We performed three other simulations to investigate the effect of rising atmospheric CO2 on

Transcription

1 In the format provided by the authors and unedited. SUPPLEMENTARY INFORMATION DOI: /NCLIMATE3299 Climate mitigation from vegetation biophysical feedbacks during the past three decades Zhenzhong Zeng 1, Shilong Piao 1,2,3 *, Laurent Z. X. Li 4, Liming Zhou 5, Philippe Ciais 6, Tao Wang 2,3, Yue Li 1, Xu Lian 1, Eric F. Wood 7, Pierre Friedlingstein 8, Jiafu Mao 9, Lyndon D. Estes 7,10,11, Ranga B. Myneni 12, Shushi Peng 1, Xiaoying Shi 9 ±, Sonia I. Seneviratne 13 and Yingping Wang 14 The surface air temperature response to vegetation changes the magnitude of evapotranspiration through canopy resistance. 1 Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing , China. 2 Key Laboratory of Alpine Ecology and Biodiversity, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing , China. 3 Center for Excellence in Tibetan Earth Science, Chinese Academy of Sciences, Beijing , China. 4 Laboratoire de Météorologie Dynamique, Centre National de la Recherche Scientifique, Sorbonne Universités, UPMC Univ Paris 06, Paris, France. 5 Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, Albany, New York 12222, USA. 6 Laboratoire des Sciences du Climat et de l Environnement, UMR 1572 CEA-CNRS-UVSQ, Gif-sur-Yvette, France. 7 Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey 08542, USA. 8 College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QF, UK. 9 Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA. 10 Woodrow Wilson School, Princeton University, Princeton, New Jersey 08542, USA. 11 Graduate School of Geography, Clark University, Worcester, Massachusetts 01610, USA. 12 Department of Earth and Environment, Boston University, Boston, Massachusetts 02215, USA. 13 Institute for Atmospheric and Climate Science, Department of Environmental Systems Science, ETH Zurich, 8057 Zurich, Switzerland. 14 CSIRO Oceans and Atmosphere, PMB #1, Aspendale, Victoria 3195, Australia. * slpiao@pku.edu.cn NATURE CLIMATE CHANGE Macmillan Publishers Limited, part of Springer Nature. All rights reserved.

2 Text S1. Effects of rising CO2 on the climate biophysical feedback of Earth greening. We performed three other simulations to investigate the effect of rising atmospheric CO2 on the climatic effects of increasing LAI. The increasing CO2 concentration affects the climate system through both physiological and radiative effects, both of which were considered in the three 60-year simulations that were configured as follows: 1. A control run (CTL) with LAI set to the observed mean and CO2 held at 341 ppm (the level of atmospheric CO2 concentration in 1982); 2. A transient run (TRN) with LAI set to the observed mean and CO2 set at 341 ppm; 3. A run simulating the recent past, in which both CO2 and LAI were perturbed (REC), having LAI set to the observed mean and CO2 set to 391 ppm We excluded the first 10 years of each simulation in order to ensure that soil-moisture fields were in equilibrium with climate, thus analyses were performed on the final 50 years of simulations. The long averaging period allowed us to quantify unforced internal climate variability, which in turn improved our ability to assess the average effects of LAI changes on climate. Model runs for this experiment were performed on the Tianhe supercomputer (TH- 1A) in China, with a resolution of 1.5 latitude 3.0 longitude, 19 vertical levels, and a 1.5 minute time step We evaluated the effect of rising atmospheric CO2 based on the differences between the CTL, TRN, and REC simulations. The difference between the TRN and CTL runs reveals the simulated climatic response to increasing LAI. The difference between the REC and CTL results demonstrates the climatic response to both increased LAI and atmospheric CO2. The difference between these two differences (REC CTL and TRN CTL) isolates climate

