Interactive comment on “ The impact on the surface climatology from changing the land surface scheme in the ACCESS ( v 1 . 0 / 1 . 1 ) climate model ”

To follow-up the request from reviewer 1 for significance testing, we have now also explored the use of the modified t-test (Zwiers and von Storch, 1995). This accounts for temporal correlation and the relatively small sample size which we have available (∼20 years). Compared to the standard t-test, the modified t-test generally gives less widespread areas of significance, particularly for the model-model differences in northern winter. Based on this new testing, we propose showing significance stippling for both the model-ERAi temperature biases and the model-model differences in Fig 4, as these are the critical results that we are trying to explain. Since the model-model differences shown in the remainder of the paper are primarily for explaining the tem-


Introduction
One of the main issues in climate modelling is understanding the dependence of climate on the interaction between clouds, radiation, precipitation and the land surface processes.A land surface model (LSM) is a key component of a climate model, providing information on surface exchange processes.The LSM includes a representation of the turbulent transport of momentum, heat and water between the land surface, canopy and the atmospheric boundary layer, as well as descriptions of thermal and hydrological processes in the soil and snow.A number of studies have been conducted to understand land-atmosphere interactions, in particular the feedbacks between soil moisture and meteorology (e.g.Koster et al., 2004;Seneviratne et al., 2010).With rapidly increasing changes in land management and land use producing complex feedbacks between the biosphere and climate, LSMs have become increasingly complex.The performance of different LSMs has been compared using prescribed meteorological forcing (e.g.Slater et al., 2001;Luo et al., 2003;Abramowitz et al., 2008;Best et al., 2015) and atmosphere.Both models use tiles to represent land cover types in each grid-cell and calculate a separate energy balance for each tile to provide area-weighted grid mean fluxes and temperatures.
A basic configuration of MOSES version 2.2 was used in the ACCESS1.0 simulation (Cox et al., 1999;Essery et al., 2001) and is also used for ACCESS numerical weather prediction.The MOSES code formed the scientific core of the Joint UK Land Environment Simulator (JULES) (Best et al., 2011), which has both stand-alone and Unified Model (UM) implementations and has had ongoing development since the version of MOSES used here.In MOSES, the canopy is modelled as one big leaf model and is represented in the surface energy balance equation through the coupling to the soil underneath.The soil underneath is not tiled and hence a homogenous soil moisture and temperature is common to all tiles within a grid cell.Subsurface tiling is used in CABLE.
The CABLE model (v1.8) has been coupled to the UM and is used in ACCESS1.1 simulations.CABLE is a one layer two-leaf canopy model, as described in Wang and Leuning (1998) and was formulated on the basis of the multilayer model of Leuning (1995).CABLEv1.8 is derived from CABLEv1.4b (Kowalczyk et al., 2006;Wang et al., 2011) with the changes for CABLEv1.8 detailed in K2013.

