This section gives an overview of the processes that are considered for calculating the surface temperature (T*) in ACCESS in order to show where the model code has been changed (including the names of the subroutines). The calculations for surface temperature are given in more detail by ; however, this section only describes the equations that are changed (see Sect. ) to prescribe T*.

A schematic of the model process for updating T*, the surface long-wave (LW, W m-2) and short-wave (SW, W m-2) radiative fluxes, and the surface sensible (H, W m-2) and latent (λE, W m-2) heat fluxes is shown in Fig. . Initially the values of SW, LW, H, and λE are calculated explicitly at the start of a time step (in SF_EXCH; see Fig. ) using surface, soil, and boundary layer temperatures from the previous time step seefor more details. The fluxes are then updated implicitly, at which point the initial estimate of the new value of the surface temperature is calculated from T*=Ts+1A*Rs-H-λE+CcΔtT*prev-Ts, where Ts is the temperature of the first soil layer beneath the surface at the end of the previous time step (K), Rs is the net radiation (SW and LW) into the soil layer through the surface (W m-2), A* is the coefficient to calculate the surface heat flux (W m-2 K-1), Cc is the areal heat capacity of the surface (J m-2 K-1), Δt is the time step length (s), and T*prev is the surface temperature from the previous time step (K); all other variables have the same definition as described above. The term Cc/Δt (T*prev-Ts) represents the conductive energy flux from the first soil layer to the surface of the soil during the previous time step and is equivalent to the ground heat flux (G). More details on the derivation of Eq. (1) can be found in and .

Adjustments to the surface sensible and latent heat fluxes are then calculated implicitly in SF_EVAP depending on the availability of surface moisture . The value of T* calculated in Eq. (1) then needs to be adjusted by an amount that is consistent with (and proportional to) the updated values of the sensible and latent heat fluxes via ΔT*EVAP=-ΔH+Δ(λE)A*,T*EVAP=T*+ΔT*EVAP, where ΔT*EVAP (K) is the land surface temperature increment resulting from the adjustments to the sensible (ΔH) and latent heat (ΔλE) fluxes (W m-2) and T*EVAP (K) is the adjusted value of land surface temperature following evaporation (T* and A* have the same definition as those in Eq. 1). If there is no snow present within the grid box, then T*EVAP is that final value of land surface temperature (T*final, K) and is passed back into ATMOS_PHYSICS2. If there is lying snow however, then T*EVAP is passed into the SF_MELT routine (Fig. ) to account for any melting ice and snow on land tiles. The surface energy fluxes over snow and ice (sublimation and sensible heating) are also adjusted in SF_MELT. If the value of T*EVAP from Eq. (4) is above freezing for water (Tm, 273.15 K), then the temperature is adjusted by a value ΔT*MLT (K), which is either

back to freezing if there is sufficient snow that it cannot be melted within a time step (30 min in this case) or

The final value of surface temperature that the atmosphere uses in the rest of the time step (T*final, K) is therefore given as T*final=T*EVAP+ΔT*MLT.

If there is no melting, then ΔT*MLT is zero, but if melting does occur, then the surface fluxes are updated by an amount proportional to the value of ΔT*MLT. Therefore, the value of T* may differ within the model time step between the first guess (Eq. 1) and the final value (Eq. 4), which also applies to the surface fluxes (H, LE, and sublimation flux).

Given that ACCESS uses a 30 min time step, in order to prescribe the land surface temperatures, a data set that is available for all surface tiles and at 30 min intervals is required. Such a data set does not exist in the observational record and so, therefore, in order to represent both the diurnal and seasonal cycles, the optimal solution is to take the surface temperatures from a simulation where they are allowed to vary freely. In this study, surface temperatures are taken from each time step and tile from a 50-year long simulation that uses prescribed climatological SSTs and sea ice fractions (denoted as FREE in Table ). Data are stored from each time step and surface tile type so that the prescribed temperature field can account for 1.

