We first examine changes in warm extremes and surface moisture fluxes as illustrated in Fig. 1 showing the difference in mean Boreal summer (June-July-August) daily maximum temperature (T MAX , Fig. 1a), warmest yearly maximum temperature (TXx, Fig. 1b) and evapotranspiration (ET, Fig. 1c), averaged over 20 year intervals (2020–2099), between the new and the default g s scheme (i.e., Experiment minus Control). T MAX increases commonly by ∼1 °C but by more than 1.5 °C over Western Europe and 2 °C in some regions. The impact of the new g s scheme on TXx is larger, reaching 5 °C over widespread regions. Not surprisingly, there is a strong similarity between the patterns of temperature increases, decreases in ET (Fig. 1c) and subsequent decrease in precipitation (Fig. 1d), consistent with our previous work39. We note that the difference in both T MAX and TXx between models is largest during the period 2040–2059 and decreases towards the end of the century. One possible explanation for this decrease is that at high leaf temperatures (ca. 30 °C), photosynthesis and stomatal conductance (and thus transpiration) are reduced due to photosynthetic inhibition (Fig. S2). This response to high temperature minimises the differences in transpiration between the models that originally resulted from the more conservative water use paramaterisation in the new scheme.

Figure 1 Difference (Experiment minus Control) in mean Boreal summer (June-July-August) (a) daily maximum temperature (T MAX , top row), (b) warmest maximum temperature (TXx, middle row) and (c) evapotranspiration (ET, bottom row) and (d) precipitation (mm day−1), averaged over 20 year intervals between 2020–2099. Stippling shows regions where differences are statistically significant at the 95% level using the student’s t-test and the false discovery method for field significance. This figure was created using NCLV6.2.1 (http://www.ncl.ucar.edu/). Full size image

Furthermore, the two g s schemes have different sensitivities to vapor pressure deficit (VPD), with the default model showing stronger sensitivity at high VPD (>3 kPa)38. Thus, as dryland expansion accelerates under climate change44 and the air temperature and VPD increase towards the end of the 21st century, the difference in predicted transpiration between the two models becomes smaller (Fig. S2 and related text), which potentially accounts for the smaller effect on T MAX and TXx compared to earlier in the century. Nevertheless, there are still large differences between the models across most of Eurasia at the end of the century (2 to 4 °C for TXx).

The increases in T MAX and TXx and a decrease in ET can be clearly seen in the probability density functions (PDFs, Fig. 2). There is a clear shift to the right for the PDF of T MAX and TXx, but the limits of the lower and upper tails are mostly unchanged. The new g s scheme does not lead to the emergence of temperatures not previously experienced across the region; rather, it leads to a much more frequent occurrence of hot temperatures. Clearly, this change is linked to a shift in the PDF of ET to the left, such that ET exceeding 4 mm day−1 is rare with the new g s scheme, but common using the old scheme.

Figure 2 Probability distribution function (PDF, %) of monthly mean Boreal summer (June-July-August) daily maximum temperature (T MAX , left plot) warmest maximum temperature (TXx, middle plot) and evapotranspiration (ET, right plot) over the period 2020–2099. Results using the new g s are shown in blue (i.e., experiment) and the default g s scheme is in black (i.e., the control). This figure was created using NCLV6.2.1 (http://www.ncl.ucar.edu/). Full size image

We next examined the influence of the change in g s on heatwave duration, frequency and intensity (see Methods for definition). The changes in heatwave duration and frequency were very small, but changes in heatwave intensity (HWI) were large (Fig. 3). During the earlier part of the century (2020–2039), there are regions of both increases and reductions in HWI indicating that the forcing associated with the change in g s is commonly smaller than internal model variability. However, by 2040–2059, the new scheme results in an increase in HWI everywhere, with particularly large increases over western Europe, western Russia and eastern China, where HWI increases by 6–7 °C. Similarly to the changes in T MAX and TXx, the magnitude of the increase in HWI decreases towards the end of the century, but remains higher than 5 °C in many regions.

