Estimates from marine sedimentary records within the Indo-Pacific Warm Pool (IPWP) suggest SSTs were broadly similar to present during 7–5 ka, with temperatures around 301–302 K . There is some uncertainty, however, as there is evidence of IPWP SSTs being higher ( – specifically in the TNE region), equivalent or lower at 6 ka relative to 0 ka. In comparison, the multi-model mean differences (6 - 0 ka) in temperature for the TNW and TNE domains are -0.17 ± 0.05 and -0.21 ± 0.05 K, respectively (both statistically significant with 81 % model agreement; see Fig. 3a). The model results therefore agree with the study of , who indicate that the lower SSTs result from an enhancement of the equatorial Pacific easterlies.

Despite the apparent agreement between the PMIP models and , there is an important caveat regarding the mean climate state over the Pacific in the AOGCMs that merits discussion. Many coupled AOGCMs are known to have poor representation of the SST field across the equatorial tropical Pacific. Typically, the SSTs are too low along the equatorial Pacific and those negative SST errors extend into the western tropical Pacific , which is known as the “cold-tongue” bias. Furthermore, the same errors are also visible in the PMIP2 and PMIP3 simulations used in this study . A simple way to remove the impact of the error is to assume that it remains unchanged in a different climatic state (such as changing the Earth's orbital parameters). Such an assumption implies that the difference between two simulated climate states is representative of the “observed” (e.g. proxy data) difference despite the initial error as could be done when comparing to. Nevertheless, it seems that for 6 ka conditions, the SST errors may actually be enhanced and therefore the change in the climate state is dependent on the initial error in the background state.

Previous work by suggests that there was a strengthening of the Pacific trade winds in the austral spring around 6 ka, which has been attributed to a strengthening of the south-east Asian summer monsoon and is also evident in the PMIP2 and PMIP3 models . If the tropical Pacific easterlies are already too strong in the 0 ka simulations, however, any further strengthening could enhance the existing cold-tongue bias through the Bjerknes feedback mechanism . To illustrate this, the differences between the PMIP ensemble mean and HadISST (1870–1899) SSTs are plotted in Fig. a. Overlaid on the figure are the differences in the 850 hPa zonal wind for the ensemble mean relative to ERA-Interim (averaged over 1979–2008). It is immediately obvious that there is a strong (< -1.5 K) SST anomaly along the western equatorial Pacific, which coincides with an easterly zonal wind bias. Recent work by suggests that the zonal wind error is responsible for the cold-tongue bias through the Bjerknes feedback; however, suggest other mechanisms may be responsible. Regardless of the actual cause, both the cold-tongue bias and easterly errors are present in the PMIP ensemble in Fig. a. While there are uncertainties associated with both the HadISST and ERA-Interim datasets seerespectively, the consistency between the SST (negative anomalies) and circulation (easterly anomalies) errors suggests that the biases in the models are more important than any uncertainty in the reference datasets. To this end, Fig.  is reproduced in the Supplement (Fig. S3a), except HadISST data are averaged over the period 1979–2008 to correspond with the ERA-Interim data. While the magnitude of the SST anomalies are larger (∼ -2.5 K), they are still evident regardless of the averaging period and the interpretation above remains valid.

When the 6 ka SST and 850 hPa flow are considered relative to 0 ka, both the negative SST anomalies (relative to HadISST) and easterlies (relative to ERA-Interim) are larger in magnitude for the PMIP2/3 multi-model mean (Fig. b). The difference between the 6 and 0 ka simulations (Fig. c) may therefore be the result of enhancing the errors that already exist in the 0 ka simulations. Hence, the models may be simulating the same conditions at 6 ka as those described in for the wrong reason. Conversely, if the SST at 6 ka were higher or equivalent to 0 ka then the models are not representing the IPWP correctly. It is also important to note the uncertainty in the proxy-derived SSTs in the western Pacific around 6 ka given the different estimates from , , and . Indeed, show that there was a transition in the IPWP SSTs from relatively high around 6.6–6.3 ka to relatively low around 5.5 ka, compared with 0 ka. The models (given they simulate perpetual 6 ka conditions) may therefore be more representative of ∼ 5.5 ka. It should be noted that the record is located at the southern margin of the IPWP, and thus may represent a contraction of the IPWP at this specific time. Given the different proxies (e.g. corals versus sediment cores) and different localities, it is important to bring these various lines of evidence together i.e. in order to infer the mean climate state within the TNE zone at 6 ka, such that AOGCMs can be evaluated. Overall, the discussion above indicates that fixing the cold-tongue bias is still a very high priority for the AOGCMs.

