On the role of land-surface hydrology schemes in simulating the daily maximum and minimum air temperatures of Australia using a regional climate model (RegCM4)

The climate system comprises several interactive components such as the atmosphere, biosphere, hydrosphere, cryosphere, and geosphere. These components interact with each other on multiple time scales ranging from days, seasons, and years to millennia with complex feedback mechanisms. In particular, studying the hydrological cycle is important because of the considerable influence of climate change on the water cycle budget such as precipitation, soil moisture, surface and sub-surface runoff, and evapotranspiration (Bouraoui et al. 2004; Imbach et al. 2012; Allan et al. 2020). In return, the hydrological cycle affects the climate system through the transfer of water vapor to the atmosphere. Concerning soil moisture, the hydrological cycle can be also examined through changes in the balance between the total precipitation as an input, runoff, and total evapotranspiration as an output (Peng et al. 2019; Pereira et al. 2020). Moreover, there is a direct link between the hydrology cycle and the surface energy balance and eventually to the surface climate, because solar radiation drives the vertical transfer of water vapor from the earth's surface to the atmosphere via evaporation from bare soil and transpiration from vegetation (Siler et al. 2018).

Due to the significance of the land surface condition in modeling the regional surface climate; several researches discussed the comparison between various land surface model versions. In terms of reproducing mean air temperature and total surface precipitation, the Community Land Model version 3.5 (CLM3.5; Oleson et al. 2008) outperforms the Biosphere-Atmosphere Transfer System (BATS; Dickinson et al. 1993) as reported by Steiner et al. (2009), Wang et al. (2015), and Maurya et al. (2017). Additionally, when it comes to modeling mean air temperature and total precipitation, the Community land model version 4.5 (CLM4.5; Oleson et al. 2013) performs better than the BATS scheme (Maurya et al. 2017; Chung et al. 2018).

Soil moisture plays an important role in controlling the climate system, particularly in semi-arid and arid regions, which account for 40% of the world's area (Reynolds et al. 2007). Such importance is given to investigating the factors that control soil moisture variability (Srivastava et al. 2021a). Furthermore, soil moisture derives from physiological and biogeochemical processes such as plant transpiration and photosynthesis (Seneviratne et al. 2010; Lemoine & Budny 2022). Offline land-surface (or regional climate models; RCMs) are considered important tools to investigate the response of surface climate/terrestrial carbon fluxes to soil moisture changes. For instance, Lei et al. (2014) used the offline community land model version 4 (CLM4.0; Oleson et al. 2010) to examine the role of soil moisture changes (represented by the land-surface hydrology scheme) on the terrestrial gross primary production (GPP). They reported that soil moisture considerably affects the GPP through its influence on photosynthesis and leaf area index.

Concerning surface energy balance and surface climate, RCMs were used to address this issue. For example, Anwar et al. (2019) examined the influence of two land-surface hydrology schemes (TOP and VIC) on the surface climate over Africa using version four of RegCM (RegCM4; Giorgi et al. 2012). They found that the VIC scheme outperforms the TOP scheme compared with reanalysis products despite noted biases in the summer season. In one of the recent studies, Anwar et al. (2022) examined the influence of soil moisture changes on the surface energy balance and climate of the Amazon (AMZ) using RegCM4. They found that the VIC scheme underestimates (overestimates) latent heat (sensible heat) fluxes more than TOP. In addition, the VIC showed a high warm bias than the one observed in the TOP scheme. Moreover, no scheme performs better than the other with respect to reanalysis products. Wang et al. (2021) examined the mutual interaction between different convection and land-surface hydrology schemes in simulating the Tibetan Plateau (TP) climate using the RegCM4 model. They showed that the VIC land-surface hydrology scheme reproduces the climate aspects more than TOP. Anwar & Diallo (2021) showed that RegCM4 model bias is amplified through the influence of soil moisture on vegetation status (e.g., Leaf Area Index; LAI).

Until now, the sensitivity of the Australian's daily maximum and minimum air temperatures to varied land-surface/soil moisture variations has received less attention when using a regional climate model (e.g., RegCM4); the target of our study is to address the following points:

• 1.

  Evaluate the potential influence of soil moisture changes on Australia's maximum and minimum air temperatures using the regional climate model (RegCM4).