Differences between CABLE and MOSES
The main difference between CABLE and MOSES is the representation of the canopy processes including structural placing of the canopy above the bare ground; there are also significant differences in snow submodels.In MOSES a "two-patch" approach is used in which the canopy is modelled by conceptually placing it beside bare ground and calculating entirely separate energy balances for bare ground and vegetation, hence neglecting radiative and aerodynamic interaction between the two systems and their mediation of each others microclimate.Figure 1a gives an example of a mean grid-cell flux density calculation i.e. sensible heat flux is calculated from the weighted fraction of the vegetation fraction tile (σ) and the bare ground tile (1σ).
Surface temperature, T r (K), in MOSES is interpreted as a surface skin temperature (Essery et al., 2001) and is obtained for both vegetation and bare ground tile from the surface energy balance calculated as where R n is surface net radiation (Wm −2 ), H, LE are surface heat fluxes (Wm −2 ), L is the latent heat of vaporization (Jkg −1 ).
C s is a surface heat coefficient (JK −1 m −2 ) and G 0 is the heat flux (Wm −2 ) into the ground parameterised as where ∆z s and T s are the thickness (m) and temperature (K) of the top soil layer respectively, f r is a radiative canopy fraction, σ = 5.67 × 10 −8 Wm −2 K −4 is the Stefan Boltzmann constant and c is the thermal conductivity (Wm −1 K −1 ).Components of net radiation (R n ): incoming long wave (Wm −2 ) and the net short wave (Wm −2 ) are calculated outside of the LSM by the UM atmospheric radiation model.The heat diffusion equation is solved to calculate the soil temperature, T s (K).
By contrast, in CABLE the canopy is placed conceptually above the ground (Fig. 1b) hence removing a need for a separate bare ground tile in canopy covered areas, Fig. 2. A combined energy balance for the soil-vegetation system is calculated allowing for the aerodynamic and radiative interaction between the canopy and the ground (Kowalczyk et al., 2006).The mean grid flux density is a sum of the soil flux and the canopy flux, Fig. 1b.When solving the combined energy balance, the calculation of surface fluxes depends on stability and the surface temperature and simultaneously the surface temperature depends on the stability and fluxes.Therefore, an iterative procedure is used to allow for the simultaneous calculation of all the required variables.We first calculate the radiation absorbed by the canopy, differentiating between sunlit and shaded leaves.
We iterate for the thermal stability parameter and soil heat fluxes simultaneously with the solution of the coupled model of stomatal conductance calculating photosynthesis, heat fluxes, leaf (T l ) and vegetation temperatures, T v (K).At this stage the soil surface temperature from the previous time step is being used in the iteration.Having obtained canopy/soil fluxes and canopy temperature, the heat flux into the ground is obtained by where S abs is net short wave at the soil surface (Wm −2 ), L in is incoming long wave (Wm −2 ) which includes terrestrial and canopy irradiances.T s (K) is the soil surface temperature which in CABLE is the temperature of the top thin soil layer of 0.022 m.The heat diffusion equation is solved to calculate the soil temperature profile.l and s are leaf and soil emissivity and H s and LE s are soil heat fluxes (Wm −2 ).The surface radiative temperature (T r ) is obtained from vegetation T v (K) and soil surface temperatures; where τ is a canopy transmission τ = exp(-c LAI), c is a function of the plant function type and LAI is leaf area index.
CABLE has a more complex representation of canopy turbulent transport than many other land surface models.In particular, features of the canopy representation in CABLE that are not present in MOSES are: -Turbulent transport within the canopy based on Localised Near-Field theory, (Raupach, 1989) and transport just above the canopy in the roughness sublayer (RSL) is simulated.The inclusion of a representation of the RSL is critical to the performance of CABLE.
-The model differentiates between sunlit and shaded leaves for the calculation of canopy radiation, photosynthesis, stomatal conductance and leaf temperature (Wang and Leuning, 1998).
-The canopy albedo is resolved diurnally as a function of beam fraction, the sun angle, canopy leaf area index, leaf angle distribution and the transmittance and reflectance of the leaves.
In CABLE the two main canopy parameters affecting turbulent exchange i.e. the displacement height, d, and the roughness length for momentum, z0 c , have more complex representation than many other LSMs.The displacement height, which de-Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-35, 2016 Manuscript under review for journal Geosci.Model Dev.
scribes the mean level of momentum absorption by the canopy, is a function of canopy height and leaf area index as given in Raupach (1994) (Eq. 8).The canopy roughness length, z0 c , is determined by matching the mean wind speed profiles within and above the canopy as described in Raupach (1994).In MOSES, a more conventional rough wall boundary theory is used, with roughness length being a constant fraction of the canopy height (h) i.e. z0 c = h/20 for trees and h/10 for other vegetation types.Displacement height is not explicitly included in its formulation, with the result being that the reference level for wind is the height above the displacement height for each tile and consequently the ground surface is uneven.Both models use the same prescribed value of soil roughness length, z0 soil = 3 × 10 −4 , as well as a common geographically explicit snow free soil albedo dataset.
Both LSMs use multiple surface types in each grid-cell, but with different numbers of vegetated and non-vegetated types (Sec.3.1).Subsurface tiling is used in CABLE, where each surface tile has a corresponding soil tile for the calculation of soil temperature, moisture and runoff, while in MOSES the soil is common to all tiles within a grid cell.Soil processes are modelled similarly in both models but with different vertical resolution.Soil temperature and moisture are calculated for four soil layers to a depth of 3.0m in MOSES and six layers to a depth of 4.6m in CABLE.In both models soil moisture is calculated using Richards' equation.The evolution of soil moisture depends on the rates of infiltration, plant transpiration, soil evaporation and deep drainage.The heat diffusion equation, including an explicit freeze-thaw scheme, is solved to calculate the soil temperature profile.In CABLE soil water is assumed to be at the ground temperature so there is no heat exchange between the soil moisture and the soil due to vertical movement of water.MOSES does account for this heat exchange.
There are also significant differences between snow model components used in ACCESS1.0 and ACCESS1.1 simulations.
In both models snow cover evolution is based on the mass budget between the snowfall, sublimation and the snowmelt.The amount of snow deposited on the surface depends on the amount of solid/liquid precipitation which in the UM is computed by the cloud microphysics parameterizations.Both models accumulate solid fraction at the snowpack surface but the treatment of liquid fraction is different.In CABLE rain falling on snow freezes within the snowpack while MOSES diverts the rainfall straight to runoff.
The total surface albedo is calculated from the contribution from vegetation, snow and bare ground, the last one being the same in both models.In the version of MOSES used here the albedo for soil, vegetation, ice and snow are specified as single values for all radiation bands.The snow albedo in MOSES remains constant when the surface air temperature is below -2 • C, and undergoes an aging process decreasing its value above -2 • C, see Essery et al. (2001).In CABLE only snow free soil albedo is prescribed.The canopy albedo is resolved diurnally while the snow albedo depends on snow depth, a spectral mix of the incident solar radiation, soot loading, snow melting/freezing and snow age, which is parameterized as a function of snow density, see Dickinson et al. (1993).
In CABLE, the snow metamorphism and the bulk snow properties are accounted for through changes in snow density, see Gordon et al. (2002).In CABLE the density of the fresh snow is 120 kg/m 3 , and with time it may increase to 400 kg/m 3 while in MOSES it remains constant at 250 kg/m 3 .Snow density affects the temperature of the snow through its effects on the snow albedo and thermal conductivity.In CABLE, thermal conductivity for new snow is 0.2 Wm −2 K −1 and increases with snow density up to 0.5 Wm −2 K −1 while in MOSES it remains constant at 0.265 Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-35, 2016 Manuscript under review for journal Geosci.Model Dev.
3 Data and model setup