The TSE proxy data indicate similar to present conditions at 6 ka from marine records and warmer conditions from the terrestrial island records and coral records from the Great Barrier Reef , with slightly lower temperatures in the hinterlands . Speleothem records from north-west Australia i.e. TSW; suggest mean annual temperatures were equivalent to or slightly cooler at 6 ka than at 0 ka. Therefore, there appears to be a signal of lower temperatures over the land and evidence of slightly higher SSTs in the TSE at 6 ka.

The models (on average) simulate lower surface temperatures at 6 ka (-0.09 ± 0.06 K) for the ensemble mean in the TSE and TSW, which are both statistically significant differences. Nevertheless, the change in temperature is very small (< 0.1 K) and could be interpreted as similar to present (given the weak model consensus), in agreement with the proxies. It is likely that the lower 6 ka temperatures in the AOGCMs are caused by the lower annual mean insolation at these latitudes relative to 0 ka (see Fig. b), which agrees with the terrestrial proxies. It is clear from Fig. c, however, that the multi-model mean SSTs are lower in the 6 ka simulations than in the 0 ka simulations, in disagreement with the proxy evidence outlined above. Given the negative SST biases in the 0 ka simulations (relative to HadISST, Fig. a), the model–proxy disagreement may also be related to the cold-tongue bias (as described for the TNW and TNE above) and is further evidence of the need to improve the representation of the mean climate state over the Pacific.

In the TNW, slightly drier than modern conditions are apparent at 6 ka in a lake record from Sulawesi ; however, speleothem records from Borneo indicate similar or slightly higher annual mean precipitation at 6 ka relative to present . The multi-model mean change in precipitation for the TNW is -0.31 ± 1.34 % (Fig. b), which agrees with the proxy interpretation of similar precipitation amounts around 6 ka relative to 0 ka.

There is an interesting point raised in the work by , that an increase in insolation over Borneo in September, October and November (SON) around 5.5 ka corresponded with increased convective activity there and also, more broadly, across the IPWP as indicated in. The data from the AOGCMs gives us an opportunity to test this hypothesis for 6 ka, i.e. whether the change in insolation is directly driving any changes in seasonal precipitation.

In both the 6 and 0 ka simulations, the peaks in insolation (Fig. a) and surface temperature (Fig. b) occur in September/October and March–April–May. The highest precipitation occurs in November to April (in the 0 and 6 ka simulations, Fig. c) for both total (blue line) and convective (turquoise lines) precipitation. The model-simulated seasonal cycle in precipitation is consistent with the observed rainfall seasonality within the TNW domain . At 6 ka (relative to 0 ka), insolation and surface temperature are higher in August to November, but precipitation is higher in November to March (Fig. d). Therefore, neither the mean seasonal cycle of precipitation in either period (6 and 0 ka) nor the changes in precipitation (6 ka relative to 0 ka) are driven directly by higher insolation and surface temperatures as suggested by .

