Emissions calculations

The simulated LGM CH 4 emissions budget is shown in Fig. 1. This compares the predicted changes in emissions from wetlands, biomass burning, oceans, hydrates and termites, as well as bacterial uptake of methane in dry soils. Simulated wetland CH 4 emissions at the LGM are 70% of the pre-industrial (that is, a reduction from 138 to 97 TgCH 4 yr−1). Previous work shows large uncertainty in wetland emissions at the LGM, from no change7 to reductions similar to those we simulate8,9,10. The wetland scheme in HadGEM2-ES accounts for changes in soil carbon as a substrate for methanogenesis21,23 and also the impacts of low LGM atmospheric CO 2 on the terrestrial carbon cycling. However, in common with the majority of other wetland models24,25, it does not have a specific representation of hydrological and biogeochemical processes in organic soils or several other peatland-specific processes such as soil water-table variations. We addressed this issue by performing offline simulations using the LPJ-WHyMe model of peatlands and permafrost26 forced with HadGEM2-ES climate variables and the atmospheric CO 2 for each time period. With this model, the high-latitude distribution of wetland emissions is increased, possibly improving on the underestimation of wetland regions in this area under modern climate conditions27, and LGM emissions reduce from 70 to 58% of pre-industrial. This enhanced sensitivity is due to the higher proportion of the global emissions deriving from northern latitudes in the peatland model (44 TgCH 4 yr−1 versus 16 CH 4 TgCH 4 yr−1 in HadGEM2-ES), where additional ice-sheets and enhanced cooling strongly reduce emissions at the LGM. Peatland processes could therefore be important for understanding wetland emissions under LGM conditions. Given the differences in sensitivity of the peatland model, it also highlights the potential for different responses in a future warmer climate.

Figure 1: Last glacial maximum minus pre-industrial CH 4 emissions. Emissions change (TgCH 4 yr−1) for each source term and total. Values in pink include peatland emissions in northern extra-tropics. The sum uses the standard-fire flux. Values from previous work have been scaled to the same pre-industrial fluxes as used in our study for comparison. Wetland emissions from ref. 9 (dots) based on PMIP2 coupled GCM simulations, V: Valdes et al.8, K: Kaplan et al.7,14, T: Thonicke et al.29 and S: Singarayer et al.10. More details are provided in Table 1. Full size image

We simulated biomass burning CH 4 emissions with LPJ-LMfire28 forced offline with HadGEM2-ES climate variables and atmospheric CO 2 . This model shows a 34% reduction (from 14 to 9.2 TgCH 4 yr−1), consistent with the range in previous work8,29. This is termed standard-fire in the following. Inclusion of an estimated human contribution to fires at the LGM gives higher LGM fire emissions, so that the reduction in emissions at the LGM is somewhat smaller at 16% (that is, LGM emissions of 12.1 TgCH 4 yr−1), as shown in Fig. 1. This is termed standard+LGM humans. We also include a low-fire scenario as used in previous work16 in which all fire emissions are reduced by 90% from pre-industrial values at the LGM. This is included to bracket potential uncertainty in this term, because charcoal-based inferences imply substantial reductions in global biomass burning during the late glacial period30. In this scenario, the total fire CH 4 source term reduces by a factor of 10 (from 14 to 1.4 TgCH 4 yr−1). The additional effects due to changes in other trace gas emissions from biomass burning on CH 4 lifetime are evaluated in the following section.

For the CH 4 flux from oceans the Guenther et al. BVOC scheme31 simulates a 20% reduction in BVOC emissions between the pre-industrial and LGM. This is mostly driven by cooler sea-surface temperatures, with a small contribution from reduced surface dissolved organic carbon concentrations. To estimate emissions of CH 4 from termites, we used the observed relationships between vegetation biome type, termite species and termite CH 4 emissions per unit biomass to predict termite emissions as a function of vegetation coverage (see ‘Methods’ section). We calculated a 40% reduction at the LGM, mostly resulting from reduced tropical forest coverage. In the absence of appropriate process-based schemes for predicting hydrate and other geological CH 4 emissions, these sources are either assumed to remain unchanged at pre-industrial levels during the LGM, or alternatively were set to zero for the LGM.