Model Datasets
The simulation results of MOSES and CABLE also depend on the values of their parameters, with some vegetation or soil type dependent, and others having an explicit geographical distribution.Both models use a number of surface datasets to derive the distributions of vegetation and soil types as well as some of the parameters required for the vegetation and soil, see K2013.
Both ACCESS1.0 and ACCESS1.1 use the IGBP (Global Soil Data Task Group, 2000) soil data.The hydraulic properties are determined from information on soil texture based on empirical relationships (Jones, 2008).Each soil type is described by the following hydraulic characteristics: volumetric water content at saturation, wilting point, field capacity, hydraulic conductivity and matrix potential at saturation.These properties define soil water holding capacity and control the rate of water infiltration through the soil.Soil thermal conductivity and heat capacity depend on soil moisture and ice content.
Both CABLE and MOSES use the same spatially varying snow free soil albedo data set which was obtained by blending soil albedo from Wilson and Henderson-Sellers (1985) with MODIS-derived albedo as described in Houldcroft et al. (2009), for details see Jones (2008).
ACCESS1.0 with MOSES uses five vegetated surface types (broadleaf trees, needleleaf trees, C3 grass, C4 grass and shrubs) and four non-vegetated types (urban, inland water, bare soil and ice).The spatial distribution of surface types is derived from 0.5 • by 0.5 • International Geosphere Biosphere Program (IGBP) data (Loveland et al., 2000).The implementation of CABLE in ACCESS1.1 uses ten vegetated surface types and three non-vegetated types.A dataset prepared for the Common Land Model 4 (CLM4) (Lawrence et al., 2012) at 0.5 • by 0.5 • resolution was mapped to CABLE vegetated types; and wetlands, lakes and permanent ice where taken from IGBP and do not change in time, see K2013.The vegetation distribution used for both models in this study is for present-day conditions, i.e. 2005.Figure 2 in K2013 shows the differences in vegetation fractions.In general the distributions are broadly similar for both models.The main difference is in the representation of bare ground underneath a canopy, shown here in Fig. 2. CABLEs vegetation is above the ground hence there are many grids-cells in CABLE without bare ground tile (Fig. 2b).By contrast, MOSES' vegetation is placed beside bare ground, and hence every grid-cell is allocated a separate bare ground tile to account for bare ground under a canopy (Fig. 2a).The vertical placement of the vegetation above the ground also has implications on the calculation of the surface albedo and roughness length which in CABLE are the integral part of the model.
The key parameters for each CABLE surface type used in the simulation are given in K2013.A description of vegetation parameters used by MOSES can be found in Cox et al. (1999), Cox (2001) and Jones (2008).MOSES uses prescribed monthly varying Leaf Area Index (LAI) which depends on vegetation type and canopy height.In the ACCESS1.1 simulations described here, LAI is prescribed from MODIS satellite estimates (Yang et al., 2006).However, unlike MOSES, a constant value is used across all tiles within a grid-cell.This consequently limits the differentiation of vegetated surfaces within a grid-cell, a limitation that needs to be addressed in future implementations of CABLE in ACCESS.Both MOSES and CABLE are able to predict canopy LAI when coupled to appropriate submodels that simulate plant growth: TRIFFID (Cox, 2001) for MOSES, CASA-CNP (Wang et al., 2010) for CABLE.However these submodels are not used in the ACCESS simulations described here.

Observations
Two datasets have been used to provide an observational context to the comparison between model versions: ERA Interim Reanalysis (ERAi, Dee et al., 2011) and FLUXNET flux station data (Baldocchi et al., 2001).ERAi has been used to evaluate model differences at regional to continental scale while the flux station data allow a more detailed analysis to help identify which processes contribute to generating those differences.

Model setup
We perform atmosphere-only simulations following the Atmosphere Model Intercomparison Project (AMIP) experimental design with prescribed sea surface temperature and sea-ice to constrain the climatology and aid in interpreting the differences between ACCESS1.0 (MOSES) and ACCESS1.1 (CABLE) simulations.We run both models for 20 years for the period 1979-1998 at a resolution of 1.875 N96).The same atmospheric model and cloud scheme are used in ACCESS1.0 and ACCESS1.1.Similar to a previous study by K2013, both simulations use initial conditions from a pre-industrial simulation.
Global atmospheric CO 2 is also prescribed, increasing from 337 ppm in 1979 to 379 ppm in 2005.We also perform single-site offline simulations to explain some of the implications of different processes between models.Note that the offline models are not identical to that used in the ACCESS simulations (for MOSES we use JULES v3.0 and for CABLE we use v2.1.2) but the core science parameterisations are essentially the same between the online and offline versions of the model.Hence the aim is not to exactly reproduce the online behaviour but rather to characterise differences in model behaviour when using common meteorological forcing.We also make reference to the 27 year  ACCESS1.3AMIP simulation submitted to CMIP5, which employed CABLE but with additional changes to the atmospheric physics parameterisations.

Model results
We focus our assessment of the land surface climatology on the seasonal means of screen level temperature and precipitation for present-day conditions.We calculate means for December-January-February (DJF) and June-July-August (JJA) for 1979-1998 and plot the biases between modelled and reanalysis fields from ERAi.We also present components of the water and energy balance over all the land area in comparison with various other estimates.

