Abstract
Global warming is increasing due to the ongoing rise in atmospheric greenhouse gases, and has the potential to threaten humans and ecosystems severely. Carbon dioxide, the primary rising greenhouse gas, also enhances vegetation carbon uptake, partially offsetting emissions. The vegetation physiological response to rising carbon dioxide, through partial stomatal closure and leaf area increase, can also amplify global warming, yet this is rarely accounted for in climate mitigation assessments. Using six Earth System Models, we show that vegetation physiological response consistently amplifies warming as carbon dioxide rises, primarily due to stomatal closure-induced evapotranspiration reductions. Importantly, such warming partially offsets cooling through enhanced carbon storage. We also find a stronger warming with higher leaf area and less warming with lower leaf area. Our study shows that the vegetation physiological response to elevated carbon dioxide influences local climate, which may reduce the extent of expected climate benefits offered by terrestrial ecosystems.
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Introduction
Increasing atmospheric greenhouse gas (GHG) levels, caused by the human burning of fossil fuels and land-cover and land-use change, substantially influence human society and ecosystems1. As atmospheric carbon dioxide (CO2) concentration (the dominant changing GHG) rises, it enhances carbon sequestration2,3, and currently offsets about 30% of CO2 emissions4. The increased carbon removed by vegetation results in complex land biogeochemical cycling (BGC) changes, many of which further affect climate5. In addition to the BGC effects, ecosystems with increased canopy cover generally have negative (i.e. cooling) biophysical climate feedbacks under current atmospheric CO2 levels6,7,8, further strengthening the capacity of terrestrial ecosystems as natural solutions to curb climate change. Large-scale afforestation is, therefore, offered as a promising and cost-effective climate mitigation solution9,10,11. However, knowledge gaps exist in whether the strength and even if the sign of the biophysical climate feedbacks will persist from current to future atmospheric CO2 concentrations. One such knowledge gap is the quantification of how CO2-driven physiological changes will influence near-surface air temperature, and how these compare against broader mitigation gains by enhanced carbon sequestration that slows the rise in global mean temperatures.
Multiple vegetation characteristics drive synergistic changes of vegetation biophysics, mainly through changes in leaf area index (LAI) and stomatal conductance. Different levels of such changes may force different biophysical feedbacks on climate as CO2 rises11. Indeed, increasing atmospheric CO2 induces simultaneously an enhancement of photosynthesis which often increases LAI, and a reduction in stomatal conductance12,13,14. Recent analyses that focus on these physiological feedbacks (PHY) imply that they may exacerbate future heat extremes15,16 and amplify regional and global warming17,18. However, the contributing processes of PHY feedbacks that affect local warming changes remain highly uncertain and are rarely considered in the assessments of the mitigation capacity of the terrestrial ecosystem. Evaluation of any extra PHY-induced warming is required, as its magnitude will influence emissions compatible with given overarching global temperature goals19.
Thus, our primary objective is to evaluate the overall mitigation capability of terrestrial ecosystems by comparing the PHY-induced warming against the BGC-driven temperature cooling caused by terrestrial carbon accumulation. To do so, we investigate the vegetation feedbacks of PHY, at different atmospheric CO2 levels and across the global vegetated land, on near-surface air temperature change. We use simulations from six Earth System Models (ESMs; see “Methods”) with a cumulative 1% per year increase in atmospheric CO2 concentration ranging from 284 to 1132 ppm over 140 years20. We find that PHY consistently amplifies global warming as CO2 rises, partially offsetting cooling through CO2-driven carbon storage increase. We attribute the PHY-induced temperature increase primarily to stomatal closure-induced evapotranspiration reductions. We find more PHY-based warming at locations with higher LAI (e.g. tropical forests) and less warming with lower LAI (e.g. Australia). We highlight that vegetation physiological response to increasing atmospheric CO2 amplifies warming, which requires consideration with the cooling effect vegetation offers by “drawing down” atmospheric CO2.
