The UM (Davies et al., 2005; version 10.6) is the grid-point atmospheric model used in BARRA and ACCESS. 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 (SL), 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 grid (Charney and Phillips, 1953). The BARRA-C model has a horizontal spacing of 0.0135∘ × 0.0135∘ (about 1.5 km at the Equator), and its vertical levels follow the modelled orography at the surface and relax to surfaces of uniform radial height after 62 model levels (∼ 17 km above ground) in the upper atmosphere, with the model top height at 40 km. At this resolution, the model is run with an integration time step of 60 s.

The science configuration of the model in BARRA-C is based on the UK Met Office operational suite OS36, while BARRA-R is based on the Global Atmosphere (GA6) configuration of Walters et al. (2017). While the OS36 model configurations preceded the release of the first UM Regional Atmosphere and Land (RAL1) configuration of Bush et al. (2020), BARRA-C implements some of the improvements from RAL1. Table 1 summarizes the differences between BARRA-C, BARRA-R, and RAL1. The physical parameterization schemes common to BARRA-C and BARRA-R include a variant of Wilson and Ballard (1999) for mixed-phase cloud microphysics, the large-scale cloud scheme of Smith (1990), and the radiation scheme of Edwards and Slingo (1996), all of which have been improved since publication. BARRA-R uses a convection parameterization scheme based on Gregory and Rowntree (1990), which is not used in BARRA-C. With a grid length of 1.5 km, the horizontal grid length approaches the depth of the boundary layer (Hanley et al., 2015), and as such it is no longer appropriate to use the 1D boundary layer parameterization that restricts mixing to the vertical. BARRA-C therefore uses a blended boundary layer parameterization (Boutle et al., 2014b) whereby the scheme transitions from the 1D vertical turbulence scheme of Lock et al. (2000) to a 3D subgrid turbulence scheme based on Smagorinsky (1963) as a function of the grid length to the turbulent length scale (Halliwell et al., 2007). The mixing length, which can be tuned to control the smoothness of the fields and the number of small cells, is taken as 300 m, which is used in operational systems.

The cloud scheme uses a profile of critical relative humidity values (RHcrit), above which a grid box contains some cloud if the relative humidity is exceeded. Based on the assumption that there should be less subgrid variability in humidity for smaller grid boxes, BARRA-C uses higher RHcrit values that BARRA-R in the lowest few layers, decreasing smoothly above to 0.8.

Without the convection parameterization scheme, BARRA-C relies on the model dynamics to represent convective motions. While convection remains unresolved in 1.5 km models, removal of the cumulus parameterization has been shown to result in more realistic behaviour (Clark et al., 2016). In particular, the model can explicitly capture processes with convective-like characteristics, which can subsequently drive scales that the model can properly resolve. BARRA-C also reduces the appearance of unrealistically strong vertical velocities and “grid-point storms” seen in BARRA-R due to the inability of convective parameterization to stabilize the air column (Su et al., 2019). Nevertheless, convection can remain under-resolved, leading to cases of small, shallow showers that are too early or no rain at all. The midlatitude version of RAL1 therefore includes stochastic perturbations of temperature and moisture as well as relatively weak turbulent mixing to encourage the model fields to be less uniform and help convection to initiate. BARRA-C does not use stochastic perturbations for moisture and may thus still suffer from convection initiation issues.

Another distinguishing feature of BARRA-C is the handling of mass conservation during the advection of moisture prognostic variables. This is one of the key science developments in RAL1. BARRA-C and RAL1 use the zero lateral flux scheme of Zerroukat and Shipway (2017) for moisture conservation at the model's lateral boundaries, avoiding spurious extreme precipitation caused by the SL treatment of moisture variables near partially resolved convection.

BARRA-C is missing some of the configuration improvements introduced in RAL1 because production runs had already commenced. BARRA-C does not include a set of changes to the representation of the land surface and the canopy radiation model, which improve the damped diurnal cycle issue in near-surface temperatures. BARRA-C also does not benefit from the improved treatment of gaseous absorption in both longwave and shortwave regimes in GA7 and RAL1, which improves interaction with band-by-band aerosol and cloud forcing.