Mean climate
Modelling climate over the land is critically dependent on the interaction between clouds and the surface.Clouds are precursors of precipitation, reflect solar radiation and absorb outgoing long-wave radiation affecting the surface energy balance.Figure 3a shows Zonally averaged mean precipitation shown in Fig. 3b was similar in ACCESS1.0 and ACCESS1.1 and significantly lower than in ACCESS1.3, again showing the impact of the new physics and the cloud scheme in ACCESS1.3.In comparison to ERAi it was overestimated by up to 0.5 mm/day across the mid latitudes.In the mid latitudes of the southern hemisphere the precipitation was overestimated despite the underestimation of the total cloud cover.Table 1 presents model computed and 'observed/estimated' components of the water balance over the global land area.The estimates come from Baumgartner and Reichel (1975) and Legates and Willmott (1990).Globally, both ACCESS1.1 and ACCESS1.0 produced similar means for precipitation and evapotranspiration but larger differences are found for boreal summer over the Northern mid-high latitudes (Table 2).This is consistent with the larger cloud fraction simulated in these areas by ACCESS1.1 (Fig. 3a).
Patterns and signs of the precipitation biases are similar in ACCESS1.0 and ACCESS1.1 as well as in ACCESS1.3 over most of the land areas, (not shown).Common strong dry biases occur in the Amazon and equatorial Africa in both seasons, weak dry biases occur in central Europe and the eastern part of North America in JJA.There are also common strong biases in the equatorial ocean area and wet biases in the south eastern part of Africa in DJF and the central part of South America in JJA.
Weak wet biases occur in ACCESS1.1 but not ACCESS1.0over North Asia in JJA.In both simulations the Indian monsoon is severely under-predicted resulting in insufficient evapotranspiration in JJA (K2013).Many GCMs including HadGEM, which also uses the UM for its atmospheric component, show systematic biases in their simulation of monsoonal rainfall and dynamics.
As for precipitation, there is a similarity in the pattern and sign of the mean screen temperature biases especially in the tropics (Fig. 4).In JJA common warm biases occur across central Europe and the Great Plains of North America.These are coincident with the underestimation of precipitation in these regions; however in ACCESS1.0 the biases are larger than in ACCESS1.1.
Likewise, the underestimation in rainfall seen in both simulations in the Indian peninsula, equatorial Africa and part of the Amazon induces the warm temperature biases which are enhanced further by a positive feedback between the decrease in evapotranspiration and increased solar radiation due to a deficit in cloud cover fraction (Fig. 3a).The warm biases which are specific to ACCESS1.0 include those in the high and mid latitudes of Asia and high latitudes of North America in JJA and unexpectedly, a strong bias over Antarctica in DJF.The ACCESS1.1 simulation tends to have a warm bias in mountainous snow covered regions.For example, in East Siberia (in DJF) and Antarctica (in JJA) where the mean winter temperature drops below -20 • C, ACCESS1.1 overestimates the daily temperature by up to 5 • C over areas of high topography.There are also common cold biases occurring over arid areas of North Africa and the Middle East.Table 1 summarises annual mean, minimum and maximum temperature for all land and excluding Antarctica, and energy budget components with estimates from Henning (1989), Budyko (1978) and Smith et al. (2008).• N latitude (Table 2), ACCESS1.1 is cooler by about 0.5 • C and the mean maximum temperature was cooler by about 1.3 • C and the minimum temperatures warmer by 0.4 • C. The largest differences are in seasonal temperatures for the land above 30 • N latitude.In boreal winter the minimum temperature was warmer by 1.7 • C and in summer in ACCESS1.1 the maximum temperature was cooler by 2.7 • C.
In the comparison below we will focus on the Northern hemisphere, where the surface air temperature shows significant differences between both simulations (Fig. 4).We separately discuss boreal summer and winter, as the cold season with surface snow has a mostly stable boundary layer in contrast to the warm season which has an unstable daytime boundary layer.