Results and discussion
Global vegetation physiological response to increasing atmospheric CO2 and its reduction of mitigation potential
We calculate different effects of increasing CO2 on mean annual near-surface air temperature change over global vegetated land. We compare the direct atmospheric radiative effect (RAD)-induced climate warming to the temperature reductions caused by the BGC effect. These two global temperature changes are, in turn, then compared against our main focus of aggregated local PHY-induced temperature contributions (PHYall; Fig.1). The main finding is that the spatial aggregation of PHY feedbacks on temperature over global vegetated land (green bars) offsets a substantial amount of the cooling effect through enhanced terrestrial carbon storage because of the BGC effects (blue bars). Terrestrial carbon storage continuously increases with rising atmospheric CO2, and reaches a global total of 621 ± 260 Pg C under 4 × CO2 (Supplementary Fig. 1). This increased land carbon storage is equivalent to 293 ± 122 ppm of CO2 removal from the atmosphere and results in a temperature cooling of −1.24 ± 0.57 °C. The PHYall-induced land temperature increase is 0.83 ± 0.47 °C, from the ensemble of five ESMs we use (here we excluded bcc-csm1-1; see “Methods”), corresponding to a large offset of the cooling effect by terrestrial ecosystems through BGC. However, our estimated temperature cooling induced by the BGC effect is a transient response. This cooling effect will be larger for subsequently stabilised CO2 concentrations, since terrestrial ecosystems continue to fix carbon until reaching equilibrium. We note that BGC-induced cooling may be overestimated in the absence of land-cover change effect in the simulations, as the latter may reduce terrestrial carbon stores (Supplementary Fig. 2). However, inter-model differences, such as different parameterisation or different biogeochemistry module20, may prevent a definitive answer as to how land-cover change influence global temperature change through BGC (Supplementary Fig. 2–5). We also note that we focus our analysis on CMIP5 data mainly due to the fact that the parameters in Eqs. (1) and (2)21,22 (see “Methods”) for calculating BGC-induced cooling are only currently available for CMIP5 ESMs.
The RAD response to rising CO2 projects a global warming of 5.25 ± 0.65 °C, again under 4 × CO2, corresponding to roughly four times the magnitude of BGC-induced cooling. Hence, PHYall-induced temperature increase adds about 16 ± 8% to the RAD-induced warming globally. The relative magnitude of warming/cooling effects is similar for the lower CO2 levels of 500 ppm and 800 ppm (Fig. 1), illustrating the importance of accounting for PHYall-induced warming and how it affects the ability of terrestrial vegetation to mitigate global warming, irrespective of CO2 concentration. Our identified PHYall feedbacks are a combination of different altered land properties. The balance of changed land components will depend on location, and so spatial variations in the overall warming effect may suggest a reappraisal of some climate change adaptation measures. Hence to aid such assessments, we now consider in detail the global contributions of individual drivers of the PHY-based local warming, and then any geographical variations.
In Fig. 2a, we show changes in global vegetated land air temperature associated with PHYall with increasing atmospheric CO2 from our ensemble of six ESMs (see “Methods”). PHYdir (see “Methods”; Supplementary Table 1) is based on the direct CO2 effect on vegetation physiology. PHYall represents all the vegetation physiology-related feedbacks, and captures any additional interactions between RAD and PHYdir, due to all effects not being a simple linear addition of RAD and PHY responses. The difference, PHYall minus PHYdir, is termed PHYint. Although inter-model difference exists, warming levels are projected to increase, by all the ESMs, as CO2 concentration rises, and for both PHYdir and PHYall (Fig. 2a). The interactive effects result in PHYall-induced temperature change being higher than PHYdir throughout the period. For the smaller increases in atmospheric CO2 of up to ~450 ppm, interactive effects dominate with PHYdir being almost zero up to that concentration. However, above the CO2 concentration of 450 ppm, PHYdir increases global warming reaching 0.17 ± 0.06 °C for CO2 of 500 ppm, and climbing to 0.62 ± 0.48 °C under quadrupled atmospheric CO2 (Fig. 2a). At that 4 × CO2 level, interaction term PHYint increases warming by approximately one-fifth of that induced by PHYdir. Hence, the overall physiological feedbacks described by PHYall produce a global temperature increase of 0.74 ± 0.47 °C under 4 × CO2 (again from multi-model mean of six ESMs; see “Methods”).
To better understand the factors influencing CO2 physiological drivers of temperature change, we decompose the global PHYall into individual biophysical components6 (see “Methods”). These five aspects are albedo, aerodynamic resistance (ra), evapotranspiration (ET), downwelling shortwave radiation (SW) and near-surface air emissivity (ɛa). These five-component changes result in climate forcings with different signs and magnitudes (Fig. 2b), and thus perturb the surface energy balance, where positive values correspond to an increase in temperature. Specifically, ET, SW, and albedo generates positive climate forcings that increase local temperature, while ra and ɛa produce negative effects and thus offset local temperature increase. The relative role of each biophysical component in influencing PHYall-induced temperature change remains largely invariant in the transition from low to high CO2 concentrations. In addition, the changes in these five quantities affecting PHYdir show similar variations with that of PHYall as atmospheric CO2 rises (Supplementary Fig. 6). Those results are generally valid for CMIP6 results but with a lower magnitude of change (Supplementary Fig. 7).