3 responses due solely to elevated CO2 from those caused by increased LAI. By increasing plant water-use efficiency, rising atmospheric CO2 might diminish the climatic effects of greening by decreasing evapotranspiration rates. However, such physiologically driven declines in evapotranspiration were largely offset by increases caused by the higher levels of radiative forcing caused by increased CO2. Rising CO2 does not significantly change the ΔLAI-induced increase in global land evapotranspiration (TRN CTL, REC CTL; p > 0.05; Supplementary Fig. 15). This finding is consistent with previous studies (e.g., refs 49-51) Text S2. Simulation protocol to investigate the climate effects of Earth greening. Model spin-up and the creation of initial conditions: We first performed a long-term control simulation forced by SST climatology, LAI climatology, and atmospheric CO2 fixed to 1982 levels to ensure that soil-moisture fields equilibrated with climate. The number of simulation years required is model-dependent, but generally 30-year-simulations are long enough to achieve equilibrium, after which the control simulation was continued for another 30 years to create different initial conditions. Note that LAI and SST climatologies were obtained from the averages of observations from each dataset, and therefore provide typical seasonal cycles, i.e. the values vary between months in each year, but the same monthly values were applied in all simulation years Initial condition ensemble simulations (IC-ensemble, ensemble member size = 30): Starting in January 1982 with the initial conditions created above, we performed three transient simulations: S1: Forced with observed SSTs and CO2, and LAI climatology; S2: Forced with observed SSTs, CO2, and LAI; S3: Forced with climatological SST, fixed CO2, and observed LAI.

4 Our study seeks to understand how the climate system responds to a boundary perturbation. Given that the climate system is highly sensitive to initial conditions, and exhibit chaotic behavior 38, we used an IC-ensemble approach to reduce uncertainties related to initial conditions so that we could more effectively isolate responses to perturbations. In this study, each 30-year simulation was replicated 30 times with different initial conditions. Total simulation years: more than 30 (ensemble member size) 30 ( , 30 years) 3 (S1, S2 and S3) + 30 (spin-up) + 30 (create initial conditions) = 2, Text S3. Brief introduction on the ACME and ACCESS models. The three models used in this study (IPSLCM, ACME and ACCESS) are Earth System Models consisting of atmosphere, land, ocean and sea-ice components linked through a coupler that exchanges information among the components 19,26,27,34,35. The IPSLCM, ACCESS models and the precedent of ACME have participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5; ref. 52) for the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report 53. The land surface components in the three models were modified to replace LAI at each grid point and for each plant functional type (PFT) with the satellite-observed values. IPSLCM, ACME and ACCESS were ran following the identical simulation protocol in the supercomputers in France, United States and Australia, respectively IPSLCM has been described in detail in the Methods, here we briefly introduce ACME and ACCESS. We applied ACME branched from the Community Earth System Model (CESM, version 1.2; ref. 26) (details see

5 and ACCESS1.4 (refs 27,54) in this study. ACME uses the Community Atmosphere Model, version 5 (CAM5; ref. 55) and the Community Land Model, version 4.5 (CLM4.5; ref. 56). There are 17 PFTs in the land-surface model. The spatial resolution of ACME is 2.5 o o. In ACCESS1.4, the atmospheric component is the UK Meteorological Office Unified Model, version 7.3 (UM7.3; ref. 57) and the land-surface model is the CSIRO Community Atmosphere Biosphere Land Exchange, version 1.8 (CABLE1.8; refs 54,58). Nine PFTs are used to describe the vegetation cover in CABLE. The spatial resolution of ACCESS is o 1.25 o Text S4. Method to calibrate modelled evapotranspiration with the suggested proportion of transpiration to terrestrial evapotranspiration. Using a recently developed stable-isotope-based methodology 28 the global transpiration is estimated to be about 64% (56% - 74%) of total terrestrial evapotranspiration. The observed proportion of transpiration to terrestrial evapotranspiration has been reproduced by IPSLCM, but is underestimated by ACME and ACCESS (Fig. S9b). The underestimation of the transpiration-evapotranspiration proportion in the latter two models gives rise to a very low sensitivity of modelled evapotranspiration to increasing LAI in ACME, and even a negative sensitivity of modelled evapotranspiration to increasing LAI in ACCESS (Fig. S10a). The misrepresentation of the sensitivity of evapotranspiration to LAI further leads to the non- significant effects of increasing LAI on the modelled T a in both these GCMs (p > 0.05, Fig. S8) To calibrate the modelled evapotranspiration with the observation-based proportion of transpiration to terrestrial evapotranspiration suggested by ref. 28 for both ACME and