Boreal summer
In boreal summer in the Northern Hemispheres canopy covered areas mean screen level temperatures are lower in CABLE (ACCESS1.1)by up to several degrees (Fig. 4f) despite CABLE giving lower surface albedo (Fig. 5b).The maximum temperature is significantly cooler in most areas while the minimum temperatures are slightly lower in canopy covered areas as shown in Figs.6b and 6d.The 2.1 • C difference in northern continental JJA temperature between model simulations (Table 2) is larger than the interannual variability in either simulation (standard deviation=0.4-0.5 • C) with the interannual variability being moderately well correlated between the two simulations (R 2 =0.7).This suggests the JJA temperature difference between ACCESS1.0 and ACCESS1.1 is robust.There are two main reasons for these differences: the first one is each models approach to canopy representation i.e. the "two-patch" approach conceptually placing canopy beside bare ground in MOSES compared to above the ground in CABLE (Fig. 1).The second are feedbacks enhancing the precipitation due to larger evaporation fluxes.
To illustrate the first difference we show an offline simulation for a single location, a 15m tall Scots pine forest at Hyytiälä (61.85 • C cooler than the bare ground tile temperature in JULES (Fig. 7) since CABLEs soil is shaded by the canopy while JULES' bare ground tile is exposed to the full atmospheric forcing.In July LAI is about 2.4 at this site resulting in a low canopy transmission coefficient and a mean grid radiative temperature in CABLE (Eq.4) that is close to the canopy temperature, with only a slight reduction in midday temperature due to the lower soil temperature.By contrast, the averaging of bare ground and vegetated tiles in JULES leads to a midday grid temperature slightly higher than that obtained for the vegetated tile alone.The consequence is that JULES is warmer by up to In summer, in the mid and high latitudes, the weather and the climate are driven by large scale synoptic systems, and interactions between clouds, precipitation and the Atmospheric Boundary Layer (ABL).The land surface determines the partitioning of the available energy and provides the moisture and heat fluxes to the ABL.In these regions with relatively moist soils, the key contribution to the climate from the land surface is evapotranspiration that depends on soil moisture.Table 2 shows that north of 30 • N summer mean evaporation and precipitation are larger in ACCESS1.1 by about 0.2 mmday −1 .Figure 8a shows that around 60-70 • N where soil moisture is in abundance, ACCESS1.1 shows significantly larger cloud fraction over canopy covered areas.Total evapotranspiration is also higher in at least half of these areas, (Fig. 8b).Increased evapotranspiration influences cloud formation and rainfall which in turn replenishes the soil moisture availability for evapotranspiration (Bierkens et al., 2008) (Fig. 8c, 8d).However we cannot separate cause and effect here i.e. whether higher evaporation fluxes induced higher cloud cover and precipitation or vice versa.Also, not all clouds produce precipitation as water droplets/ice crystals may remain suspended in the atmosphere until they are converted back into vapour.Also note, that most of the areas with the largest model differences in daily maximum temperature (Fig. 6d) coincide with the areas of largest differences in mean precipitation, (Fig. 8c).
The links between moisture and temperature presented in 140km), and model errors in simulating the site meteorology but gives useful information on the overall model performance and differences between the models.In this grid ACCESS1.1 has 3 tiles; needleleaf trees (0.83), grass (0.07) and lakes (0.10) and ACCESS1.0 has 6 tiles; broadleaf and needleleaf trees (0.09, 0.50), grass (0.17), shrubs (0.02), lakes (0.10) and bare ground (0.12).In ACCESS1.1, the cloud cover fraction is slightly larger than in ACCESS1.0 during the daytime and much larger at night, (Fig. 9a).The intense summer rainfall events are not reproduced with precipitation slightly larger for ACCESS1.1, (Fig. 9b).The maximum daily net radiation in ACCESS1.1 (Fig. 9c) is lower by up to 35 Wm −2 due to the larger cloud cover fraction while at night time the outgoing long wave flux is smaller up to 40 Wm −2 due to significantly lower surface temperatures.Partitioning of the net radiation is different with CABLE giving larger latent than sensible heat (Fig. 9d, 9e) due to greater moisture availability.Boreas grid-cell is located within an area where ACCESS1.1.has larger soil moisture and precipitation (up to 1mm/day) than ACCESS1.0,Fig. 8c, 8d.Smaller daytime net radiation, larger evapotranspiration and larger fraction of grid covered with trees shading the ground in CABLE resulted in cooler diurnal screen level temperatures (Fig. 9f) with the difference in maximum temperature being larger than for the minimum temperature.However, for the Boreas grid-cell, the MOSES partitioning is closer to that observed at the flux station.This difference in partitioning is also seen when averaged across the northern continents (Table 2) with MOSES producing a sensible to latent heat ratio of 0.7 compared to 0.5 for CABLE. in Fig. 9d.The fog layer dissipates when the surface warms after sunrise.In much of the tundra and taiga regions high levels of humidity, fog and mist are observed in summer (Przybylak, 2003).This is captured well in the ACCESS1.1 simulation, with the occurrence of fog rapidly decreasing with latitude.
Over the desert and semi desert areas of the Middle East both models showed cold biases in the mean temperature (Fig. 4), but differ from each other in their diurnal range.Figure 6 showed that ACCESS1.1 gives warmer minimum and cooler maximum temperatures than ACCESS1.0.This is especially noticeable in large parts of Iran and Saudi Arabia in JJA.Most model grids in ACCESS1.1 in these areas are represented by one bare ground tile while ACCESS1.0 there may have two tiles; bare ground tile and a small fraction canopy tile, see Fig. 2. Larger bare ground area results in slightly higher surface albedo in CABLE (Fig. 6), which contributes to cooler daytime temperature.There is limited cloud cover and precipitation in these areas and the latent heat flux is small or negligible.The maximum daytime surface radiative temperatures in both models were similar but the night time temperatures were warmer in ACCESS1.1.The daytime maximum sensible heat flux in MOSES was slightly larger cooling the surface and providing more heat to the atmosphere resulting in warmer daytime air temperature.In CABLE smaller daytime sensible heat under similar radiative forcing allowed for larger ground heat flux which combined with the deeper soil column (4.7 vs. 3m) allowed a larger heat storage modulating the daily temperature amplitude.In both models the diurnal pattern of sensible heat flux is phase shifted after local midday.This phase shift occurs in the deserts due to the diurnal radiative cycle being not in phase with the soil heat storage cycle.

