and Global Water Partnership (2016) some of the properties of deciles are noted. In particular: deciles are “easy to calculate” and “examples from Australia are useful”. “Daily, weekly, monthly, seasonal and annual values can all be considered in the methodology, as it is flexible when current data are compared to the historical record for any given period.”. “Applications: With the ability to look at different timescales and time steps, deciles can be used in meteorological, agricultural and hydrological drought situations.” Table 2 of the Handbook also lists some of the Meteorological Institutions, in addition to those in Australia and USA, that use deciles.
  Deciles of rainfall and temperatures have also been used in the horticultural and agricultural industries on a variety of time scales including the extended seasonal time scale. For example: Cool season – April to October – rainfall deciles were used in the South Australian Government study of climate change, wheat production and erosion risk by Sweeney and Liddicoat (2012). In a study of “The Riverland Climate for Almond Production” Thomas and Hayman (2019) examine September – April deciles of temperature. Hayman and Hudson (2021) explore the value of recent new BoM forecast products of weekly, monthly, and seasonal rainfall and temperature deciles for grain production.
  The Bureau of Meteorology (2022a) publishes monthly, seasonal, extended seasonal and annual deciles of rainfall and temperatures and has published numerous reports in which they are employed for various purposes.
  In view of the above background, we do not understand why the reviewer thinks that extremes-based results using deciles for long seasons should be artifacts. There is no essential difference in studying time span averaged data in decile 5 – the median for general distributions and the mean for symmetric distributions – and any other decile. As noted by Keyantash (2021) the decile approach is nonparametric so there are no fitted distributions or assumptions. The approach involves a simple time span averaging, ranking, ordering, and binning of the observed results and the values, in a given decile, at a particular time, and time scale, represent just the reality of the observations. If the regime transitions are more obvious for decile 10 than decile 5 then that is the reality of the meteorology, the climatology, the physics and the chemistry just as they are for streamflow compared with rainfall. This is the case whether the resulting distribution is Gaussian, Poisson or fat-tailed and whether the future is just a shift in the mean or a significant change in the tail. The frequency of bushfires and the failure of crops are the reality of phenomena that depend more on the extremes than the mean or median.
  The broad conclusions of our study are borne out by monthly, seasonal, extended seasonal and annual data. Monthly data are, of course, noisier which is why for many purposes seasonal, extended seasonal or annual deciles are preferred as generally presented by the Australian Bureau of Meteorology.
  
  3 Other comments
  L16-17: Yes, agreed – thank you.
  L86: Yes, agreed – thank you.
  Section 2.2: Yes, we will expand on this section in the revision and point out that the NCEP data is not used for the extremes but only the mean circulation and reference comparisons.
  L95-97: Thank you – will be referenced in the revision.
  L124-125: The purpose of the quadratic fit is just to indicate why the streamflow is more sensitive to changes in rainfall than mean rainfall itself. In that sense the streamflow acts like the decile 10 rainfall. We will rephrase this to make it clearer in the revision.
  L140: SWWA rainfall has often historically been considered for the SWS season of April to November and for the winter season of June to August and more recently also for the CS season of April to October. We feel that we should point out that our conclusions are broadly the same for all these periods.
  L208-209: Yes, the time series are essentially synchronous – thank you.
  L234: Yes, agreed – thank you.
  Section 5; We feel that it is important to point out that the rainfall changes in Northern Australia have been largely opposite to those in Southern Australia. We will consider this point further in the revision.
  L251: Yes, agreed – thank you.
  
  Acknowledgements. We wish to thank David Jones and Blair Trewin of the Australian Bureau of Meteorology for informative discussions on the rigour, robustness and value of the BoM decile temperature and rainfall data for months, seasons, extended seasons, and years.
  References
  