Overall, the strength of the total CH 4 source is reduced by 32–46, 28–42 and 26–41% in the low-fire, standard and standard+LGM human fire scenarios, respectively, where the range depends on whether the peatland model is used and whether emissions from hydrates are reduced (Table 1).

Table 1 Global CH 4 emissions diagnosed (wetlands and OH and stratospheric loss) or else prescribed within HadGEM2-ES climate-chemistry simulations. Full size table

Using the offline models for BVOC, CO and NO x emissions (as described in ‘Methods’ section and in the Supplementary Table 1), we find relative changes that are similar to the simulated changes in CH 4 emissions. The fluxes of CO, acetone and NO x from biomass burning are reduced by 32%, 34% and 30%, respectively, or by 21%, 15% and 7% when LGM human emissions are included. Soil NO x emissions increase by 37%. This is partly as a result of increased LGM land area in the tropics. Transitions from dry to wetter months stimulate emissions in the soil NO x scheme, and a general increase in the precipitation variability across the tropics stimulates NO x emissions in the LGM simulation. The ocean CO flux is reduced by 20%. While acetone emitted from vegetation is reduced by 34%, isoprene emissions are reduced by only 19%, significantly smaller than reductions of up to 31–61%8,14 in previous work. This is because the stimulation of emission by low CO 2 during the LGM was not accounted for in these past studies. Lightning NO x is modelled interactively within HadGEM2-ES (see ‘Methods’ section). There is a 25% reduction in this source, from 6.1 TgNyr−1 in the pre-industrial simulation to 4.6 TgNyr−1 for the LGM.

Factors influencing the glacial CH 4 lifetime

We ran HadGEM2-ES using the CH 4 source terms summarized in Fig. 1, other trace gas emissions and a soil uptake term for CH 4 , all described in Table 1 and Supplementary Table 1. In this model, the source trace gases are mostly monthly globally gridded inputs, whereas wetland CH 4 and lightning NO x are calculated interactively, as are the main sinks of CH 4 by tropospheric OH oxidation and stratospheric loss (see Supplementary Note 1 for details).

The emissions-driven model predicts a global mean concentration of 659 pbv for the pre-industrial close to the observed AD 800–1,600 average value of 680 p.p.b.v.1. The lifetime of CH 4 with respect to OH is calculated using mixing ratios for CH 4 and OH up to the diagnosed model tropopause22 and is equal to 10.4 years, within the range inferred for present day32, and close to the multi-model mean lifetime 10.1±1.7 years for AD1850 conditions found in the Atmospheric Chemistry and Climate Model Intercomparison Project33. For the LGM, the lowest LGM concentration is expected in the low-fire LGM scenario, and so this is the only emissions-driven LGM scenario integrated to equilibrium. This results in a concentration of 447 p.p.b.v., which is almost 75 p.p.b.v. greater than the observed LGM concentration of 375 p.p.b.v.

Given that the emissions-driven model over-predicts the LGM CH 4 concentration, the model-derived methane lifetime will be too long given the self-feedback effect34. The self-feedback effect is the positive feedback whereby any change in CH 4 concentration will influence the amount of OH available for CH 4 oxidation, which in turn further impacts on the concentration of CH 4 . By running concentration-driven rather than emissions-driven simulations for the two time periods (pre-industrial and LGM), the change in CH 4 lifetime can be modelled more realistically.

In the concentration-driven simulations, the LGM lifetime increases by 2.3% relative to the pre-industrial value of 10.4 years for the low-fire scenario, and 6.5% for the standard-fire scenario as shown in Fig. 2. When the standard+LGM human fire scenario is used, the lifetime difference increases to 7.7% (see Table 2). The lifetime differences (LGM-PI) between the three fire scenarios are comparable to that reported in previous work16.