the diurnal and seasonal cycles in surface temperature and

Starting at 00:00 UTC on 1 January, all 50 values for that specific time produced by the FREE simulation (i.e. one for each year) are averaged together to produce a representative mean temperature on each land tile and saved. The process is then repeated on all land tiles for 00:30 UTC on 1 January. The process is repeated for all time steps over the year to produce a climatological land temperature field that contains a mean diurnal cycle for each day of the year on each land surface tile. This is illustrated in Fig.  for a selection of different grid points in the model (values are the grid-box means across all surface tiles). These grid points are located within a tropical (Amazonia), sub-tropical (central Australia), high-latitude (northern Asia), and mid-latitude (Europe) region. The grey lines show the 30 min surface temperatures at those points for all 50 years of FREE on 1–2 January and the black solid line is the average over those 50 years for each 30 min time step (Fig. , middle column). The variability in surface temperatures is reduced by taking the average; however, diurnal variability in the surface temperature field can be seen at each of those grid points, which is larger in the tropics than at mid-latitudes. There are also some discontinuities in the original time step data, which are likely to be associated with the radiative calculations within ACCESS (occurring every 3 h).

In Fig.  (third column), the mean diurnal cycle for each day (black) and the daily mean surface temperature (yellow) are plotted. There is a clear seasonal and diurnal cycle, which is representative of the FREE simulation at each of those selected grid points.

Initial test experiments with the time step data resulted in two problems. 1.

The time step (30 min) data set is too large to be read into the current ACCESS framework as one single input field.

To combat the first problem, surface temperatures are read into the model every 3 h and interpolated hourly between those points (orange line overlaid in Fig. , middle column). The results of the 30 min and 3-hourly temperature simulations have almost indistinguishable mean climate states (not shown). Therefore, the 3-hourly data are used in the simulations outlined below.

In order to prevent the negative temperature anomalies from developing over Antarctica in the prescribed runs relative to the FREE simulation, the surface temperatures on permanent land ice tiles were allowed to vary freely. The impact of this exception is small and discussed in Sect. .

In order to prescribe the land surface and sea ice temperature, Eq. (1) in SF_IMPL (Fig. ) is simply changed to T*=TPRES, where TPRES is the input land surface temperature (K) field. Furthermore, the increments to the surface H, LE, sublimation, and snowmelt are still calculated in SF_EVAP and SF_MELT (Fig. ), but the surface temperature increments (Eqs. 3 and 4) are removed so that the surface temperature cannot change. The variables in the surface radiation budget are then set to their final values, which depend upon TPRES only.

The differences in the annual mean precipitation between CON1 and FREE are generally within ±8 % (Fig. a). The largest percentage differences primarily occur over the Arctic circle (reductions > 4 %) and the Amazon (increases > 4 %). Nevertheless, the differences in precipitation outside these two regions (Arctic and Amazon) are largely statistically insignificant. Furthermore, for CON2 relative to CON1 (Fig. b), there are only small and non-significant differences in precipitation (within ±8 %), which suggests that there is little impact on precipitation from changing the initial conditions.

For ALL10K relative to CON1 there are statistically significant changes to the precipitation over all land areas (Fig. c); however (unlike with T1.5), the differences are not all the same sign. Precipitation increases by more than 30 % over northern South America, Africa, South-east Asia, the islands of the Maritime Continent, and northern and eastern Australia, but decreases by more than 30 % over central North America, central Asia, and India. There are also large reductions (greater than 30 %) in precipitation over the central Atlantic Ocean, Indian Ocean and much of the Pacific Ocean, while there is an approximate 10 % increase in precipitation over the Southern Ocean.

In both of the tropical experiments (AMA10K and MC10K), precipitation increases by > 50 % where the surface temperatures are increased (compare Fig. d and e with Fig. d and e, respectively). There are also precipitation anomalies of alternating sign that extend from the Amazon and the Maritime Continent to the north-east and south-east that are statistically significant (similar to the T1.5 differences – Fig. d and e), which suggests the increased tropical land surface temperatures are affecting precipitation remotely (Fig. d and e). Moreover, the response of tropical precipitation in AMA10K over Africa, India, the tropical Atlantic, and Pacific is much stronger than in MC10K (the largest differences are confined to the western Pacific in MC10K).