  Bureau of Meteorology: Australian climate variability and change - Time series graphs 2022a: http://www.bom.gov.au/climate/change/index.shtml#tabs=Tracker&tracker=timeseries, last access: 04/01/2022.
  Bureau of Meteorology: Rainfall Map Information 2022b: http://www.bom.gov.au/climate/austmaps/about-rain-maps.shtml#deciles, last access: 04012022.
  Bureau of Meteorology: 122 years of Australian rainfall 2022c: http://www.bom.gov.au/climate/history/rainfall/, last access: 04/01/2022.
  Bureau of Meteorology and CSIRO: State of the Climate 2020: www.csiro.au/state-of-the-climate, last access: 04/01/2022.
  Gibbs, W.J. and Maher, J.V.: Rainfall deciles as drought indicators, Australia Bureau of Meteorology, Melbourne, Bull 48, 1-85, 1967.
  Hayman, P. and Hudson, D.: Forewarned Is Forearmed – Exploring The Value Of New Forecast Products From The BOM To Enable More Informed Decisions On Profit And Risk On Grain Farms 1-11, 2021: https://grdc.com.au/__data/assets/pdf_file/0024/445902/Paper-Hayman-and-Hudson-May-2021.pdf, last access: 04/01/2022.
  Keyantask, J.: Indices for Meteorological and Hydrological Drought, Hydrological Aspects of Climate Change, 11, 215-236, 10.1007/978-981-16-0394-5, 2021.
  Keyantask, J. and Dracup, J.A.: The Quantification of Drought: An Evaluation of Drought Indices, Bulletin of the American Meteorological Society, 83, 1167-1180, 0.1175/1520-0477-83.8.1167, 2002.
  Lenton, T. M. R., J.; Gaffney, O.; Rahmstorf, S.; Richardson, K.; Steffen, W.; Schellnhuber, H. J. : Climate tipping points — too risky to bet against, Nature, 575, 592 - 595, 2019.
  Sweeney, S. and Liddicoat. C.: Climate change, wheat production and erosion risk in South Australia’s cropping zone:
  Linking crop simulation modelling to soil landscape mapping, Department of Environment, Water and Natural Resources, Government of South Australia, Technical Report 2012/05, 1-151, 2012: https://cdn.environment.sa.gov.au/environment/docs/kb-gen-climate-change-wheat-production-and-erosion-risk.pdf, last access: 04/01/2022.
  Thomas, D. and Hayman, P.: The Riverland Climate for Almond production: Analysis of strengths and challenges, South Australian Research & Development Institute, 1-44, 2019: https://www.horticulture.com.au/globalassets/hort-innovation/resource-assets/al14006-climate-strengths-and-challenges---riverland.pdf, last access: 04/01/2022.
  World Meteorological Organization and Global Water Partnership: Handbook of Drought Indicators and Indices, 1-52, 2016: https://www.droughtmanagement.info/literature/GWP_Handbook_of_Drought_Indicators_and_Indices_2016.pdf, last access: 04/01/2022.
  
  Citation: https://doi.org/10.5194/wcd-2021-72-AC1
RC2:
'Comment on wcd-2021-72', Anonymous Referee #2, 27 Jan 2022

The main idea of the paper is interesting and deserves to be published, but I have some concern about the shape of the paper and the way is it presented. The paper doesn't really flow and it would benefit from presenting the results in a way such as the reader know what question is to be addressed in each section and how it is related to the rest of the paper. Also, the main question(s) of the paper should be clarified. Does it focus on transition periods? or regional differences?.....

For most of the sections, there is a lack of conclusions. Results are presented in details but the key message of a section should be summarized so the reader can have a clear idea about what he got from the analyses.

Part 3.1: I didn't really understand the way authors defined the periods to average results (Fig.1 / Table 1). It seems a bit random. Also, authors refer to some particular signal for the last of the periods (2014-2018) but it is not surprising as it only concerns 5 years whereas most of the other periods refer to multi-decade averages. This whole paragraph should be written in a more consistent and justified way. Also, instead of Table 1, period-averaging results could be displayed directly in Fig.1 (as horizontal bars for example).

This comment is also valid for all other tables and results showing period averaging.

In a same way, figures showing 10-year running means could simply be plotted on the top of annual results (with a curve or something similar).

Also, a lot of litterature review is merged with the results and many references are quite old. Although I totally agree that it's important to refer to previous studies, in this paper there's sometime a mixed approach between a review paper and a new study paper. I'd suggest to limit more the review part to the introduction, to focus more on the most recent studies, and only refer to previous papers in a limited way in method and result sections. But in general I appreciate that authors did an extensive review work, especially for people (like myself) who are not familiar with Australian climate.

