Towards ACCESS-Based Regional Climate Projections for Australia

Towards ACCESS-based regional climate projections for Australia

Chun-Hsu Su1, Harvey Ye1, Andrew Dowdy1, Acacia Pepler2, Christian Stassen1, Andrew Brown1, Simon O. Tucker3, Peter J. Steinle1 1Bureau of Meteorology, Docklands, Australia 2Bureau of Meteorology, Sydney, Australia 3Met Office, Exeter, UK

July 2021

Bureau Research Report – 057 TOWARDS ACCESS-BASED REGIONAL CLIMATE PROJECTIONS FOR AUSTRALIA

AUSTRALIAN TROPICAL CYCLONE-INDUCED EXTREME COASTAL WINDS IN CLIMATE DATASETS

Towards ACCESS-based regional climate projections for Australia

Bureau Research Report No. 057

July 2021

National Library of Australia Cataloguing-in-Publication entry

Authors: Chun-Hsu Su, Harvey Ye, Andrew Dowdy, Acacia Pepler, Christian Stassen, Andrew Brown, Simon Tucker, Peter Steinle

Title: Towards ACCESS-based regional climate projections for Australia

ISBN: 978-1-925738-33-9 ISSN: 2206-3366

Series: Bureau Research Report – BRR057

TOWARDS ACCESS-BASED REGIONAL CLIMATE PROJECTIONS FOR AUSTRALIA

Enquiries should be addressed to:

Lead Author: Chun-Hsu Su

Bureau of Meteorology GPO Box 1289, Melbourne Victoria 3001, Australia [email protected]

© 2021 Bureau of Meteorology. To the extent permitted by law, all rights are reserved and no part of this publication covered by copyright may be reproduced or copied in any form or by any means except with the written permission of the Bureau of Meteorology.

The Bureau of Meteorology advise that the information contained in this publication comprises general statements based on scientific research. The reader is advised and needs to be aware that such information may be incomplete or unable to be used in any specific situation. No reliance or actions must therefore be made on that information without seeking prior expert professional, scientific and technical advice. To the extent permitted by law and the Bureau of Meteorology (including each of its employees and consultants) excludes all liability to any person for any consequences, including but not limited to all losses, damages, costs, expenses and any other compensation, arising directly or indirectly from using this publication (in part or in whole) and any information or material contained in it.

AUSTRALIAN TROPICAL CYCLONE-INDUCED EXTREME COASTAL WINDS IN CLIMATE DATASETS

Contents Abstract ...... 1 1. Introduction ...... 2 2. Experimental design for BARPA-R ...... 4 3. Model bias studies ...... 6 3.1 Daily maximum temperature ...... 7 3.2 Daily minimum temperature ...... 9 3.3 Vapour pressure ...... 11 3.4 Precipitation ...... 12 4. Added value analysis for extreme weather ...... 15 4.1 Temperature ...... 16 4.2 Precipitation ...... 18 5. Severe thunderstorm potential and extreme wind gusts ...... 18 6. Cyclone climatology ...... 21 7. Discussion and outlook ...... 22 8. Appendix ...... 24 8.1 Model bias studies ...... 24 8.2 Added value analysis for extreme weather ...... 27 8.3 Severe thunderstorm diagnostic CS6 ...... 27 Acknowledgements ...... 28 References ...... 28

iii

AUSTRALIAN TROPICAL CYCLONE-INDUCED EXTREME COASTAL WINDS IN CLIMATE DATASETS

ABSTRACT

A new modelling framework for dynamical downscaling from global climate model (GCM) data has been developed for application in the Australian region, known as Bureau of Meteorology Atmospheric Regional Projections for Australia (BARPA). BARPA is intended for use in producing fine-scale projections of historical and future simulated climates. It uses the atmosphere and land model components from the Australian Community Climate and Earth- System Simulator (ACCESS) that are based on a global atmosphere configuration of UK Met Office Unified Model, complementary to other regional climate model (RCM) approaches currently available for Australia and surrounding regions. The use of the ACCESS model is also intended to provide some similarities to other modelling suites used in the Australian Bureau of Meteorology including for operational weather forecasting, seasonal prediction and historical reanalysis data sets, as part of broader goals towards providing services across a range of time scales that are as consistent (seamless) as possible.

Here we describe the moderate horizontal resolution (~12 km) BARPA-R modelling framework and present results from its application to global reanalysis data from ERA-Interim. The BARPA-R output is assessed against observations-based data, including analysis of the added value it provides as compared to the host model. This work demonstrates that BARPA yields stable and realistic simulations of near-surface meteorological parameters and provides added regional information to the host global data. It is intended that BARPA-R will help contribute to a broader set of RCMs available for understanding future climate changes in the Australian region, including for enhanced planning and preparedness in relation to phenomena such as wildfires, cyclones and rainfall extremes.

TOWARDS ACCESS-BASED REGIONAL CLIMATE PROJECTIONS FOR AUSTRALIA

1. INTRODUCTION

Australia is a place of large inter-annual variability and has experienced extreme weather events including precipitation (Ashcroft et al., 2019), drought (Van Dijk et al., 2013), heatwaves (Perkins-Kirkpatrick et al., 2016), cyclones (Chand et al., 2019; Dowdy et al., 2019a), thunderstorms (Allen et al., 2011) and bushfires (Dowdy, 2018). With extreme events projected to become more pronounced with climate change including in Australia (Meehl et al., 2000; Coumou et al., 2012; Seneviratne et al., 2012; CSIRO and Bureau of Meteorology, 2015; Bell et al., 2019; Dowdy et al., 2019b), there is an increasing need for robust fine-scale projections of key climate variables. Spatially and temporal high-resolution climate model output is generally needed in climate change impact and adaptation studies particularly when conducted on regional and local scales. While modern global climate models (GCMs) are technically able to simulate the climate on fine spatial and temporal scales (Schär et al., 2020) this is often out of reach given the current computational constraints, such that regional climate modelling approaches can be beneficial.

Nested limited-area regional climate models (RCMs) first started being developed about 30 years ago to overcome the limited computational resources by providing fine-resolution climate data only for a specific region rather than globally (e.g., Tapiador et al., (2020) for a review). RCMs are deployed over a region of interest and because of their smaller domain size can be run at an increased spatial and temporal resolution as compared to GCMs (Laprise et al., 2008; Tapiador et al., 2020). This allows them to account for local details such as complex topography, land-sea contrasts and regional surface characteristics that cannot be resolved or considered in GCMs. Consequently, RCMs have the potential to more accurately simulate processes relating to precipitation and extreme events in areas with complex landscape (Torma et al., 2015), and thus are increasingly important for regional impact and physical risk assessment (Giorgi et al., 2009; Di Virgilio et al., 2019). Further, beyond the moderate resolution of ≥ 10 km, regional models with kilometre or smaller length scales can explicitly simulate some convective cloud processes and replace parameterizations of moisture convection in coarser models. More accurate representation of local storm dynamics and physical processes (including microphysics and thermodynamics) are still needed in convective-permitting models to further improve the credibility of climate projections of convective extremes (Kendon et al., 2014). Postprocessing of model simulations to produce environmental diagnostics can be beneficial for examining convective extremes (Brown and Dowdy 2021).

While most dynamical downscaling studies report an improvement of key climate variables (e.g., Torma et al., 2015; Di Luca et al., 2016; Olson et al., 2016; Zhao et al., 2020), quantifying the added value (AV) of RCMs over their host driving models can be challenging and is an ongoing topic of research (Di Luca et al., 2016; Di Virgilio et al., 2019). Additionally, some studies have highlighted that RCMs do not unequivocally add value, but rather the AV depends on a variety of factors such as the region, season, variable, time scale and climate statistic of interest (Prömmel et al., 2010; Feser et al., 2011; Di Luca et al., 2013). Di Luca et al. (2016) have shown that about the half of the AV relating to the representation of the spatial variability of the

AUSTRALIAN TROPICAL CYCLONE-INDUCED EXTREME COASTAL WINDS IN CLIMATE DATASETS

climate statistics is due to increased spatial variability of climate statistics can be achieved via simple postprocessing of the GCMs, and the other half due to fine-scale details simulated by the RCMs.

For the Australian domain, several dynamical downscaling projects exist. So far, three different models have been considered within the COordinated REgional Downscaling EXperiment (CORDEX, Giorgi et al., 2009) framework for the Australasian domain (Di Virgilio et al., 2019 and references therein). Higher resolution (< 10 km horizontal grid spacing) downscaling with CSIRO Conformal Cubic Atmospheric Model (CCAM) over smaller Australian sub-domains has also been considered in several State Government-funded projects, including the Climate Futures for Tasmania project (Corney et al., 2013; Grose et al., 2013; Bennett et al., 2014), Victorian Climate Projections 2019 project (Clark et al., 2019), and Queensland Future Climate project. Weather Research and Forecasting (WRF) model is used in the NSW and ACT Regional Climate modelling (NARCliM) projects (Evans et al., 2012; 2014; Olson et al., 2016). WRF has also been applied to downscale projections over Western Australia (Andrys et al., 2016). COSMO-CLimateMode (CCLM) is another model applied to the CORDEX-Australasian domain. Di Virgilio et al. (2019) found that the biases in output from these modelling approaches for near-surface temperature maximum and minimum and precipitation vary markedly in terms of spatial extent, sign and statistical significance. Thus, making inference for future impact assessments needs to consider post-processing techniques (including calibration methods) as well as using projections of future change based on an ensemble of RCM approaches. Such inference was made by Grose et al. (2019) for the projected change in precipitation over Australian Eastern Highlands, where the multi-model ensembles are used to inform levels of confidence on simulated changes. It is also noted that larger uncertainty in projected change remains for autumn precipitation and for summer since these models remains non convective permitting.

