Vegetation dynamics and plant CO 2 responses as positive feedbacks in a greenhouse world

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1 GEOPHYSICAL RESEARCH LETTERS, VOL. 36, L11706, doi: /2009gl038217, 2009 Vegetation dynamics and plant CO 2 responses as positive feedbacks in a greenhouse world Ryouta O ishi, 1 Ayako Abe-Ouchi, 1,2 I. Colin Prentice, 3 and Stephen Sitch 4,5 Received 21 March 2009; revised 6 May 2009; accepted 13 May 2009; published 6 June [1] An atmosphere-ocean-vegetation coupled model is used to quantify the biogeophysical feedback that emerges as vegetation adjusts dynamically to a quadrupling of atmospheric CO 2. This feedback amplifies global warming by 13%. About half of it is due to climatically induced expansion of boreal forest into tundra, reinforced by reductions in snow and sea ice cover. The other half represents a global climatic effect of increased vegetative cover (an indirect consequence of plant physiological responses to CO 2 )inthe semi-arid subtropics. Enhanced absorption of shortwave radiation in these regions produces a net surface warming, which the atmosphere communicates poleward. The greatest vegetation-induced warming is co-located with large, vulnerable carbon stores in the north. These lose carbon, so that in the long term, the biospheric response to CO 2 and climate change becomes dominated by positive feedbacks that overwhelm the effect of CO 2 fertilization on terrestrial carbon stocks. Citation: O ishi, R., A. Abe-Ouchi, I. C. Prentice, and S. Sitch (2009), Vegetation dynamics and plant CO 2 responses as positive feedbacks in a greenhouse world, Geophys. Res. Lett., 36, L11706, doi: /2009gl Introduction [2] Increasing atmospheric CO 2 concentration and global warming [Meehl et al., 2007] are expected to change the distribution of vegetation [Cox et al., 2000; Joos et al., 2001; Cramer et al., 2001; Scholze et al., 2006; Fischlin et al., 2007] with potential additional effects on climate through alteration of land surface properties [Bonan et al., 1992; Thomas and Rowntree, 1992; Chalita and Le Treut, 1994; Betts, 2000; Gibbard et al., 2005] and carbon stocks [Cao and Woodward, 1998; Dufresne et al., 2002; Friedlingstein et al., 2006; Sitch et al., 2008]. In transient 21st century simulations with coupled climate-carbon cycle models [Cox et al., 2000; Dufresne et al., 2001; Friedlingstein et al., 2006], two opposing biogeochemical mechanisms dominate: CO 2 fertilization increases land carbon storage, while warming (primarily through accelerated soil organic matter decomposition) reduces it [Sitch et al., 2008]. However, a general (but little noted) feature of transient coupled climate-carbon cycle simulations 1 Center for Climate System Research, University of Tokyo, Kashiwa, Japan. 2 Frontier Research Center for Global Change, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan. 3 QUEST, Department of Earth Sciences, University of Bristol, Bristol, UK. 4 Met Office Hadley Centre, Exeter, UK. 5 School of Geography, University of Leeds, Leeds, UK. Copyright 2009 by the American Geophysical Union /09/2009GL [Friedlingstein et al., 2006] is that by 2100, land carbon storage has always increased. In other words, the negative feedback due to CO 2 fertilization outweighs the positive feedback due to warming, when assessed in terms of accumulated carbon storage by the end of the 21st century. This applies even in simulations where the land shifts from being a CO 2 sink to a source (i.e., from a state where the annual net exchange of CO 2 between land and atmosphere is negative, or downward, to a state where it is positive, or upward) during the course of the century [e.g., Cox et al., 2000]. [3] The present study investigates what may happen to land carbon storage in the longer term, when (a) vegetation patterns are allowed to equilibrate interactively with a high- CO 2 climate, and (b) carbon stores are subsequently allowed to stabilize in the changed climate. The results indicate an important role for biogeophysical feedback mechanisms, involving climate- and CO 2 -induced changes in vegetation patterns, in determining both climate and the terrestrial carbon balance. 