The vegetation physiological response to rising CO2 causes changes in LAI and stomata closure, which adjust in parallel with more detailed attributes of the land surface. The ESM simulations of changes in LAI and transpiration compare moderately well with available field measurements23,24,25,26,27,28,29,30,31,32 (Supplementary Fig. 8). Here we focus on their effects on the near-surface thermal changes, expressed as climate forcings on near-surface energy fluxes (Fig. 2b). The LAI increase due to elevated CO2 (Supplementary Fig. 9a) leads to decreases in albedo11,33 (Supplementary Fig. 10a; Supplementary Table 2) and ra7 (Supplementary Fig. 10c). These changes are continuous with rising atmospheric CO2 and result in positive and negative effects on global land air temperature change, respectively (Fig. 2b). Specifically, the albedo reduction increases solar radiation absorption by the land surface11,33, and imposes a positive forcing of 0.30 ± 0.14 W m−2 for 500 ppm, and of 0.51 ± 0.45 W m−2 for a quadrupling of CO2 (Fig. 2b). The decreased ra favours the turbulent transport of heat from land to atmosphere7, and leads to a persistent surface cooling with increasing atmospheric CO234,35, which is −1.66 ± 0.44 W m−2 for 500 ppm and −3.15 ± 2.05 W m−2 for 4 × CO2. In contrast, ET decreases considerably (Supplementary Fig. 10e; Supplementary Table 2) due to decreased stomatal conductance responding to increasing atmospheric CO2. These reductions in ET reduce evaporative cooling15,17, and therefore result in a strong positive climate forcing on global warming (Fig. 2b), which increases with rising atmospheric CO2 and reaches 0.88 ± 0.25 W m−2 for atmospheric CO2 of 500 ppm and 2.88 ± 1.49 W m−2 with quadrupled atmospheric CO2 concentration (Fig. 2b).
Moreover, ET reduction decreases the inflow of evaporative water to the atmosphere, reducing cloud fraction and water vapour content, thereby feeding back to impose indirect effects that influence temperature change. We quantify these indirect effects through their effects on the components of SW and ɛa, (Fig. 2b). The lower ET values reduce cloud fraction (Supplementary Table 2; Supplementary Fig. 9c) thereby increasing the amount of SW to the land surface6,35,36 (Supplementary Fig. 10g), and producing a moderate positive forcing of 0.67 ± 0.16 W m−2 for 500 ppm and 1.76 ± 1.26 W m−2 under 4 × CO2 (Fig. 2b). However, the reduced cloud fraction and water vapour content decreases ɛa (Supplementary Fig. 10i), which weakens the absorption of longwave radiation37 (Supplementary Fig. 9e). The ɛa changes result in a small negative forcing (−0.28 ± 0.11 W m−2 for 500 ppm and −0.57 ± 0.43 W m−2 for 4 × CO2), and thereby lowering global temperatures. Of particular note is that the direct changes in ET produce the strongest positive forcing for warming (Fig. 2b). Hence, ET changes have a dominant role in influencing the magnitude and sign of PHYall feedbacks on global temperature change. The changes in these five factors for PHYdir are very close in relative terms to those of PHYall (Supplementary Fig. 10), driving climate forcings in a similar magnitude and sign to influence PHYdir-induced global temperature change (Supplementary Fig. 6).
Overall, as CO2 rises, these five biophysical factors cause vegetation to amplify warming locally, and when aggregated spatially act to raise planetary global warming. This additional warming effect is primarily driven by changes in ET, with smaller warming contributions from changes in SW and albedo, but also compensated with cooling effects from ra and ɛa changes. The substantial role of ET on biophysical climate feedbacks is consistent with a previous study6, which investigates the biophysical feedbacks because of vegetation greening. That study proposed that CO2-driven greening will enhance ET and thereby producing a net cooling effect. We build on that analysis by here additionally including the CO2-induced partial stomatal closure17,18. We find this inclusion overtakes the LAI influence on ET changes, resulting in large reductions in ET that will instead contribute to net warming as CO2 rises. Large inter-model differences in simulating ra dynamics (Supplementary Fig. 10c, d) mostly explains the substantial spread of ra-induced climate forcing (Fig. 2b). The different extents of LAI increase (Supplementary Fig. 9a, b) also contribute to such differences in simulated ra changes and effects on temperature change. Our noted large cooling through ra changes is also indicated in a recent study7, suggesting the importance of ra in affecting vegetation biophysical climate feedbacks, and the importance of constraining this factor in climate models.