• 2.

  Compare the results of this work to those of Anwar et al. (2019, 2022) and Wang et al. (2021) to investigate how the climatic regime affects the action mechanism of the land-surface hydrological scheme.

Section 2 describes the study area, data, and methods used, while Section 3 provides the results of simulations. Section 4 provides the discussion; while Section 5 shows the final conclusion and future recommendations.

The present study used the International Center of Theoretical Physics (ICTP) regional climate model version 4.7 (RegCM-4.7.0; hereafter RegCM4; Giorgi et al. 2012). Giorgi et al. (2012) reported that the RegCM4 model performance shows a remarkable improvement in comparison with previous versions: RegCM version 3 (RegCM3; Pal et al. 2007). In addition, RegCM4 includes a tropical band mode (RegT-Band; Coppola et al. 2012) to simulate the tropical climate processes (e.g., El Niño-Southern Oscillation; ENSO). Besides, it includes an interactive chemistry module, online gas-phase chemistry scheme (Shalaby et al. 2012). The performance of the RegCM4 was extensively evaluated using different physics configurations across the globe: South America (Llopart et al. 2017), Southeast Asia (Chung et al. 2018; Zhengqi et al. 2020), and Tibetan Plateau (Gu & Wang 2020) and India (Verma & Bhatla 2021). Recently, the RegCM4 has been upgraded to work as an earth system model (RegCM4-ES; Reale et al. 2020) with implementation of a non-hydrostatic dynamical core (Coppola et al. 2021).

In this study, the following physical schemes were used: (1) Rapid Radiation Transfer Model (RRTM; Clough et al. 2005) as the radiation scheme, (2) local prognostic 1.5-order scheme of the University of Washington (UW; Grenier & Bretherton 2001) as the boundary layer scheme; and (3) convection scheme of Emanuel (1991) over land and ocean. It is worth noting that the community land model version 5 (CLM5.0; Lawrence et al. 2019) has not been yet coupled to the RegCM4. In the present study, the CLM4.5 was used as the land surface model for several reasons such as: (1) the CLM4.5 offers substantial improvements such as: a revised canopy radiation scheme, canopy scaling of leaf processes, and reducing the excessive tropical GPP (Bonan et al. 2011) and (2) in previous studies, the CLM4.5 land model was used to investigate how changes in soil moisture affected surface climate simulations of the surface climate over various geographic locations (Anwar et al. 2019, 2022).

Australia continent's climate is characterized by a large spatial variability owing to its geographical nature. The majority of its land is classified as arid to semi-arid. An important future of Australia is its relatively flat landscape. Along the coastal strip, the climate regime shows a considerable gradient from humid-subtropical at its north to temperate at its south (Peel et al. 2007). An important feature of Northern Australia is its tropical savanna climate with a monsoonal wet season. Specht (1970) reported that land unit/vegetation of the Australian continent can be categorized as closed-forest, open-forest, woodland, shrub-land, scrub, health, and herbland. The RegCM4 model domain was customized with 25°S latitude, 132°E longitude, 60 km horizontal grid spacing and 80 grid points in the zonal direction, and 60 grid points in the meridional direction and 18 vertical levels.

Note that, elevation above 1,200 m will not appear (see Figure 1) because of using quite coarse resolution (60 km). Two 13-year simulations were conducted and integrated from 01 January 1998 till 31 December 2010. This period was chosen based on availability of the computational power at the time of conducting the RegCM4 simulations. The first two years of each simulation were not considered in the analysis to allow equilibration of the soil moisture (Anwar & Diallo 2022). Therefore, the actual period of analysis starts at 01 January 2000 till 31 December 2010. The two simulations were driven by the National Centre for Environmental Prediction/National Centre for Atmospheric Reanalysis version 2 of 1.8 × 1.8 degrees (NCEP/NCAR-R2; Kanamitsu et al. 2002) and the National Oceanic and Atmospheric Administration (NOAA) Optimum Interpolation Sea Surface Temperature of 1 × 1 degrees (OISST) (Reynolds et al. 2002). It is worth mentioning that the utilized version of the RegCM4 does not support ERA5 as a lateral boundary condition. Also, sensitivity of RegCM4 to atmospheric forcing (and its horizontal resolution) is beyond the scope of the present study.

