SUPPLEMENTARY INFORMATION

Transcription

1 Supplementary and Additional information Uncertainties on terrestrial GPP reconstructed from oxygen-18 In the calculation of terrestrial gross primary production using Equation (1) (see main text), a biogeochemical model of the Dole effect, in other words 18 O in molecular oxygen (1) was run with different ocean model NPP results (2-7) using a marine NPP to GPP ratio (8) of 0.48 and a photosynthetic quotient (9) of 1.2 for converting CO 2 to O 2 fluxes. Uncertainties on P OCEAN are derived from the spread of ocean model NPP results (2-4, 6, 7) and the spread of modern NPP estimates from observations and models (5) (Table S1). DE OCEAN comes from the global Dole Effect model of ref (1) but accounting for higher 18 O respiration fractionation from new observations of the triple oxygen isotope composition of dissolved O 2 (10). The value of DE OCEAN is 19.8 for PRE, and 20 for LGM. Given earlier values of DE OCEAN (11) (1) but noting that the 18 O respiration fractionation seems to be independent of surface temperature or latitude, we estimate the error in DE OCEAN to 1. These values allow us to reproduce the observed DE in ice cores with the whole range of ocean NPP estimates, which is a first independent verification of their validity. The model was then optimized by trial and error in order to match exactly the observed PRE value of the Dole Effect, for all marine NPP estimates and DE OCEAN plus and minus 1, resulting in a DE TERR of 28.3 ± 0.8 for PRE conditions. The optimized Dole Effect model was then run for LGM climatic conditions, resulting in a calculated DE TERR of 29.5 ± 0.7. All three input errors on marine GPP, DE OCEAN and DE TERR were propagated in Equation (1) by a Monte Carlo method to infer the terrestrial GPP and its uncertainty. Equation (1) was then calculated 1 million times to deduce P TERR with samples from the three input intervals for PRE: P OCEAN = Pmol (O 2 ) a 1, DE OCEAN = , DE TERR = ; and for LGM: P OCEAN = Pmol (O 2 ) a 1, DE OCEAN = , DE TERR = A log-normal distribution was fitted to the resulting empirical distribution of P TERR. The median and square-root of variance of the log-normal distribution are reported here. Terrestrial GPP was obtained from P TERR by dividing by 1.8 for PRE and 2.1 for LGM, which results from the biogeochemical model and accounts for O 2 emissions from photorespiration and other auto-oxidative processes such as the Mehler reaction. Ocean NPP (PRE) Ocean NPP (LGM) Reference (7) (2) best circulation (Circ-A) (4) (6) (5) Table S1: Different marine NPP estimates for PRE and LGM periods (units Pg C y 1 ) NATURE GEOSCIENCE 1

2 Uncertainty on ocean and terrestrial stocks reconstructed from carbon-13 All uncertainties are reported as 1-sigma, unless an uncertainty range is otherwise specified. In the calculation of global carbon stocks M TERR and M OCEAN during the LGM, Equations (2-3) in the manuscript are parameterized with observations whose individual errors are propagated using a Monte-Carlo method. These observations are: Pre-industrial ocean carbon stock. We used present-day global observations from the GLODAP dataset (12), giving an estimate of Pg C. To get the PRE ocean stocks, we added all the ocean regions not covered by the GLODAP inventory, the Arctic Ocean, the Mediterranean Sea and marginal seas, and removed the input of anthropogenic carbon from ref. (13). This gives a PRE ocean carbon stock of Pg C. Spatially variable errors (12) integrated over the entire ocean gives an uncertainty of 170 Pg C. However, uncertainties associated with the interpolation technique (12) are not accounted for. δ 13 C of pre-industrial ocean carbon. For estimating PRE δ OCEAN (δ 13 C of pre-industrial ocean dissolved inorganic carbon DIC), we have used modern observations (ocean surveys data) adjusted to their PRE value using a 3-D global model of ocean circulation and biogeochemistry. The modern d OCEAN data from ocean surveys were compiled and harmonized by Gruber et al. (1999) (ref. 14). These data differ from the PRE period to varying degrees due to the invasion of 13 C depleted fossil-fuel carbon since the industrial revolution (socalled 'Suess Effect'). The Suess Effect shows spatial variability, with surface waters impacted by 13 C depleted carbon to a much greater degree than deeper waters as shown by Tagliabue and Bopp, 2008 (ref. 15). We used the modelled Suess Effect from the simulation of Tagliabue and Bopp (2008) that best matches modern δ 13 C of DIC,called PISCES-D' in their study to correct each observation in the Gruber et al. (1999) dataset at its individual latitude, longitude, depth and year of sampling. The resulting degree of correction from modern to PRE period was greatest at the surface (0.22 to 1 ), intermediate where non-negligible amounts of anthropogenic carbon have been subducted to depth (e.g., North Atlantic Deep Water, around 0.1 to 0.2 ) and negligible in deep ocean locations where little anthropogenic carbon has accumulated. This defines the PRE δ OCEAN value at the sampling locations of Gruber et al.. A simple mean of these PRE δ OCEAN data would result in an unrealistically high value of the mean ocean PRE δ OCEAN as the data set is dominated numerically by surface samples, which occupy little ocean volume, but have relatively higher values of PRE δ OCEAN and a larger contamination by 13C-depleted fossil fuel carbon that invaded the upper ocean. To overcome this sampling bias, we re-calculated the global mean PRE δ OCEAN from our PRE dataset within 3 different depth horizons (0 to 100 m, m and > 500m) and for 5 different ocean basins (North Atlantic, South Atlantic, North Pacific, South Pacific and Indian) individually. The PRE total DIC (DI 12 C) using the 2 NATURE GEOSCIENCE