BARRA uses the land surface 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 with subsurface temperature updated using a heat diffusion equation and with vertical moisture flux estimated using the Richard's equation and Darcy's law. The soil hydraulics are computed using the van Genuchten equation. 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). Urban surfaces are represented only by a single urban tile such that street canyons and roofs are not distinguished.

The characteristics of the lower boundary, climatological fields, and natural and anthropogenic emissions are specified using static ancillary fields. These are created as per Bush et al. (2020; Table A1), with the exception of ancillaries for the land–sea mask, canopy tree heights, and land usage. The land–sea mask is created from the 1 km resolution International Geosphere–Biosphere Programme (IGBP) land cover data (Loveland et al., 2000) for SY and TA and from Shuttle Radar Topography Mission (SRTM) orography data for AD and PH. Land cover data based on the Climate Change Initiative (CCI; Hartley et al., 2017) are not adopted here as their mapping to the nine land surface tiles over the Australian region remains untested. The canopy tree heights are derived from satellite light detection and ranging (lidar; Simard et al., 2011; Dharssi et al., 2015). The land usage ancillary, created from IGBP, is modified for AD and PH to match the water fractions in the Water Observations from Space (WOfS; Mueller et al., 2016). Aerosol absorption and scattering in the radiation scheme assume climatological aerosol properties. A climatological ozone field is also prescribed.

The BARRA-C model hindcast is re-initialized with 6-hourly initial conditions at the synoptic hours t0 = 00:00, 06:00, 12:00, and 18:00 UTC created by downscaling from BARRA-R analyses (Fig. S2 in the Supplement). These fields are taken from the centre of BARRA-R's 6 h analysis windows. A two-component reconfiguration approach is used in which BARRA-R winds, moisture, and temperature are downscaled separately with different resolution topography sets to remove model instability due to large horizontal topography gradients. BARRA-C is further constrained by BARRA-R at the lateral boundaries without nudging based on the prescription described in Bush et al. (2020) and a boundary rim width of 0.34∘. The boundary conditions force the development of the larger-scale features within the BARRA-C domains. These setups follow the Bureau's NWP system and ensure that the benefits of the BARRA-R analysis are inherited by BARRA-C, wherein the nested model is treated as a physically consistent interpolator of the driving model.

The JULES soil moisture and temperature are prescribed by BARRA-R. Consistent with BARRA-R, daily sea surface temperature and sea ice 0.05∘ × 0.05∘ analysis from reprocessed (1985–2007; Roberts-Jones et al., 2012) and near-real-time Operational Sea Surface Temperature and Ice Analysis (OSTIA; Donlon et al., 2012) are used as lower boundaries over the water after being interpolated to the BARRA-C grid. The near-real-time data are used from January 2007.

Each hindcast in BARRA-C is a 9 h simulation but only 6 h are used. The model data during the first 3 h are discarded as the fine detail is only partially established from the coarse-resolution initial conditions due to model spin-up. Therefore, the hindcast fields between t0 + 4 h and t0 + 9 h form the BARRA-C datasets. Such a hindcast length is considered short but is chosen to meet computational constraints when regular re-initialization is needed for running the model for such an extended period. One clear limitation of our setup is that model spin-up artefacts are expected to still be present, particularly for convective clouds and rain.