Increasing Australian land surface temperatures causes precipitation to increase in the north and east of the continent but to decrease over the eastern Indian Ocean (Fig. f). There is very little significant change in the precipitation field away from the Australian continent and eastern Indian Ocean.

For AM10K, increased precipitation coincides with the surface heating except in the centre of the continent (this also occurs in ALL10K – compare Fig. g and c). There is also higher precipitation over the Arctic and Greenland. Conversely, there is lower precipitation in the Gulf of Mexico and the eastern Pacific. For AMm10K, there is a reduction in precipitation throughout North America, which extends over Greenland and into the Arctic (Fig. h). There are also significant increases in precipitation over the North Atlantic and the North Pacific, with decreased precipitation over northern Africa.

When prescribing the surface temperatures it is important to maintain the diurnal cycle, particularly in regards to the impact of the daily heating and cooling of the land surface on tropical convection. Accepting that ACCESS and other GCMs produce convective rainfall too early in the day relative to observations, the same process should also occur in the prescribed simulations outlined in Sect. . Nevertheless, the model needs to be representative of the free-running simulation, and therefore the early triggering of convective rainfall is expected. In order to assess this, the mean diurnal cycle of convective rainfall is plotted in Fig.  for tropical land grid points in

West Africa, 0∘ E, 15∘ N (June–July–August, JJA, mean for a NH monsoon region), Fig. a;

In West Africa (Fig. a), convective rainfall peaks around 10:30 local time (LT) in FREE. Both CON1 and CON2 have peak rainfall around 10:30–13:30 LT, with higher rainfall between 13:30 and 19:30 LT. Despite these differences the diurnal cycle of rainfall is maintained in both CON1 and CON2.

Convective rainfall in northern Australia peaks at 11:00 LT in FREE, CON1, and CON2; however, as over West Africa, the prescribed simulations have higher precipitation in the afternoon (around 17:00 LT). Despite the higher rainfall around 17:00 LT, the diurnal cycle still occurs in the prescribed simulations. Interestingly, the secondary peak in rainfall (around 02:00 LT) associated with the modelled diurnal cycle of the heat low circulation as discussed by is represented in each of the prescribed simulations. This suggests that the diurnal cycle of the low-level atmospheric circulation at this point is also maintained in CON1 and CON2.

For the Maritime Continent (Fig. c), the peak in convective rain occurs at 11:30 LT in all simulations; however, the rainfall amounts are slightly higher in CON1 and CON2. Moreover, the afternoon rainfall is slightly higher in CON1 and CON2 relative to FREE (as with northern Australia and West Africa), but the overall diurnal cycle is maintained (including the secondary peak around 02:30 LT).

Finally, peak convective rainfall occurs at 13:30 LT in all simulations for the Amazonian point (Fig. d); however, CON1 and CON2 both have higher accumulated precipitation than the FREE simulation between 07:30 and 19:30 LT, which agrees with the region of increased annual mean precipitation in Fig. a. Nevertheless, the diurnal cycle in convective precipitation is again maintained in both CON1 and CON2 when the temperatures are prescribed as they are in the other tropical regions.

Over most of the globe, the differences in T1.5 between FREE and CON1 are within ±0.1 K (unshaded in Fig. a). Importantly, the differences in T1.5 over the Antarctic in CON1 relative to FREE are not statistically significant (Fig. a). Therefore, despite allowing the Antarctic surface temperatures to vary freely in CON1, the surface air temperatures over the Antarctic are unaffected as a result of prescribing the surface temperatures over all other land surface tiles. Nevertheless, there are some regions where T1.5 is significantly different between FREE and CON1, for example over the NH high latitudes (Fig. a). The largest difference in T1.5 between CON1 and FREE (-1.32 K) occurs at 277.5∘ E (82.5∘ W) and 67.5∘ N (in northern Canada), and the anomaly is particularly pronounced between September and May (and particularly in December to February – not shown). It is hypothesized that the prescribed surface temperatures in the CON1 simulation may be changing the surface snow cover relative to FREE over the NH high latitudes.