3 GLODAP DIC (anthropogenic DIC removed) data set, was then calculated in a similar fashion for each depth range and each basin to permit the determination of the PRE DI 13 C concentration for the corresponding regions/depths. The change in mean δ OCEAN from PRE to modern is With both values volume weighted in the same way by depth and ocean basin. The overall ocean mean DI 12 C and DI 13 C, weighted by the ocean volume of each depth horizon and basin, was calculated and a value of global ocean average PRE δ OCEAN = was obtained. A first source of uncertainty in this estimate is due to modeled ocean circulation changes between 1850 and This model induced error is estimated to be of 0.04, as given by a range of sensitivity tests in Tagliablue and Bopp, 2008 (ref. 15). A second large source of uncertainty is the unknown exact sampling date of each observation in ref. (14). This time-stamp induced error is estimated to be of 0.1. The overall uncertainty of global PRE δ OCEAN is thus set to 0.11 in our calculations. δ 13 C change in ocean carbon between LGM and PRE. We used reconstructed δ 13 C values from a new dataset of Cibicides benthic foraminifera calcite shells measurements ( ). The dataset contains 133 LGM sediment cores and 1033 PRE cores, measured by different laboratories. The instrumental error on each sample is The different laboratories are well inter-calibrated, but possible drifts in calibration are not assessed. Depending on sedimentation rates and fractionation effects (E. Michel, Personnal Comm.), the foraminifera proxy error impacting the reconstructed ocean carbon δ 13 C value is estimated to 0.1. In addition, a sampling error associated with incomplete coverage of the entire ocean by the sediment cores was calculated using the 3D ocean carbon isotopes of ref (2), from the simulated difference in δ 13 C between the average value of the entire ocean and the values at the sediment cores exact locations. This sampling error is found to equal The quadratic sum of instrumental error, proxy error and sampling error provides a total uncertainty of 0.13 for a signal representing the δ OCEAN change of ocean carbon between LGM and PRE of Pre-industrial terrestrial carbon stock. We used present-day measurements of carbon in vegetation and soils (16). To get the PRE terrestrial carbon stocks, we removed the incorporation of anthropogenic carbon into the land biosphere as deduced from cumulated anthropogenic emissions minus ocean anthropogenic carbon increase (13) and atmospheric accumulation. This gives a PRE value of 2370 Pg C. The uncertainty around this non-inert carbon stock is of 125 Pg C, according to the literature survey of ref (17) (their table 4). To this noninert carbon stock, must be added a high-latitude inert stock permafrost and peat deposits of 1600 Pg C according to ref (18) with an uncertainty of 300 Pg C (deduced from ref 18.). This brings the total PRE terrestrial carbon stock up to 3970 ± 325 Pg C, as reported in Table 1 of the manuscript. δ 13 C of the terrestrial biosphere during LGM and PRE. The isotopically enabled LPJ land ecosystem model (19) simulates the 13 C discrimination of photosynthesis and traces this isotopic signature through all of the NATURE GEOSCIENCE 3