The two simulations considered the satellite phenology mode (SP; Lawrence et al. 2011). In this mode, the vegetation parameters are prescribed from the Moderate Resolution Imaging Spectroradiometer dataset (MODIS; Lawrence & Chase 2007). Besides, soil moisture changes and climate variability do not affect the vegetation status (Anwar 2019). The first simulation was referred to as TOP (control simulation), while the other simulation was designated as VIC (test simulation). The difference between each simulation and the observational dataset was referred to as TOP-OBS for the TOP simulation, VIC-OBS for the VIC simulation, and the difference between the two simulations themselves (Diff). OBS is the reanalysis product (with which RegCM4 simulations were compared).

To assess the RegCM4 model performance, various datasets were utilized. For instance, the ERA5 reanalysis product was used to evaluate the simulated surface net radiation, total cloud cover (TCC), sensible, and latent heat fluxes. ERA5 was used in the present study because of its higher temporal and spatial resolutions (0.25° horizontal grid spacing; Hersbach et al. 2020; Tarek et al. 2020). Also, it was used to evaluate the RegCM4 performance over the AMZ (Anwar et al. 2022). ERA5-land product was used to evaluate the simulated sensible heat flux as an example and it is available at 0.1° grid spacing (Muñoz-Sabater et al. 2021). Another reason to use the ERA5-land product is to check whether the simulated sensible heat flux is sensitive to the spatial resolution of the ERA5/ERA5-land reanalysis product. To account for the uncertainty associated with the observational dataset, the simulated daily maximum and minimum air temperatures were compared with two reanalysis products. The first one is the NOAA-CIRES-DOE 20th Century Reanalysis V3 (20CRv3; Compo et al. 2011; for short Century).

Century is available at 1° × 1° global grid and it covers the period of January 1836 to December 2015. In addition, it reasonably produces good estimates of the atmospheric variables throughout the 20th Century. Moreover, it has a good performance on long time scales (e.g., climate scale; Slivinski et al. 2019). The second product is the Australian Gridded Climate Data (AGCD; Jones et al. 2009). AGCD is the Bureau of Meteorology's official dataset, and it offers a high-quality set of historical and ongoing real-time climate analyses for Australia for the meteorological variables: rainfall, temperature (maximum and minimum), as well as vapor pressure at daily and monthly timescales. Also, it is available in 0.05° horizontal grid spacing and it covers the period of 1910–2020.

It is worth mentioning that the accuracy of AGCD is limited to regions with a low density of stations. To evaluate the RegCM4 model performance in simulating the monthly and climatological seasonal cycle of the mean air temperature (TMP) over the period 2000–2010, station data of the Ozflux network were used (Srivastava et al. 2021b). To serve the purpose of the present study, the time period of 2000–2010 was chosen for the ERA5, ERA5-land, Century, and AGCD. Moreover, the aforementioned reanalysis products were bi-linearly interpolated on the RegCM4-CLM4.5 curvilinear grid (Anwar & Diallo 2022).

In this section, two steps were carried out to analyze the role of land-surface hydrology schemes in controlling the maximum, minimum, and mean air temperatures of Australia following Anwar et al. (2022). Firstly, a diagnostic analysis was applied to the following variables: infiltration rate (QINFL), soil moisture of depth of 10 cm (SM10), vegetation transpiration (QVEGT), vegetation evaporation (QVEGE), soil evaporation (QSOIL), and solar radiation absorbed by vegetation and ground (SABV and SABG), sensible heat emitted from the ground and vegetation (SHG and SHV), as well as relative humidity (RH). Such analysis was performed by calculating the significant difference between the two simulations using the Student's t-test with a significance level of 0.05 (α = 5%).

These variables were retrieved from the CLM4.5 output results using the clm4.5_1dto2d program of the RegCM4. The second step considers a quantitative evaluation of the surface net radiation (R_n), TCC, surface energy balance (sensible and latent heat fluxes; SHF and LE), and maximum and minimum air temperatures (TMX and TMN) with respect to ERA5/ERA5-land, Century, and AGCD reanalysis products. The significant bias/difference was also calculated using a Student's t-test always with a significance level of 0.05 (α = 5%).