The t0 + 6 h model hindcasts of screen-level temperature, 10 m wind speed, and surface pressure are evaluated against land station observations during the 2010–2012 period, following the approach of Su et al. (2019). These observations have no direct relation to BARRA-C, since there is no analysis in BARRA-C, and they are not used in the associated BARRA-R cycle t0. These fields are interpolated from the model levels using surface similarity theory (Walters et al., 2017). Our benchmarks include BARRA-R and ERA-Interim t0 + 6 h hindcasts, the MERRA-2 hourly time-averaged hindcast fields, and the ERA5 hourly analysis. The models are interpolated to be coincident with the observed locations and times. As the observations are irregularly distributed in time, all observations within a t0 + 5 to t0 + 7 h time window for t0 = 00:00 and 12:00 UTC are considered. The root mean square difference (RMSD), Pearson's linear correlation, additive bias, and variance bias are calculated at each station between observed (do) and model (dm) data. Additive bias is defined as Bias =E(dm)-E(do), where E(⋅) is the expectation operator, and the variance bias as Mbias=var(dm)var(do)-1 so as to capture differences in the dispersion, where var(⋅) computes the variance in time. This assessment does not serve to provide information on the true quality of the various reanalyses at their native resolutions; rather, it indicates whether the models contain finer-scale information captured by point measurements. Based on Di Luca et al. (2016), we distinguish three distinct regions with characteristics of complex topography (stations with an elevation higher than 500 m – topo), land–sea contrasts (stations that are within 1.5∘ of the coast – coast), or relatively smooth terrain (stations far from the coast – flat) (Fig. S3 in the Supplement).

The comparisons of scores across all BARRA-C domains are shown in Fig. 2. For temperature, BARRA (i.e. BARRA-R and BARRA-C) and ERA5 show better agreement with the station data than the other coarser reanalyses for most metrics. For instance, BARRA-C shows lower RMSD than ERA-Interim at 80 % of stations. BARRA shows greater contrast from the global reanalyses than between them. ERA5 shows a warm (additive) bias, while BARRA appears cooler. ERA-Interim and ERA5 generally show less variability in temperature than observations (Mbias < 0), while the other models tend to have more similar temperature variability with observations. This is related to the cold bias in ERA during high temperature (shown in the next section). On average, BARRA scores lower for RMSD than ERA5 at elevated stations (e.g. Snowy Mountains in SY) and smaller for Mbias at near-coast stations. In general, BARRA-C shows more visible improvements to BARRA-R at stations near coasts or over complex topography in terms of RMSD, correlation, and Mbias (Fig. S3 in the Supplement). Consequently, BARRA-TA scores higher than BARRA-R on average. However, BARRA-C shows higher RMSD in the flat regions than in the other regions, unlike the other reanalyses. The degradation is small (within 0.6 K in terms of RMSD), and for AD, this is related to overdispersion (Mbias > 1).

For 10 m wind speed, BARRA-C, BARRA-R, and ERA5 similarly exhibit lower RMSD and higher correlation with the station data than the other global reanalyses, and the differences between these three models are not pronounced. BARRA's largest enhancement to ERA-Interim is found at elevated stations and near coasts, benefitting Tasmania particularly. Contrasting with BARRA-R, BARRA-C tends to show lower RMSD at these stations (Fig. S3, Supplement), and while we observe higher RMSD in BARRA-C, the difference is within 1 ms-1. The wind estimated by all the models tends to be underdispersed (Mbias < 1), relating to a positive (negative) bias during light (strong) wind conditions. Such a model underdispersion is more striking in the TA and SY domains than in the other domains as well as over coastal regions.

For surface pressure, the higher-resolution models, including ERA5, show markedly lower RMSD near coasts. There is very good agreement between ERA5 and the observations. BARRA-C shows some improvements over BARRA-R in correlation and Mbias as well as over coastal regions and mountains.

The reanalyses are compared against a gridded daily 0.05∘ × 0.05∘ analysis of observed maximum and minimum screen temperature from the Australian Water Availability Project (AWAP; Jones et al., 2009) in Fig. 3. BARRA outperforms the driving model ERA-Interim in reducing the cold (warm) bias during summer DJF (winter JJA), particularly over the SY and TA domains. BARRA-C shows a smaller extent of summer cold bias in daily maximum temperature over the Great Dividing Range than both BARRA-R and ERA5, but it shares a similar bias with BARRA-R elsewhere. BARRA and the global reanalyses also exhibit a considerable warm bias in the northwest of the AD domain, the Nullarbor Plain, but this is likely an artefact of the AWAP station density and is discussed later.