To investigate this hypothesis, the snow mass at 277.5∘ E and 67.5∘ N during September, October, and November (SON) is plotted in Fig. a. The values for each individual day of SON are averaged over all 50 simulation years to give the mean time series of snow accumulation in FREE (solid line) and CON1 (dashed line) during that season (Fig. a). From approximately day 29, the CON1 simulation has (on average) more snow lying on the surface than FREE (Fig. a), which continues into boreal winter (not shown). The prescribed surface temperatures in CON1 therefore are causing more snow to accumulate relative to FREE, and the reason for this can be seen in Fig. b. The daily maximum surface temperature at 277.5∘ E and 67.5∘ N during SON in CON1 (black, solid line) is plotted in Fig. b. The day on which the maximum surface temperature drops below 0 ∘C is denoted by the dashed lines and corresponds with day 29 (as also marked in Fig. a). After this point, the surface temperature does not rise above the freezing point of water, and therefore the surface snow cannot melt away. Conversely, in many of the 50 realizations of SON in FREE (grey lines, Fig. b), the maximum surface temperatures remain above 0 ∘C past day 29 of SON, and so the snow can still melt after this point. Therefore, due to prescribing the surface temperatures, snowmelt is typically prevented earlier in CON1 than FREE, and so snow amounts are, on average, higher in CON1 during the cold season, which causes T1.5 to be systematically lower.

The lower values of T1.5 within the Arctic Circle appear to cause a reduction in precipitation westward of Greenland and to the north-east of Asia; however, the differences in precipitation over the rest of the globe between CON1 and FREE are largely insignificant (Fig. a). Moreover, the differences in mean sea level pressure between CON1 and FREE are also largely insignificant (Fig. a). It appears that differences in T1.5 between CON1 and FREE have relatively little impact on the global precipitation and circulation fields. Therefore the prescribed land surface temperature simulation (CON1) is broadly able to reproduce the climate of the original simulation (FREE) from which the land surface temperatures are derived.

The differences in T1.5 (Fig. b), precipitation (Fig. b), and mean sea level pressure (Fig. b) between CON2 and CON1 are climatologically indistinguishable. The climatological states of the modelled atmospheres in CON1 and CON2 are therefore not sensitive to changes in the initial conditions and show further that this model setup is reliable for other users to perform idealized simulations without the need to use the same initial conditions as this study.

Previous work by shows that induced heating of the land surface causes an increase in tropical precipitation in GCM experiments with prescribed SSTs. Nevertheless, in order to induce that surface warming, either quadrupled CO2 concentrations or increased the solar constant; therefore, the surface temperature response to those perturbations would have been unknown until after the experiments were run. The method of prescribing surface temperatures shown in this study therefore presents an opportunity to assess the impact of increasing land surface temperatures – by a pre-determined quantity – on tropical (and global) precipitation in comparison to those of , who increase land surface temperatures indirectly.

An increase in precipitation over almost all tropical land surfaces can be seen in the ALL10K experiment (Fig. c). To first order, the changes in precipitation appear to be caused by enhanced convection over the land (uplift) and suppressed convection over the ocean (subsidence), which coincide with a reduction in MSLP (Figs. c and c) as suggested by . Nonetheless, the pattern correlation between the differences in precipitation and MSLP in Figs. c and c is weak (-0.20) and there are several regions where the MSLP and precipitation differences are the same sign (e.g. over the Atlantic and central Asia). Therefore MSLP may not be a good indicator of the changes in circulation that are causing the changes in precipitation.