Spatial patterns and attributions of vegetation physiologically induced warming
We present in Fig. 3 the area-weighted regional contributions to global temperature change of the physiological responses and BGC under 4 × CO2. PHYall-based warming and BGC-induced cooling both show larger values in East and Central North America (ENA and CNA), North and Central Europe (NEU and CEU), Amazon (AMZ) and North Asia (NAS). Whereas, the smallest changes are in the Sahel region (SAH) (Fig. 3a). PHYall-induced warming reduces large proportions of the temperature cooling through BGC in the northern mid-to-high latitudes (Fig. 3b). Furthermore, this cooling effect is fully offset by warming through vegetation physiological response in Alaska (ALA), Canada/Greenland/Iceland (CGI), NAS, NEU, SAH, Tibetan Plateau (TIB), Central (CAS) and West Asia (WAS), and West North America (WNA), resulting in slight warming in these regions. Presented as a map of the net effects of BGC and PHYall (Fig. 3b), we further illustrate this overall cooling effect from PHY and BGC for the tropical regions and South Hemisphere. Additionally, the balance of PHY and BGC to influence regional temperature change under relatively low atmospheric CO2 level of 500 ppm (Supplementary Fig. 11) is very close to that for 4 × CO2, suggesting consistency of vegetation biophysical and biogeochemical effects on climate irrespective of CO2 levels. In summary, the PHYall feedbacks offset the cooling benefits from ecosystem “draw-down” of CO2 to a large extent, in line with the global average values presented in Fig. 1. Northern mid-to-high latitudes may contribute less than expected to slow global warming (Fig. 3). However, as also noted for the global change values, the estimated temperature cooling by BGC is a transient effect that would keep increasing as the ecosystems approach their equilibrium. However, BGC-induced cooling may be overestimated without consideration of the effect of land-cover change (Supplementary Fig. 2), which is often associated with the deliberate removal of terrestrial carbon.
The regional pattern in Fig. 3b provides a motivation to investigate further the five climate forcings-driven PHYall contributions (Fig. 2a) to near-surface temperature change triggered by rising CO2. Here, we first examine the much finer spatial patterns and component contributions of PHYall, PHYdir and PHYint on warming for 4 × CO2. Focussing on PHYdir and typically for 4 × CO2, there is a larger local warming in the tropical forests (Fig. 4a), where vegetation shows higher potential to stabilise carbon than other ecosystems10 (Fig. 3). Within these tropical regions, the PHYdir-forced temperature increase is the highest in the Amazon forests (Fig. 4a), reaching approximately 30% of that induced by RAD (Supplementary Fig. 12b). Strong warming by PHYdir is also found in the northern mid-to-high latitudes (>40°N). In contrast, smaller temperature increases due to PHYdir are seen in arid and semi-arid regions, such as Australia and Sahel (Fig. 4a). The spatial variations of PHYall-forced temperature change (Supplementary Fig. 13a) have strong similarities to those of PHYdir (Fig. 4a). These similarities again suggest a relatively small role of interactions on temperature change (Supplementary Fig. 13b) under quadrupled CO2. Additionally, the agreements across the six ESMs are reasonably high, with all the models agreeing that PHYall, PHYdir and PHYint result in local warming for 4 × CO2 across most of the global vegetated land (Supplementary Fig. 14).