Variable and indices used should be described to the methodology instead of being presented in the main text (deciles, percentage area coverage.....). Especially, % area coverage should be clearly defined. Does it use a single threshold for whole period/season? In that case, it may explain the dramatic change for extreme temperatures (bascially it's just a shift in seasonal temperatures). It would be more interesting to use calendar day thresholds for example. But I'm not sure about which methodology has been used here....

Some other comments:

Fig.1(a): why is it displayed as centered values around the mean whereas all other similar plots are displayed as absolute values?

Fig,4: When plotting differences between 2 periods it's good to show where it is significant or not (with a t-test for exemple).

Fig5: although the decrease then flattening is quite clear, I'm wondering how reliable are reanalysis before 1970s, as there weren't satellite observation at that time?

L109-110: Figure description should be in the figure caption, not in the text.

Section 5 is not really useful...

Citation: https://doi.org/10.5194/wcd-2021-72-RC2
- AC2: 'Reply on RC2', Jorgen Frederiksen, 01 Mar 2022
  
  Interactive comment on “Regime transitions of Australian climate and climate extremes” by Jorgen S. Frederiksen and Stacey L. Osbrough
  Jorgen S. Frederiksen^1,2, Stacey L. Osbrough^1,2
  ¹CSIRO Oceans and Atmosphere, Aspendale, 3195, Australia
  ²Monash University, Clayton, 3800, Australia
  Correspondence to: Jorgen S. Frederiksen (jorgen.frederiksen@csiro.au)
  Reply to RC2 - doi.org/10.5194/wcd-2021-72-RC2
  
  We are pleased to learn that the Referee thinks that the main idea in the paper is interesting and deserves to be published. Here we present a summary of how we have addressed the Reviewer’s comments with more details in Response to RC2 (Part 2) accompanying the revised paper.
  1 Motivation and summary of findings for major sections
  We have added questions to be answered at the start of the major sections and a summary of findings at the end. The conclusions provide further summaries of our findings. We are interested in both the timings of the transition periods and how this varies regionally or not. Much of the discussion on anthropogenic climate change tipping points has focussed on major tipping points of global extent that may be exceeded in the future. For example, Lenton et al. (2019) discuss tipping points such as the possible acceleration of the melting of the Greenland ice sheet that could occur with a 1.5^o C warming. As noted, our particular interest in this article is whether the changes that have already occurred in Australian climate and climate extremes over the last seventy years are indicative of regime transitions in a noisy environment. Further discussion on the aims and purpose, and main findings, is given in the main sections of the paper that have been updated. We also discuss the relationship between these changes and the large-scale circulation and in the revision further expand on additional implications and connections.
  2 Data and Methods 2.1 Rainfall, temperature and streamflow data sets
  The average and extreme – decile 10 – rainfall and temperature data used in this paper have been obtained from the Bureau of Meteorology (2022a) website. In this study we focus on various regions such as SWWA, SEA, Northern Australia, and Australian states, shown in Fig. 1. These Bureau of Meteorology (BoM) data sets are based on averages of station data and are of higher quality than earlier BoM data sets (Jones et al., 2009; Trewin 2013). Gridded data sets based on observed station data are also generally more consistent than reanalysis data sets particularly in the pre-satellite era (e.g., Donat et al., 2014). The data for streamflow into Perth dams has been obtained from the Water Corporation (2020) of Western Australia.
  2.2 Reanalysis data sets
  The investigation of the changes in atmospheric circulation in our study uses the reanalysis data set of the National Centers for Environmental Prediction (NCEP) and the National Centre for Atmospheric Research (NCAR), (Kalnay et al., 1996). It will be referred to as the NNR data set. The NNR data set is available for the whole period from 1948 to the present and has been one of the most studied reanalysis data set incorporating the pre-satellite period. We focus here on changes in the Southern Hemisphere zonal jet in the Australian region and their strong relationships to rainfall changes in Southern Australia. In Supplement S1, we summarize the results of studies of the comparison of the NNR data set in the Australian region with other reanalysis data sets and with observations.
  2.3 Methods
  Next, we summarize the method of determining decile data from the BoM station data and our method for establishing the changing trends and critical points in data sets.
  2.3.1 Determination of decile data
  Gibbs and Maher (1967) developed a decile-based methodology for characterizing meteorological drought and presented Australian maps of the distribution of decile ranges of annual rainfall for the years 1885 to 1965. This method of determining decile data from station rainfall data (Jones et al., 2009) and temperature data (Trewin 2013) is detailed on the websites of the Bureau of Meteorology (2022b, 2022c). Briefly, deciles are a convenient way of coarse-graining the frequency distribution of a variable into ten bands each with 10% of the values. Decile 1 corresponds to the lowest 10%, decile 5 gives the median and decile 10 the highest 10% of the data which is generally monthly, seasonal, multi-month or annual data. The method makes no assumption about the distribution – it is nonparametric – and is based on all the data for a given time span. In practice, for a given time span, gridded data in each grid box, are sorted from lowest value to highest value and placed into ten equal bands, labelled decile 1 to 10, so that any value in a lower decile is smaller than those in the next decile. The percentage of grid boxes (percentage area) with values in a given decile and year and in a particular geographical region are then calculated based on all the grid boxes, which may be as small as circa 5 km by 5 km (0.05 degrees by 0.05 degrees) for regional rainfall.
  The utility and applications of decile data is further described in Supplement S2.
  