The Bureau of Meteorology (Bureau) observes the importance of ‘seamless’ modelling that can underpin a consistent service for hazards across different timescales, spanning both weather and climate timescales, from the historical past decades through to the present as well as predictions for upcoming seasons and decades (e.g., Dowdy 2020a). This includes a goal of producing data sets that are as consistent as possible from historical reanalyses through nowcasting, weather and seasonal predictions to future climate change projections. The Bureau’s operational weather and multi-week-to- seasonal forecasting over the Australian region is based on the Australian Community Climate and Earth-System Simulator (ACCESS; Puri et al., 2013; Hudson et al., 2017) that uses atmosphere and land components from the UK Met Office (UKMO) Unified Model (UM; Brown et al., 2012). The same approach is taken in the first Australian regional reanalysis (Su et al., 2019). Such seamless modelling is advantageous scientifically; because the physical processes are the same in the models which are used at different timescales, it allows understanding of model skill and biases at one timescale to inform that of other timescales so consistent assessment of uncertainties comes more readily. This ACCESS model is also driving the development of new regional climate change projections with the Bureau of Meteorology Atmospheric Regional Projections for Australia (BARPA) modelling framework. This development

TOWARDS ACCESS-BASED REGIONAL CLIMATE PROJECTIONS FOR AUSTRALIA

includes creation of moderate (horizontal) resolution projections at 0.11⁰ (BARPA-R) over large parts of Australia (spanning all of eastern and central Australia together with the surrounding maritime region), as well as kilometre-scale projections over smaller sub-regions (for exploratory examples of convection-permitting simulations based on this modelling approach). It is intended that BARPA-R will contribute to a broader set of RCMs available for understanding future climate changes in Australia and surrounding regions.

In this paper we describe the development of the BARPA-R and its application over central and eastern Australia, forced by ERA-Interim reanalysis (Dee et al., 2011). The forecast model and experimental setup are described in Section 2. Section 3 compares BARPA-R to observations and ERA-Interim. In Section 4, the value added by BARPA-R to ERA-Interim is shown, especially for extreme events of precipitation and temperature. Section 5 illustrates differences in BARPA-R compared to ERA-Interim for diagnosing severe thunderstorm environments and extreme winds. Section 6 compares the BARPA-R cyclone climatology with ERA-Interim and other reanalysis datasets. Section 7 provides a discussion of the results and an outlook for future research.

2. EXPERIMENTAL DESIGN FOR BARPA-R

The BARPA-R system is based on the UKMO’s regional climate modelling system for UK Climate Projections 2018 (UKCP18, Murphy et al., 2018; Tucker et al., 2021), but without the perturbed parameter ensemble. BARPA-R has been applied to produce downscaled data based on the ERA-Interim reanalysis over the period from January 1990 to December 2015. The domain covers about 68% of Australian land mass as well as surrounding maritime regions, covering 127°E – 168.81°E in longitude and 53°S – 3.21°N in latitude (Figure 2.1). This region was selected to enable a focus on southeast Australia, where the most populous Australian cities are situated, together with the island of Tasmania, as well as northeast Australia where tropical rainforest and the Great Barrier Reef are situated.

The UM (Davies et al., 2005) is the grid-point atmospheric model used in BARPA-R and the Bureau’s numerical weather and seasonal forecasting systems. It uses a non-hydrostatic, fully compressible, deep atmosphere formulation and its dynamical core (Even Newer Dynamics for General atmospheric modelling of the environment, ENDGame) solves the equations of motion using mass-conserving, semi- implicit, semi-Lagrangian, time integration methods (Wood et al., 2014). The prognostic variables are three-dimensional wind components, virtual dry potential temperature and Exner pressure, dry density, and mixing ratios of moist quantities. These variables are discretized horizontally onto a regular longitude-latitude grid with Arakawa-C staggering (Arakawa and Lamb, 1977), and vertically with the Charney– Phillips staggered 63-level grid (Charney and Phillips, 1953). The physical parameterization schemes in UM include a variant of Wilson and Ballard (1999) for mixed-phase cloud microphysics, the large-scale cloud scheme of Smith (1990), the radiation scheme of Edwards and Slingo (1996), the boundary layer parameterization

AUSTRALIAN TROPICAL CYCLONE-INDUCED EXTREME COASTAL WINDS IN CLIMATE DATASETS

scheme of Lock et al. (2000), and the convection parameterization scheme based on Gregory and Rowntree (1990), all of which have been improved since publication. The UM is coupled to the surface-layer scheme of Best et al. (2011), implemented in the Joint UK Land Environment and Simulator (JULES). It describes a 3 m, four-layer soil column, along which vertical heat and water transfer are modelled with van Genuchten hydraulic parameters. It uses a nine-tile approach to represent subgrid scale heterogeneity in land cover, with the surface of each land point subdivided into five vegetation types (broadleaf trees, needle-leaved trees, temperate cool-season (C3) grass, tropical warm-season (C4) grass, and shrubs) and four non-vegetated surface types (urban, inland water, bare soil, and land ice). In particular, the urban surfaces are represented only by a single urban tile, where street canyons and roofs are not distinguished.

BARPA-R is a limited-area version of the global climate (GA7.05) atmosphere and land surface model described in Walters et al. (2019). Here the model has a horizontal spacing of 0.11° × 0.11° (about 12 km at the equator) and its vertical levels follow model orography at the surface and relax to surfaces of uniform radial height after 50 model levels (~18 km above ground) in the higher levels of the atmosphere with a top of model height of 40 km. At this resolution, the model is run with an integration time step of 5 minute. Some differences from GA/L7 include locally optimised JULES urban parameters as described in Dharssi et al. (2015) and use of Brooks-Corey (1964) soil hydraulic model instead of Van Genuchten. The latter was used to correct an error where vertical moisture transport through the soil was too slow (internal comm. Gedney, UKMO). The GA/L7 configurations are closely related to the UKMO’s submission to CMIP6, HadGEM3-GC3.1, but differ from the older GA/L6 configurations used in the regional reanalysis (Su et al., 2019) and the current operational global weather forecasting systems (Bureau of Meteorology, 2019). The GA/L7 improves treatment of gaseous absorption in the radiation scheme and of warm rain and ice cloud, and the numerics in the model’s convection scheme, amongst others (Walters et al., 2019).

The characteristics of the lower boundary, ozone concentrations, and the radiative forcing are prescribed using ancillary files. We follow the specifications described in Walters et al., (2019, Table 3), with the following exceptions. We use the plant canopy heights derived from satellite light detection and ranging (lidar; Simard et al., 2011; Dharssi et al., 2015). Changes in aerosol optical and cloud properties due to radiative forcing are included by prescribing monthly 3D fields of shortwave and longwave optical properties and cloud droplet number concentration. These fields are generated by combining background aerosol climatology estimated from a pre- industrial forced global atmosphere simulation with a prognostic aerosol scheme at N96 resolution (about 135km in the mid-latitude), with time-varying anthropogenic aerosol changes from the MACv2-SP (Max Planck Institute Aerosol Climatology v2; Stevens et al., 2016) and stratospheric volcanic forcing.

TOWARDS ACCESS-BASED REGIONAL CLIMATE PROJECTIONS FOR AUSTRALIA

Figure 2.1: (a) BARPA-R modelling domain. White dashed boxes in (a) indicate the spatial extent of sub-daily variability of precipitation examined in Section 3. (b) shows the area of near- surface assessment and indicates three distinct regions of added-value analysis: complex topography (‘topo’), coastal (‘coast’), and flat (Section 4).

The model is driven at its lateral boundaries in a one-way nesting setup, using 6- hourly timeseries of surface pressure, wind, temperature, and specific humidity from ERA-Interim reanalysis. The lateral forcing is applied across a 10-grid cell relaxation zone and the orographic heights are blended across a further three grid cells. As such, a 13-grid cell margin is discarded. The model was initialized with ERA-Interim in September, and the first four months (September-December) are treated as a spin-up period to allow fine-scale circulations to form. It is of note that sub-surface properties with long memory such as root-zone soil moisture does not reach equilibrium after this period, but this is sufficient to stabilise the initial biases in screen temperature. To reduce simulation time, the 26-year (January 1990 to December 2015) record is created by combining three parallel model simulations initialized in 1989, 1999 and 2009.

3. MODEL BIAS STUDIES

In the first part of our assessment, we examine biases in BARPA-R, and how it changes from the host model, by comparing against gridded observation analyses for daily maximum temperature (Tmax), minimum temperature (Tmin), precipitation and partial pressure of water vapour (or vapour pressure) at 3 pm local time using data from the Australian Water Availability Project (AWAP, Jones et al., 2009). Given that BARPA- R is closely related to the regional reanalysis BARRA-R in terms of model physics and configurations, BARRA-R and ERA5 are both included as other points of reference. To directly compare the models with different resolutions, one can interpolate them onto a common coarser grid, but this will diminish the potential benefits of the higher resolution modelling. Thus, we use the common AWAP’s 0.05×0.05⁰ grid to assess

AUSTRALIAN TROPICAL CYCLONE-INDUCED EXTREME COASTAL WINDS IN CLIMATE DATASETS

whether the models contain finer-scale information captured by the gridded analysis; it therefore does not provide an assessment of the true quality of the models at their native resolutions. The models are bilinearly regridded to the AWAP grid prior to comparisons. It is of note that both BARRA-R and ERA5 assimilated a wide range of observations, including conventional observations from land-surface stations, ships, drifting buoys, aircrafts, radiosondes, wind profilers, and satellite observations, namely retrieved wind, radiances, and bending angle. Rain gauge observations are not assimilated in both reanalyses.

3.1 Daily maximum temperature

BARPA-R shows a mean cold bias of around 0.7 K across the domain, which is smaller than the bias in ERA reanalyses (i.e., ERA-Interim and ERA5) but larger than in BARRA-R. Figure 3.1(a) shows the seasonal variability of the bias across the period, with the bias smaller during austral summer months (DJF) than during the austral winter months (JJA). This inter-seasonal variability over a range of 1.5 K is larger than the ERA reanalyses and BARRA-R, but bias in BARPA-R shows more stable inter-annual variability than the host model.

Figure 3.1: Seasonal mean bias in (a) daily maximum temperature (Tmax) and (b) daily minimum temperature (Tmin) with respect to AWAP, averaged across the BARPA-R Australian domain.

TOWARDS ACCESS-BASED REGIONAL CLIMATE PROJECTIONS FOR AUSTRALIA

Figure 3.2: Bias in the (a) mean DJF Tmax, (b) mean annual number of hot days (Tmax > 35⁰C), (c) 99th percentile of DJF Tmax, and (d) mean JJA Tmax. This is shown for BARPA-R and the various reanalyses, with respect to AWAP gridded analysis (first column).