Therefore, the two simulations show a quite similar pattern of SM10 (Figure 2(d) and 2(e)), and the difference between them ranges from 2 to 4 mm approaching 6 mm over Western Australia and coastal regions (Figure 2(f)). The noted behavior of the QINFL and SM10 is reflected in partitioning the total ET budget. For instance, the two simulations show a quite similar pattern of the QVEGT (Figure 2(g) and 2(h)) and the difference between them is around 0.2–0.4 mm day⁻¹ (Figure 2(i)). For the QVEGE, the two simulations match each other (Figure 2(j) and 2(k)) and there is no obvious difference between them (Figure 2(l)). The situation for the QSOIL is quite different (Figure 2(m) and 2(n)) because the difference between them is ranging between 0.4 and 0.8 mm day⁻¹ approaching 1 mm day⁻¹ over all the continent except for the Northern part of West Australia where the difference ranges from −0.2 to −0.4 mm day⁻¹ (Figure 2(o)).

Furthermore, it can be observed that the VIC has a higher LE than the TOP by 5–15 W m⁻² over the Northern Territory and it is small elsewhere. In the austral JJA and SON seasons, both simulations exhibit a negative bias of 10–25 W m⁻² over all the continent except for Western Australia which has a positive bias of 20–30 W m⁻² (Figure 4(g)–4(k) and 4(m)–4(q)). Also, the VIC differs from the TOP by 5–15 W m⁻² over the same region in the austral JJA season (Figure 4(l)) and by 5–10 W m⁻² in the lower region of Western Australia in the austral SON season (Figure 4(r)).

In addition, both simulations exhibit a positive bias of 15–50 W m⁻² over the majority regions and by ∼60 W m⁻² over Western Australia (Figure 4(s)–4(w)). Quantitatively, VIC has a higher LE than TOP by 5–25 W m⁻² over the Continent except for the Western region (Figure 4(x)). It can be noticed that the influence of soil moisture changes depends on the region of interest as well as the austral season. Also, the noted positive bias of the LE (in the austral MAM and DJF seasons) can be attributed to the fact that the VIC has higher soil evaporation than the TOP (Section 3.1).

This negative bias can be attributed to the fact that the RegCM4 shows the same bias order of magnitude with opposite signs in simulating the LE. In addition, VIC has a lower SHF than TOP by 10–15 W m⁻² (Figure 5(x)). From Figures 4 and 5, it can be observed that there is no obvious difference between the two simulations (except for some regions), suggesting that soil moisture changes do not determine the surface energy budget. However, there are some differences between the two simulations themselves; which vary with the region under investigation as well as the austral season. In comparison with the ERA5-land, the RegCM4 shows a bias similar to the one observed using ERA5 (Supplementary Figure S2). Such a comparison suggests that the spatial resolution of the observed data does not considerably affect the model bias. Therefore, observed data with quite coarse resolution can be used to evaluate the model performance.

In the DJF austral season, the RegCM4 model bias is restricted to the coastal regions of the Northern Territory and Victoria. Furthermore, the negative bias noted in the MAM, JJA, and SON austral seasons is quite shrunk (Figure 6(v) and 6(w)). Moreover, there is no notable difference between the two simulations themselves (Figure 6(x)). The quite small difference of the two simulations either with respect to the ERA5 or between themselves can be attributed to the small difference between the two simulations regarding the RH. Furthermore, the negative bias noted in all austral seasons, particularly those over the coastal regions, can be attributed to the convection parameterization of the RegCM4.

In the austral SON season, the RegCM4 model bias ranges from 20 to 40 W m⁻² over the majority of the continent except for the coastal region of Western Australia where the bias ranges from 60 to 80 W m⁻² approaching 80 to 100 W m⁻² over Victoria (Figure 7(p) and 7(q)). Furthermore, there is no significant difference between the two simulations themselves (Figure 7(r)). Finally, in the austral DJF season, the RegCM4 model bias ranges from +20 to 60 W m⁻² approaching 80 W m⁻² particularly over Victoria, New South Wales, South Australia, and Western Australia (Figure 7(v) and 7(w)). It is worth mentioning that the noted bias of the simulated LE, SHF, R_n, and TCC is not only because of the uncertainty associated with the RegCM4 physical parameterization, but also with the uncertainty associated with the ERA5 because ERA5 uses the TESSEL land surface model (Anwar et al. 2022). It can be noted that, the VIC has a higher R_n than the TOP by ∼10 W m⁻² over Western Australia, Queensland, and Northern Territory (Figure 7(r)).