We next analyse spatially the decomposition of PHYall- and PHYdir-induced temperature change into the five biophysical factors shown in Fig. 2b, and with findings shown in Fig. 4b–f. In response to quadrupled CO2, LAI shows increases over most global vegetated land (Supplementary Fig. 15b), leading to a positive climate forcing (and thus warming) through reducing albedo (Fig. 4b), and especially in the south Sahel and Tibetan Plateau. Moreover, a large albedo decrease occurs in the northern mid-to-high latitudes, such as for most of Siberia. This albedo decrease may be due to LAI increase combined with reduced snow cover promoted by PHY-driven warming17. The LAI increase also contributes to a strong negative forcing through ra decrease7. This ra decrease enhances the energy exchange between land and atmosphere, lowering the local warming effect in response to 4 × CO2, particularly in Australia, Sahel, South Africa and South Asia (Fig. 4c). In comparison, the projected forcing from ET reductions (Supplementary Fig. 15d) causes strongly positive warming, and especially in the tropical and boreal forests (Fig. 4d). The larger ET reductions in the tropical and boreal forests also lead to stronger cloud fraction decreases (Supplementary Fig. 15f). These feedbacks induce a positive climate forcing for additional warming by increasing SW reaching the land surface (Fig. 4e). Simultaneously, the decreased cloud cover and water vapour by the ET reductions generate a cooling effect (Fig. 4f) through decreasing net longwave radiation absorption in most locations. The PHYdir-induced warming and the associated climate forcings at an atmospheric CO2 concentration of 500 ppm (Supplementary Fig. 16) show similar spatial distributions, but smaller magnitude, compared with that adjustment for 4 × CO2 increase (Fig. 4). This result manifests that PHY continues to amplify global warming through the combined climate forcings from our identified five biophysical factors, irrespective of CO2 concentration.
Substantial differences exist between PHYall- and PHYdir-induced temperature changes in northwestern Eurasia, implying pronounced PHYint-based feedbacks there under 4 × CO2 (Supplementary Fig. 13b). A relatively large change in SW, inducing a warming effect (Supplementary Fig. 13j), mainly contributes to the large PHYint-coupled changes in this region. Elsewhere, for the Amazon forests, the cooling effect in PHYint, through changes in ET (Supplementary Fig. 13h) and SW (Supplementary Fig. 13j), cancels out the warming due to changes in ra (Supplementary Fig. 13f) and ɛa (Supplementary Fig. 13l). This cancellation causes negligible PHYint feedback on temperature there (Supplementary Fig. 13b). In summary, local warming due to biophysical feedback of increasing CO2 is regulated primarily by the positive forcing from ET reductions (with smaller positive forcings from albedo and SW changes), which is partly compensated by reductions in ra (and a small contribution from ɛa).
Strong physiologically induced warming in dense ecosystems
We further investigate the finding presented in Fig. 4 that PHYdir-forced temperature increase is higher in the tropical forests, while lower in the arid and semi-arid ecosystems. This finding suggests that there may be a relationship between the forced temperature change and background baseline LAI, which we confirm in Fig. 5a (for both PHYall and PHYdir). We find widespread warming amplification with increasing LAI for lower background LAI levels. Such rates of increase in temperature per unit of LAI show evidence of flattening at higher LAI values38. That is, the CO2-fertilised LAI increase saturates in ecosystems with dense canopy cover such as tropical forests (Supplementary Fig. 17a). However, the stomatal closure-induced ET decrease varies almost linearly with baseline LAI, although the reduction breaks down when the baseline LAI approaches six (Supplementary Fig. 17b). Hence, we conclude that ET reductions caused by stomatal closure are the primary cause of PHY-induced temperature rises, although changes in ra because of LAI increases remain an important factor (Fig. 4; Supplementary Fig. 13). Taking all the components together, PHYdir-triggered temperature change increases almost linearly with baseline LAI gradients under 4 × CO2 (Fig. 5a). Of particular interest is that when expressing PHYdir-induced temperature increase as a fraction of warming induced by RAD, it also increases with the baseline LAI value (Fig. 5b). Hence, Fig. 5 provides strong evidence that background vegetation functional structure (LAI) influences the feedback of CO2 physiological forcing on warming in response to 4 × CO2. In terms of change, as the trend of global LAI increase slows down as atmospheric CO2 concentration increases to very high levels (Supplementary Fig. 9a, b), this suggests that the magnitude of the cooling effect (via ra) will decrease relative to the ET-based warming.
Implications and conclusions
Terrestrial ecosystems respond to increasing atmospheric CO2 not only through accumulating carbon (the “BGC” effect) but also through their physiological response (the “PHY” feedback), which can have opposing effects on local surface temperature. As such, to fully understand the net climate benefits of terrestrial ecosystems, comprehensive assessments need to account for both PHY and BGC effects39,40. We find that the warming through PHY feedback largely reduces the capacity of vegetation to slow global warming through BGC. In particular, vegetation transiently operates to amplify local warming in the northern mid-to-high latitudes, as PHY-induced warming is larger than BGC-induced cooling (Fig. 3). Tropical forests have net cooling effects, and forests and ecosystem restoration efforts there would have much higher net cooling benefit for mitigation than if they are placed in the northern mid-to-high latitudes. At the global scale, this PHY-induced warming can offset by up to 67% of the cooling gains from the transient BGC effect (Fig. 1). This is likely an upper-bound value, because the steady-state BGC effect is larger than the transient one. Thus, afforestation can still result in a net cooling effect, especially in the tropics, although the cooling achieved may be less than first expected. Of particular interest is that the PHY-based warming is generally stronger for high baseline LAI values (Fig. 5). This result further suggests the PHY produces extra warming through afforestation due to higher LAI for forests than non-forests. However, this does not contradict the finding for the tropics (where LAI is high) as possibly the most beneficial regions for afforestation, due to the compensating much larger BGC potential to lower temperature for such locations.