The probability density function (PDF: i.e., histogram) comparison of spatial variation of Tmax shows that BARPA-R improves upon ERA-Interim by shifting the temperature distribution to the higher values in both summer and winter months and narrowing the distributions around the median values (Figure A.1 in the Appendix). Over the summer months, BARPA-R yields better agreement with AWAP and the reanalyses than ERA-Interim; the cold bias of mean summer Tmax in ERA-Interim is

AUSTRALIAN TROPICAL CYCLONE-INDUCED EXTREME COASTAL WINDS IN CLIMATE DATASETS

greatly reduced in BARPA-R across Australian mainland, although the model appears cooler than AWAP in Tasmania by around 1K (Figure 3.2(a)). This improvement is also found for the mean annual count of hot days (with Tmax > 35 °C, Figure 3.2(b)). BARPA-R simulates higher temperature extremes than ERA-Interim and ERA5, particularly over southeastern Australia, Tasmania, and the Top End (Figure 3.2(c)).

For the winter months, BARPA-R significantly reduces the bias in ERA-Interim, but retains some cold bias of around 1.5 K, which is more pronounced over the midlatitude regions (Figure 3.2(d)). There are some spatial similarities with the cold bias (of smaller magnitude) in BARRA-R. This suggests that the cold bias is related to the model physics or setup. Development of Regional Atmosphere and Land (RAL version 1) science configuration has shown that several land surface changes, namely an increase in vegetation cover at the expense of bare soil using the ancillary based on Climate Change Initiative (CCI) and reducing the near-infrared albedo of vegetated tiles, have the benefits of improving daytime temperature. The increased vegetation cover results more rapid evolution of surface and near-surface temperature due to more insulation between the atmosphere and the surface soil (Bush et al., 2020). These changes are not included in GAL configurations of BARRA-R and BARPA-R. Further, they use land cover fractions estimated from International Geosphere–Biosphere Programme (IGBP, Loveland et al., 2000) and show lower bare soil cover, higher shrubs and C4 grass cover than the CCI counterparts. In a further examination of temperature bias during June and July of 2007 (not shown), BARPA-R tends to simulate more (total and low-level) cloud cover than BARRA-R by 0.1–0.2 fraction, when averaged over the southeastern part of the domain including New South Wales, Victoria and Tasmania, during both day and night times, which will reduce Tmax.

3.2 Daily minimum temperature

For daily minimum temperature, all the models are warmer than AWAP on average, and similar observations as were shown for Tmax can be seen in Figure 3.1(b). The mean warm bias in Tmin is around 0.3 K, which is better than the ERA reanalyses, but worse than BARRA-R. The bias also shows a larger seasonal variance than the reanalyses. The bias is smaller during DJF, but particularly warmer than BARRA-R and ERA- Interim during JJA. Specially, in the summer months, BARPA-R captures a more pronounced bimodality in the PDF of DJF Tmin that is less marked with ERA-Interim (Figure A.1(b)) and agrees well with AWAP and BARRA-R across Australia in Figure 3.3(a).

By contrast, during winter months, BARPA-R and BARRA-R overestimate the temperature over some regions. While BARPA-R improves the warm bias in seasonal means over the tropic and extratropic present in ERA-Interim, it is warmer by 2 K in the midlatitudes (Figure 3.3(b), Figure A.1(d)). This warm bias is even larger during the cold extremes when we look at the 1st percentile of wintertime temperature (Figure 3.3(c)). It is also noticeable that some of the warm spots in BARPA-R are similar, with a lesser extent, to those in BARRA-R. Similarities with BARRA-R are also apparent in the interannual variability of bias in Figure 3.1(b). We find that data assimilation in

TOWARDS ACCESS-BASED REGIONAL CLIMATE PROJECTIONS FOR AUSTRALIA

BARRA-R reduces the warm bias across two (of four) analysis cycles at 12 and 18 UTC. For instance, averaging over the southern part of the domain including New South Wales, Victoria and Tasmania, the warm bias in BARRA-R during June and July of 2007 is reduced by around 0.8 K (not shown). Higher night-time cloud cover will also contribute to warm bias in Tmin.

Figure 3.3: Bias in the Tmin during (a) DJF and (b) JJA, and (c) 1st percentile of Tmin, during JJA, and (d) mean annual number of tropical nights (Tmin > 20 ⁰C). This is shown for BARPA-R and various reanalyses, with respect to AWAP gridded analysis.

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Figure 3.3(d) shows that BARPA-R and BARRA-R broadly agree with AWAP in terms of the number of tropical nights with daily Tmin > 20 °C, which is generally fewer than ERA reanalyses that show high bias in the tropics and inland Australia. The biases are lowest in the southeastern region and Tasmania, where all the models agree well.

The combination of cold bias in Tmax and warm bias in Tmin in the mid-latitude region during the winter months leads to the wintertime diurnal temperature range being too narrow by around 3-4 K (Figure A.2). Without these issues during summer, BARPA-R (and BARRA-R) agrees with AWAP more than the ERA reanalyses. The inter-annual change in bias in Tmax from 2011 (Figure 3.1(a)) also leads to a similar change in bias in diurnal range amongst BARPA-R and the reanalyses (Figure A.2).

3.3 Vapour pressure

BARPA-R shows a low bias in the afternoon vapour pressure, which is largest during DJF (Figure 3.4(a)). The bias has a spatial long-term average of -4 Pa and possibly shows a positive trend to lower bias after 2002. ERA-Interim and BARRA-R also show low but smaller bias, but the interannual change in bias and spatial variability of the low bias are very similar in BARPA-R. From Figure 3.5, during DJF, the low bias is largely uniform over the mainland arid and tropical interiors. By contrast, during JJA, the low bias is seen over Cape York Peninsula through the more northern (tropical) parts of the region.

Figure 3.4: Seasonal mean bias in (a) vapour pressure at 3 pm local time, and (b) total precipitation and (b), with respect to AWAP (first row), averaged across the BARPA-R Australian domain.

TOWARDS ACCESS-BASED REGIONAL CLIMATE PROJECTIONS FOR AUSTRALIA

Figure 3.5: Bias in the 3 pm (local time) vapour pressure during (a) DJF and (b) JJA for BARPA-R and various reanalyses, with respect to AWAP gridded analysis.

3.4 Precipitation

The bias in precipitation estimated by BARPA-R also varies seasonally. It is mainly wetter during the summer months as compared to observations-based data and has smaller bias during other months (Figure 3.4(b)). Its range of seasonal variability is the largest amongst the models (i.e., larger than for ERA-Interim or BARRA), but also shows the most stability for the interannual changes.

Figure 3.6(a) shows the spatial variability of the DJF bias. This wet DJF bias in BARPA-R owes to the higher frequency of light-moderate rain days across Australia, with decreasing North-to-South gradient (Figure 3.6(c)). These are absent during winter months (Figure 3.6(b)), during which BARPA-R improves over ERA-Interim over the Eastern Highlands and western Tasmania by providing added spatial details. The excess light rain in moderate-resolution configurations of UM is known in the literature due to its convection parameterization scheme (Lean et al., 2008, Hanley et al., 2015). We find that BARPA-R simulates 3–4 more light rain days per month across the domain during DJF, compared to 1–2 days for BARRA-R and ERA-Interim (not shown).

At the same time, BARPA-R and ERA-Interim share similar dry bias around the Top End of the Australia’s North Territory. This is due to fewer heavy rain days simulated in northern Australia than observed in AWAP (Figure 3.6(d)). These contrasting frequencies of light and heavy rain days over different regions are also seen in BARRA-R. The convection parameterization scheme tends to underestimate extreme precipitation when it cannot explicitly simulate mesoscale convective organization (Su

AUSTRALIAN TROPICAL CYCLONE-INDUCED EXTREME COASTAL WINDS IN CLIMATE DATASETS

et al., 2020) as well as noting the broad range of factors that can potentially influence the occurrence of extreme precipitation (e.g., including microphysical processes not represented in the modelling framework such as influences from cloud condensation nuclei concentrations).

Figure 3.6: Bias in the mean daily precipitation during (a) DJF and (b) JJA. We also plot the bias in the frequency of (b) light-moderate (1-10 mm) rain days and (c) heavy (>10 mm) rain days.

TOWARDS ACCESS-BASED REGIONAL CLIMATE PROJECTIONS FOR AUSTRALIA

To explore the realism in the evolution of simulated storms, we plot, in Figure 3.7(a), the extreme rain events of February 2010 over New South Wales brought on by the ex-tropical cyclone Olga in the north. Here comparisons are made with the reanalyses and Tropical Rainfall Measuring Mission (TRMM) multi-satellite 3-hourly precipitation analysis (TMPA 3B42 version 7; Huffman et al., 2006), although we generally do not expect an RCM to exactly reproduce observed events. In this example, we find that BARPA-R captures the successive rain events and with higher rainfall intensity than ERA-Interim. However, it does not propagate the rainbands further inland during the periods at the beginning and towards end of the month. There are three thunderstorms recorded near Sydney on 5th, 13th and 14th. All but the 5th event can be seen in BARPA-R, TMPA and the reanalyses; the 5th event is not sufficiently organised and does not propagate further inland in BARPA-R.

Figure 3.7: Hovmoller plots, averaged along latitude, of hourly to 3-hourly precipitation from BARPA-R, TMPA and the reanalyses during (a) Sydney storm event of February 2010 over New South Wales domain (38-24⁰S, 140-165⁰E) and (b) Brisbane flood event of 2010/2011 over Queensland domain (23-10⁰S, 140-156⁰E). The analysis domains are shown in Figure 2.1.

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BARPA-R has generated heavy rain events between 20–25 February 2010, which are absent in the reanalyses and TMPA, and subsequent rain events between 25 February – 5 March 2011 that are too intense. The precipitation for these events is largely diagnosed by its deep convection parameterization scheme in GA/L7. In the present scheme, fully developed deep convective clouds can appear instantaneously within a model timestep without the need to develop or grow. This is partly because a fixed entrainment rate for fully developed deep convection is given when the model diagnoses deep convection. In GA8 configuration that is under development (as of December 2020), a prognostic entrainment scheme (ProgEnt) has been developed to provide a mechanism to allow the parameterization scheme to have “memory” of recent convective activity. It uses a variable entrainment rate that has a physically meaningful inverse relation with recent convective precipitation rate (Willet and Whitall, 2017). The use of this scheme has the impact of suppressing this event, reducing the intensity of subsequent events, and propagating the rainbands further inland (Figure A.5(a)), and thus produces rainfall patterns that are subjectively more similar to TMPA.