4 terrestrial carbon pools. Simulated variations in isotopic discrimination are due to the balance of C 3 and C 4 plants in the modelled vegetation, and variations in stomatal conductance (associated with moisture status, and plant-type specific differences in unstressed stomatal behaviour) in C 3 plants. For PRE the model was run with climate of the period and atmospheric CO 2 fixed at 280 ppm to infer a value of 23.9 for the average δ 13 C isotope composition of terrestrial ecosystems. This value lies in the range of surface soil samples analysis given by reference (20) ( 25 to 22 ). It is combined with frozen and wetland carbon stocks with an assumed isotopic composition of 24, to give an overall δ 13 C of the terrestrial biosphere of δ TERR = 24.3 with an uncertainty of 1.5, the latter according to reference ((20)). For LGM we used the LPJ land ecosystem model with reduced atmospheric CO 2, forced by an ensemble of LGM climate model simulations from the Paleoclimate Model Intercomparison Programme Phase-2, PMIP-2 ( and run under a similar protocol to that described below for LPX simulations, to calculate δ TERR = (range 23.8 to 22.9 obtained from the spread of LPJ results driven by three different climate models). The reduced isotopic discrimination at the LGM arises mainly because of the (modelled) reduced competitive efficiency of C 3 plants relative to C 4 plants at low CO 2 concentration, resulting in a greater area of C 4 -dominated vegetation. This value is comparable to, but lower than previous calculation from the CARAIB vegetation model 5 of 22.8 to Description of the LPJ model enabled for 13 C simulation. The Lund-Potsdam-Jena (LPJ) dynamic vegetation model combines process-based descriptions of terrestrial ecosystem structure (vegetation composition, biomass and height) and function (energy absorption, carbon cycling). Vegetation composition is described by nine different plant functional types, which are distinguished according to their bioclimatic (boreal, temperate, tropical), physiological (C3, C4 photosynthesis), morphological (tree, grass) and phenological (deciduous, evergreen) attributes. The model runs on a regular latitude-longitude grid (here at the climate model resolution) with atmospheric CO 2 concentration, soil texture, and monthly fields of temperature, precipitation and fractional sunshine hours as input. The fractional coverage of a PFT within a grid-cell depends on its specific environmental limits and on resource competition among the PFTs. Photosynthesis is calculated by a Farquhar-Collatz scheme coupled to a twolayer soil water model on a daily basis. Isotopic discrimination during CO2 photosynthesis is calculated following Lloyd and Farquhar (1994) as a daily average value and the 13 C value of the biomass is tracked through the entire model state. Assimilated CO 2 is allocated to four different tissue pools (leaves, sap- and heartwood, roots) on an annual basis. Soil and litter C pools are updated monthly and decomposition rates depend on soil temperature and soil moisture. Vegetation dynamics are simulated annually based on the productivity of the different PFTs as well as on disturbance, mortality and establishment. Natural disturbance is included by computing fire occurrence as a function of a threshold litter load, surface soil moisture and temperature. Climate input data for LPJ were calculated using anomalies of monthly mean temperature, precipitation and cloud cover from the LGM climate simulations from 3 climate models from the Paleoclimate Modelling Intercomparison Project PMIP (IPSL, HadCM3 and MIROC that were available by June 2009). Climate anomalies were defined as differences from the 30-year mean for the baseline period, , in the 20 th century model simulations of the four selected climate models. They were applied to a baseline climatology ( ) from CRU (New et 4 NATURE GEOSCIENCE

5 al., 1999). LPJ was spun up by repeating a 30-year cycle of climate anomalies over a 1000-year simulation period. Atmospheric carbon stock and atmospheric δ 13 C. We used for the PRE period an average CO 2 concentration of 280 ± 1 ppm, with δ 13 C of 6.4 ± 1 from refs (21, 22). A factor of 2.12 is used to convert atmospheric CO 2 concentration into atmospheric carbon stock. For the LGM between 22 and 17.6 kyr BP in the ice core record, we diagnose an average CO 2 of 188 ± 1 ppm and δ 13 C of 6.6 ± 0.1. This value is from new ice core measurements (23). Its uncertainty reflects both replicate samples and instrumental error (23). Note that during the Holocene, the atmospheric δ ATM is observed to be subject to significant fluctuations, on the order of 0.2 between the early Holocene and the PRE period, as pointed out by reference (21). Summary of error budget We carried out sensitivity tests on the uncertainty of each parameter entering Equations (2) and (3) (see main text). We set to zero successively the uncertainty of each parameter while uncertainties on all other parameters are maintained at their standard estimates. The results are summarized in Fig. S1. Zero uncertainty of δ TERR during PRE period (red). Zero uncertainty of δ TERR during LGM period (green). Zero uncertainty of δ OCEAN during PRE period (blue). Zero uncertainty of the change of δ OCEAN between LGM and PRE (cyan). Zero uncertainty of the ocean carbon stock M OCEAN during the PRE period (magenta). Zero uncertainty of the terrestrial carbon stock M TERR during the PRE period (yellow). One can see that the main sources of uncertainty on the inferred M OCEAN and M TERR during the LGM are δ OCEAN during the PRE period as well as the change of δ OCEAN between LGM and PRE obtained from foraminifera. Setting to zero the error on the ocean and terrestrial C stock PRE, obtained from inventories, has the effect to tighten the anti-correlation between inferred M OCEAN and M TERR in the LGM. The uncertainty of atmospheric CO 2 content and its 13 C isotopic composition has a negligible impact on the errors of inferred M OCEAN and M TERR. NATURE GEOSCIENCE 5