Our analysis highlights the importance of including PHY feedbacks in any assessments of future levels of global warming. Such understanding may be especially important for the northern mid-to-high latitudes. We point out that only one-third of ESMs we use incorporate dynamic global vegetation schemes (DGVMs). Since land-cover change acts to influence PHY and BGC effects (Supplementary Fig. 2–5), future simulations with dynamic vegetation may allow refining these results. However, we note that inter-model structural differences (e.g. different ratio of transpiration to ET15,41) contribute to large uncertainties in these results. In particular, we note the potential implications of our results for climate mitigation policies with substantial reforestation as a mechanism to slow global warming through emissions offsetting. Reforestation may cause a near-surface cooling through the BGC effect. However, the PHY-based warming effect is stronger for higher LAI values as atmospheric CO2 rises (Fig. 5a). This must be accounted for in any major global reforestation plans with consideration of particular location of reforestation measures42. In general, the effects of anthropogenic land use and land cover change on vegetation biophysical and biogeochemical processes are important and complex with substantial spatial heterogeneity42,43,44,45,46,47. Given the lack of exploitable factorial simulations designed to separate land use and land-cover change effects, these are ignored in the idealised simulations, and may introduce a bias in our results. We note, however, that these idealized “4 × CO2” simulations broadly capture the sign and spatial distributions of land-atmosphere coupling effects on temperature change through comparisons to the ESM results under the RCP8.5 scenario (Supplementary Fig. 18) which include anthropogenic land use and land-cover change. We suggest that future climate projections should much more routinely account for the effect of anthropogenic land use and land-cover change together with PHY and BGC effects42,47, and so that this can be considered in reforestation climate mitigation strategies.
An additional caveat of our research is that the PHY and RAD factorial ESM simulations that inform our analysis, are at present only available for the illustrative but potentially unrealistic exponential increase in atmospheric CO2 (1% per year). For this scenario, the systems can exhibit a unrealistic linear behaviour48. PHY and RAD simulations under other scenarios, such as abrupt 4 × CO2 or even historical forcing followed by a potential future scenario, would greatly help to improve projections of PHY feedbacks. We suggest this should be made a high priority of climate modelling exercises, especially if additionally including land use and land-cover change representation. We recognise that the newer CMIP6 ESMs contain improved representation of many processes (e.g. atmospheric aerosols, clouds, and land processes)49. We anticipate our findings to remain broadly valid with the CMIP6 models, although the magnitude of PHY-induced warming is lower for CMIP6 than CMIP5 ESMs18, especially in the northern high latitudes. We hope our analysis provides an incentive to others to undertake such analysis as all the calculations (BGC, PHY, and RAD) become available for that newer CMIP6 ensemble. In summary, our results illustrate that vegetation physiological response to increasing atmospheric CO2 has a substantial local warming effect, which requires consideration alongside the cooling effect vegetation offers by “drawing down” atmospheric carbon dioxide.
Methods
CMIP5 outputs
The results from six Earth System Models (ESMs) that participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5) were obtained from the CMIP5 archive (https://esgf-node.llnl.gov/search/cmip5/). We used the ESMs of bcc-csm1-150, CanESM251, CESM1-BGC52, HadGEM2-ES53, IPSL-CM5A-LR54, and MPI-ESM-LR55. For each model, we adopted the simulations of 1% per year cumulative increase in atmospheric CO2 concentration, which corresponds to a rise from 284 to 1132 ppm over 140 years. Anthropogenic land-use and land-cover changes are not included in these simulations. HadGEM2-ES and MPI-ESM-LR incorporate full dynamic global vegetation models, allowing natural vegetation coverage and competition changes with CO2 levels20. The remaining four ESMs do not include DGVMs, and therefore the nature vegetation distributions remain fixed over time, although they do project LAI changes. Any effects related to actual land-use change on temperature are not considered in this study. We selected this scenario, and these models, as projections are available with these ESMs that not only account for both radiative and physiological together (“1pctCO2”), but critically there are also factorial simulations of radiative only (“esmFdbk1”) and physiological only (“esmFixClim1”) effects20,56. The radiative only simulations include just the radiative effects of CO2, which means only the atmospheric system experiences the 1% per year cumulative increase in atmospheric CO2 concentration. In contrast, only the land surface responds to increasing atmospheric CO2 in physiological only simulations20. The monthly outputs we used are from these three scenarios include near-surface air temperature at 2 m, evapotranspiration (ET), transpiration, LAI, upwelling and downwelling shortwave radiation, upwelling and downwelling longwave radiation, cloud fraction, latent heat, sensible heat, vegetation, and soil carbon storage. These variables were averaged temporally to annual scale. We also re-gridded, spatially, to a common 1° × 1° grid, before further averaging inter-ESMs to obtain a multi-model ensemble.