The mean pressure vertical velocity at 500 hPa (ω500) is plotted for CON1 in Fig. a with dashed lines indicating areas of climatological ascent and solid lines for subsidence. The same field is given for ALL10K in Fig. b (contours), with the difference in ω500 for ALL10K relative to CON1 overlaid (red indicating relative subsidence and blue relative ascent). There is a strengthening and expansion of the ascent regions over central–southern Africa, northern South America, the islands of the Maritime Continent, and northern Australia, with increased subsidence over the tropical–sub-tropical Atlantic, Indian Ocean and the ocean surrounding the Maritime Continent. Moreover, the pattern correlation between the ω500 anomalies in Fig. b and the precipitation anomalies in Fig.  is -0.69, which indicates that ω500 is a better indicator of the circulation-induced precipitation changes than the MSLP. These results also agree with the results of , who show that the spatial patterns of tropical precipitation response are also driven by circulation changes and not just the local thermodynamic influence (i.e. increased surface temperatures). While it should be expected that the largest changes in precipitation should be over the land (given the pattern of surface temperature increases), precipitation does not increase over all land grid points. This is most apparent over the Indian sub-continent where (to first order) the increased surface temperatures should enhance precipitation; however, the large-scale re-organization of the tropical circulation (seen in Fig. ) results in positive differences in ω500 for ALL10K relative to CON1 over southern India, which would suppress precipitation.

In both the AMA10K and MC10K experiments, there is evidence of alternating T1.5, precipitation, and MSLP anomalies emanating from the region of increased surface temperatures and extending into the mid-latitudes of both hemispheres (see Sect. ). These T1.5, precipitation, and MSLP anomalies that alternate in sign suggest that there are waves propagating away from the imposed tropical heat sources , which in this case are from increasing surface temperatures by 10 K and the resultant increase in latent heat release (inferred from the increase in precipitation; see Fig. d and e). Such a response is consistent with the modelling study of where low-latitude diabatic heating can excite Rossby wave propagation into the high latitudes provided there was a background westerly flow. Moreover, and showed that the excitement of Rossby waves from a tropical source depends on the location of the diabatic heating and the background zonal flow in the tropics and mid-latitudes, which vary seasonally. In order to identify whether the T1.5, precipitation, and MSLP features are associated with wave propagation away from the tropics, the characteristics of the upper-level atmospheric flow need to be considered. and primarily focus on the 300 hPa fields, which are also considered here for ease of comparison.

The differences in the zonal mean deviation of the 300 hPa streamfunction (contours) for AMA10K and MC10K relative to CON1 are plotted in Fig. . The fields are time-averaged annually (ANN), for December–February (DJF), and for June–August (JJA). The orange boxes denote the land areas where the surface temperature has been increased by 10 K. In both the AMA10K and MC10K experiments (Fig. a and d), alternating positive and negative streamfunction anomalies can be seen emanating from the region of increased land surface temperatures and into the high latitudes of both hemispheres. The magnitudes of the streamfunction anomalies appear to be stronger in the AMA10K simulation than the MC10K simulation, which may be due to the smaller areal extent of the Maritime Continent islands and therefore their impact on the atmospheric circulation. Nevertheless, and show that if the heating anomaly is located in background easterly flow, then this can suppress the development of waves that propagate towards higher latitudes. Regions where the 300 hPa mean flow is negative (easterly) are stippled in blue in Fig. . The surface temperature perturbations in the MC10K experiment lie completely within a region of background easterly flow, whereas the AMA10K heating region extends into areas with background westerly flow in both hemispheres. Therefore the background atmospheric state is likely to be playing a role in weakening the teleconnections between the tropical convection and mid-latitude circulation in the MC10K experiment relative to AMA10K.