Previous work by and also shows that precipitation is not primarily driven by insolation in the TNW region but by the seasonal cycle in the large-scale circulation from a relatively dry southeasterly flow in April to October to a relatively moist easterly to northeasterly flow in November to March. The models also represent the seasonal change in wind direction from southeasterly during April to October (Fig. b) to easterly–northeasterly during November to March (Fig. c), which corresponds with the seasonal peak in rainfall. Furthermore, the precipitation is higher in November to March in the 6 ka simulations (relative to 0 ka), which corresponds with anomalous east to northeasterly 850 hPa flow over the TNW during October to March (Fig. h). It is therefore clear that the seasonality of precipitation over the TNW is driven by the large-scale circulation and not directly through higher insolation. There is one caveat, however: the change in the seasonal circulation is likely to have been caused by a strengthening of the south-east Asian monsoon in response to insolation forcing, which is also seen in the PMIP simulations . Therefore, changes in insolation are likely to be responsible for the change in circulation plotted in Fig.  and thereby indirectly change the precipitation seasonality over the TNW.

Speleothem records from Flores (TSW) indicate similar amounts of precipitation fell there at 6 ka relative to 0 ka ; however, marine records indicate higher precipitation in the TSW domain at 6 ka as do speleothem records of warm season precipitation over north-west Australia . The multi-model annual mean precipitation (Fig. b) is higher in the TSW at 6 ka relative to 0 ka (1.63 ± 0.92 – significant), which is primarily from an increase in October–March rainfall (5.95 ± 1.27 % – see Supplement, Fig. S1b). The models therefore largely agree with the proxy evidence.

When the TSW mean insolation and surface temperatures are plotted seasonally (Fig. a, b and d), the higher insolation (and surface temperatures) during October to December corresponds with higher rainfall around the same time (Fig. c and d). Furthermore, the higher simulated 6 ka rainfall is primarily from convection (turquoise line, Fig. c and d), indicating a thermally driven, direct response to the change in seasonal insolation at 6 ka relative to 0 ka. The models therefore agree with the proxies for higher rainfall during the warm season (i.e. October to March).

As in the TSW, the TSE zone also received equivalent or slightly more precipitation at 6 ka than at present in both terrestrial and offshore records . Furthermore, pollen records also indicate higher wet-season precipitation . Likewise, the models simulate higher mean precipitation (2.01 ± 0.67 % – significant) in the TSE (with high model agreement, 81 %) and also higher October–March precipitation (3.52 ± 1.02 % – see Supplement, Fig. S1b).

The cause of the higher rainfall at 6 ka (particularly in October to March) over TSE can be seen when the seasonal cycle of precipitation is considered (Fig. ). The higher insolation in June to December (Fig. a and d) causes SST at 6 ka to be higher in August to January (Fig. b and d; SST response lags the insolation change), which coincides with the period where both the convective and total precipitation are higher in the 6 ka simulations relative to 0 ka (Fig. c and d). Furthermore, higher land surface temperatures also coincide with the higher convective precipitation at 6 ka relative to 0 ka (Fig. d), which causes an increase in low-level convergence over the land that is consistent with the stronger easterlies (see Fig. c and Supplement Fig. S1c). The earlier onset of the monsoon from increased continental heating, stronger onshore flow and higher SST adjacent to the land (i.e. higher evaporation) is therefore likely to be responsible for the higher precipitation over the TSE at 6 ka in the models. Conversely, the impact of reduced land and sea temperatures from April to July has little impact on precipitation during the dry season.

The precipitation characteristics for both of the TSW and TSE domains appear to respond directly to insolation (Figs.  and , respectively). In both regions, lower annual mean insolation causes surface temperatures to be lower around 6 ka relative to 0 ka; however, the lower annual mean temperatures do not result in reduced precipitation. The higher insolation from July to November at 6 ka relative to 0 ka causes the wet season precipitation to start earlier (September–October) than at 0 ka (October–November). As the response of the SST lags the insolation changes by 1–2 months, the average difference in SST in the 0 and 6 ka simulations during the middle of the wet season (December to February) is approximately ± 0.2 K (i.e. very little difference). Therefore, given the insolation and resulting SST conditions, an overall increase in wet season precipitation occurs.