We calculated the difference between the 1pctCO2 and esmFdbk1 simulations to evaluate the total PHY feedback (PHYall), as this allows the inclusion of any nonlinear interactions between CO2 physiological and radiative forcings15,17. The effects of RAD, however, were calculated as simply the difference between each of the 140 years in the esmFdbk1 experiment and its average over the first 20 years. We use esmFedbk1 simulations, rather than 1pctCO2 minus esmFixClim1, to directly calculate the RAD effects, to exclude any interactions between PHY and RAD, i.e. some warming-induced vegetation feedbacks. The estimated temperature change from five model ensemble was highly comparable with that from ensemble of six ESMs (see below for why the bcc-csm1-1 was not included in all the analyses). The direct PHY feedback (PHYdir) was calculated as the difference between each year over the whole study period in the esmFixClim1 and its average over the first 20 years. The interaction (PHYint) of CO2 physiological and radiative forcing was estimated as the difference between the PHYall and PHYdir calculations. We smoothed all the PHYdir- and PHYall-induced variable changes using a twenty-year running window. In addition, we used the averaged data of the final 20 years (~ 1032 ppm atmospheric CO2 concentration, nearing 4 × CO2) when presenting the spatial patterns of PHYall, PHYdir, and PHYint-related changes. The averaged LAI of the first 20 years, in the 1pctCO2 scenario from the ensemble of the six selected models, was considered as the baseline LAI (as required for Fig. 5). We screened out non-vegetated land locations with LAI < 0.1. Moreover, when considering any dependence on background LAI, we binned PHYall- and PHYdir-induced temperature change at a step of 0.1 LAI ranging from 0.0 to 6.0. This range accounted for 98% of LAI global distributions. We binned PHYall- and PHYdir-induced temperature change in the pixels with LAI value larger than 6.0 into the bin of 6 to include all the available vegetated pixels in the analysis.
Potential of terrestrial ecosystem carbon storage for climate mitigation
Estimates of carbon storage in vegetation (cVeg) and soil (cSoil) were obtained from the three scenarios of five ESMs (the bcc-csm1-1 model was not included since it does not have cVeg and cSoil outputs). The total carbon storage (cTot) of terrestrial ecosystem was calculated as the sum of cVeg and cSoil. As for other quantities, the annual cTot of each model was re-gridded to a common 1° × 1° grid for calculating a multi-model ensemble. The PHYall-induced cTot changes were calculated similarly, and based on the difference between the 1pctCO2 and esmFdbk1 scenarios.
To estimate the implication of increasing carbon storage changes due to the biogeochemical effect (BGC) on slowing the rate of warming (ΔTa), as derived from the PHY simulations, we calculate this as a function of the time-evolving CO2 increase and ESM-projected alteration to carbon storage change ΔcTot. We used the method from previous studies21,22, with the derived equation of:
Quantities, \({a}_{s}\), \({a}_{f}\), \({\tau }_{s}\), \({\tau }_{f}\), λ (radiative feedback parameter) and \(F\) (radiative forcing) were parameters22. Note, the estimated temperature change is transient and has inertia, as illustrated by the time-delay function in the integral of Eq. (1). Hence ΔTa would ultimately be larger for ecosystems approaching to an equilibrium. Three horizons of atmospheric CO2 concentration were selected of 500 ppm, 800 ppm and 1032 ppm (latter 4 × CO2), to account for ΔTa under different atmospheric CO2 concentrations. We converted the total carbon storage changes (Pg C) to equivalent atmospheric CO2 concentration (ppm) using a conversion ratio57 of 2.123 Pg C ppm−1. The regional contributions (Fig. 3) of increased carbon storage to global temperature change was then determined using the proportion of regional to global carbon storage change. The standard IPCC AR5 SREX regions (https://www.ipcc-data.org/guidelines/pages/ar5_regions.html) were used to provide the regional boundaries to such contributions.