Figure 3.7(b) illustrates the case of Brisbane flood event in 2010/2011. Here BARPA-R captures an increased rainfall intensity of Tropical Cyclone Tasha during 19–27 December 2010 better than ERA-Interim. The precipitation is diagnosed almost entirely by the convection parameterization scheme. However, like ERA-Interim, the model does not show the organization and intermittency of preceding smaller systems during the seasonal wet monsoon period. Comparing to TMPA, the peak precipitation rates are still too low and the precipitation is too spatially diffuse. Willet and Whitall (2017) have shown that ProgEnt improves the precipitation seen in GA/L7, by having more coherent convection and higher peak precipitation rate and less light precipitation (Figure A.5(a)). Other cases over Tasmania and other time periods were considered (not shown). Generally, BARPA-R captures the events, which are modelled with more realistic intensities and organized propagating systems than ERA-Interim, particularly over Tasmania.

4. ADDED VALUE ANALYSIS FOR EXTREME WEATHER

Here we apply the method of Di Luca et al. (2011; 2016) to measure the added value (AV) of BARPA-R over the host ERA-Interim for climate statistics of temperature and precipitation by using AWAP as the reference. The method assumes that a climate th statistic 푋푖 (e.g., 99 percentile of precipitation over time) for a given data set i can be linearly decomposed into two components at different spatial scales given by the three 80푘푚 5푘푚 80푘푚 data sets, namely 푋푖 = 푋푖 + 푋푖 , where 푋푖 is the statistic defined on the 5푘푚 ERA-Interim’s 80 km grid and 푋푖 contains the extra information provided by the 5 km data (i.e., BARPA-R linearly interpolated on the AWAP grid) compared to the 80 km data.

When defined this way, the AV can be quantified by comparing the relative skill of the GCM and the RCM to represent the statistic, using the normalised quantity 푛 푛 푛 푛 푛 푛 퐴푉푑 = ∑푛[푑(푋퐸푅퐴퐼, 푋퐴푊퐴푃) − 푑(푋퐵퐴푅푃퐴, 푋퐴푊퐴푃)] / ∑푛[푑(푋퐸푅퐴퐼, 푋퐴푊퐴푃) +

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푛 푛 푑(푋퐵퐴푅푃퐴, 푋퐴푊퐴푃)] , where d is some distance metric between the pairs of data computed spatially across the AWAP grid in our analysis. The upscaling to compute 80푘푚 푋푖 is based on taking the average of the higher-resolution values within the coarser- resolution grid boxes. Positive values of AV indicate where the RCM improves over the GCM whereas negative values show less skill in the RCM. Here the AV is calculated for different seasons (DJF, MAM, JJA, SON and annual), regions (coastal, complex topography, and flat, shown in Figure 2.1(b)), variables (Tmax, Tmin, and daily precipitation) and statistical measures for extremes (99th, 99.5th and 99.9th percentiles). In other words, we focus on the warm and wet extremes but distinguish differences between different time and spatial domains. Additionally, two different distance measures d are used to calculate AV, namely the mean-square error (MSE) to capture added value in absolute values and spatial correlation (Corr) for indicating improvements in spatial coherence. It is of note that AVCorr cannot be shown on a map, contrary to AVMSE.

4.1 Temperature

MSE-based AV for daily Tmax and Tmin in Figure 4.1(a, b, d, e) suggests that smaller errors in BARPA-R for estimating these daily values over most parts of eastern Australia (135-160°E, 10-45°S). Over the east coast and the Great Dividing Range the signal is somewhat mixed for Tmax, with both positive and negative AV for different locations through those regions. Some potential reasons for negative values could include the lack of resolution of BARPA-R to simulate temperature variability around the complex orography or the observing network used by AWAP not being sufficiently dense in some locations along the Great Dividing Range. Tasmania is better represented in the RCM, leading to large positive AV for Tmin for all parts of the island (Figure 4.1(a, d)). The Flinders Ranges (137°E, 30-34°S) also stands out as area where the RCM consistently outperforms the GCM. It is also noted that AV is just one metric for indicating relative errors and that post-processing techniques such as calibration using quantile matching to observations can also help improve these, particularly in cases where there is a reasonably consistent bias over time (such as was noted for BARPA-R in Section 3.4).

The AV analysis shows similar features to the temperature biases discussed for BARPA-R in Section 3. The warm bias found in summer (DJF) Tmin is generally smaller than during winter (JJA) which leads to higher AV in the summer months (compare Figure 4.1(a, d)). The warm bias in BARPA-R JJA Tmin in midlatitude regions (Figure 3.3(b, c)) lowers the average added value in those areas (Figure 4.1(d)) and is even more pronounced for cold extremes (Figure A.6(b)). The warm bias found in extreme summer Tmax over the Top End of Australia and cold bias in central Australia (Figure 3.2(c)) lead to a reduction of the average added value (Figure 4.1(b)).

On average, the AV for Tmin and Tmax for all regions, seasons and percentiles is around 0.4 (Figure 4.2), which is broadly consistent with previous RCM downscaling efforts for the Australian region (Di Luca et al. 2016). Tmin shows higher AV in absolute

AUSTRALIAN TROPICAL CYCLONE-INDUCED EXTREME COASTAL WINDS IN CLIMATE DATASETS

values (MSE) than Tmax, while Tmax shows higher AV values than Tmin in its spatial representation (Corr).

Figure 4.1: Grid-point added Value (AV) metric for Tmin (a, d), Tmax (b, e) and daily precipitation (c, f), shown for DJF (a-c) and JJA (d-f). These are calculated using MSE for their 99th, 99.5th and 99.9th percentiles, with their averages shown.

Figure 4.2: The total added value for (a) MSE and (b) spatial correlation, averaged over specific regions (labelled as 'coast', 'flat' and 'topo' based on details as shown in Figure 2.1(b)), seasons (DJF, MAM, JJA and SON as well as annual), variables (precipitation, Tmin and Tmax) and percentiles (99th, 99.5th and 99.9th).

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4.2 Precipitation

For precipitation, AV is largest along the east coast and areas with complex topography (Figure 4.1(c, f)) and AV is larger for austral winter (JJA) than for austral summer (DJF). Plausible reasons for the relatively low AV in the summer months, include that this might be related to too frequent heavy rain days along the east coast of Australia and south of 24°S (Figure 3.6(d)), while the negative AV in far north Australia might be related to fewer heavy rain days (see Section 3.4). In addition to positive AV in coastal and mountainous regions, especially during JJA, some other regions show negative AV. Overall, this reduces the total AVMSE of precipitation to an average of 0.1. However, AVCorr shows a good improvement in spatial variability of the downscaling over the global model (Figure 4.2(b)), especially for regions with complex topography.

Overall, the AV analysis indicates that the RCM precipitation simulations are an improvement over those from the GCM when compared to observations (noting that average AV values are all positive on average for the different metrics tested here). The largest variation in average AV arises from the variable analysed (Figure 4.2(a)) and the region of interest (Figure 4.2(b)). While the RCM improves the spatial distribution coherently for all seasons and quantiles, this is less the case for absolute values (Figure 4.2(a)). Absolute values improve least for the austral summer and more extreme percentiles. The highest average AV typically comes from regions with complex topography.

5. SEVERE THUNDERSTORM POTENTIAL AND EXTREME WIND GUSTS

The representation of severe thunderstorm potential in BARPA-R is assessed by analysing the distribution of relevant large-scale environmental conditions. The environments in which severe thunderstorms occur are typically characterised by thermodynamic instability, such as described by convective available potential energy (CAPE) and other factors related to convective organisation such as vertical wind shear. Similar to previous studies in Australia (Allen et al., 2011; Dowdy 2020b) we use a measure referred to here as CS6, calculated as the product of CAPE and vertical wind shear from the surface to a height of 6 km above ground level, to indicate environments conducive to the formation of thunderstorms (see Appendix 8.2 for details). This is applied to BARPA-R and ERA-Interim, and to rawinsonde observations taken 1-3 times daily at four locations (shown in Figure 5.1(a)) during the period from 2005 to 2018. These locations have unique climatic conditions, and include Darwin (tropical coastal), Sydney (sub-tropical coastal), Adelaide (mid-latitude coastal) and Woomera (mid- latitude inland).

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Figure 5.1: (a) Spatial distribution of mean CS6 from 6-hourly instantaneous data, for ERA- Interim and BARPA-R over 1990-2015. (b) Monthly domain-mean CS6 for BARPA-R and ERA-Interim. Locations of rawinsonde observations for use in Figure 5.2 are represented by black crosses in (a).

Figure 5.1 shows that BARPA is broadly consistent with ERA-Interim in terms of both the spatial distribution and seasonal cycle of CS6, noting some differences over the Coral Sea (northeast of the domain) and over southern Australia. At the four rawinsonde locations (Figure 5.2), BARPA-R produces more extreme values of CS6 than ERA-Interim over the range CS6 > 105, which is in greater agreement with observations, indicating improved potential to represent thunderstorm-related hazards such as severe wind gusts (Brown and Dowdy, 2021), lightning (Dowdy et al., 2020b) and hail (Dowdy et al., 2020c) based on that CS6 metric.

Extreme surface wind gusts over the east Australia domain can be produced by a variety of phenomena, including severe thunderstorms, as well as other synoptic-scale systems such as mid-latitude cyclones and associated fronts, cut-off low pressure systems and tropical cyclones (Walsh et al., 2016; Dowdy et al., 2019a). Thus, the BARPA-R extreme wind climate is also assessed using the annual maximum parameterised surface wind gust at each grid point over the entire study period, representative of a 3-second average wind speed at a height of 10 m. Annual maximum BARPA-R gusts are compared to ERA-Interim, as well as observed wind gusts at the same four locations as used for CS6.