6 Figure S1. Sensitivity of inferred terrestrial and oceanic LGM carbon stocks to different sources of uncertainties. Bayesian inversion of biome areas during the LGM constrained by global carbon stocks and GPP estimates. First guess values of biome areas, stock densities and GPP flux densities The LPX Global Dynamic Vegetation Model is used to provide first guess estimates for the Bayesian inversion. This model is a development from the LPJ model as described by ref. (19). It differs principally in its process-based treatment of firevegetation interactions. These broadly follow ref (24), but with changes as follows: (a) a single global parameterization to account for the concentration of lightning events on wet days, (b) independent drying rates for different size classes of fuel (as opposed to a single rate for the composite fuel), and (c) removal of human ignition sources (this leads to the under-estimation of deforestation fires in recent decades, but enables the model to be applied to palaeoclimate scenarios). The process-based fire treatment allows for a more realistic simulation of vegetation biomass. Climate models used to provide LGM climates for forcing LPX were HadCM3, IPSL and MIROC from climate simulations of the Palaeoclimate Modelling Intercomparison Project Phase 2, PMIP-2. The three climate models were selected because the data were available in the project database in June 2009 ( Simulations with LPX were run with LGM low 6 NATURE GEOSCIENCE

7 CO 2 concentration and palaeoclimate fields that were derived by interpolating gridpoint climate model anomalies (differences between the climate models simulated average values for LGM and PRE, i.e. the pre-industrial control run) of mean monthly maximum and minimum temperatures, precipitation and fractional sunshine hours, to a 0.5-degree grid, and adding the interpolated anomalies to a repeated, detrended time-vaying CRU global field of modern climate variables. Maximum and minimum temperatures and fractional rain days are required to drive the models simulation of fuel drying and hence fire ignition probability and variables influencing the rate and duration of fire spread (24). Fractional rain days were assigned to each grid cell and month based on a two-parameter empirical model, based on modern observations, fitted separately to each grid cell for contemporary observations. CRU climate data were interpolated on to the exposed continental shelves, using standard lapse rates to account for elevation effects, before the addition of model-based anomalies. The LGM palaeotopography was determined based on relative sea-level lowerings consistent with the ICE-5G model, which was also used to provide an ice-sheet mask where no vegetation was simulated. The model was spun up to closely approach equilibrium total carbon storage under PRE CO 2 concentration and climate, then shifted to the LGM CO 2 concentration and palaeoclimate fields and allowed to approach equilibrium again. Cited values of simulated ecosystem properties are averages over the past century of each model run, for the three LGM climate forcings. Bayesian inversion of biome areas We designed a Bayesian inversion. In this optimization, the first guess (prior) values are calculated by the LPX model (24, 25). We regrouped the 12 original biomes calculated by LPX into 5 megabiomes (groups of biomes) plus one inert terrestrial pool, equated with the carbon stored in permanently frozen soils and in peat: Megabiome 1 Tundra + Shrub-tundra + Boreal grassland Megabiome 2 Desert + Dry Grassland + Sclerophyll + Savanna Megabiome 3 Temperate Forest + Warm Temperate Forest + Temperate grassland Megabiome 4 Tropical forest Megabiome 5 Boreal Forest Megabiome 6 Inert C pool with zero GPP (permafrost, peat) Table S2. The 5 megabiomes used in the following and how they are constructed from the 12 biomes used by default in the LPX terrestrial carbon model. Each megabiome is assigned its average GPP density (g C m -2 y -1 ), and biomass and soil carbon density (g C m -2 ) calculated by LPX under LGM conditions. This defines a set of first guess values for the Bayesian inversion. The inert terrestrial pool is set to 1000 ± 1000 Pg C, the large uncertainty ensuring that this pool can be adjusted freely by the inversion to match the total terrestrial stock M TERR. Let s be a vector describing the area of each megabiome, the quantity that we seek to optimize. The first guess (prior) area value s 0 has an assigned error of 70% of the mean, defining a diagonal error covariance matrix C(s 0 ). The global GPP and NATURE GEOSCIENCE 7