2. Experimental Setting 2.1. Models [4] The MIROC atmospheric general circulation model (AGCM) [Hasumi and Emori, 2004], asusedintheipcc Fourth Assessment Report [Meehl et al., 2007], was coupled synchronously to the Lund-Potsdam-Jena (LPJ) dynamic global vegetation model [Sitch et al., 2003]. Ten plant functional types (PFTs) are modelled. Photosynthesis, plant respiration, carbon allocation, plant establishment, growth, tissue turnover, mortality, competition among PFTs, disturbance (fire) regime and soil carbon dynamics are calculated. Monthly mean temperature, precipitation and fractional cloud cover required as input to LPJ are derived from MIROC as running means of the most recent 20 years of simulation. In each model year, the combination and proportion of PFTs in every grid cell is used to re-assign the grid cell in the land-surface classification system. This assignment updates the parameter values (vegetation height, root depth, leaf albedo and photosynthetic properties) in MATSIRO [Takata et al., 2003], the land-surface component of MIROC. Sea surface temperature and sea ice extent are predicted by a slab-ocean model with prescribed seasonal ocean heat transport. All components of the coupled model run at resolution, providing reasonable representations of large-scale vegetation and climate patterns. Simulated global net primary productivity (NPP) of 64.5 PgC yr 1 is realistic and in the range of previous studies [Cramer et al., 1999], as are the simulated pre-industrial carbon stocks of 1073 PgC in vegetation and 1466 PgC in soils Experimental Set-up [5] Coupled model experiments (Table 1) were designed to quantify the effects of vegetation dynamics, with and L of5

2 Table 1. Summary of Model Experiments a Name of Run Vegetation Treatment [CO 2 ] clim (ppm) [CO 2 ]veg (ppm) T (K) DT (K) Cveg (PgC) C soil (PgC) C tot (PgC) DC tot (PgC) A1V1 dynamic A4V4 Dynamic A4V1 Dynamic A4V4F from A1V A4V1F from A1V a [CO 2 ] clim,co 2 concentration for the atmospheric model; [CO 2 ] veg,co 2 concentration for the biosphere model; T, global mean temperature; DT, change in global mean temperature relative to A1V1; C veg, global carbon storage in vegetation; C soil, global carbon storage in soil; C tot, global carbon storage in vegetation and soil (total carbon storage); DC tot, change in total carbon storage relative to A1V1. All carbon storages are values obtained at the end of a long ( years) offline simulation with LPJ, with climate prescribed from the end of the coupled model experiment. without the plant-physiological effects of high (quadrupled) CO 2 concentration, on climate (after sufficient time for equilibration between vegetation and climate in the coupled model), and carbon storage (after a longer period of offline simulation with the LPJ model, to allow equilibration of carbon stores in the changed climate). CO 2 quadrupling is comparable with concentrations approached by the end of the 21st century in business as usual scenarios with coupled climate-carbon cycle models [Cox et al., 2000; Friedlingstein et al., 2006]. We performed a pre-industrial control simulation with atmospheric CO 2 concentration at 285 ppm (A1V1), a 4 CO 2 simulation with CO 2 at 1141 ppm (A4V4), and a climate change only simulation in which the vegetation was permitted to adjust to climate change but with no physiological effects of elevated CO 2, i.e., the vegetation model sees pre-industrial CO 2 (A4V1). The coupled model was run for long enough to allow the simulated global vegetation patterns to stabilize. A1V1 and A4V4 were started from an unvegetated state, and required 150 years to achieve a stable vegetation pattern. A4V1 started from the final state of A4V4, and required a further 90 years to achieve a new stable vegetation pattern. In an additional 4 CO 2 climate simulation, the vegetation was fixed at the pre-industrial equilibrium state, as simulated in the