Figure 2: Last glacial maximum atmospheric CH 4 lifetime change relative to the pre-industrial (%) and estimates of the separate contributions to this change. LGM minus pre-industrial relative CH 4 lifetime change (dark upper bar), and sensitivity experiments (perturbation minus LGM), light bars below. V: Valdes et al.8, K: Kaplan et al.7,14, L: Levine et al.15, S: Singarayer et al.10 and Murray et al.16, for which only warm-LGM simulations are included and where m denotes the low-fire scenario. The effects of physical climate changes on reaction rates were not accounted for by Kaplan et al.14 or by Singarayer et al.10 shown in blue. Note for the BVOC sensitivity tests, Valdes et al.8 only changed isoprene and terpene emissions, Levine et al.15 also changed CO, ethane, propane and acetone; while here soil NO x was also included. Details of all of the lifetime results are given in Table 2. Full size image

Table 2 Global summary of the simulated CH 4 lifetime with respect to OH . Full size table

In each of the three fire cases, three main factors influence the CH 4 lifetime: reduced atmospheric temperatures and humidity levels, changes in surface emission rates of CO, NO x , isoprene and acetone, and a reduction in the lightning generation of NO x as simulated within HadGEM2-ES. Physical climate changes can be summarized by a global mean cooling of 4.8 °C and a global reduction in water vapour of 24% at the surface, with similar humidity reductions throughout the atmospheric column. The other tropospheric O 3 precursor emission changes are summarized above, with full details in the ‘Methods’ section.

We quantified the relative influences on the CH 4 lifetime with three additional LGM sensitivity tests with the low-fire interactive CH 4 (that is, emissions-driven) set-up. The three additional emissions-driven simulations were LGM but with pre-industrial tropospheric O 3 precursor emissions excluding methane, LGM but with pre-industrial lightning NO x generation rates and LGM but with pre-industrial physical climate. We find that LGM non-CH 4 emissions cause a 26% decrease in lifetime at the LGM. The LGM lightning reduction is responsible for a lifetime increase of 7%, while the physical climate changes increase the lifetime by 19%. These components of the total lifetime change are shown in Fig. 2. They are comparable with previous work8,15,16 though the stronger cooling in HadGEM2-ES compared with the previous model simulations, is likely responsible for the larger climate term.

For computational efficiency HadGEM2-ES uses pre-computed photolysis rates from an offline 2D model35. We also evaluate a model version including an interactive photolysis scheme36, which responds dynamically to changes in clouds and aerosols within HadGEM2-ES. This allows the effects from changes in stratospheric O 3 concentrations on incoming radiation incident on the troposphere, and hence on the production of OH, to be included. Switching from prescribed photolysis rates to interactive photolysis rates together with a prescribed increase in stratospheric ozone16 has a very small impact on the methane lifetime, as detailed in the Supplementary Note 2.

CH 4 concentration sensitivity to source and lifetime changes

We now combine the model predicted estimates of source and lifetime changes to derive a possible range of concentration predictions for the LGM. Because of the high computational cost of the coupled HadGEM2-ES model, we use a simplified mass balance formulation (described in ‘Methods’ section) to calculate the concentration as a function of the global CH 4 source and the CH 4 lifetime, and assuming steady state conditions in the two time periods, though this may not hold for all of the late Holocene pre-industrial period37. In terms of the sources, we incorporate all of the different source possibilities, including peatlands, changes to hydrates and the three fire scenarios described previously. For lifetime, we apply three different possible changes which correspond to the simulated changes in CH 4 lifetime in response to the three fire scenarios. We consider changes in sources and sinks separately and then combinations of the two, to give a summary of how the LGM CH 4 budget depends on the processes analysed. This analysis follows a similar approach recently presented for glacial CO 2 (ref. 38).

The individual contributions of the source and sink terms considered in this work are shown for comparison in Fig. 3. Our analysis shows that the largest changes in CH 4 concentrations in absolute terms arise from changes in either the wetland source or tropospheric O 3 precursors including BVOCs, justifying their focus in previous work. However, the combined contribution from other terms is significant, especially for the sinks. In past work, only relatively few of these terms have been quantified. The most studied aspect is the LGM wetland source for which previous work shows a significant spread, which encompasses the difference between the wetland and peatland models used here.