Proxy records indicate that marginally higher than modern SSTs are present through the Great Australian Bight TeS zone;. Terrestrial records from pollen and charcoal from the TeE and TeS, however, both indicate slightly lower temperatures at 6 ka than present . The proxy-derived changes in temperature are of opposing signs over the land and ocean, however, which indicates uncertainty over whether the regional mean temperature was higher or lower at 6 ka. Lower temperature at 6 ka relative to 0 ka is simulated in the TeE (-0.11 ± 0.07 K – significant) region in agreement with the proxies. Given that the annual mean insolation is lower between 20 and 33∘ S at 6 ka (Fig. b) it is likely that the models and proxies are responding to that change. Conversely, in the TeS zone, the change in surface temperature at 6 ka relative to 0 ka is not significant (Fig. a). The annual mean difference in insolation between 6 and 0 ka is approximately zero between 33 and 40∘ S, which may also explain the non-significant change in temperature from the models and uncertainty in the proxies.

Pollen and isotope records from North Stradbroke Island in the TeE indicate higher precipitation at 6 ka than at present , although records from Fraser Island suggest lower precipitation . The pollen and charcoal records indicate drier conditions in the Sydney Basin and wetter conditions to the south at 6 ka . The lack of a regionally coherent signal in the proxies for precipitation at 6 ka relative to 0 ka across the TeE region indicates there is uncertainty in the proxy estimates. There is also no statistically significant change in precipitation in the models (-0.46 ± 1.01 %), which is consistent with no regional consensus of higher or lower precipitation in the proxy record.

Sedimentology-, palaeoecology- and geochemistry-based lake records from western Victoria (TeS) indicate higher lake levels than present ; however, in some circumstances lower than their maximum at 7.5 ka . Furthermore, records from the western Victorian crater lakes in the TeS suggest highly variable conditions (i.e. regularly fluctuating between high and low rainfall), with a marked decrease in effective precipitation from 7 to 6 ka and around 1750 CE e.g.. Further south, pollen and charcoal records from Tasmania reveal overall wetter conditions to the west and drier to the east . There is also evidence from both New Zealand and South America that indicates wet conditions on the western (windward) flanks of the mountains that intercept westerly flow between 40 and 44∘ S across the hemisphere, suggesting that, while possibly attenuating, there was a persistence of relatively strong westerly flow at this latitude at ca. 6 ka .

The strength of the westerly winds and their influence on precipitation in the TeS for both the present-day and ∼ 6 ka e.g.and references therein periods have been widely discussed. In general, stronger (weaker) westerly flow corresponds with more (fewer) extratropical disturbances and therefore higher (lower) precipitation. Nevertheless, the correlations between precipitation and 850 hPa flow strength are ∼ 0.3 over southern Australia , which indicates that other processes must be important. Indeed, show that the correlations are higher when both the 850 hPa zonal flow and relative humidity are considered over Australia, which indicates that moisture availability is also an important limiting factor for precipitation over the TeS and not just the circulation strength. Both and indicate that precipitation was higher around 6 ka but for different reasons. state that it is “a phase of enhanced westerly flow that led to a moisture increase in western Tasmania and south-west Victoria”, whereas state that “strengthening of this narrow, warm surface water [Leeuwin Current] may increase convection and precipitation locally, resulting in shifted pressure gradients causing wetter conditions in south-east Australia… and south-west Australia” (i.e. not directly caused by stronger westerlies). The AOGCMs on the other hand indicate no change/slightly higher precipitation (only +0.3 %) at 6 ka relative to 0 ka with 65 % of models having higher precipitation, despite there being weaker westerly flow (see Fig. c). A similar anti-correlation exists in October to March where 71 % of the models simulate higher precipitation at 6 ka (+2.14 ± 1.39 % – significant, Supplement Fig. S1b) even though the westerlies are weaker (Supplement Fig. S1c). Interestingly, there is little consensus on reduced April to September rainfall (56 % of the models simulate reduced precipitation, Supplement Fig. S2b) when the westerlies should have their greatest influence on southern Australia precipitation through frontal cyclones .

As the physical processes responsible for precipitation can be diagnosed directly from the PMIP simulations, there is an opportunity to explain (i) why the mid-latitude westerlies are likely to have been weaker at 6 ka relative to 0 ka and, (ii) despite those weaker westerlies, precipitation over the TeS domain may have been equivalent to or slightly higher at 6 ka than around 0 ka – in agreement with both and .