control run. [6] Because carbon stocks equilibrate more slowly than vegetation patterns [Sitch et al., 2008], steady-state vegetation and soil carbon storages were calculated for each experiment by continuing to run the LPJ model, offline, for years in the changed climate. In the fixedvegetation case, two such offline runs were performed: one with 4 CO 2 maintained to allow physiological effects on carbon storage (A4V4F), and one with pre-industrial CO 2 to eliminate these effects (A4V1F). A4V4 absorbs a greater proportion of shortwave radiation, because of increased forest area in high latitudes (boreal forest expanding into tundra [e.g., Levis et al., 1999]) and the encroachment of vegetation on sparsely vegetated areas in the subtropics (Figure 1). [8] The simulated additional warming due to vegetation dynamics is concentrated in the northern mid- to high latitudes, reaching 5 K in the Arctic, and in semiarid regions of the subtropics with a warming typically of 2 3 K (Figure 2a). This spatial pattern of additional warming closely follows the response of surface albedo changes (Figure S1) and consequent changes in the surface energy balance (Figure S2). The simulated surface albedo changes are greatest in the northern high latitudes because 3. Results 3.1. Global Climate and Vegetation [7] The high-co 2 coupled model experiment (A4V4) produced a global mean temperature increase of 9.8 K relative to the control run, and a substantial change in vegetation distribution (Table 1 and Figure 1). Without vegetation dynamics (A4V4F), the warming was 8.7 K. In other words, vegetation dynamics amplified the global warming due to CO 2 by 13%. The main cause of this amplification was reduced surface albedo (see Figure S1 of the auxiliary material). 1 The vegetation simulated in 1 Auxiliary materials are available in the HTML. doi: / 2009GL Figure 1. Equilibrium vegetation distribution simulated with the coupled atmosphere-mixed layer ocean-dynamic vegetation model for (a) pre-industrial CO 2 (A1V1), (b) 4 CO 2 (A4V4). 2of5

3 Figure 2. Annual mean temperature changes due to the inclusion of dynamic vegetation: (a) with plant CO 2 response (A4V4 A4V4F), (b) without plant CO 2 response (A4V1 A4V1F). of the mutually reinforcing effects of increased snow masking by vegetation, reduced snow cover, and reduced Arctic sea ice cover Plant Physiological Response [9] In semi-arid subtropical regions, the simulated changes in surface albedo are primarily forced by increases in vegetation cover promoted by high CO 2 concentration, through the combination of CO 2 fertilization (increased photosynthesis due to higher substrate concentration and reduced competition with O 2 at the Rubisco reaction sites) and partial stomatal closure, which tends to conserve the ratio of leaf-internal to ambient CO 2 concentration in the face of a diminishing biochemical return on internal CO 2 concentration [Sellers et al., 1996]. Under high CO 2,in conditions where water supply is limiting, a strong CO 2 effect on plant growth is expected [Long et al., 2006] as increased photosynthesis and reduced water loss act in combination to increase the water use efficiency of vegetation. In seasonally dry climates the effect is to increase the growing season even if annual precipitation is unchanged, and to increase annual average NPP, leaf area index, and the fractional absorption of solar radiation, relative to dry and unvegetated soil surfaces. [10] The importance of these plant-physiological responses to CO 2 in producing the additional warming due to vegetation dynamics is illustrated by comparison of Figures 2a and 2b. The climate change only effect of vegetation dynamics (A4V1, Figure 2b) on the climate in the subtropics is slight. Many regions show a cooling, associated with reduced vegetation cover. The vegetation pattern simulated in A4V1 (Figure S3) differs notably from that simulated in A4V4, with extensive replacement of tropical forests by savannas; the physiological effects of high CO 2 counteract tropical forest dieback in