Boreal winter
In boreal winter ACCESS1.1 is warmer than ACCESS1.0 (Fig. 4e), with mean screen level temperature up to several degrees higher in most northern areas where snow occurs.The minimum temperatures are 1.7 • C warmer and maximum temperatures 0.3 • C warmer (Table 2).The interannual variability in each simulation is comparable to these differences with the standard deviation of annual minimum and maximum temperatures being around 0.8-1.2 • C and the correlation between simulations being 0.4-0.5.Thus the winter temperature differences between simulations appear less robust than those in summer.leaves senescence, and with plant metabolism slowed down vegetation enters a dormant phase.At this phase vegetation impact on the surface temperature is reduced to an effect of lowering surface albedo in areas with vegetation protruding through the snow cover.In these environments, the differences between ACCESS1.0 and ACCESS1.1 simulated winter temperatures are attributed to the different representation of the snow processes by the models; these include the parameterization of snow albedo, accumulation, density and thermal conductivity.
The calculated total surface albedo is significantly lower in ACCESS1.1 (Fig. 5a), with the exception of a band of higher albedo stretching from the northern parts of the Scandinavian Peninsula across Russia.These areas have high grid fraction of needle leaf trees/shrubs in both models (see Fig. 2, K2013), however ACCESS1.0 has much larger value of LAI protruding through the snow which results in lower surface albedo.In ACCESS1.1 much lower surface albedo occurred in the areas of intermittent snow cover i.e. central USA and central Asia.This difference is due to later onset of snow cover in autumn and earlier melting.Over the permanent ice, CABLEs total surface albedo is higher than for MOSES due to snow albedo refreshing process allowing albedo to remain around its maximum value.
To illustrate how the snow processes differ between the two land surface models and the consequent impacts on the ACCESS simulations, we have performed offline simulations, forced with observed meteorology, for a single location, for the [2003][2004] snow season in Hyytiälä.In winter in Hyytiälä, LAI decreased from summer maximum of 2.85 to 0.71.The widespread lower ACCESS1.1 albedo in winter is reproduced in the offline simulation.For both models the time evolution of surface albedo reflects snowfall/melting events (Fig. 10a) and CABLE also represents the diurnal variation of snow and canopy albedo on cloud free days.MOSES albedo response to snowfall/snowmelt events is larger than in CABLE as its variation depends only on snow depth and melting temperature.In CABLE albedo of the surface is affected by overlying canopy albedo as well as snow age and density.In early winter snow albedo in MOSES increases more rapidly and remains higher through the rest of the season.During the melting period the surface albedo in MOSES oscillated with the daily temperature variation around -2 • C while in CABLE the albedo decreased rapidly allowing for faster melting of the snow, Fig. 10b.
As in CABLE rain falling on snow freezes within the snowpack while MOSES diverts the rainfall straight to runoff; this results in a deeper snow cover (Fig. 10b) at this site and contributes to warmer snow temperatures Fig. 10e.In early spring when liquid precipitation frequently occurs, warm rainfall falling on snow accelerates snow melting in CABLE decreasing the snow albedo.In the ACCESS1.1 simulation there is slightly more snow in the northern part of the continent and less in the south (not shown).This is broadly consistent with more frequent occurrence of liquid precipitation in the south.
Parameterization of snow thermal conductivity and density contribute to a warmer surface temperature in CABLE.In early winter, the snow has low thermal conductivity (0.2) preventing heat loss from the underlying soil.With time, both snow thermal conductivity and density increase (Fig. 10c and 10d), allowing for more heat to be absorbed by the snow cover and the ground below.The differences in daily mean surface radiative temperatures between the offline simulations are shown in Fig. 10e.
In early winter when the snow cover is shallow, the differences tend to be smaller and are related to snowfall/melting events but with time they increase, with maximum differences occurring as melting begins.Consistently, in the A.1.1 simulation variable thermal conductivity and density of snow contribute to warmer mean temperatures and in particular warmer minimum temperatures over the snow areas.and is closer to the observed.The latent heat is negligible; sensible heat flux is small and underestimated in both models due to underestimated net radiation, Fig. 11d and 11e.Precipitation is overestimated in both models.We did not have flux stations data for Siberia; however we investigated diurnal cycles at a number of grids between locations (58.6 • N, 49.7 • E) and (61.3 • N, 110.6 • E).In all of the investigated grids ACCESS1.1 has slightly warmer temperatures consistent with the results shown in Fig. 6a and 6c.
Parameterization of the cold climate processes in CABLE, which include liquid precipitation freezing within the snowpack, age dependant diurnally resolved snow albedo, prognostic snow density and variable snow thermal conductivity, resulted in warmer snow surface temperatures than compared to MOSES.ACCESS1.1 mean, maximum and minimum temperatures were warmer than in ACCESS1.0 (Fig. 4 and 6), with the largest difference of 1.74 • C in the mean minimum temperature (Table 2).
The mean winter precipitation, evaporation, runoff and the heat fluxes were similar in both models while net radiation was only slightly larger in CABLE than is the case for MOSES.
One of the consequences of the seasonal temperature difference between ACCESS1.0 and ACCESS1.1 in the Northern hemisphere high latitudes is the timing of the calculated snowmelt and runoff.Spring snowmelt is an important source of water to replenish soil water reservoirs, with an excess of water diverted to runoff.In the high latitudes snowmelt is also a source of the fresh water flux to the Arctic sea.An earlier spring and snowmelt affects land-atmosphere carbon exchange, permafrost thaw and ecosystem carbon sequestration in high latitude tundra ecosystems (Li et al., 2014;Humphreys and Lafleur, 2011;Tang and Zhuang, 2011).Figure 12 shows difference in mean monthly runoff generated from the snowmelt.In ACCESS1.1 in spring, the soil moisture in these regions is close to saturation and thus snowmelt flows on the surface along topography as surface runoff.In ACCESS1.0 there is significantly less soil moisture, Fig. 8d, so the snowmelt water enters partially unfrozen soil and seeps slowly through the soil column before emerging months later as drainage from the bottom layer.Hence a substantial amount of runoff is not generated in ACCESS1.0 until June.In ACCESS1.1 the main contribution to the total runoff in Fig. 12 comes from the surface runoff while in ACCESS1.0 it comes from the drainage.In high latitude regions soil moisture is high because the moisture evaporates slowly and the soil drainage conditions are poor because of the underlying permafrost.These processes are captured in ACCESS1.1 simulation.Also, the timing of runoff as simulated in ACCESS1.1 is more consistent with the observations from the three main Siberian river watersheds (Yang et al., 2007) than in ACCESS1.0 confirming that the land surface scheme is the main driver of similar differences noted between ACCESS1.0 and ACCESS1.3 by K2013.Table 2 shows that in JJA the total runoff for ACCESS1.0 is almost twice as large due to ACCESS1.1 simulating surface runoff from the spring snowmelt in April and May.In boreal summer in the Northern Hemisphere, in spite of the overall lower surface albedo in canopy areas, ACCESS1.1 is generally cooler over high latitudes.Cooler surface temperatures are attributed to two factors; the first one being the representation of the canopy in CABLE with the vertical placement of the vegetation above the ground which allows for radiative and aerodynamic interaction between the canopy and the ground below.An offline simulation showed that in CABLE the net available radiation flux at the ground surface below the canopy was much lower than for a separate bare ground tile directly exposed to the atmospheric forcing in MOSES.Hence, the ground temperature in CABLE being shaded by vegetation was cooler than vegetation temperature while in MOSES it is the opposite; day time bare ground tile temperature was significantly higher than canopy tile temperature.The MOSES configuration of land cover with a separation of the canopy covered grid into bare ground and canopy tile resulted in larger areas of bare ground surface as shown in Fig. 2. A larger area of bare ground exposed directly to the atmosphere contributed to larger diurnal temperature amplitude with a tendency to dry out earlier due to an absence of physiological control over the evaporation flux.Cooler summer temperatures are also attributed to larger soil moisture, precipitation and day time cloud cover fraction in most of the areas in ACCESS1.1.In high latitudes the low level cloud cover fraction over the canopy covered area at night was higher in ACCESS1.1 due to the presence of fog.