Explaining the above may provide a useful alternative mechanism to account for periods in the past when the rainfall–westerly wind strength relationship may weaken or break down.

In order to explain the cause of the weaker mid-latitude westerlies at 6 ka (relative to 0 ka), the thermal wind balance equation is considered: ΔUgΔz=gfTΔTΔy, where ΔUg/Δz is the change in the zonal geostrophic wind (ΔUg, m s-1) with height (Δz, m – also called the vertical shear of the geostrophic wind with respect to height), f is the Coriolis parameter (s-1), T is the temperature at a reference point (K), g is the acceleration due to gravity (m s-2), and ΔT/Δy is the change in surface temperature (K) per distance of latitude (m). Following Eq. (), reducing equatorial surface temperatures and increasing them at high latitudes would reduce the ΔT/Δy term (i.e. weaker Equator-to-pole temperature gradient). If all other parameters are held fixed then ΔUg/Δz will also reduce. In order to identify whether the Equator-to-pole temperature gradient has changed, the following calculation is undertaken:

DTep=T‾30N to 30S-T‾60S to 90S, where T‾30N to 30S is the area averaged surface temperature (K) between 30∘ N and 30∘ S, T‾60S to 90S is the area averaged surface temperature (K) between 60 and 90∘ S and DTep is the Equator-to-pole temperature difference (K). The values of DTep are calculated for each individual model and plotted in Fig.  for the 0 ka simulations (white box, left axis), the 6 ka simulations (amber box, left axis) and the difference in DTep for 6 ka relative and 0 ka (pink box, right axis). The median DTep from the models is 44.7 K for 0 ka and 44.0 K for 6 ka; however, all 32 models simulate a reduction in the difference in temperature between the Equator and poles (pink box plot – upper whisker is less than zero). The weaker Equator–pole temperature gradient is consistent with lower insolation in the tropics and higher insolation at high latitudes at 6 ka compared to 0 ka (Fig. b). Therefore, given the insolation and surface temperature characteristics of the PMIP simulations visible in Figs.  and (respectively), the westerly winds should be weaker in the mid-latitudes at 6 ka relative to 0 ka (as seen in Fig. c).

So, given that there is a physically plausible reason why the westerlies would have been weaker at 6 ka relative to 0 ka, why is there little change to the annual mean rainfall (or even a tendency for a small increase)? To answer this question, the seasonal cycles of insolation, temperature and precipitation are plotted for the 0 and 6 ka simulations in Fig. a–c and for 6 - 0 ka in Fig. d. At 0 and 6 ka the insolation peaks in December and is lowest in June; however, at 6 ka insolation is higher in July to November and lower in December to May than at 0 ka. There is also a shift in the seasonal surface temperature and local SST with higher surface temperatures at 6 ka relative to 0 ka between August and January. Between August and November there is lower convective precipitation and, between December and June, higher convective precipitation in the 6 ka simulations relative to 0 ka. This suggests that the non-convective rainfall (e.g. frontal rain) is not changing. Furthermore, in April to June, the convective precipitation has increased more than the total precipitation change, which indicates that the non-convective rainfall has actually reduced during those months. So, there has been an increase in convective rainfall in the PMIP models (through increased frequency and/or intensity), which has compensated for any reduction in precipitation caused by the weaker westerlies and is consistent with . Nevertheless, the flow in the models is still predominantly westerly over the TeS at 6 ka in agreement with despite being slightly weaker than at 0 ka. Overall, the model assessment given above provides an example of where the relationship between westerly wind strength and precipitation could weaken through changes in convective precipitation (which can be directly diagnosed from the models). Such a process would be very difficult to decipher from the proxies and shows where model data may be useful to elucidate the key processes that induce past climatic states.