6 ACCESS, terrestrial evapotranspiration in each pixel is calculated as the sum of evapotranspiration from vegetation and soil as follows: 133 obs obs obs cali tran inte soil E E E E mod tran mod inte mod soil tran inte soil (17) 134 where cali E is the calibrated total terrestrial evapotranspiration, E tran, E inte and E soil are the 135 modelled plant transpiration, interception and soil evaporation, respectively; mod tran, mod inte and 136 mod soil are the modelled proportion of these fluxes to total evapotranspiration in the GCMs 137 (multiyear average); and obs tran, obs inte, obs soil are the observed proportion of these fluxes to total evapotranspiration suggested by ref. 28 with values of 64%, 27%, 9%, respectively. This approach only calibrates the partitioning of terrestrial evapotranspiration, and barely 140 influences the magnitude of evapotranspiration. Note that obs tran, obs inte, obs soil provided by ref are the values at the global scale, our approach have only calibrated the global T/ET at this stage. As shown in Fig. S10b, the sensitivities of the calibrated evapotranspiration to increasing LAI in ACME and ACCESS fall within the range of satellite-derived sensitivity values Lastly, neglecting the influence of the atmospheric circulations, we applied equation (1) to calculate the LAI-induced variation in T a using the calibrated evapotranspiration and other model outputs including surface air temperature, wind speed, incoming solar radiation at the top of atmosphere, incoming and reflected solar radiation at the land surface. The results suggest significant and strong correlations between LAI and T a in ACME (R = -0.97, p < 0.001), and between LAI and T a in ACCESS (R = -0.88, p < 0.001). The increasing LAI leads to a cooling of o C per decade in ACME, o C per decade in ACCESS (Fig. S11). 153

7 References. 49. Betts, R. A. et al. Contrasting physiological and structural vegetation feedbacks in climate change simulations. Nature 387, (1997). 50. Bounoua, L. et al. Interactions between vegetation and climate: radiative and physiological effects of doubled atmospheric CO2. J. Clim. 12, (1999). 51. Cao, L. et al. Importance of carbon dioxide physiological forcing to future climate change. Proc. Natl. Acad. Sci. U.S.A. 107, (2010). 52. Taylor, K., Stouffer, E., R. J. & Meehl G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, (2012). 53. Stocker, T. F. et al. Working Group I Contribution to the IPCC Fifth Assessment Report (AR5), Climate Change 2013: The Physical Science Basis. Intergovernmental Panel on Climate Change (edited, Geneva, Switzerland, 2013). 54. Law, R. M. et al. The carbon cycle in the Australian Community Climate and Earth System Simulator (ACCESS-ESM1) Part 1: model description and pre-industrial simulation. Geosci. Model Dev. Discuss. 8, (2015). 55. Neale, R. B. et al. Description of the NCAR community atmosphere model (CAM 5.0). NCAR Technical Notes (2010). 56. Oleson, K. W. et al. Technical description of version 4.5 of the Community Land Model (CLM). NCAR Technical Notes (2013). 57. Hewitt, H. T. et al. Design and implementation of the infrastructure of HadGEM3: the next-generation Met Office climate modelling system. Geosci. Model Dev. 4, (2011). 58. Kowalczyk, E. A. et al. The land surface model component of ACCESS: description and impact on the simulated surface climatology. Aust. Meteorol. Oceanogr. J. 63, (2013)

8 Figure S1. Temporal variation of global land average LAI (green line) and ΔLAI- induced variation in annual average land-surface air temperature ( T a, blue line) from LAIobs_OCNclim. The black straight line is the least squares regression of T against time. a 182 The red curve is the smoothed ΔLAI-induced variation in T a using LOESS local regression 183 with a default span value 0.75, and the red straight line is its regression