The importance of the location of the surface temperature perturbation relative to the background flow, rather than simply the areal extent of the heating source, is more obvious when the seasonal (DJF and JJA) averages are considered. In DJF (Fig. b and e), background easterly flow is located between 0–150∘ E and 5∘ N–10∘ S and over a small region of northern South America. As the AMA10K surface temperature perturbation zone extends into regions of westerly background flow in both hemispheres during DJF, there is strong wave activity in both the NH and SH (Fig. b), although the streamfunction anomalies are stronger in the winter hemisphere. As the Maritime Continent lies within climatological easterlies in the MC10K simulation, the waves appear weaker in the streamfunction field in both hemispheres, although the waves are still present (Fig. e).

In JJA, the Amazonian heating source lies entirely south of the band of background easterly flow at 300 hPa, and there is little wave activity apparent in the streamfunction field in the NH as a result (Fig. c). Moreover, there is a much broader band of background easterly flow northward of the Maritime Continent heating source and subsequently there is no evidence of wave activity propagating into the NH high latitudes (Fig. f). There is however strong wave activity in the SH during JJA in both the AMA10K and MC10K experiments (Fig. c and f), where the background westerly flow adjacent to the region of increased surface temperatures allows for Rossby wave propagation into the higher latitudes. Therefore, based on the evidence given above, it is more likely to be the background atmospheric state, as opposed to the areal extent of the surface temperature perturbation, that is causing the stationary Rossby waves in each hemisphere. Nevertheless, the larger areal extent of the diabatic heating (and higher precipitation amounts) in AMA10K relative to MC10K is also likely to be an important factor in the different wave responses between those two simulations.

Overall, the circulation responses to both of these tropical heating sources are broadly consistent with the results of , , and . Nevertheless, there are cases where the cross-equatorial meridional flow can allow Rossby wave propagation through easterly flow as discussed in. For example, show that wave sources in the summer hemisphere can excite wave activity in the winter hemisphere if the meridional flow is from the summer to the winter hemisphere. Therefore, the idealized GCM with prescribed land surface temperatures in this study is likely to be useful for running similar experiments that address all of these features (where easterlies do and do not act as a barrier to wave propagation).

Previous work has shown that Australian rainfall has changed regionally over the last 60 years ; however, there has only been one study that perturbed the local surface conditions over the continent in order to account for the changes . decreased the land surface albedo by a factor of 4 over the whole of the Australian continent to induce an increase in surface temperature and cause an increase in monsoon rainfall. The AUS10K experiment (Table ) now provides an opportunity to qualitatively compare the impact of directly increasing Australian land surface temperatures with an indirect method i.e. reducing the surface albedo as in.

Precipitation increases are primarily in the north and east (Fig. f), which implies that the monsoon driven rainfall is responding the strongest (the largest changes occur in DJF – not shown). Moreover, the increase in precipitation is primarily through increased convective precipitation, which suggests an increase in ascending air over the continent, which causes the MSLP to be lower over Australia (Fig. f). Reduced MSLP and increased monsoon rainfall also occur with decreased surface albedo and show the increased surface temperature in AUS10K is likely to be having a similar impact.

The change in convective rainfall over Australia also appears to be driving changes in the SH mid-latitude circulation. MSLP increases by > 0.5 hPa over the Southern Ocean and decreases by a similar magnitude over the Antarctic (Fig. f). The MSLP changes are consistent with a transition towards the positive phase of the Southern Annular Mode SAM,. Moreover, there is also a poleward shift in the annual mean location of the SH mid-latitude jet (Fig. a), which is consistent with a more positive phase of SAM. The largest changes in the zonal wind occur in DJF (0.5–2.0 m s-1, Fig. b) rather than JJA (typically < 0.5 m s-1, Fig. c), which coincides with the periods where the Australian monsoon is active and inactive, respectively.

Such an impact on the SAM was not discussed in and warrants further investigation – especially given that there has been a shift towards a more positive phase of the SAM in DJF over the last 60 years . The majority of the trend towards a more positive SAM is attributed to SH stratospheric ozone depletion ; however, greenhouse gases also play a weaker role in in the positive trend in the SAM index, which may in part be caused by an increase in the SH meridional temperature gradient . Given that land surface temperatures are expected to increase more than SSTs from increasing atmospheric greenhouse gas concentrations , the model developed in this study could be used to understand the impact of the land–sea surface temperature contrast on large-scale modes of atmospheric variability (such as the SAM).