  2.3.2 Determination of changes in trends and critical points
  The critical times of large and sustained changes in the trends of the rainfall, streamflow and temperature data considered in this study have been determined as follows. The data have been low-pass filtered by applying a 10 year running mean to reduce noise due to the interannual variability. Graphs of the filtered data indicate time periods when these trends change significantly. Regression of the filtered data against time over each time periods incorporating these trend changes are then used to focus in on the critical times. Firstly, regression is applied against a quadratic function of time which highlights the critical time of gradient change. Then regressions against linear functions of time are performed between the beginning and first critical time, between the last critical time and the end of the timeseries and between any two adjacent intermediary critical points. This then determines the large changes in linear trends we find that are sustained for 15 to 20 years or longer. Averages of the unfiltered data are calculated for the associated time periods.
  S1 Reanalysis data sets and observations
  We have noted the consistency between aspects of the large-scale circulation in the Australian region as characterized by the NCEP-NCAR reanalysis data set (Kalnay et al., 1996) (hereafter NNR) and Southern Australian rainfall in this study and in our earlier works discussed in the Introduction. In our earlier works (e.g., Frederiksen et al., 2017) we also compared results based on NNR reanalyses with those for European Centre for Medium Range Weather Forecasting (ECMWF) Reanalysis (ERA-40) project (Kållberg et al., 2007) and Twentieth Century Reanalysis (20CR v2) project (Compo et al., 2011) that include the pre-satellite era. Here we consider the relationships between NNR reanalyses and other reanalyses and observations in the Australian region including during the pre-satellite era.
  Hertzog et al. (2006) compared the results of the August 1971 to December 1972 EOLE (from the Greek God of the Winds) experiment, involving flights of super-pressure balloons in the Southern Hemisphere upper troposphere, largely between 230 hPa and 190 hPa and 20S-70S, with NNR and ERA-40 data. They argued that their findings are representative of reanalysis accuracy for the pre-satellite era between 1957 and 1979. They noted that their analysis of the zonal wind structure in their Fig. 6 shows that both NNR and ERA-40 largely capture the meridional structure of the mean upper tropospheric jet although ERA-40 has a spurious double-jet peak structure. As well, ERA-40 has much larger errors in capturing upper tropospheric synoptic-scale variability. ERA-40 also has larger errors than NNR in representing mean sea level pressure and 500 hPa geopotential heights in the mid to high latitudes of the Southern Hemisphere in the pre-satellite era as shown in Figs. 3 and 6 of Bromwich and Foght (2004).
  Frederiksen and Frederiksen (2005, 2007; hereafter FF05, FF07) found broad consistency between changes in the Southern Hemisphere July large-scale circulation determined by NNR data, and the results of instability calculations of synoptic disturbances based on this data, and changes in rainfall over south-west Western Australia before and after the mid-1970s. FF07 (Table 7) also compared results for ERA-40 data with those for NNR and found very close agreement for leading synoptic scale modes with growth rates differing by less than 4% and pattern correlations between 0.94 and 0.99. The similar weakening of the mid-winter Southern Hemisphere subtropical jet at 200 hPa around the Australian region, in both NNR and ERA-40 data, in the 1990s compared with the 1950s and 1960s, was also noted by Joseph and Sabin (2008; Fig. 4).
  Frederiksen et al. (2017; Figs. 1 and 2) compared the Southern Hemisphere linear trend in Phillips (1954) criterion (discussed in our Section 3.2), in each of the four seasons, for NNR and 20CR v2 over the period 1950-1999 and for ERA-40 over 1958-1999. Of particular interest here are the consistent negative trends in the criterion upstream of Australia in NNR and ERA-40 with generally poorer agreement with 20CR. Rikus (2018), in a study of mid-latitude jetstreams in 9 reanalyses, including NNR, ERA-40 and 20CR, over the period 1979-2009, noted that 20CR had some systematic biases in upper-level winds compared with the the other reanalyses. Nevertheless, as shown in Fig. 1 of Freitas et al. (2015), the mid-winter reduction in the Southern Hemisphere subtropical jet that occurred in NNR data between the periods (1949-1968) and (1975-1994), and the increase further south, is evident in 20CR v2 data but with peak values at slightly lower levels, consistent with the findings of Rikus (2018).