Figure 5.3(a) shows this comparison using the 10-year average recurrence interval (ARI) gust, which is estimated by fitting a generalised extreme value distribution to annual maxima at each grid point using an l-moments technique. BARPA-R shows improved representation (stronger gusts) relative to ERA-Interim at coastal locations, owing to an increase in the winds associated with synoptic systems off

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the east coast and over tropical oceans (further details in Section 6). Stronger gusts over the inland locations may also be associated with stronger synoptic systems, as well as smaller grid sizes and enhanced topography in BARPA-R than ERA-Interim.

Figure 5.2: Frequency histograms of CS6 values from BARPA-R, ERA-Interim and rawinsonde observations at four locations (Darwin, Sydney, Adelaide, Woomera; crosses in Figure 5.1). Only model data corresponding to rawinsonde launch times have been used to construct the histograms, over an 11-year period (2005-2015).

Figure 5.3: (a) 10-year ARI of extreme 10 m wind gusts for ERA-Interim and BARPA. (b) Annual maximum wind gusts shown individually for four locations (black crosses on a), compared with observed wind gusts from automatic weather stations over a common period (1990-2015).

AUSTRALIAN TROPICAL CYCLONE-INDUCED EXTREME COASTAL WINDS IN CLIMATE DATASETS

Figure 5.3(b) compares the distribution of modelled annual maxima with that of the gust observations at each of the four locations. BARPA-R shows improvements (stronger gusts) relative to ERA-Interim for Sydney, Woomera and Darwin but not for Adelaide. For Woomera, a mid-latitude inland location, gusts over 30 ms-1 (corresponding to approximately a 3-year ARI event based on the observations data) are rarely produced by BARPA-R and are not simulated by ERA-Interim. For locations in sub-tropical and southern Australia, many of the observed annual maximum gusts are convective (Holmes, 2002), which occur on spatial scales smaller than model grid boxes and are better represented by environmental conditions associated with severe thunderstorms (such as CS6) rather than simulated model gusts (Brown and Dowdy, 2021).

6. CYCLONE CLIMATOLOGY

Cyclones are tracked using the University of Melbourne cyclone tracking scheme (Murray and Simmonds, 1991; Simmonds et al., 1999), which first regrids the raw data to a common polar stereographic grid with resolution equivalent to 1.5° at 30°S before identifying maxima in the Laplacian of MSLP and associated MSLP minima. Both open and closed lows are considered, and cyclones are required to have an average Laplacian of at least 1 hPa (deg. Lat.)-2 within a 2° radius of the cyclone centre.

Figure 6.1 shows that in eastern Australia (135-160°E, 10-45°S) the spatial patterns of cyclones in BARPA are similar to reanalyses and previous studies (Quinting et al., 2019; Pepler and Dowdy, 2019), with highest frequencies in the Tasman Sea to the east of Australia. Overall, BARPA has 22% more cyclones than ERA-Interim, but only 3% more than BARRA-R and 8% more than ERA5, consistent with the tendency for cyclones to be stronger and therefore more frequent for a given threshold when using higher resolution reanalyses (Di Luca et al., 2015). Compared to ERA-Interim, the largest differences in BARPA-R are over the tropics (north of 20°S), where BARPA-R produces twice as many cyclones per year; this pattern of increased cyclone frequency in the north is also apparent in the other high-resolution reanalyses. In comparison, the number of cyclones south of 20°S is very consistent between the four reanalyses, with variations less than 7%. Differences are larger in the west of the domain, which may be due to cyclone propagation near the western boundary in the more limited BARPA-R domain.

Figure 6.1: Proportion of all observations with a cyclone detected in each 1×1° grid, 1990- 2015. This is shown for BARPA-R and the three reanalyses including ERA-Interim, ERA5 and BARRA-R.

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7. DISCUSSION AND OUTLOOK

Our results in Section 3 show that BARPA-R simulations do not inherit large biases from ERA-Interim for the near-surface temperature, vapour pressure, and precipitation. Specifically, BARPA-R improves upon ERA-Interim to show reduced biases for summertime daily maximum and minimum temperature, and thus is able to reflect more accurate diurnal temperature ranges, and other statistics such as relating to the frequency of hot days and of warm nights. For the winter months, BARPA-R also shows lower bias for daily minimum and maximum over the tropics. In mid-latitude areas BARPA-R shows some biases including some that are also characteristic of our regional reanalysis BARRA-R. BARPA-R also shows similar characteristics to BARRA-R in having a relatively low bias for vapour pressure during the summer months. These commonalities between BARPA-R and BARRA-R suggest that the underlying model setups (e.g., specifications of land cover and vegetation albedo) are likely the contributing factors, such that this potentially could be improved by using the later model configurations in GA/L8.

For precipitation, by using the same convective parameterization scheme, BARRA-R and BARPA-R also share the same biases in the frequencies of light and heavy rain days during the warm months, but leading to more striking bias in BARPA- R. These issues of convective parameterization schemes are well known, and can be partly addressed with higher-resolution convection-permitting models where small convective cloud processes are explicitly modelled (Lean et al., 2008; Clark et al., 2016; Su et al., 2020). This may potentially alleviate the low bias in heavy rain days seen in BARPA-R over the tropics. However, the UM-based convective-permitting models can also produce storm cells in some cases that are too intense, too far apart and with not enough light rain (Lean et al., 2008; Hanley et al., 2016). Biases can continue to be addressed in ongoing development activities for UM-based modelling applications in the Australian region, as well as noting benefits from post-processing methods such as using multi-variate regression models (e.g., Glahn and Lowry, 1972) or quantile matching (e.g., Bennett et al., 2014; Cattoën et al., 2020; Dowdy 2020a).

Given the largely similar model setups in BARPA-R and BARRA-R, this study provides added clarity on biases inherit to the underlying modelling systems. An understanding of strengths and limitations in the reanalysis setup at one timescale (e.g., recent decades) could help inform that of a climate modelling setup at another timescale (e.g., future projected decades). For instance, our comparison reveals that the observations-based analysis step in BARRA-R (i.e., data assimilation) could be an important factor to help reduce the warm bias in wintertime Tmin. By contrast, BARPA- R does not include data assimilation, due to its intended future application for producing downscaled projections of future climate. Improvements to the convective parameterization scheme and land surface characteristics in UM can be expected to benefit both modelling systems (BARPA-R and BARRA-R).

While the deeper sub-surface soil moisture has not stabilised during the initial years of each model run that made up the 26-year modelled timeseries, there is no discernible evidence that BARPA-R show drifts in biases as a result. BARPA-R

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however displays some inter-annual variations of biases in various studied parameters, which are also mirrored in the other reanalyses. BARPA-R also appears to show greater inter-annual stability than ERA-Interim such as for Tmax and precipitation. That is, BARPA-R is less susceptible to artificial shifts or spurious trends in a reanalysis, which can be resulted from incremental or abrupt changes in observing systems (e.g., Thorne and Vose, 2010), noting the increase in satellite coverage in recent decades as compared to earlier time periods. Thus, BARPA-R may be more suited to defining long-term climate changes in some cases. This temporal stability over time suggests potential benefits from the application of systematic calibration methods in post-processing steps, such as the use of quantile matching to observations-based data for improved confidence in climate model output products (Dowdy 2020a).

The added value of BARPA-R over ERA-Interim is also shown for a variety of different regions, seasons, variables and percentiles, with two distance measures also used to calculate the added value (Section 4). The results demonstrate that on average the RCM is improving on the GCM. The largest variation in average added value arises from the variable analysed and the region of interest. The most averaged added value comes from regions with complex topography, broadly similar to previous findings (Di Luca et al. 2016). The low MSE added value of precipitation in some cases could potentially be partly influenced by biases that are either inherent to the RCM or that feed into the RCM through the lateral boundary conditions. This is supported by the generally low added value when bias is used as distant measure (see Figure A.7).

BARPA-R can produce a realistic distribution of severe thunderstorm environments, including more frequent extreme conditions as compared with ERA- Interim, which is in greater agreement with rawinsonde observations (Section 5). Neither BARPA-R or ERA-Interim can produce extreme wind gusts associated with severe thunderstorms given the small spatial scales on which they occur, although BARPA-R is able to produce more extreme surface wind gusts associated with synoptic systems, which is in greater agreement with observed gust distributions at some locations. It is also noted that BARPA-R is likely to share the same similar biases in the near-surface (10 m) wind speed as previously reported for BARRA-R (Su et al, 2019) where negative (positive) bias occurs for high (low) wind speeds. In Section 6, the climatology of cyclones in BARPA-R is found to be very similar to that in BARRA-R, both of which produce more cyclones on average than the lower-resolution ERA- Interim data, particularly north of 20°S. This finding for northern Australia suggests a potentially improved ability to represent smaller-scale tropical cyclones, which will be investigated in more detail in future work.

BARPA-R is intended to help contribute to the set of RCM approaches available for Australia and surrounding regions, including for producing downscaling of future climate projections. The set of BARPA-R simulations examined here, based on ERA- Interim reanalysis as the host global model, demonstrates the general stability of the modelling suite including relatively consistent biases over time, considerable added value over the host model in many cases, and realism of simulated storms, thunderstorm environments, wind gusts and cyclone climatology. Future work could also potentially expand the spatial coverage of BARPA-R to include the full Australian continent,

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whilst noting that in larger domains, large-scale departures from the driving model are more likely. Finer-scale 'nested' versions of the BARPA modelling suite are also being considered, including using the BARPA-R simulations as the host model and downscaling further to convection-permitting (kilometre) scale horizontal grid spacings. It is intended that BARPA-R will help contribute to an enhanced ability to plan and prepare for hazards in the future climate, including for phenomena such as cyclones, thunderstorms, wildfires, severe wind and rainfall extremes.

8. APPENDIX

8.1 Model bias studies

Figure A.1: Probability density functions of seasonal (DJF and JJA) daily maximum temperature (Tmax) and minimum temperature (Tmin), averaged across the BARPA-R Australian domain. This is shown for BARPA-R, AWAP and the reanalyses (ERA-Interim, ERA5 and BARRA-R).