Decomposition of CO2 physiological forcing feedback
We decomposed the feedbacks of PHYall and PHYdir into changes in five factors6, which was based on perturbations to the surface energy balance. Here, the resulted temperature change (still referred to ΔTa), was decomposed using the following equation:
Here \({T}_{a}\) represents air temperature, \(f\) is the conversion ratio of climate forcing to actual air temperature, SW is downwelling shortwave radiation (W m−2), \(\alpha\) is albedo, \(\lambda\) is the latent heat of vaporisation (J kg−1), ET is evapotranspiration (mm d−1), \({\varepsilon }_{s}\) is land surface emissivity, \(\sigma\) is Stephan-Boltzmann constant (5.67 × 10-8 W m−2 K-4), \({\varepsilon }_{a}\) is near-surface air emissivity, \(\rho\) is air density (1.21 kg m-3), \({C}_{d}\) is specific heat of air at constant pressure (1.013 J kg−1 K−1), \({T}_{s}\) is land surface temperature, \({r}_{a}\) is aerodynamic resistance (s m−1) and \({T}_{a}^{{{{{{{\rm{cir}}}}}}}}\)represents the influence of atmospheric circulation. Variable \({r}_{a}\) is estimated by inversion of the equation of sensible heat (H):
In addition, \({\varepsilon }_{a}\) was given by the net longwave radiation calculation:
where \({L}_{{{{{{{\rm{in}}}}}}}}\) was downwelling longwave radiation (W m−2), and \({L}_{{{{{{{\rm{out}}}}}}}}\) was upwelling longwave radiation (W m−2).
The five decomposed items, \(-{{{{{{\rm{SW}}}}}}}\triangle \alpha\), \(-\lambda \triangle {{{{{{\rm{ET}}}}}}}\), \(\triangle {{{{{{\rm{SW}}}}}}}\left(1-\alpha \right)\), \({\varepsilon }_{s}\sigma {T}_{a}^{4}\triangle {\varepsilon }_{a}\), and \(\frac{\rho {C}_{d}\left({T}_{s}-{T}_{a}\right)}{{r}_{a}^{2}}\triangle {r}_{a}\), respectively represent the climate forcing induced by changes in albedo (\(\triangle \alpha\)), evapotranspiration (\(\triangle {{{{{{\rm{ET}}}}}}}\)), downwelling shortwave radiation (\(\triangle {{{{{{\rm{SW}}}}}}}\)), air emissivity (\(\triangle {\varepsilon }_{a}\)) and aerodynamic resistance (\(\triangle {r}_{a}\)). Detailed derivation of Eqs. (3)–(5) are provided in the previous studies6,35, and we follow directly the methodologies of those analyses. As with the total PHY feedback, we used a 20-year moving average spanning the full atmospheric CO2 concentration ranges. The difference of the decomposition results between the PHYall and PHYdir simulations was calculated to identify the magnitude of the individual climate forcings contributing to the extra interaction-based warming, PHYint. The decomposition results of the final 20 years were retained in spatial format (i.e. not averaged to global temperature changes) to understand the spatial patterns of PHYall and PHYdir feedback on temperature change in response to 4 × CO2.
Data availability
The CMIP5 and CMIP6 model outputs are available in https://esgf-node.llnl.gov/search/cmip5/ and https://esgf-node.llnl.gov/search/cmip6/, respectively.
Code availability
All the analyses and figures are made using MATLAB 2019b, and the codes are available from the corresponding author on reasonable request.
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Acknowledgements
This study was supported by the National Natural Science Foundation of China (41988101 and 42001089). CH acknowledges the NERC National Capability Fund made to the UKCEH.
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S.P. designed the research; M.H. performed analysis; M.H., S.P., and C.H. drafted the paper; M.H., S.P., C.H., H.X., X.W., A.B., J.C., and T.G. contributed to the interpretation of the results and to the text.
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He, M., Piao, S., Huntingford, C. et al. Amplified warming from physiological responses to carbon dioxide reduces the potential of vegetation for climate change mitigation. Commun Earth Environ 3, 160 (2022). https://doi.org/10.1038/s43247-022-00489-4
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DOI: https://doi.org/10.1038/s43247-022-00489-4
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