  The study of Osbrough and Frederiksen (2021), based on six hourly NNR 850 hPa data, found there was good correspondence between the reduction in fast growing storms in the Australian region since the late 1960s and the reduction in Southern Australian rainfall providing further evidence of the general consistency of NNR data with rainfall variability.
  S2 Utility and applications of decile data
  Since the first introduction by Gibbs and Maher (1967), the decile representation of meteorological data has been widely used in the study of droughts and rainfall and temperature variability. As noted in Section 2, the decile data is available from the website of Bureau of Meteorology (2022a) and the decile method is described on the websites of Bureau of Meteorology (2022b, 2022c). Decile data for seasons, extended seasons and years have been presented in many reports (e.g., Bureau of Meteorology and CSIRO 2020) and BoM publishes Australian maps of annual rainfall deciles from 1900 to the present (Bureau of Meteorology 2022d). Deciles of rainfall and temperatures have also been used in the horticultural and agricultural industries. For example, cool season – April to October – rainfall deciles were used in the South Australian Government study of climate change, wheat production and erosion risk by Sweeney and Liddicoat (2012). In a study of “The Riverland Climate for Almond Production” Thomas and Hayman (2019) examined September – April deciles of temperature. Hayman and Hudson (2021) explored the value of recent new BoM forecast products of weekly, monthly, and seasonal rainfall and temperature deciles for grain production.
  Keyantash (2021) reviewed indices of meteorological and hydrological drought and compared the established quantile methodology of deciles with other approaches. He noted the simplicity and nonparametric nature of deciles that make no assumption about the distribution function but determines a coarse-grained version directly from the total available data. Keyantash and Dracup (2002) noted that, in the USA context, for Meteorological Drought the rainfall decile index as used at BoM is the superior index overall and particularly in terms of robustness, transparency and extendability (their Table 3). In the Handbook of Drought Indicators and Indices by the World Meteorological Organization and Global Water Partnership (2016) some of the properties of deciles are noted. In particular: deciles are “easy to calculate” and “examples from Australia are useful”. “Daily, weekly, monthly, seasonal and annual values can all be considered in the methodology, as it is flexible when current data are compared to the historical record for any given period.”. “Applications: With the ability to look at different timescales and time steps, deciles can be used in meteorological, agricultural and hydrological drought situations.” Table 2 of the Handbook also lists some of the Meteorological Institutions, in addition to those in Australia and USA, that use deciles.
  3 Presentational and minor comments
  The Referee’s presentational and minor comments will also be answered in Response to RC2 (Part 2) accompanying the revised paper.
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  Citation: https://doi.org/10.5194/wcd-2021-72-AC2

Jorgen Segerlund Frederiksen and Stacey Lee Osbrough

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Short summary

Southern Australian rainfall and temperatures have experienced dramatic systematic changes that became evident for extreme rainfall and streamflow in the south-west since the 1960s and in the south-east since the late 1990s. Notable increases in average and extreme surface temperatures over all of Australia have occurred since the start of the 21^st century. Southern Australian climate has transitioned into a drier warmer state associated with shifts in the Southern Hemisphere circulation.


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