Increasing (AM10K) and decreasing (AMm10K) the North American continental surface temperatures induce local decreases and increases in MSLP, respectively (Fig. g and h). Moreover, precipitation increases over most of North America in AM10K (except the central plains, Fig. g) and decreases in AMm10K (Fig. h) in response to the respective surface temperature perturbation. The atmospheric responses to the ±10 K surface temperature perturbations over North America also appear to be of almost equal and opposing sign in each respective simulation, which suggests the circulation and precipitation respond in a linear way to the different surface temperature conditions.

The largest changes in precipitation occur in JJA (boreal summer, not shown) where the increased surface temperature (Fig. a) causes an increase in convective rainfall in AM10K (Fig. b) and vice versa for AMm10K (Fig. e and f). It is also in JJA when the positive and negative anomalies in the annual mean T1.5 over northern Asia and northern Africa (Fig. g and h) are at their strongest (Fig. a and e). Therefore, the rest of this section will focus on the changes in the JJA circulation in response to the surface temperature perturbations imposed on the North American continent.

Locally, the increased surface temperatures and induced convection act to decrease the surface MSLP in AM10K (relative to CON1), which can also be seen as a negative 850 hPa geopotential height (Zg850) anomaly over North America (Fig. c) and an associated anomalous cyclonic flow over the continent. Conversely, the Zg850 field is higher in AMm10K than CON1 over North America and is associated with anomalous anticyclonic flow (Fig. g) in response to the lower surface temperatures and suppressed convection. There are also large differences in the Zg850 and 850 hPa wind field to the west of North America, with an anomalous anticyclone and positive Zg850 values over the North Pacific in AM10K (Fig. c) and negative Zg850 values and cyclonic flow in AMm10K (Fig. g).

show that the land–sea thermal contrast along the western coast of North America is important in causing the formation and maintenance of the Northern Hemisphere, summertime sub-tropical high-pressure cell over the North Pacific. show that the increase in low-level potential temperatures from boreal spring to summer over the North American continent in July (and May) acts to increase cyclonic vorticity (cyclone stretching) over the continent, which strengthens the northerly flow along the western coast. Strengthening of the northerlies then increases the advection of polar air over the ocean, enhances evaporation from the ocean surface, and encourages the development marine stratocumulus, which all act to reduce SSTs. The cooling of the air column causes subsidence visible at 500 hPa; see Fig. 8d in and enhances the anticyclonic circulation (vortex compression) within the sub-tropical high-pressure cell over the ocean and strengthens the northerly flow and subsidence further.

Interestingly, the differences in circulation in Fig. c are qualitatively very similar to those produced by , which suggests that increasing North American surface temperatures by 10 K may result in a strengthening of the Pacific sub-tropical high-pressure cell. To illustrate this further, the values of ω500 from CON1 (black solid and dashed lines) and the difference between AM10K and CON1 (coloured shading) are plotted in Fig. d. The largest increases in subsidence (red shading) at 500 hPa occur over the centre and to the north of the maximum subsidence in CON1 (Fig. d), which may indicate a strengthening and northward shift of the summertime high-pressure cell. Conversely, the opposite circulation anomalies occur in the AMm10K simulation (and with very similar magnitude), which suggests that the same process may be reversed by decreasing North American land surface temperatures (also seen in the ω500 field, Fig. h). It is therefore likely that increasing or decreasing the North American land surface temperatures in ACCESS may act to enhance or weaken the strength of the Pacific sub-tropical high-pressure cell given that SSTs in the AM10K simulation do not respond to and feed back on the atmospheric circulation in the way described in. These results therefore indicate that this version of ACCESS (with prescribed land surface temperatures) may be useful for investigating the impact of regional land–sea thermal contrasts on the location and strength of the summertime sub-tropical high-pressure cells, for example.