Warmer winter temperatures simulated by ACCESS1.1 over the snow covered areas of mid and high latitudes are attributed to differences in the snow parameterization in CABLE compared with MOSES.In particular, CABLE accounts for liquid precipitation freezing within the snow pack, prognostic snow density and variable snow thermal conductivity.These differences contribute to warmer winter temperatures.Accounting for liquid precipitation freezing within the snowpack delays the build up of the snow pack in autumn and accelerates snow melting in spring.Snow density is simulated to increase through the winter which lowers the snow albedo and allows for an increased absorption of solar radiation.Variable snow thermal conductivity increases over the snow season, initially preventing heat loss and later allowing more heat to enter the snow/ground.
One of the deficiencies of the modelled climate in both versions of ACCESS model was the overestimation of evapotranspiration.In some regions this is due to overestimated precipitation caused by continuous but low intensity events in lieu of less frequent but more intense rainfall which would allow for an increase in the surface runoff and drier soil.The excessive evapotranspiration is a common problem for a number of other models, (Mueller and Seneviratne, 2014).The sensitivity of the Geosci.ModelDev.Discuss., doi:10.5194/gmd-2016-35,2016   Manuscript under review for journal Geosci.Model Dev.Published: 1 March 2016 c Author(s) 2016.CC-BY 3.0 License.
the zonally averaged simulated total cloud cover fraction in comparison with ERAi derived cloud fraction.ACCESS1.1 7 Geosci.Model Dev.Discuss., doi:10.5194/gmd-2016-35,2016 Manuscript under review for journal Geosci.Model Dev.Published: 1 March 2016 c Author(s) 2016.CC-BY 3.0 License.producedmuch smaller fractions than ACCESS1.3 especially in the tropics and the polar regions, illustrating the large impact of changing the atmospheric physics settings and cloud scheme in ACCESS1.3 (K2013).In comparison with ACCESS1.0,ACCESS1.1 produced slightly larger fractions around the equator, in mid-high latitudes in the Northern hemisphere and in the polar regions.However, in comparison with ERAi, the simulated fraction was significantly lower, particularly in the tropics and southern hemisphere by up to 15% and in the northern polar regions by up to 20%.This underestimation of the cloud cover impacts the latitudinal distribution of cloud forcing that will strongly affect the surface energy budget.
With an exclusion of the Antarctic continent, where the largest 8 Geosci.Model Dev.Discuss., doi:10.5194/gmd-2016-35,2016 Manuscript under review for journal Geosci.Model Dev.Published: 1 March 2016 c Author(s) 2016.CC-BY 3.0 License.temperature differences occur, ACCESS1.1 model produced a cooler mean screen temperature by 0.5 • C, and in particular the maximum temperature was lower by 0.86 • C and the minimum temperature higher by 0.09 • C.Over the Northern hemisphere above 30 1.5• C at midday.In ACCESS1.1 large areas of the globe do not have a bare ground tile while in ACCESS1.0 up to 20% of the grid-cell fraction in the canopy covered area is designated for bare ground tile, see Fig. 2.This representation impacts overall Geosci.Model Dev.Discuss., doi:10.5194/gmd-2016-35,2016 Manuscript under review for journal Geosci.Model Dev.Published: 1 March 2016 c Author(s) 2016.CC-BY 3.0 License.calculation of the grid surface temperature.In particular, it is well know that seasonal depletion of soil moisture over the bare ground is larger than in the canopy covered areas due to an absence of plant physiological control over the evapotranspiration fluxes.
Figs. 6 and 8 are explored for a typical mid-latitude grid-cell; online simulations of the diurnal cycle of fluxes, temperatures, cloud cover and precipitation are compared with observations for the grid-cell closest to the Boreas flux tower site (55.88 • N, 98.48 • W), in North America.Comparing a grid-cell from the model simulations with flux tower observations has limitations due to model resolution (grid area of about 200km x Geosci.Model Dev.Discuss., doi:10.5194/gmd-2016-35,2016 Manuscript under review for journal Geosci.Model Dev.Published: 1 March 2016 c Author(s) 2016.CC-BY 3.0 License.The large cloud fraction overnight in ACCESS1.1 is due to the presence of fog, shown by the fraction of very low cloud, < 111m, in Fig. 9a.The radiative cooling of the surface in the stable nocturnal boundary layer causes the overlying air to cool to the dew point temperature, generating saturation and cloud in the lowest model levels.The cooler surface temperatures simulated with CABLE require a smaller amount of radiative cooling before saturation of the overlying air is reached compared to the case with MOSES.Once the fog has formed, longwave radiation cools the cloud top rather than the surface and drives the cloud layer through the generation of turbulence.The presence of fog increases the incoming longwave radiation at the surface, leading to an increase in the net surface radiation and the larger sensible heat fluxes seen in the early morning in ACCESS1.1 Snow constitutes a dominant part of the winter environment in mid and high latitudes.It strongly reduces the available energy at the surface through its high reflectivity of solar radiation.The insulating properties of the snow reduce the soil heat to the atmosphere, thus allowing soil temperature to remain warmer.The surface energy, water budget and seasonal freezing and thawing of the ground are affected by snow processes.Processes of infiltration, soil water transfer and transpiration are suspended upon soil freezing and resumed with thawing.During winter LAI is significantly reduced by snow cover and the Geosci.ModelDev.Discuss., doi:10.5194/gmd-2016-35,2016   Manuscript under review for journal Geosci.Model Dev.Published: 1 March 2016 c Author(s) 2016.CC-BY 3.0 License.
Geosci.Model Dev.Discuss., doi:10.5194/gmd-2016-35,2016 Manuscript under review for journal Geosci.Model Dev.Published: 1 March 2016 c Author(s) 2016.CC-BY 3.0 License.The warmer surface temperature in ACCESS1.1 occurs throughout the diurnal cycle, as can be seen for a location, Boreas (55.88 • N, -98.48 • E).We present the 20 year mean diurnal cycle for January temperature, fluxes, precipitation and cloud cover fraction in Boreas, Fig. 11.The Boreas flux station with needle leaf forest is an example of a site dominated by snow and frozen soil processes in winter.Both models underestimate the temperature in winter but ACCESS1.1 is warmer than ACCESS1.0 by approximately 2 • C, see Fig. 11f.Also, the maximum daily screen level temperature occurs an hour or more later in ACCESS1.1 Geosci.ModelDev.Discuss., doi:10.5194/gmd-2016-35,2016   Manuscript under review for journal Geosci.Model Dev.Published: 1 March 2016 c Author(s) 2016.CC-BY 3.0 License.Kowalczyk et al. (2013) highlighted differences in the present day land surface climatology of the two ACCESS submissions to CMIP5, but the impact of the different land surface model used in each simulation was difficult to determine due to other differences in atmospheric settings.The simulations presented here, using the same atmospheric settings, have allowed the impacts of the land surface model to be determined, with a focus on the processes driving those impacts.Differences found in K2013 that we can now largely attribute to the land surface processes and model configuration include smaller seasonal temperature amplitude manifested by a warmer winter and a cooler summer, and an earlier runoff from snowmelt in the Northern Hemisphere in ACCESS1.1 (CABLE).CABLE also gives smaller mean diurnal temperature range in JJA and DJF in most of the areas including sparsely vegetated regions.