AUSTRALIAN TROPICAL CYCLONE-INDUCED EXTREME COASTAL WINDS IN CLIMATE DATASETS

Figure A.2: Bias in the mean (a) DJF and (b) JJA diurnal temperature range (Tmax – Tmin), for BARPA-R and the various reanalyses (ERA-Interim, ERA5 and BARRA-R), with respect to AWAP gridded analysis (first column).

Figure A.3: Seasonal mean bias in diurnal temperature range (Tmax – Tmin), with respect to AWAP (first row), averaged across the BARPA-R’s Australian domain. This is shown for BARPA-R, AWAP and the reanalyses (ERA-Interim, ERA5 and BARRA-R).

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Figure A.4: Probability density functions of seasonal mean diurnal temperature range (Tmax –

Tmin), across the BARPA-R’s Australian domain. This is shown for BARPA-R, AWAP and the reanalyses (ERA-Interim, ERA5 and BARRA-R).

Figure A.5: Hovmoller plots, averaged along latitude, of 3-hourly precipitation from BARPA-R during (a) Sydney storm event of February 2010 over New South Wales domain (38-24⁰S, 140- 165⁰E) and (b) Brisbane flood event of 2010/2011 over Queensland domain (23-10⁰S, 140- 156⁰E). For each event, two BARPA-R simulations are compared, one with and without prognostic entrainment scheme. The ones without replicates the BARPA-R plots shown in Figure 3.7 (leftmost).

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8.2 Added value analysis for extreme weather

Figure A.6: Minimum daily temperature added value for 1st percentile for DJF (a) and JJA (b).

Figure A.7: The total added value for the bias distance measure averaged over specific regions (labelled as 'coast', 'flat' and 'topo' based on details as shown in Figure 2.1(b)), seasons (DJF,

MAM, JJA and SON as well as annual), variables (precipitation, Tmin and Tmax) and percentiles (99th, 99.5th and 99.9th).

8.3 Severe thunderstorm diagnostic CS6

CS6 is the product of CAPE and vertical wind shear from the surface to a height of 6 km above ground level (S06), and has been found to relate represent broad-scale thunderstorm occurrence globally (Brooks et al., 2003), and in Australia, where raising the value of vertical wind shear to the power of 5/3 is a more regionally appropriate formulation (Allen et al., 2011) such that,

퐶푆6 = 퐶퐴푃퐸 × 푆065/3

In this work, CAPE and S06 are both derived from pressure and surface level model output and the full rawinsonde soundings below 100 hPa, with pseudo-adiabatic lifting used for CAPE applying a virtual temperature correction. The number of vertical levels

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used for calculating CAPE is 28 using ERA-Interim, 13 using BARPA-R and between 45 and 65 on average using rawinsonde soundings, which varies with location. The most-unstable CAPE is used here corresponding to the parcel of air which has maximum CAPE in the vertical.

ACKNOWLEDGEMENTS

This project is funded by the Australian Government Department of Industry, Science, Energy and Resources through the Electricity Sector Climate Information (ESCI) Project. BARPA-R is set up with assistance from UKMO colleagues involved UKCP18 project. This work has also benefitted from valuable contributions from Elizabeth Kendon and Joao Teixeira at UKMO; Marcus Thatcher at the Commonwealth Scientific and Industrial Research Organisation; Greg Roff, Charmaine Franklin, Hongyan Zhu, Imtiaz Dharssi, and Dörte Jakob at the Bureau; Nathan Eizenberg at University of Melbourne. The development and implementation of BARPA-R system were undertaken with the assistance of resources and services from NCI, which is supported by the Australian Government. This study uses the ERA-Interim and ERA5 data provided through the ARC Centre of Excellence for Climate System Science (Paola Petrelli) at NCI. ERA-Interim can be retrieved from the ECMWF at https://www.ecmwf.int/en/forecasts/datasets/archive-datasets/reanalysis-datasets/era- interim. ERA5 can be retrieved from Copernicus Climate Data Store, at https://cds.climate.copernicus.eu/. The AWAP data can be requested from http://www.bom.gov.au/climate (last access: 12 December 2020). The near-surface and rawinsonde observation data was retrieved from Australia Data Archive for Meteorology (ADAM) database. We thank Mitchell Black and Hongyan Zhu for reviewing early drafts of this paper.

REFERENCES

Allen, J. T., Karoly, D. J., and Mills, G. A.: A severe thunderstorm climatology for Australia and associated thunderstorm environments. Aust. Meteorol. and Oceanog. J., 61(3), 143-158, 2011. Andrys, J., Lyons, T.J., and Kala, J.: Evaluation of a WRF ensemble using GCM boundary conditions to quantify mean and extreme climate for the southwest of Western Australia (1970–1999). Int. J. Climatol., 36: 4406-4424, doi:10.1002/joc.4641, 2016. Ashcroft, L., Karoly, D. J., Dowdy, A. J.: Historical extreme rainfall events in southeastern Australia, Weath. and Clim. Extremes, 25, 100210, doi:10.1016/j.wace.2019.100210, 2019. Arakawa, A. and Lamb, V. R.: Computational design of the basic dynamical processes of the UCLA general circulation model, Methods of Comp. Phys.: Adv. Res. Appl., 17, 173–265, doi:10.1016/B978-0-12-460817-7.50009-4, 1977. Bennett, J.C., Grose, M.R., Corney, S.P., White, C.J., Holz, G.K., Katzfey, J.J., Post, D.A. and Bindoff, N.L.: Performance of an empirical bias‐correction of a high‐ resolution climate dataset. Int. J. Climatol., 34: 2189-2204. doi:10.1002/joc.3830, 2014.

AUSTRALIAN TROPICAL CYCLONE-INDUCED EXTREME COASTAL WINDS IN CLIMATE DATASETS

Bell, S. S., Chand, S. S., Tory, K. J., Dowdy, A. J., Turville, C. and Ye, H.: Projections of southern hemisphere tropical cyclone track density using CMIP5 models. Clim. Dyn., 52(9-10), 6065-6079, doi:10.1007/s00382-018-4497-4, 2019. Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, doi:10.5194/gmd-4-677-2011, 2011. Brooks, R.H. and Corey, A.T.: Hydraulic Properties of Porous Media; Hydraulic Papers; Colorado State University, Fort Collins, CO, USA, 1964. Brooks, H. E., J. W. Lee, and J. P. Craven: The spatial distribution of severe thunderstorm and tornado environments from global reanalysis data. Atmos. Res., 67– 68, 73–94, doi:10.1016/S0169-8095(03)00045-0, 2003. Brown, A., Milton, S., Cullen, M., Golding, B., Mitchell, J., & Shelly, A.: Unified modeling and prediction of weather and climate: A 25-Year journey, Bull. Am. Meteorol. Soci., 93(12), 1865-1877, doi:10.1175/BAMS-D-12-00018.1, 2012. Brown, A., and Dowdy, A.: Extreme wind gusts and thunderstorms in South Australia analysed from 1979-2017, Bureau Research Report – 034, available at: http://www.bom.gov.au/research/publications/researchreports/BRR-034.pdf (last accessed: 15 December 2020), 2019. Brown, A. & Dowdy, A. (2021). Severe convection-related winds in Australia and their associated environments. J. South. Hem. Earth Sys. Sci. doi:10.1071/ES19052 Bureau of Meteorology: APS3 upgrade of the ACCESS-G/GE numerical weather prediction system, National Operational Centre Operations Bulletin No. 125, available at http://www.bom.gov.au/australia/charts/bulletins/opsbull_G3GE3_external_v3.pdf (last accessed: 12 December 2020), 2019. Bush, M., Allen, T., Bain, C., Boutle, I., Edwards, J., Finnenkoetter, A., Franklin, C., Hanley, K., Lean, H., Lock, A., Manners, J., Mittermaier, M., Morcrette, C., North, R., Petch, J., Short, C., Vosper, S., Walters, D., Webster, S., Weeks, M., Wilkinson, J., Wood, N., and Zerroukat, M.: The first Met Office Unified Model–JULES Regional Atmosphere and Land configuration, RAL1, Geosci. Model Dev., 13, 1999–2029, https://doi.org/10.5194/gmd-13-1999-2020, 2020. Cavicchia, L., Pepler, A., Dowdy, A., & Walsh, K.: A physically based climatology of the occurrence and intensification of Australian East Coast lows, J. Clim., 32(10), 2823- 2841, doi: 10.1175/JCLI-D-18-0549.1, 2019. Chand, S. S., Dowdy, A. J., Ramsay, H. A., Walsh, K. J. E., Tory, K. J., Power, S. B., Bell, S. S., Lavender, S. L., Ye, H., Kuleshov, Y.: Review of tropical cyclones in the Australian region: Climatology, variability, predictability, and trends. WIREs Clim. Change, 10:e602. doi:10.1002/wcc.602, 2019. Charney, J. G. and Phillips, N. A.: Numerical integration of the quasi-geostrophic equations for barotropic and simple baroclinic flows, J. Meteorol., 10, 71–99, doi:10.1175/1520-0469(1953)010<0071:NIOTQG>2.0.CO;2, 1953.