Furthermore, it can be observed that the two simulations show a high R_n because of the low TCC over all the continent suggesting that R_n is mainly constrained by the convection scheme rather than the soil moisture changes. Also, there is no considerable difference between the two simulations because the vegetation status is prescribed in both simulations (i.e., vegetation parameters retrieved from the MODIS; Section 2.2); therefore, there is no change in simulated total albedo and eventually R_n. It is important to mention that Equation (4) has been examined to check whether the surface net radiation equals the sum of the sensible, latent heat fluxes, and the heat storage in soil. After some mathematical calculations, it was found that the surface net radiation is larger than the sum of the sensible and latent heat fluxes by ∼150 W m⁻² suggesting that the remaining part can be attributed to heat storage in soil. Also, shortwave/longwave parameterization is an important source of uncertainty in the simulated R_n.

Furthermore, the difference between the two simulations is less than 1 °C (Figure 8(l)). Furthermore, the two simulations exhibit a warm bias of 1–2 °C over all the continent and 3 °C over Western Australia and the Northern Territory (Figure 8(p) and 8(q)). Similar to the austral MAM and JJA seasons, there is no clear difference between the two simulations themselves (Figure 8(r)). It can be noticed that the RegCM4 behavior in the austral DJF is quite different. For instance, the two simulations show a cold bias of 1–2 °C approaching 4–5 °C over the borders of Western Australia and Northern Territory (Figure 8(v) and 8(w)). This cold bias can be attributed to the fact that the RegCM4 overestimates (underestimates) the latent (sensible) heat flux in this austral season (see Figures 4 and 5) because of the soil moisture surplus in this region (Sharmila & Hendon 2020). Moreover, the TMX cold/warm bias can be attributed to biases of the TCC, R_n, and SHF because low TCC leads to high R_n, SHF and therefore high TMX.

To account for the uncertainty associated with the observed dataset, the TOP simulation was taken as an example to be compared with Century and AGCD reanalysis products (Supplementary Figure S3). From Supplementary Figure S3, it can be noted that TOP can reproduce the spatial pattern of the TMX with respect to Century and AGCD (Supplementary Figure S3(a)–S3(c), S3(g)–S3(i), S3(m)–S3(o), and S3(s)–S3(u)). Furthermore, it can be noted that Century is colder than AGCD by 1–3 °C in all austral seasons except for the DJF, where Century is warmer than AGCD by 1–4 °C particularly over Western Australia (Supplementary Figure S3(f), S3(l), S3(r), and S3(x)). In the austral MAM, it can be observed that TOP shows a lesser bias (by 1–2 °C) when it is compared with AGCD than Century (Supplementary Figure S3(d) and S3(e)). Also, the TOP shows a lower bias (by 2 °C) when it is compared with AGCD relative to Century in the austral JJA and SON (Supplementary Figure S3(j), S3(k), S3(p), and S3(q)). Lastly in the austral DJF, the TOP shows quite similar bias for both Century and AGCD except for Western Australia where the bias with AGCD is lower than Century by 1–2 °C (Supplementary Figure S3(v) and S3(w)).

Similar to the TMX, the simulated TMN of the TOP was compared with the Century and AGCD reanalysis products (Supplementary Figure S4). From Supplementary Figure S4, it can be noted that Century is warmer than AGCD by 1–6 °C, particularly in the austral JJA and SON (Supplementary Figure S4(f), S4(l), S4(r), and S4(x)). In the austral MAM, the TOP shows a bias of 1–4 °C in comparison with Century particularly over South Australia and New South Wales (Supplementary Figure S4(d)), while TOP shows a high warm bias 1–6 °C over the same regions when it is compared with AGCD (Supplementary Figure S4(e)). Compared with Century, the TOP shows a bias up to 1–2 °C and 2–4 °C in the austral JJA and SON, respectively (Supplementary Figure S4(j) and S4(p)), while in comparison with the AGCD, the TOP shows a high warm bias (3–8 °C) over the Australian continent (Supplementary Figure S4(k) and S4(q)). In the austral DJF, the TOP shows a warm bias of 1–3 °C over a majority of the Australian continent and a cold bias of 1–3 °C over Western Australia in comparison with the Century (Supplementary Figure S4(v)). Moreover, the TOP shows a warm bias of 1–4 °C over a majority of Australia's continent and up to 6 °C over Queensland and New South Wales (Supplementary Figure S4(w)).