The warm bias in daily minimum temperature in winter is also similar between BARRA-C and BARRA-R. BARRA-C has largely inherited the biases from BARRA-R but with small local-scale differences. Despite such similarities in summer bias, there are more hot days (i.e. days exceeding 35 ∘C or 308.15 K) in Fig. 3c in BARRA-C than in BARRA-R over inland Australia. By contrast, the summer cold temperature bias in both ERA reanalyses is also reflected by fewer hot days and vice versa for MERRA-2. Further analysis of the temperature extremes is considered in Sect. 3.6.

Figures 4 and 5 examine the inter-seasonal and inter-annual variations in temperature bias with respect to AWAP for daily maximum and minimum temperature, respectively. They are similar between BARRA-C and BARRA-R, with BARRA-C showing slightly wider inter-seasonal variability. The inter-seasonal range of bias in BARRA is around 2 K, which is similar to ERA-Interim and MERRA-2 in most domains but is larger than ERA5 with the exception of TA. For AD and PH, the daily maximum temperature is positively biased during summer months (DJF) and is negatively biased during winter (JJA). The negative bias in daily maximum temperature is smallest during summer for SY and TA and is largest during winter for SY. For daily minimum temperature these are reversed; e.g. the associated positive bias peaks during winter for AD, PH, and SY, and the negative bias is maximum during summer for AD and PH.

There is both inter-annual variability and a trend of the temperature biases in BARRA. For daily maximum temperature bias, there is a cooling trend in AD and PH and a warming trend in TA. These trends can also be seen in ERA5 and MERRA-2. For daily minimum temperature bias, trends in BARRA are less apparent than in ERA5 and MERRA-2. We also observe in the TA domain that BARRA shows a small warming trend with respect to AWAP.

This analysis of variability in the bias is repeated for the standard deviation of the modelled temperature and AWAP in Figs. S4 and S5 in the Supplement. BARRA-C shows a slightly wider dispersion of daily maximum temperatures than AWAP (by 0.4 K) and BARRA-R (by 0.1 K), with the exception of the TA domain. For BARRA-TA, the standard deviation of BARRA is similar to AWAP and is higher than the global reanalyses. For daily minimum temperature, both BARRA versions are similar and generally underdispersed by 0.3 K compared to AWAP.

Hourly modelled precipitation from BARRA and ERA5 is compared against observations from 27 rain gauges within a 1∘ radius around Sydney during the warmer months (NDJF) in 2008–2013 in Fig. 6. During these months convection processes dominate and can produce a distinct diurnal distribution in thunderstorm activity. The greatest frequency of severe thunderstorms occurs in November and December (Griffiths et al., 1993). ERA5 and to a lesser extent BARRA-R both underestimate the frequency of heavy rain rates > 8 mmh-1. By contrast, BARRA-C underestimates the frequency of light rain rate and overestimates heavy rates. BARRA and ERA5 also distribute rainfall differently over a day. BARRA-C shows a bimodal distribution similar to the observations despite showing too much rain leading up to the 06:00 UTC peak and too little rain during the daily minimum around 18:00 UTC. BARRA-R shows less diurnal variation in rainfall with too much rain distributed during 00:00–06:00 UTC, whereas ERA5 shows a pronounced early timing bias.

Figure 7a compares the modelled precipitation against the daily rain-gauge analysis from AWAP including MERRA-2's hourly time-averaged precipitation (PRECTOTCORR) product. BARRA-C shows a wet bias over the Great Dividing Range and the southeast area of the AD domain but improves the dry bias in BARRA-R and ERA reanalyses over the eastern and western seaboards and the Fleurieu and Yorke peninsulas of South Australia. BARRA-C also shows dry biases on the western borders of the AD and SY domains, possibly due to inconsistencies with the zero lateral moisture mass flux on the boundary conditions (Sect. 2.1). A striking difference between BARRA and the global reanalyses is over western Tasmania where the latter displays a dry bias.