8 terrestrial carbon stock M TERR, deduced from oxygen and carbon isotopes respectively, define the observation vector d. The observational error is a diagonal error covariance matrix C(d) containing +/- 10 Pg C y -1 error on GPP and +/- 300 Pg C error on M TERR, as given by Table 1. The linear combination of areas of each megabiome multiplied by corresponding GPP and stock density from LPX defines a matrix J, whose lines are the GPP and stock densities in megabiome. The optimal value of s assuming Gaussian errors can be calculated by minimizing the cost function χ2 with respect to s: Eq. (S1) χ 2 = 2 [ (s s 0 ) T C(s 0 ) 1 (s s 0 ) + (Js d ) T C(d ) 1 (Js d ) ] which gives an optimized s value: Eq. (S2) s = s 0 + C(s 0 ) J T J C(s 0 ) J + C(d)( d J s 0 ) associated with an error: Eq. (S3) C(s) 1 = C(s 0 ) 1 + J T C(d) 1 J The first guess and optimized area of each biome for the LGM, the latter adjusted in order to match global NPP and M TERR, are reported below. A higher error reduction indicates a better-constrained area. The final (optimized) value of GPP is 59 +/- 18 Pg C y -1. This global GPP estimate is higher than (although encompassing within its error) the observational constraint given by oxygen isotopes of 40 +/- 10 Pg C y -1. AREA 10 6 km2 First Guess Optimized tundra and cold grasslands 32 +/ /- 16 desert and warm grasslands 66 +/ /- 25 temperate forest and grass 12 +/ /- 6 tropical forests 19 +/ /- 6 boreal forests 3 +/ /- 2 Inert (permafrost, peat) 0 +/ /- 0 GPP (Pg C y -1 ) First Guess Optimized tundra and cold grasslands 8 +/ /- 4 desert and warm grasslands 36 +/ /- 14 temperate forest and grass 15 +/ /- 8 tropical forest 24 +/ /- 7 boreal forest 3 +/ /- 2 Inert (permafrost, peat) 0 +/ /- 0 ALL 86 +/ / NATURE GEOSCIENCE

9 STOCKS (Pg C) First Guess Optimized tundra and cold grasslands 323 +/ /- 157 desert and warm grasslands 198 +/ /- 74 temperate forest and grass 146 +/ /- 76 tropical forest 318 +/ /- 96 boreal forest 87 +/ /- 58 Inert (permafrost, peat) / /- 300 ALL / /- 400 Table S3. Area, GPP and carbon stock for 5 megabiomes defined above, plus an inert pool corresponding to permafrost + peat carbon. The latter inert pool is assigned no area, because it has no GPP as well. First column = biome names, Second column = First guess estimation from LPX process model simulations, Third column = Optimized estimation with Bayesian inversion constrained by global non-glaciated area, global land GPP from Dole effect observations, and global land carbon stock from 13C observations analysed in this study. Independent assessment of inversion results using a large paleo-biomes database Independent support for the inversion results is delivered by the BIOME 6000 database. The BIOME 6000 database contains reconstructions of LGM vegetation based on pollen and plant macrofossil data for 291 sites (Prentice et al., in 2011). The biome classifications used in the original publications have been standardized, and then grouped into the five major vegetation types used in the LPX inversions: tropical forest, temperate forests (there are no temperate grasslands in the dataset) boreal forest, tundra and cold grassland, and a xerophytic vegetation type which includes savanna, sclerophyll woodlands, warm grassland and desert. The resulting map shows a significant southward expansion of tundra and cold grassland in ice-free areas of the northern hemisphere. Xerophytic vegetation, which includes dry grasslands, xerophytic shrublands and woodlands, and savanna replaced forests over most of the mid-latitudes of both hemispheres. Boreal forests were very reduced in extent and temperate forests (which include warm-temperate variants) were largely confined to eastern North and central America, and China and SE Asia. Tropical forests were reduced in extent by the encroachment of xerophytic vegetation and temperate forests. NATURE GEOSCIENCE 9

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