TOWARDS ACCESS-BASED REGIONAL CLIMATE PROJECTIONS FOR AUSTRALIA

Clarke, J. M., Grose, M., Thatcher, M., Hernaman, V., Heady, C., Round, V., Rafter, T., Trenham, C. and Wilson, L.: Victorian Climate Projections 2019 Technical Report. CSIRO, Melbourne Australia, available at: https://www.climatechange.vic.gov.au/__data/assets/pdf_file/0016/435121/Vic- Climate-Projections-2019-Technical-Report_20200218.pdf (last accessed: 15 December 2020), 2019. CSIRO and Bureau of Meteorology 2015, Climate Change in Australia Information for Australia’s Natural Resource Management Regions: Technical Report, CSIRO and Bureau of Meteorology, Australia, 2015. Corney, S., Grose, M., Bennett, J. C., White, C., Katzfey, J., McGregor, J., Holz, G., and Bindoff, N. L.: Performance of downscaled regional climate simulations using a variable‐resolution regional climate model: Tasmania as a test case, J. Geophys. Res. Atmos., 118, 11,936– 11,950, doi:10.1002/2013JD020087, 2013. Coumou, D., and Rahmstorf: S. A decade of weather extremes. Nat. Clim. Change 2, 491–496 (2012). doi:10.1038/nclimate1452, 2012. Davies, T., Cullen, M. J. P., Malcolm, A. J., Mawson, M. H., Staniforth, A., White, A. A., and Wood, N.: A new dynamical core for the Met Office’s global and regional modelling of the atmosphere, Q. J. Roy. Meteorol. Soc., 131, 1759–1782, doi:10.1256/qj.04.101, 2005. Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol. C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Holm, E. V., Isaksen, L., Kallberg, P., Kohler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J. J., Park, B. K., Peubey, C., de Rosnay, P., Tavolato, C., Thepaut, J. N., and Vitart, F.: The Era- Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. Roy. Meteorol. Soc., 137, 553–597, doi:10.1002/qj.828, 2011. Dharssi, I., Steinle, P., and Fernon, J.: Improved numerical weather predictions by using optimised urban model parameter values and satellite derived tree heights, in: MODSIM2015, 21st Interna-tional Congress on Modelling and Simulation, edited by: Weber, T., McPhee, M. J., and Anderssen, R. S., Modelling and Simulation Society of Australia and New Zealand, December 2015, 1161–1167, ISBN: 978-0-9872143-5-5, available at: https://www.mssanz.org.au/modsim2015/M4/dharssi.pdf (last access: 17 May 2019), 2015. Di Luca, A., de Elía, R., and Laprise, R.: Potential for small scale added value of RCM’s downscaled climate change signal. Clim. Dyn. 40(3–4):601–18. doi: 10.1007/s00382-012-1415-z, 2013 Di Luca, A., Evans, J.P., Pepler, A., Alexander, L., and Argüeso, D.: Resolution sensitivity of cyclone climatology over Eastern Australia using six reanalysis products. J. Clim., 28(24):9530–49. doi: 10.1175/JCLI-D-14-00645.1, 2015 Di Luca, A., Argüeso, D., Evans, J.P., De Elía, R., and Laprise, R.: Quantifying the overall added value of dynamical downscaling and the contribution from different spatial scales. J. Geophys. Res. 121(4):1575–90. doi: 10.1002/2015JD024009, 2016.

AUSTRALIAN TROPICAL CYCLONE-INDUCED EXTREME COASTAL WINDS IN CLIMATE DATASETS

Di Virgilio, G., Evans, J. P., Di Luca, A., Olso, R., Argüeso, D., Kala, J., Andrys, J., Hoffmann, P., Katzfey, J. J., and Rockel, B.: Evaluating reanalysis-driven CORDEX regional climate models over Australia: model performance and errors. Clim. Dyn., 53, 2985–3005, doi:10.1007/s00382-019-04672-w, 2019. Dowdy, A. J.: Climatological variability of fire weather in Australia, J. Appl. Meteorol. Climatol., 57(2), 221-234, doi:10.1175/JAMC-D-17-0167.1, 2018. Dowdy, A.J., Pepler, A., Di Luca, A., Cavicchia, L., Mills, G., Evans, J. P., Louis, S., McInnes, K. L., and Walsh, K.: Review of Australian east coast low pressure systems and associated extremes. Clim. Dyn. 53, 4887–4910, doi:10.1007/s00382-019-04836-8, 2019a. Dowdy, A.J., Ye, H., Pepler, A., Thatcher, M., Osbrough, S.L., Evans, J.P., Di Virgilio, G. and McCarthy, N.: Future changes in extreme weather and pyroconvection risk factors for Australian wildfires. Scient. Rep., 9(1), 1-11, doi:10.1038/s41598-019- 46362-x, 2019b. Dowdy, A. J.: Seamless climate change projections and seasonal predictions for bushfires in Australia. J. Southern Hemisph. Earth Syst. Sci., 70(1), 120-138, doi:10.1071/ES20001, 2020a. Dowdy, A. J.: Climatology of thunderstorms, convective rainfall and dry lightning environments in Australia. Clim. Dyn., 54, 3041-3052, doi:10.1007/s00382-020-05167- 9, 2020b. Dowdy, A. J., Soderholm, J., Brook, J., Brown, A. and McGowan, H.: Quantifying hail and lightning risk factors using long‐term observations around Australia. J. Geophys. Res.: Atmos., 125(21), p.2020JD033101, 2020c. Edwards, J. M. and Slingo, A.: Studies with a flexible new radiation code. I: Choosing a configuration for a largescale model, Q. J. Roy. Meteorol. Soc., 122, 689–719, doi:10.1002/qj.49712253107, 1996. Evans, J.P., Ekström, M., and Ji, F.: Evaluating the performance of a WRF physics ensemble over South-East Australia. Clim. Dyn., 39(6):1241–58. doi:10.1007/s00382- 011-1244-5, 2012. Evans, J. P., F. Ji, C. Lee, P. Smith, D. Argüeso, and L. Fita: Design of a regional climate modelling projection ensemble experiment - NARCliM. Geoscient. Model Dev., 7(2):621–29. doi:10.5194/gmd-7-621-2014, 2014. Feser, F., Rockel, B., von Storch, H., Winterfeldt, J., and Zahn, M.: Regional climate models add value to global model data: A review and selected examples. Bull. Am. Meteorol. Soc., 92(9):1181–92. doi:10.1175/2011BAMS3061.1, 2011. Giorgi, F., Jones, C., and Asrar, G.: Addressing climate information needs at the regional level: The CORDEX framework. WMO Bull., 58, 175– 183, 2009. Gregory, D. and Rowntree, P. R.: A mass flux convection scheme with representation of cloud ensemble characteristics and stability-dependent closure, Mon. Weather Rev., 118, 1483–1506, doi:10.1175/1520-0493(1990)118<1483:AMFCSW>2.0.CO;2, 1990.

TOWARDS ACCESS-BASED REGIONAL CLIMATE PROJECTIONS FOR AUSTRALIA

Grose, M.R., Corney, S.P., Katzfey J.J., Bennett, J.C., Holz, G.K., White, C.J., and Bindoff, N.L.: A Regional response in mean westerly circulation and rainfall to projected climate warming over Tasmania, Australia. Clim. Dyn., 40(7–8):2035–48. doi:10.1007/s00382-012-1405-1, 2013. Hanley, K.E., Plant, R.S., Stein, T.H.M., Hogan, R.J., Nicol, J.C., Lean, H.W., Halliwell, C. and Clark, P.A.: Mixing‐length controls on high‐resolution simulations of convective storms. Q. J. R. Meteorol. Soc., 141: 272-284. doi:10.1002/qj.2356, 2015. Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D. and Simmons, A.: The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999- 2049, 2020. Holmes, J. D.: A Re-analysis of Recorded Extreme Wind Speeds in Region A. Aust. J. Struct. Eng., 4, 29–40, doi:10.1080/13287982.2002.11464905, 2002. Hudson, D., Alves, O., Hendon, H. H., Lim, E.-P., Liu, G., Luo, J.-J., MacLachlan, C., Marshall, A. G., Shi, L., Wang, G., Wedd, R., Young, G., Zhao, M., and Zhou, X.: ACCESS-S1: The new Bureau of Meteorology multi-week to seasonal prediction system, J. South. Hemisphere Earth Sys. Sci., 67(3), 132-159, doi:10.22499/3.6703.001, 2017. Huffman, G. J., Adler, R. F., Bolvin, D. T., Gu, G., Nelkin, E. J., Bowman, K. P., Hong, Y., Stocker, E. F., and Wolff, D. B.: The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales, J. Hydrometeor., 8, 38–54, doi:10.1175/JHM560.1, 2006. Jones, D. A., Wang, W., and Fawcett, R.: High-quality spatial climate data-sets for Australia, Aust. Meteorol. Oceanogr. J., 58, 233–248, 2009. Kendon, E. J., Roberts, N. M., Fowler, H. J., Roberts, M. J., Chan, S. C., and Senior, C. A.: Heavier summer downpours with climate change revealed by weather forecast resolution model. Nat. Clim. Change, 4, 570–576, doi:10.1038/nclimate2258, 2014. Laprise, R., de Elía, R., Caya, D., Biner, S., Lucas-Picher, P., Diaconescu, E., Leduc, M., Alexandru, A., and Separovic, L.: Challenging some tenets of regional climate modelling. Meteorol. Atmos. Phys., 100(1–4):3–22. doi: 10.1007/s00703-008-0292-9, 2008. Lean, H. W., Clark, P. A., Dixon, M., Roberts, N. M., Fitch, A., Forbes, R., Halliwell, C.: Characteristics of high-resolution 610 versions of the Met Office Unified Model for forecasting convection over the United Kingdom. Mon. Weather Rev. 136: 3408–3424, doi: 10.1175/2008MWR2332.1, 2008. Lock, A. P., Brown, A. R., Bush, M. R., Martin, G. M., and Smith, R. N. B.: A new boundary layer mixing scheme. Part I: Scheme description and single-column model tests, Mon. Weather Rev., 128, 3187–3199, doi:10.1175/1520- 0493(2000)128<3187:ANBLMS>2.0.CO;2, 2000. Loveland, T. R., Reed, B. C., Brown, J. F., Ohlen, D. O., Zhu, Z., Yang, L., and Merchant, J. W.: Development of a global land cover characteristics database and IGBP