In Fig. 7b, BARRA-R, ERA5, and ERA-Interim show too few heavy rain days (> 10 mmd-1) over the coastlines, SA peninsula, and western Tasmania. BARRA-C improves on this but generally simulates more heavy rain days than other reanalyses and too few moderate–light rain days (< 10 mmd-1, not shown) in all domains. BARRA-R and MERRA-2 generally show too many light rain days, and the ERA reanalyses show too many light rain days in SY and eastern Tasmania and too few in AD, PH, and western Tasmania.

The inter-seasonal and inter-annual variations in precipitation bias with respect to AWAP are plotted in Fig. 8. As with temperature (Fig. 4), they are similar between BARRA-R and BARRA-C, although the latter shows a larger range in all BARRA-C domains except TA. In particular, a wet bias is generally observed during the wet season (JJA for AD, DJF for PH), wetter months (JJA for TA), and thunderstorm season (DJF for SY). A dry bias generally occurs during the dry season or drier months, i.e. SON for AD, PH and TA. This is consistent with the tendency of BARRA-C to overestimate heavy rain rates and underestimate light rain occurrence. Some of the inter-annual variations in bias are clearly common amongst BARRA and the global reanalyses, e.g. in AD and PH domains during the Millennium Drought (1996–2009) when the various models share a dry bias. BARRA also shows different trends to the global reanalyses. There is a wetting trend post-2009 for BARRA in AD, but this is opposite for the other models. In SY, BARRA also displays a wetting trend, while ERA trends are drier.

It should, however, be noted that, as is often found for gridded interpolated data, AWAP tends to underestimate the intensity of extreme rainfall events and overestimate the frequency and intensity of low rainfall events (King et al., 2012). The errors are larger at high elevations (SY and TA) where gauges are fewer, when there is frozen precipitation, and/or topography is exposed to prevailing winds (Chubb et al., 2016).

The point gauge-based assessment in Sect. 3.3 is harsher to higher-resolution models than coarser models due to the compound error of space and time near-misses, which increases as the grid cells shrink. Therefore, we compare the simulated rainfall from BARRA-SY with the Bureau's radar nowcasting rainfall product (Rainfields2; Seed et al., 2007) and use the fractions skill score (FSS) to allow assessment at different spatial scales following the approach described in Roberts and Lean (2007), Jermey and Renshaw (2016), and Acharya et al. (2020). The FSS provides an evaluation of the rainfall skill as a function of spatial resolution. The radar product, blended with gauge observations using conditional merging (Sinclair and Pegram, 2005), is available from 2014 onwards on a mosaic grid consisting of the domains of multiple radars. Following Acharya et al. (2020), the largest 36 storm events during 2014–2016 are selected based on domain-averaged daily precipitation.

FSS is categorized as a “neighbourhood verification” metric (Ebert, 2009) in which fractional coverages of grid cells close to observations are valued equally. The FSS tallies the relative number of “hits” between the model and the observation at different spatial scales and different rain thresholds. An FSS of 1 represents a perfect forecast wherein the number of cells with precipitation above a threshold within a neighbourhood is identical between the model and observation grids for all possible neighbourhoods. Here, BARRA hourly rain rates are regridded to the radar grid of 1.5 km, and the accumulated rain amounts over moving 6 h windows are analysed. From the 36 multi-day storm event set, 1323 different 6 h events are produced using a moving window. FSS is computed for each 6 h event for each model, and then the scores are aggregated to give an average for all events. Given that inherent bias between the observation and the models exists due to differences in their representativity, and also to focus on the spatial accuracy of the models, we use percentile-based thresholds computed across all the storm events. This ensures that the model and observed rain fields have an identical fraction of rain events for each threshold value (explained further in Sect. E in the Supplement). Figure 9 illustrates the striking differences between BARRA-R and BARRA-SY for five events in 2014. BARRA-SY can show more realistic organization in the 1.5 km model owing to the explicit modelling of convection and can produce higher rainfall intensity. The event on 7 December 2014 in Fig. 9v illustrates a summer storm case in which BARRA-R shows rainfall accumulation lacking the spatial pattern common to convective organization that is evident in BARRA-SY and in observations. BARRA-R also shows excessive grid-point precipitation over the mountains, which is absent in both observations and BARRA-SY. At the same time, BARRA-SY can show too many cells (Fig. 9ii), which can produce streaks of light rainfall (Fig. 9iv).