Figure 1 .
Figure 1.The representation of vegetation in (a) MOSES, where vegetation is beside bare ground and in (b) CABLE, where vegetation is above the ground.The mean grid heat flux in MOSES is a weighted sum of the tile fluxes.In CABLE it is a sum of canopy and soil fluxes.

Figure 6 .
Figure 6.Northern hemisphere seasonal minimum and maximum screen temperature (K) difference between ACCESS1.1 and ACCESS1.0 for DJF and JJA.

Figure 7 .
Figure 7. Offline simulation of Hyytiälä, 2002-2005.July mean diurnal cycles of net radiation and temperature for grid-cell, vegetation and soil for CABLE and grid-cell, vegetation and bare ground tiles for JULES; a) net radiation (Wm-2) for CABLE, b) net radiation (Wm-2) for JULES, c) surface temperature (K) for CABLE and d) surface temperature (K) for JULES.

Figure 10 .
Figure 10.Offline simulation of 2003/2004 winter snow processes in Hyytiälä for CABLE (red) and JULES (black); (a) surface albedo, (b) snow water equivalent (SWE) in kg/m 2 , (c) snow density in kg/m 3 , (d) thermal conductivity in Wm −1 K −1 and e) the mean daily temperature difference, CABLE minus JULES, in K.
slightly larger value due to lower vegetation albedo.However, in CABLE, net radiation for canopy reaches a midday value of 290W/m 2 and 90 W/m 2 for bare ground underneath, adding to a total grid maximum flux around midday of 380W/m 2 .The canopy temperature in CABLE and canopy tile temperature in JULES have similar diurnal variation and amplitudes; however the midday ground surface temperature in CABLE is 6 • N, 24.3 • E) for 2002-2005.This site is represented in CABLE as a single tile with evergreen needle leaf vegetation above the ground while in JULES (based on MOSES) the site is represented with two tiles; a needle leaf canopy (tile fraction of 0.8) and bare ground (0.2), see Fig 1.JULES's calculated midday net radiation (320 W/m 2 ) is similar for both tiles with needle leaf tile having

Table 1 .
Water and energy budget components, averaged over all land surfaces for ACCESS1.0 and ACCESS1.1 compared to estimates from other sources.(Values in parenthesis are for all land excluding Antarctica).