The ability of the RegCM4 model to capture the spatial pattern of the LE, SHF, R_n, TCC, and TMP was quantitatively evaluated by plotting a spatial map of the Pearson Correlation Coefficient (PCC) with the ERA5/Century reanalysis products (Supplementary Figure S5). From Supplementary Figure S5, it can be observed that both simulations show a gradient of PCC in simulating the LE: 0.4–0.6 over the interior region of Western Australia, 0.6–0.8 over the upper region of Western Australia, South Australia, and New South Wales as well as 0.8–1 over Queensland and Northern Territory (Supplementary Figure S5(a) and S5(f)). Regarding SHF, the RegCM4 shows also a gradient of PCC ranging from 0.4 to 0.6 over the upper half of Western Australia, Northern Territory, and Queensland; 0.6–1 over lower half of Western Australia, South Australia, and New South Wales (Supplementary Figure S5(b) and S5(g)). Moreover, both simulations show a PCC of 0.8–1 overall the continent regarding the R_n and TMP (Supplementary Figure S5(c), S5(h), S5(e), and S5(j)). For the TCC, both simulations show a gradient of PCC similar to the one observed in the simulated LE (Supplementary Figure S5(d) and S5(i)).

As reported in Section 3.4, there is no considerable difference between the two simulations regarding TMX and TMN. Therefore, the TOP simulation was taken as an example to evaluate the simulated mean air temperature (TMP) with respect to the in-situ observation. Evaluation of the TOP simulation (hereafter RegCM4) was done in two routes: (1) quantitative evaluation of the RegCM4 on a monthly basis using two statistical metrics (mean bias: MB and Pearson Correlation Coefficient: PCC) and (2) plotting the climatological annual cycle of the simulated TMP versus in-situ observation.

Moreover, the RegCM4 shows a smoother annual cycle than the observation (indicated by PCC of 0.847). Over Yanco and Tumbarumba, the RegCM4 overestimates the TMP by 2.76 and 5.46 °C, respectively. However, the RegCM4 shows a smoother seasonal cycle than the one noted in the observation and it has PCC of 0.9 over both locations (Figure 10(e) and 10(f)). It can be noticed that the RegCM4 performance varies with both location and time. Furthermore, the noted bias can be attributed to the horizontal resolution adopted in this study (i.e., 60 km) as well as the RegCM4 physical parameterization. This can be applied to any location considered in this study. Another reason is the mismatch in representing the land unit/vegetation between the RegCM4 and in-situ observation.

Soil moisture plays an important role in controlling the surface energy balance and surface climate. Such a role has been documented in Llopart et al. (2017). In addition, the difference between land-surface hydrology schemes of the CLM4.5 land surface model was examined across the globe: Africa (Anwar et al. 2019), Amazon (Anwar et al. 2022), and Tibetan Plateau (Wang et al. 2021). Anwar et al. (2019, 2022) reported that the difference between the two land-surface hydrology schemes is considerable regarding the hydrological variables, surface energy balance, and surface climate. Furthermore, this difference varies with the region of study as well as the climate regime.

Anwar et al. (2019) found that the VIC scheme outperforms the TOP scheme in simulating the surface energy balance and climate. Also, Anwar et al. (2022) reported that there is no land-surface hydrology scheme that performs better than the other over the Amazon. Also, the role of RCMs in simulating the Australian surface climate is documented in Di Virgilio et al. (2019). Our main target is to examine the role of soil moisture changes in simulating the TMX and TMN of Australia using a regional climate model (RegCM4). To achieve this goal, two 13-year simulations were conducted and driven with the NCEP/NCAR reanalysis version 2. After that, the model output was evaluated with respect to various reanalysis products and in-situ observations. Also, the results of the present study were compared with those conducted over Africa and Amazon (Anwar et al. 2019, 2022).