The FSS results in Fig. 10 show that BARRA-SY is more skilful over all scales than BARRA-R for all threshold levels. FSSuniform is the FSS of a forecast field with a uniform fractional coverage equal to the fraction of points observed with any rain (> 0.2 mmh-1). Scores greater than FSSuniform are considered skilful. For the lowest threshold (56 %, i.e. 4 mm in the observed radar values), the uniform score (FSSuniform) is reached at scales of 0.3∘ (BARRA-SY) and 0.65∘ (BARRA-R). At the highest threshold (99.9 %, 64 mm), the uniform score is reached at scales of 2.4∘ and 3.35∘, respectively. The contrast between the two BARRA FSSs is therefore greater at the higher precipitation thresholds. FSSs for higher rainfall thresholds are also generally lower as the area of rain being sampled becomes more localized and is more challenging to reproduce correctly in the models.

We apply an approach similar to Di Luca et al. (2015) to quantify the added value (AV) in the representation of climatological extremes from BARRA-C by comparing its skill to the skill in BARRA-R. The warm extremes of daily maximum temperature, the cold extremes of daily minimum temperature, and the wet extremes of daily precipitation are assessed against AWAP. The statistics for extremes (X) are given by the percentiles of the daily temperature and precipitation values over the 29-year time period. We use AVd=[d(XBARRA-R,XAWAP)-d(XBARRA-C,XAWAP)]/[d(XBARRA-R,XAWAP)+d(XBARRA-C,XAWAP)] from Di Luca et al. (2016), where d defines a distance metric between the model-derived and AWAP-derived statistics computed across the grid cells. To capture both the total errors and spatial patterns of the statistics, we let d≡MSE(A,B) = E(A-B)2 to define the mean squared error and also use d≡Corr(A,B) = 1-R(A,B), with R as Pearson's correlation. Larger positive AV values suggest smaller errors in BARRA-C than in BARRA-R and thus substantial added value by the downscaling of BARRA-R.

Figure 11 plots AV scores for different BARRA-C domains, showing that AV is not gained consistently across the percentiles, variables, and domains. For warm extremes of daily maximum temperature, BARRA-C shows positive AVMSE over BARRA-R in the TA and AD domains. Low or negative AVMSE for AD, PH, and SY (inland region) is mainly due to the warm and wet bias in BARRA-C seen in Figs. 3c and 6a and b. The positive AVCorr indicates that BARRA-C captures the spatial patterns of the warm extremes across the domains, particularly over the coastal and high-topography regions (see also Fig. S6 in the Supplement).

For cold extremes in Fig. 11b, BARRA-C still shows positive AVMSE over all domains except SY. This AV is mostly contributed by coastal regions, as seen in Fig. S6. Negative AVMSE in SY is related to warmer cold extremes, particularly over the Great Dividing Range. Positive AVCorr is seen in TA but not in the other domains. However, it should be noted that the BARRA versions are generally strongly correlated with AWAP, with R mostly between 0.7 and 0.9.

AV from BARRA-C for wet extremes of precipitation relates more to the spatial patterns of the extremes (Fig. 11c). There is negative AVMSE for all domains except TA, which remains near zero, highlighting the BARRA-C rainfall bias. On the other hand, the AVCorr is positive for all domains except AD for the three highest rainfall percentiles, which indicates better spatial correlation of rainfall than BARRA-R.