9 Figure S2. Temporal variation of global land average LAI (green lines) and ΔLAIinduced variations in (a) surface albedo, (b) evapotranspiration, (c) shortwave transmissivity, (d) air emissivity, and (e) aerodynamic resistance (black lines) from experiment (1). The black straight line is the least squares regression of (a) surface albedo, (b) evapotranspiration, (c) shortwave transmissivity, (d) air emissivity, and (e) aerodynamic resistance against time. The red curve is the smoothed ΔLAI-induced variation in (a) surface albedo, (b) evapotranspiration, (c) shortwave transmissivity, (d) air emissivity, and (e) aerodynamic resistance using LOESS local regression with a default span value 0.75, and the red straight line is its regression

10 Figure S3. ΔLAI-induced variation in (a) radiative forcing over the land surface associated with the changes in surface albedo, evapotranspiration, shortwave transmissivity, air emissivity, and aerodynamic resistance from experiment (1) (i.e., C ( T T ) S E S T r )and its effect on land-surface 4 d s a (1 ) s a a 2 a ra 201 air temperature (i.e., C ( T T ) ) 1 ( (1 ) 4 d s a S E S T r s a a 2 a ) f ra (b). The black straight line is the least squares regression of (a) radiative forcing, and (b) temperature effect against time. The red curve is the smoothed ΔLAI-induced variation in (a) radiative forcing, and (b) temperature effect using LOESS local regression with a default span value 0.75, and the red straight line is its regression

11 Figure S4. Pattern of satellite-derived LAI trend between 1982 and 2011 in latitude- month space. Dots indicate a significant trend (p < 0.05)

12 Figure S5. Spatial patterns of ΔLAI-induced trends in (a) evapotranspiration, (b) surface albedo, (c) shortwave transmissivity, (d) air emissivity, and (e) aerodynamic resistance over the land surface from experiment (1). Dotting indicates a significant trend (p < 0.05)

13 Figure S6. Spatial pattern of the trend in radiative forcing associated with the ΔLAIinduced changes in surface albedo, evapotranspiration, shortwave transmissivity, air emissivity, and aerodynamic resistance from experiment (1) (i.e., C ( T T ) S E S T r ). Dotting indicates a significant trend (p < 0.05). 4 d s a (1 ) s a a 2 a ra

14 Figure S7. Spatial patterns of (a) the trend in radiative forcing associated with the ΔLAIinduced changes in surface albedo, evapotranspiration, shortwave transmissivity, air emissivity, and aerodynamic resistance from experiment (2) (i.e., C ( T T ) S E S T r ), and (b) the corresponding 4 d s a (1 ) s a a 2 a ra 229 trend in T a (i.e., C ( T T ) ). Dotting 1 ( (1 ) 4 d s a S E S T r s a a 2 a ) f ra 230 indicates a significant trend (p < 0.05)

15 233 Figure S8. Same as Fig. 1a, but from ACME (a) and ACCESS (b)

16 Figure S9. Comparisons of the modelled land evapotranspiration (a) and the modelled proportion of transpiration to total terrestrial evapotranspiration (b) against the observations. (a) Multiyear average land evapotranspiration from the models (IPSLCM, ACME, and ACCESS) and the observations (GRACE-MTE, FLUXNET-MTE, and NDVIbased E). The numbers at the top show the period for each estimate. (b) Proportion of transpiration to total terrestrial evapotranspiration in the three models against the value suggested by ref. 28 based on observations (56% - 74%)

17 Figure S10. Comparisons of the modelled sensitivity of land evapotranspiration to land LAI in ACME and ACCESS against the observed sensitivities. (a, b), The grey bars show the sensitivities from the observations and the green bars show the modelled sensitivity from experiment (1) in ACME and ACCESS. The modelled sensitivity of land evapotranspiration to land LAI is calculated with (a) the modelled evapotranspiration, and (b) the calibrated modelled evapotranspiration. Number, n, is the sample size used to estimate the sensitivity. Error bars show the standard error of the sensitivity. The significances of sensitivity is shown as ***, indicating significant at the 99% confidence interval; **, at the 95% confidence interval; *, at the 90% confidence interval; and n.s., not significant