Figure 3: Summary of the contributions to the glacial to inter-glacial CH 4 budget as modelled in this work and in previous studies. V: Valdes et al.8, K: Kaplan et al.7,14, L: Levine et al.15. T: Thonicke et al.29 and S: Singarayer et al.10. The contributions from individual sources are calculated assuming no change in lifetime, while the contributions from sinks are calculated with constant source terms. The total concentration changes take account of both source and sink changes. The separate contributions to the change in chemical lifetime of CH 4 are quantified for the low-fire scenario here. The non-CH 4 term will be smaller for the standard fire and standard+LGM humans fire scenarios. The uncertainty range in each of the ‘Net’ values, encompasses whether or not peatlands are considered. Details of these calculations are given in Supplementary Table 2. Full size image

With a constant CH 4 lifetime, the wetland and low-fire sources are the largest terms in the PI-LGM amplitude. The other fire terms are significantly smaller. Changes in the lifetime given a constant source are generally larger contributors than many of the individual CH 4 source terms. The net effect of the lifetime change is small though, because it comprises a combination of several large values of opposing signs.

A successful simulation of the LGM to pre-industrial concentration change must reproduce both the pre-industrial concentration value and the change in concentration between the pre-industrial and LGM, which is 305 p.p.b.v., as shown in Fig. 3. This ice-core inferred amplitude is associated with negligible uncertainty, so we concentrate on model uncertainty in the following. The first criterion is nearly satisfied in this model, as the pre-industrial concentration is 660 p.p.b.v. about 20 p.p.b.v. lower than observed for the period 800–1,600AD. However, a positive trend during this period may result from pre-industrial era anthropogenic activities1. Our simulations do not include any anthropogenic sources, and so the 20 p.p.b.v. under-estimate for the pre-industrial in the model is consistent with such a human contribution during this time period. However, uncertainties in each of the source and sink terms as further discussed below, preclude confidence in the attribution of the 20 p.p.b.v. difference between the simulation and observations.

We now assess whether imposing the predicted changes in CH 4 sources and CH 4 lifetime ( ) can reproduce the second of the ice-core derived criteria described above. The net effect is estimated by combining for each of the three fire scenarios, the four source estimates (hydrates changing or not, and including peatlands or not) with the respective fire-scenario lifetime change. This gives four concentrations for each of the three scenarios.

In the three fire scenarios, the total CH 4 LGM source reduction required to give the observed LGM concentration can be calculated with the mass balance formulation (see ‘Methods’ section). This results in required source reductions of 46%, 48% or 49% for the low-fire, standard-fire and standard+LGM human fire scenarios, respectively. This range is at the extreme end of our source predictions, which showed maximum reductions of 46%, 42% and 41% for the same three fire scenarios, respectively. These reductions are only 41%, 36% and 34% if the hydrate term is not reduced at the LGM.

The estimated concentration change assuming no change in hydrate emissions is shown in Fig. 3 (and details of the calculations are listed in Supplementary Table 2). For either of the process-based fire estimates (standard or with LGM humans), the resultant CH 4 concentration predictions all fall short of the observed amplitude at 125–196 p.p.b.v. and 108–181 p.p.b.v. for the standard fire and standard fire with LGM human scenarios, respectively. The range depends on whether peatland model is used or not, with larger changes when the peatland model is used. The low-fire model has both the largest decrease in the CH 4 source, and the smallest increase in lifetime at the LGM. The modelled PI-LGM concentration change is nearly consistent with the observations in the low-fire scenario when taking the very upper range of the predicted 179–248 p.p.b.v. change.

Conditional on the pre-industrial hydrates flux of 10 TgCH 4 yr−1 reducing to zero at the LGM then the predicted concentration changes are larger, at 169–240, 152–224 and 221–290 p.p.b.v. for the standard fire, standard with LGM humans and low-fire scenarios, respectively. We note that the low-fire scenario is unlikely to be consistent with the isotopic record of δ13CH 4 , because biomass burning is a source of 13C enriched CH 4 , and a significant reduction in biomass burning emissions during the LGM will lead to a depletion in atmospheric δ13CH 4 relative to the pre-industrial. This is opposite to the observed 6‰ (permille) enrichment39. For a best estimate, we therefore leave out the low-fire scenario, assume hydrate emissions did not change at the LGM (there is little evidence to support a complete shutdown of this source) and include the peatland model. This leads to a calculated LGM concentration of between 463–480 p.p.b.v. for the standard and standard with LGM human fire scenarios. This is 89–105 p.p.b.v. higher than observed for the LGM.