As reported in Section 2.2, vegetation parameters (e.g., LAI) are retrieved from the MODIS. This means that the seasonal cycle of LAI is repeated every year and it is not affected by climate or soil moisture variability. Therefore, it can induce a source of uncertainty in the simulated surface energy balance, TMX and TMN. Moreover, the role of vegetation fraction changes (i.e., by enabling the dynamic vegetation; DV) is not considered in the present study. In comparison with the results reported by Anwar et al. (2019, 2022), the difference between the two land-surface hydrology schemes over Australia (as represented by QINFL and SM10) is quite small. Such a difference can be attributed for the reasons reported in Section 3.1. Therefore, a small difference is noted between the two simulations regarding the SHF and LE with respect to the ERA5 (Section 3.2). Also, there is no considerable difference between the two simulations regarding the R_n and TCC with respect to the ERA5. In addition, warm/cold biases can be attributed to the RegCM4 physical parameterization rather than soil moisture changes.

The regional climate model (RegCM4) was used to examine the role of the land-surface hydrology schemes (of the CLM4.5 land surface model) in controlling the daily maximum and minimum air temperatures of Australia with respect to reanalysis products and in-situ observations. The results are summarized in the following points:

• 1.

  VIC has lower QINFL and SM10 than TOP. Therefore, VIC shows lower QVEGT and QVEGE with higher QSOIL than TOP. In addition, VIC has lower SABG and SHG than TOP.

• 2.

  RegCM4 succeeds in reproducing the spatial pattern of SHF and LE with respect to the ERA5. However, the difference between the two simulations themselves depends on the region as well as the austral season.

• 3.

  Soil moisture changes do not impose a notable impact on the simulated SHF and LE, R_n and TCC, TMX and TMN in comparison with the ERA5, Century and AGCD.

• 4.

  RegCM4 can reproduce the climatological seasonal cycle of the mean air temperature (TMP) with respect to the in-situ observation.

• 5.

  The simulated surface net radiation is larger than the sum of SHF and LE (for either TOP or VIC simulation).

Considering the recommendation of Anwar et al. (2022), the role of land-surface hydrology schemes need to be further examined across the globe. Our future study will include additional sensitivity studies for a long period (e.g., 30 years); revising the land-surface hydrology schemes TOP/VIC, addressing the role of different bias-correction methods to possibly reduce the surface energy fluxes and eventually the daily maximum and minimum air temperatures of Australia and finally considering the role of vegetation cover changes following Mehboob et al. (2020).

The Egyptian Meteorological Authority (EMA) is acknowledged for providing the computational power to conduct the RegCM4 model simulations. The climate group in ESP team is acknowledged for providing the RegCM4 model code and input data (http://clima-dods.ictp.it/RegCM4) to run the model. ECMWF is acknowledged for providing the ERA5/ERA5-land reanalysis product from their website at https://cds.climate.copernicus.eu.

20th Century Reanalysis V3 data was provided by the NOAA/OAR/ESRL PSL, Boulder, Colorado, USA, from their Web site at https://www.psl.noaa.gov/data/gridded/data.20thC_ReanV3.html.

The mean air temperature station data are retrieved from the Ozflux network. AGCD product is available at http://climate-cms.wikis.unsw.edu.au/AGCD. NCL plotting software is available at https://www.ncl.ucar.edu.

S.A.A.R. designed the simulation, analyzed the results, and wrote the manuscript. A.S. and B.Z. participated in analyzing the results, writing the manuscript. Furthermore, A.S. provided the observed mean air temperature data. The final version of the manuscript was revised by all authors.

The RegCM4 model code is available at https://github.com/ictp-esp/RegCM4.

We give our consent for the publication of identifiable details, which can include a photograph(s) and/or videos and/or case history and/or details within the text (‘Material’) to be published in the Journal of Water and Climate Change. We confirm that we have seen and been given the opportunity to read both the Material and the Article to be published by the Journal of Water and Climate Change.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.