AUSTRALIAN TROPICAL CYCLONE-INDUCED EXTREME COASTAL WINDS IN CLIMATE DATASETS

DISCover from 1 km AVHRR data, Int. J. Remote Sens., 21, 1303–1330, doi:10.1080/014311600210191, 2000. Meehl, G. A., Zwiers, F., Evans, J., Knutson, T., Mearns, L., and Whetton, P.: Trends in extreme weather and climate events: issues related to modeling extremes in projections of future climate change. Bull Am Meteorol. Soc., 81(3), 427-436, 2000 Murphy, J. M., Harris, G. R., Sexton, D.M.H., Kendon, E.J., Bett, P.E., Clark, R.T., Eagle, K.E., Fosser, G., Fung, F., Lowe, J.A., McDonald, R.E., McInnes, R.N., McSweeney, C.F., Mitchell, J.F.B., Rostron, J.W., Thornton, H.E., Tucker, S., and Yamazaki, K.: UKCP18 land projections: Science report, available at: https://www.metoffice.gov.uk/pub/data/weather/uk/ukcp18/science-reports/UKCP18- Land-report.pdf (last accessed: 12 December 2020), 2018. Murray, R.J., and Simmonds, I.: A numerical scheme for tracking cyclone centres from digital data. Part I: Development and operation of the scheme. Aust. Meteorol. Mag., 39(3):155–66, 1991. Oliver, H., Shin, M., Matthews, D., Sanders, O., Bartholomew, S., Clark, A., Fitzpatrick, B., van Haren, R., Hut, R., and Drost, N.: Workflow automation for cycling systems, Comp. in Sci. & Eng., 21(4), 7-21, doi:10.1109/MCSE.2019.2906593, 2019. Olson, R., Evans, J.P., Di Luca, A., and Argüeso, D.: The NARCliM Project: Model agreement and significance of climate projections, Clim. Res., 69(3):209–27. doi: 10.3354/cr01403, 2016. Puri, K., Dietachmayer, G., Steinle, P., Dix, M., Rikus, L., Logan,L., Naughton, M., Tingwell, C., Xiao, Y., Barras, V., Bermous, I., Bowen, R., Deschamps, L., Franklin, C., Fraser, J., Glowacki, T., Harris, B., Lee, J., Le, T., Roff, G., Sulaiman, A., Sims, H., Sun, X., Sun, Z., Zhu, H., Chattopadhyay, M. and Engel, C.: Implementation of the initial ACCESS numerical weather prediction system, Aust. Meteorol. Oceanogr. J., 63, 265–284, 2013. Pepler, A., and Dowdy, A.: A three-dimensional perspective on extratropical cyclone impacts. J. Clim., 33(13), 5635–5649, doi:10.1175/JCLI-D-19-0445.1, 2020. Perkins-Kirkpatrick, S.E., White, C.J., Alexander, L.V., Argüeso, D., Boschat, G., Cowan, T., Evans, J. P., Ekström, M., Oliver, E. C. J., Phatak, A. and Purich, A.: Natural hazards in Australia: heatwaves, Clim. Change 139, 101–114, doi:10.1007/s10584-016-1650-0, 2016. Prömmel, K., Geyer, B., Jones, J.M., and Widmann, M.: Evaluation of the skill and added value of a reanalysis-driven regional simulation for alpine temperature. Int. J. Climat., 30(5):760–73. doi: 10.1002/joc.1916, 2010. Quinting, J. F., Catto, J. L., and Reeder, M. J.: Synoptic climatology of hybrid cyclones in the Australian region. Q. J. Roy. Meteorol. Soci., 145(718), 288–302, doi:10.1002/qj.3431, 2019. Schär, C., Fuhrer, O., Arteaga, A., Ban, N., Charpilloz, C., Di Girolamo, S., Hentgen, L., Hoefler, T., Lapillonne, X., Leutwyler, D., Osterried, K., Panosetti, D., Rüdisühli, S., Schlemmer, L., Schulthess, T. C., Sprenger, M., Ubbiali, S., and Wernli, H.:

TOWARDS ACCESS-BASED REGIONAL CLIMATE PROJECTIONS FOR AUSTRALIA

Kilometer-scale climate models: Prospects and challenges. Bull. Amer. Meteor. Soc., 101, E567–E587, doi:10.1175/BAMS-D-18-0167.1, 2020. Seneviratne, S., Nicholls, N., Easterling, D., Goodess, C., Kanae, S., Kossin, J., et al.: Changes in Climate Extremes and Their Impacts on the Natural Physical Environment. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change 9781107025:109–230, doi:10.1017/CBO9781139177245.006, 2012. Simard, M., Pinto, N., Fisher, J. B., and Baccini, A.: Mapping forest canopy height globally with spaceborne lidar, J. Geophys. Res.-Biogeosci., 116, G04021, doi:10.1029/2011JG001708, 2011. Simmonds, I., Murray, R.J., and Leighton, R.M.: A refinement of cyclone tracking methods with data from FROST. Australian Meteorological Magazine Spec Ed:35-49, 1999. Smith, R. N. B.: A scheme for predicting layer cloud and their water content in a general circulation model, Q. J. R. Meteorol. Soc., 116, 435–460, doi:10.1002/qj.49711649210, 1990. Stevens, B., Fiedler, S., Kinne, S., Peters, K., Rast, S., Müsse, J., Smith, S. J., and Mauritsen, T.: MACv2-SP: a parameterization of anthropogenic aerosol optical properties and an associated Twomey effect for use in CMIP6, Geosci. Model Dev., 10, 433–452, doi:10.5194/gmd-10-433-2017, 2017. Su, C.-H., Eizenberg, N., Steinle, P., Jakob, D., Fox-Hughes, P., White, C. J., Rennie, S., Franklin, C., Dharssi, I., and Zhu, H.: BARRA v1.0: the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia, Geosci. Model Dev., 12, 2049–2068, doi:10.5194/gmd-12-2049-2019, 2019. Su, C.-H., Eizenberg, N., Jakob, D., Fox-Hughes, P., Steinle, P., White, C. J., and Franklin, C.: BARRA v1.0: Kilometre-scale downscaling of an Australian regional atmospheric reanalysis over four midlatitude domains, Geosci. Model Dev. Discuss., doi:10.5194/gmd-2020-366, in review, 2020. Swain, D.L., Langenbrunner, B., Neelin, J.D., and Hall, A.: Increasing Precipitation Volatility in Twenty-First-Century California. Nat. Clim. Change, 8(5):427–33. doi: 10.1038/s41558-018-0140-y, 2018. Tapiador, F.J., Navarro, A., Moreno, R., Sánchez, J.L., and García-Ortega, E.: Regional climate models: 30 years of dynamical downscaling, Atmos. Res., 235:104785. doi:10.1016/j.atmosres.2019.104785, 2020. Thorne, P. W. and Vose, R. S.: Reanalyses suitable for characterizing long-term trends, Bull. Am. Meteorol. Soc., 91, 353–361, doi:10.1175/2009BAMS2858.1, 2010. Torma, C., Giorgi, F., and Coppola, E.: Added Value of Regional Climate Modeling over Areas Characterized by Complex Terrain—Precipitation over the Alps. J. Geophys. Res., 120, doi:10.1038/175238c0, 2015.

AUSTRALIAN TROPICAL CYCLONE-INDUCED EXTREME COASTAL WINDS IN CLIMATE DATASETS

Tucker, S. O., Kendon, E. J., Bellouin, N., Buonomo, E., Johnson, B., and Murphy, J. M.: Evaluation of a new 12km regional perturbed parameter ensemble over Europe, in review, 2021. Van Dijk, A. I. J. M., Beck, H. E., Crosbie, R. S., de Jeu, R. A. M., Liu, Y. Y., Podger, G. M., Timbal, B., and Viney, N. R.: The Millennium Drought in southeast Australia (2001–2009): Natural and human causes and implications for water resources, ecosystems, economy, and society, Water Resour. Res., 49, doi:10.1002/wrcr.20123, 2013. Walsh, K., White, C. J., McInnes, K., Holmes, J., Schuster, S., Richter, H., Evans, J. P., Di Luca, A., and Warren, R. A: Natural hazards in Australia: storms, wind and hail. Clim. Change, 139, 55–67, doi:10.1007/s10584-016-1737-7, 2016. Walters, D., Baran, A. J., Boutle, I., Brooks, M., Earnshaw, P., Edwards, J., Furtado, K., Hill, P., Lock, A., Manners, J., Morcrette, C., Mulcahy, J., Sanchez, C., Smith, C., Stratton, R., Tennant, W., Tomassini, L., Van Weverberg, K., Vosper, S., Willett, M., Browse, J., Bushell, A., Carslaw, K., Dalvi, M., Essery, R., Gedney, N., Hardiman, S., Johnson, B., Johnson, C., Jones, A., Jones, C., Mann, G., Milton, S., Rumbold, H., Sellar, A., Ujiie, M., Whitall, M., Williams, K., and Zerroukat, M.: The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations, Geosci. Model Dev., 12, 1909–1963, doi:10.5194/gmd-12-1909-2019, 2019. Willett, M. R, and Whitall, M. A.: A simple prognostic based convective entrainment rate for the Unified Model: Description and tests, Forecasting Research Technical Report No: 617, UK Met Office, 2017. Williams, K. D., Copsey, D., Blockley, E. W., Bodas‐Salcedo, A., Calvert, D., Comer, R., Davis, P., Graham, T., Hewitt, H. T., Hill, R., Hyder, P., Ineson, S., Johns, T. C., Keen, A. B., Lee, R. W., Megann, A., Milton, S. F., Rae, J. G. L., Roberts, M. J., Scaife, A. A., Schiemann, R., Storkey, D., Thorpe, L., Watterson, I. G., Walters, D. N., West, A., Wood, R. A., Woollings, T., and Xavier, P. K. (2017). The Met Office Global Coupled model 3.0 and 3.1 (GC3.0 and GC3.1) configurations. J. Adv. Modeling Earth Sys., 10, 357– 380, doi:10.1002/2017MS001115, 2017. Wilson, D. R. and Ballard, S. P.: A microphysically based precipitation scheme for the UK Meteorological Office Unified Model, Q. J. Roy. Meteorol. Soc., 125, 1607–1636, doi:10.1002/qj.49712555707, 1999. Wood, N., Staniforth, A., White, A., Allen, T., Diamantakis, M., Gross, M., Melvin, T., Smith, C., Vosper, S., Zerroukat, M., and Thuburn, J.: An inherently mass-conserving semi-implicit semi-Lagrangian discretization of the deep-atmosphere global nonhydrostatic equations, Q. J. Roy. Meteorol. Soc., 140, 1505–1520, doi:10.1002/qj.2235, 2014. Wood, N., Staniforth, A., White, A., Allen, T., Diamantakis, M., Gross, M., Melvin, T., Smith, C., Vosper, S., Zerroukat, M., and Thuburn, J.: An inherently mass-conserving semi-implicit semi-Lagrangian discretization of the deep-atmosphere global nonhydrostatic equations, Q. J. Roy. Meteorol. Soc., 140, 1505–1520, doi:10.1002/qj.2235, 2014.