A4V4. In global average, the regional warming and cooling effects almost cancel, so that the global mean warming in A4V1 is barely (<0.1 K) greater than that in A4V4F (Table 1). In the northern high latitudes, however, the climate change only effect is substantial (1.2 K). Therefore, the simulated additional warming due to vegetation dynamics is robust for high northern latitudes even if the CO 2 effect on vegetation cover is neglected, or if it is less than simulated by LPJ-MIROC. [11] On the other hand, the full simulated effect of vegetation dynamics in high northern latitudes in A4V4 is about twice as large (2.3 K) as the climate change only effect, due to the remote climatic effect of CO 2 -induced vegetation changes on surface albedo in the subtropics. This is because change in surface albedo increase shortwave absorption and thereby increases the sensible heat flux, which leads to warming (Figure S2). Changes in longwave radiation and latent heat flux show smaller or opposite signals. [12] The greatest climatic effect of plant-physiological responses to CO 2 in our simulations is the effect mediated by vegetation structure and dynamics [Bala et al., 2006]. CO 2 -induced stomatal closure can also affect surface air temperature more directly, by reducing the latent heat flux [Sellers et al., 1996; Boucher et al., 2009]. We quantified this direct influence of stomatal conductance in an additional sensitivity experiment, in which the vegetation pattern of A4V4 was used but the physiological effect of CO 2 was turned off (Figure S4). This experiment showed that the stomatal conductance effect contributes to the surface warming in high latitudes, but not or only slightly (<1 K) in the tropics and mid-latitudes Change in Carbon Storage [13] We consider now the effects of the simulated climate changes on terrestrial carbon storage, after the carbon stores have equilibrated with the changed climate. The effects of climate change and CO 2 change, with fixed vegetation, are opposite: climate change causes a loss of carbon from the land ( 757 PgC in A4V1F) while climate and CO 2 change together cause a net gain of carbon to the land (+218 in A4V4F), i.e., the CO 2 fertilization effect dominates. This result is qualitatively in agreement with results obtained by 2100 in transient simulations [Cox et al., 2000; Friedlingstein et al., 2006; Sitch et al., 2008]. Some transient simulations of the 21st century have allowed for dynamic vegetation and resulting biogeophysical feedback, but the simulated vegetation patterns are not in equilibrium in 2100 [Cramer et al., 2001; Sitch et al., 2008]. Allowing the vegetation to fully adjust to new atmospheric conditions has a substantial impact on the simulated long-term carbon storage. Land carbon storage in A4V1 is reduced by a further 5% relative to A4V1F. More remarkably, land carbon storage in A4V4 is reduced by 12% relative to A4V4F. This change is large enough to overwhelm the CO 2 fertilization effect, and thus to alter the sign of the net CO 2 response from an increase (+218 PgC in A4V4F) to a decrease ( 111 PgC in A4V4) in land carbon storage. 3of5

4 Figure 3. Simulated changes in temperature (DT) and carbon storage (DC) in model simulations, relative to the control run. [14] Most of the reduction in land carbon storage in A4V4 relative to A4V4F occurs simply because the additional warming due to vegetation responses further stimulates the decomposition of soil organic matter in high northern latitudes, where low temperatures currently inhibit decomposition and allow the accumulation of large soil carbon stores, which are potentially vulnerable to warming. About three-quarters of the reduction comes from soil carbon, and the geographic patterns of carbon reduction for total carbon and soil carbon are similar (Figure S5). The additional carbon thus released to the atmosphere, due to vegetation dynamics and biogeophysical feedback, is equivalent to 154 ppm CO 2, of which about half would be expected to remain in the atmosphere on a time scale of centuries [House et al., 2002]. If CO 2 concentration had been allowed to float [e.g., Cox et al., 2000; Friedlingstein et al., 2006], the additional CO 2 thus released would have further amplified the simulated warming. 