Figure 3 Same as in Fig. 1 except showing the change in heatwave intensity (HWI). This figure was created using NCLV6.2.1 (http://www.ncl.ucar.edu/). Full size image

Discussion and Conclusion

The increase in future (2020–2099) simulated TXx resulting from changing the representation of g s is approximately 4–5 °C over Western Europe. This sensitivity to g s can be put into context by recognising that this change is equivalent to more than half the increase projected under RCP8.541 (>1370 ppm CO 2 equivalent in 2100) by an ensemble of climate models for 2081–210045. The change is similar to estimates reported for RCP4.541 (~650 ppm CO 2 equivalent at stabilization after 2100) and higher than those reported for RCP2.641 (~490 equivalent before 2100 and declining) by 2081–210046. It is also similar in magnitude to the estimate reported for the change in heatwave intensity under RCP8.547. The increases in TXx due to the change in the g s model and parameterisation are therefore of the size reported for large increases in greenhouse gases.

Over western and northern Europe, the changes in TXx and heatwave intensity due to a change in the representation of g s as reported here are similar in terms of both pattern and intensity when compared to studies which have linked these changes to projected increases in greenhouse gases46,47. There are regions where the improved parameterization of g s led to increases in temperature and improved simulations39, particularly between around 45–60°N. The increases predominately occurred across regions defined as evergreen needleleaf forest, Tundra and crop PFTs.

The stomatal parameterisation we used in ACCESS accounts for differences in stomatal behaviour between PFTs and is supported by a global synthesis of leaf-level stomatal data36, in line with both predictions from optimal stomatal theory27,48 and the leaf and wood economic spectrum49,50. This empirical basis lends support to the robustness of these model simulations, which highlight the role of stomatal conductance in influencing future heatwaves. Nevertheless, some uncertainties remain. First, the data behind this parameterisation are measured at leaf scale; it has not been confirmed that the differences among PFTs observed at this scale also emerge at canopy/ecosystem scale. In light of our results, there is an urgent need for future work which tests how the stomatal parameterisation (g 1 , the sensitivity of the conductance to the assimilation rate, see material and methods) scales from the leaf to the canopy/ecosystem. Secondly, we have assumed all vegetation to have the same drought sensitivity. Observations suggest that vegetation adapted to different hydroclimates have different sensitivity51, which has significant consequences for ecosystem-scale water flux during drought periods52. A generic parameterisation for varying drought sensitivity across different vegetation types is another important priority.

We note a further significant caveat to our study: the ACCESS 1.3b climate model, in common with all climate models, has biases in its simulation of extremes42. The new g s parameterisation resolves some of these biases, at both site38 and global scales38,39. We also note that heatwaves are coupled phenomenon linking large-scale synoptic conditions, persistence, boundary layer coupling and land processes21. While ACCESS 1.3b is similar to other models in its representation of land-atmosphere coupling strength53 it remains a limitation to our study that we used a single climate model. We therefore encourage other groups to repeat our experiments to see if they can be generalized. Our results are also influenced by our use of a prescribed monthly climatology of leaf area index (LAI) derived from remote sensing estimates (see Methods). By prescribing the LAI, we are not allowing increases in leaf area due to CO 2 to reduce any CO 2 induced “water savings”. A model inter-comparison study54 which examined the response to elevated CO 2 at two free-Air CO 2 enrichment experiments found that even when LAI was not prescribed, the land surface component of ACCESS, i.e., CABLE, predicted modest changes in LAI (~5% increase). This result suggests that the use of prescribed LAI is unlikely to affect the results shown here for ACCESS, but clearly this may vary in other climate models. As both simulations prescribed the same LAI, the result is robust to assumptions of leaf area and CO 2 and instead highlights the direct impact of the change in g s scheme and parameterization. Nevertheless, we plan to investigate the influence of prognostic LAI between the two schemes in future work.