There is, however, an important caveat associated with the model-derived precipitation estimates for 6 ka. The coarse resolution of the models means that surface topographical features on the land are not represented well. Therefore, the impact of such topography on the prevailing circulation and precipitation would also be misrepresented. Areas where such a problem may be important are over the Great Dividing Range and Tasmania. It is logical to conclude that the misrepresentation of topography may also be contributing to any interpretation of the models' simulated climate. The only way to resolve such an issue would be to run high-resolution regional climate model simulations over the TeS zone driven by output from fully coupled, multi-millennial transient global model simulations. A measure of the time-dependent change in the circulation and its interaction with the land surface could then be assessed. Such model simulations have been shown to improve the representation of present day precipitation over Southern Alps of New Zealand and have also been applied to simulations of 6 ka .

The proxies suggest that SSTs were around 284.2 K in the NSO region for the annual mean at 6 ka. The HadISST-derived 1870–1899 SSTs are 284.0 K for the NSO domain. Therefore, SSTs in the NSO are almost identical at 6 and 0 ka. The models also simulate little change in SST between 6 and 0 ka (0.04 ± 0.04; see Fig. a). Therefore, the models and proxies agree that the SSTs in the NSO region at 6 ka are likely to have been very similar to 0 ka. The lack of any SST change in NSO is also consistent with the insolation changes between 40 and 50∘ S, which are negligible (see Fig. 2b).

SSTs in the SSO zone for February are estimated to have been approximately 278.7 K . The HadISST-derived 1870–1899 mean February SSTs are also 278.7 K for the SSO region. Therefore, as with the NSO, SSTs in the SSO are almost identical at 6 and 0 ka from the proxy evidence.

The February SSO multi-model mean SSTs in the 0 and 6 ka simulations are 280.3 and 280.4 K, which are > 1.5 K higher than the HadISST and estimates given above. Furthermore, the models also simulate higher SSTs in February at 6 ka relative to 0 ka (approximately 0.11 K) with 63 % agreement. Higher SSTs are also visible for the annual mean (0.20 ± 0.07 K – significant; see Fig. a), with very high model agreement (91 %). It appears that the model SSTs are responding to the higher annual mean insolation at 6 ka relative to 0 ka (see Fig. b), which is not seen in the proxy estimate. While there are acknowledged spatial and temporal gaps in the proxy data for the Southern Ocean that may cause the slight disagreement outlined above , there are also significant known deficiencies in the model simulations within this region.

have shown that the cloud cover fraction over the Southern Ocean is too low within the CMIP3 models, which leads to a positive bias in the amount of solar radiation absorbed at the ocean surface. Furthermore, this cloud-related bias in the absorbed solar radiation approximately +8 W m-2; is still present in the next generation of coupled climate models (CMIP5 and PMIP3) and corresponds with positive SST biases see Fig. 9.2 and 9.5 in. Approximately 91 % (70 %) of the models simulate higher February (annual) mean SSTs between 50 and 60∘ S at 0 ka relative to HadISST, which is consistent with the known cloud cover and radiation errors described above. Other work by shows that the positive SST biases are strongest in the SH summer and autumn, which corresponds with the time that the cloud cover related errors in the absorbed solar radiation are at a maximum (i.e. when insolation is highest). Nevertheless, attribute the Southern Ocean warm bias to errors in the meridional overturning circulation and not the cloud radiative errors.

Overall, it is clear that there are large errors in the Southern Ocean circulation within AOGCMs that could be caused by different processes (e.g. cloud radiative forcing and the meridional overturning circulation), which may enhance (or even dampen) the SST response to a change in insolation. Therefore, while the simulated higher 6 ka SST (relative to 0 ka) in the SSO for February (∼ 0.1 K) and annually (∼ 0.2 K) may be a manifestation of these errors (especially given the large mean state bias). Given limited proxy records and the fact that they are only representative of summer conditions, estimates of seasonal or annual mean SSTs at 6 ka (and other periods) are necessary to enable better model–proxy validation. Nevertheless, improving the representation of the atmosphere and ocean at high southern latitudes should be a priority given the known errors that exist in both CMIP3 and CMIP5 .