4. Discussion [15] Ecosystem-level responses to a quadrupling of CO 2 as modeled here cannot be directly verified, as ecosystemlevel manipulations have not considered such high CO 2 concentrations. However, Free Air Carbon Dioxide Enrichment (FACE) experiments in forests, where CO 2 concentrations have been elevated by around 200 ppm, have yielded NPP enhancements in agreement with predictions of the LPJ model [Hickler et al., 2008]. In addition, analysis of last glacial maximum vegetation patterns has shown that the extent of reduction of tropical forests at that time cannot be accounted for without the effect of low (<200 ppm) iceage CO 2 concentration on plant water use efficiency and competition between C 4 grasses and trees [Harrison and Prentice, 2003]. These findings are consistent with the expectation that high CO 2 concentrations would shift lowlatitude vegetation boundaries through physiological mechanisms, even in the absence of climate change. [16] According to current thinking, the main global consequence of CO 2 fertilization and related effects on plant processes is a negative feedback due to the enhancement of NPP, even if the magnitude of this effect is subject to large uncertainty [Meehl et al., 2007; Friedlingstein et al., 2006; Sitch et al., 2008; Denman et al., 2007], while the predominant global consequence of CO 2 -induced climate change for terrestrial carbon cycling is the positive feedback due to the temperature dependence of soil organic matter decomposition [Cao and Woodward, 1998; Dufresne et al., 2002; Friedlingstein et al., 2006; Sitch et al., 2008; Denman et al., 2007; Knorr et al., 2005]. It has been suggested that this positive feedback will overwhelm CO 2 fertilization if CO 2 concentration continues to rise toward saturating levels [Cox et al., 2000; Cao and Woodward, 1998; Dufresne et al., 2002; Friedlingstein et al., 2006; Sitch et al., 2008]. Nevertheless, current model projections [Friedlingstein et al., 2006; Sitch et al., 2008] show the net effect of the CO 2 increase over the 21st century as an increase in carbon storage, i.e., the total, cumulative response of the terrestrial biosphere over the whole period provides a negative feedback to atmospheric CO 2. Our results indicate that the sign of the feedback remains negative after the multi-century period needed to stabilize terrestrial carbon pools but only if vegetation patterns are held constant. The sign of the feedback switches to positive (Figure 3) when vegetation patterns are allowed to adjust to a warm, high-co 2 world. The additional biogeophysical feedback due to plant physiological responses to CO 2, paradoxically, is what tips the balance. References Bala, G., et al. (2006), Biogeophysical effects of CO 2 fertilization on global climate, Tellus, Ser. B, 58, Betts, R. A. (2000), Offset of the potential carbon sink from boreal forestation by decreases in surface albedo, Nature, 408, Bonan, G. B., D. Pollard, and S. L. Thompson (1992), Effects of boreal forest vegetation on global climate, Nature, 359, Boucher, O., A. Jones, and R. A. Betts (2009), Climate responses to the physiological impact of carbon dioxide on plants in the Met Office Unified Model HadCM3, Clim. Dyn., 32, Cao, M. K., and F. I. Woodward (1998), Dynamic responses of terrestrial ecosystem carbon cycling to global climate change, Nature, 393, Chalita, S., and H. Le Treut (1994), The albedo of temperate and boreal forest and the Northern Hemisphere climate: A sensitivity experiment using the LMD GCM, Clim. Dyn., 10, Cox, P. M., R. A. Betts, C. D. Jones, S. A. Spall, and I. J. Totterdell (2000), Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model, Nature, 408, Cramer, W., et al. (1999), Comparing global models of terrestrial net primary productivity (NPP): Overview and key results, Global Change Biol., 5, Cramer, W., et al. (2001), Global responses of terrestrial ecosystems to changes in CO 2 and climate, Global Change Biol., 7, Denman, K. L., et al. (2007), Couplings between changes in the climate system and biogeochemistry, in Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by S. Solomon et al., pp , Cambridge Univ. Press, Cambridge, U. K. Dufresne, J.