18 Figure S11. Time series of LAI and ΔLAI-induced trends in annual average land-surface air temperature ( T a ) from ACME (a) and ACCESS (b). The ΔLAI-induced variation in is calculated with the calibrated modelled evapotranspiration and other model outputs following equation (1), neglecting the influence of the atmospheric circulations (i.e., C ( T T ) ). The black straight line is 1 ( (1 ) 4 d s a S E S T r s a a 2 a ) f ra T a 261 the least squares regression of T a against time. The red curve is the smoothed ΔLAI-induced 262 variation in T a using LOESS local regression with a default span value 0.75, and the red 263 straight line is its regression

19 Figure S12. Global average land-surface air temperature for 1982 to 2011 from climate simulations forced by SST climatology and LAI climatology (CTRL). The grey lines are from the 30 simulations with the same setup but different initial conditions, and the thick black line shows the initial-condition ensemble average

20 Figure S13. Global average land-surface air temperature for 1982 to 2011 from climate simulations (LAIobs_OCNobs, forced by observed SSTs and LAI) and that from the observations (CRU, Climatic Research Unit). The grey lines are from the 30 simulations with the same setup but different initial conditions, and the thick blue line shows the initialcondition ensemble average

21 Figure S14. A conceptual diagram showing the LAI-induced change in land surface air temperature ( T a ). Two factors drive the change of T a : first, the variations in the radiative and thermodynamic forcing over the land surface change land surface temperature ( T s ), the 282 latter drives the change in T a via the variation in surface heating rate; second, the direct effect 283 of the change in atmospheric circulation on T a, generally cooling due to northerly-wind induced invasion of cold air and warming effect due to southerly-wind-induced warm air. Because T and T are fully coupled and evolve together, the trends of T and T in response a s a s to the Earth greening are synchronous

22 Figure S15. Global spatially averaged annual land evapotranspiration in the three equilibrium simulations (CTL, TRN and REC). Error bars show one standard variation of annual land evapotranspiration within each 50-year-long simulation. ***, significance of differences to CTL at the 99% confidence interval; n.s., not significant according to a two-sample t-test

23 Table S1. GCM simulations for the 30-year period from 1982 to Four simulations were prescribed with different land LAI and ocean SSTs. Each 30-year simulation was replicated 30 times with different initial conditions. Experiment (1) examines the trend in LAIobs_OCNobs LAIclim_OCNobs; experiment (2) examines the trend in LAIobs_OCNclim. Here OCN represents SSTs. Climatology is obtained from the multiyear ( ) average of the observations (LAI and SSTs). It refers to mean seasonal cycles, i.e. the values change from month to month but the same monthly values are repeatedly applied for all simulation years. Experiment Simulation LAI SSTs Control CTRL climatology climatology 1 LAIclim_OCNobs climatology observed LAIobs_OCNobs observed observed 2 LAIobs_OCNclim observed climatology

24 Table S2. Land surface energy budget and the LAI-induced changes in the budget from experiment (1). Multiyear average (Mean), interannual variability (IAV) and trend (Trend) for: S, solar radiation; L, longwave radiation; λe, evapotranspiration; H, sensible heat flux; and G ground heat flux. Subscript n indicates net flux, arrows indicate direction. Each term is calculated from 1982 to The uncertainty represents the standard error of the trend. The significance of the trends are shown as ***, indicating significance at the 99% confidence interval; **, at the 95% confidence interval and *, at the 90% confidence interval. Budget Mean (W m -2 ) IAV (W m -2 ) Trend (W m -2 decade -1 ) S at the top of atm S ±0.03*** S ±0.01*** Sn ±0.02*** L ±0.04*** L ±0.02 Ln ±0.03*** λe ±0.07*** H ±0.05*** G ±0.01** 312