-L., L. Fairhead, H. Le Treut, M. Berthelot, L. Bopp, P. Ciais, P. Friedlingstein, and P. Monfray (2002), On the magnitude of positive feedback between future climate change and the carbon cycle, Geophys. Res. Lett., 29(10), 1405, doi: /2001gl Fischlin, A., et al. (2007), Ecosystems, their properties, goods and services, in Climate Change 2007: Impacts, Adaptation and Vulnerability, edited by M. L. Parry et al., pp , Cambridge Univ. Press, Cambridge, U. K. Friedlingstein, P., et al. (2006), Climate-carbon cycle feedback analysis: Results from the C4MIP model intercomparison, J. Clim., 19, Gibbard, S., K. Caldeira, G. Bala, T. J. Phillips, and M. Wickett (2005), Climate effects of global land cover change, Geophys. Res. Lett., 32, L23705, doi: /2005gl of5

5 Harrison, S. P., and I. C. Prentice (2003), Climate and CO 2 controls on global vegetation distribution at the last glacial maximum: Analysis based on palaeovegetation data, biome modeling and palaeoclimate simulations, Global Change Biol., 9, Hasumi, H., and S. Emori (2004), K-1 coupled GCM (MIROC) description, K-1 Tech. Rep. 1, Cent. for Clim. Syst. Res., Kashiwa, Japan. Hickler, T., et al. (2008), CO 2 fertilization in temperate FACE experiments not representative of boreal and tropical forests, Global Change Biol., 14, House, J. I., I. C. Prentice, and C. Le Quéré (2002), Maximum impacts of future reforestation or deforestation on atmospheric CO 2, Global Change Biol., 8, Long, S. P., E. A. Ainsworth, A. D. B. Leakey, J. Noberger, and D. R. Ort (2006), Food for thought: Lower-than-expected crop yield stimulation with rising CO 2 concentrations, Science, 312, Joos, F., I. C. Prentice, S. Sitch, R. Meyer, G. Hooss, G.-K. Plattner, S. Gerber, and K. Hasselmann (2001), Global warming feedbacks on terrestrial carbon uptake under the Intergovernmental Panel on Climate Change (IPCC) emission scenarios, Global Biogeochem. Cycles, 15, Knorr, W., I. C. Prentice, J. I. House, and E. A. Holland (2005), Long-term sensitivity of soil carbon turnover to warming, Nature, 433, Levis, S., J. A. Foley, and D. Pollard (1999), Potential high-latitude vegetation feedbacks on CO 2 -induced climate change, Geophys. Res. Lett., 26, Meehl, G. A., et al. (2007), Global climate projections, in Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by S. Solomon et al., pp , Cambridge Univ. Press, Cambridge, U. K. Scholze, M., W. Knorr, N. W. Arnell, and I. C. Prentice (2006), A climatechange risk analysis for world ecosystems, Proc. Natl. Acad. Sci. U. S. A., 103, 13,116 13,120. Sellers, P. J., et al. (1996), Comparison of radiative and physiological effects of doubled atmospheric CO 2 on climate, Science, 271, Sitch, S., et al. (2003), Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model, Global Change Biol., 9, Sitch, S., et al. (2008), Evaluation of the terrestrial carbon cycle, future plant geography and climate-carbon cycle feedbacks using 5 Dynamic Global Vegetation Models (DGVMs), Global Change Biol., 14, Takata, K., S. Emori, and T. Watanabe (2003), Development of the minimal advanced treatments of surface interaction and runoff, Global Planet. Change, 38, Thomas, G., and P. R. Rowntree (1992), The boreal forests and climate, Q. J. R. Meteorol. Soc., 118, A. Abe-Ouchi, Frontier Research Center for Global Change, Japan Agency for Marine-Earth Science and Technology, Yokohama , Japan. R. O ishi, Center for Climate System Research, University of Tokyo, Kashiwanoha, Kashiwa , Japan. (ryo@ccsr.u-tokyo.ac.jp) I. C. Prentice, QUEST, Department of Earth Sciences, University of Bristol, Wills Memorial Building, Bristol BS8 1RJ, UK. S. Sitch, School of Geography, University of Leeds, Leeds LS2 9JT, UK. 5of5