Future projections of Siberian wildfire and aerosol emissions

Прогнозы будущих лесных пожаров и эмиссий аэрозолей в Сибири
Tomomichi Kato, Hisashi Sato, Reza Kusuma Nurrohman, Hideki Ninomiya, Lea Végh, Nicolas Delbart, Tatsuya Miyauchi, Tomohiro Shiraishi, Ryuichi Hirata
2024-09-26

PM2.5 and CO2 emissions projectionsRepresentative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, RCP8.5)SEIB-DGVMSPITFIRESiberian wildfire emissions
Abstract. Wildfires are among the most influential disturbances affecting ecosystem structure and biogeochemical cycles in Siberia. Therefore, accurate fire modeling via dynamic global vegetation models is important for predicting greenhouse gas emissions and other biomass-burning emissions to understand changes in biogeochemical cycles. We integrated the widely used SPread and InTensity of FIRE (SPITFIRE) fire module into the spatially explicit individual-based dynamic global vegetation model (SEIB-DGVM) to improve the accuracy of fire predictions and then simulated future fire regimes to better understand their impacts. The model can reproduce the spatiotemporal variation in biomass, fire intensity, and fire-related emissions well compared to the recent satellite-based estimations: aboveground biomass (R2=0.847, RMSE =18.3 Mg ha−1), burned fraction (R2=0.75, RMSE=0.01), burned area (R2=0.609, RMSE =690 ha), dry-matter emissions (R2=0.624, RMSE =0.01 kg DM m−2; dry matter), and CO2 emissions (R2=0.705, RMSE =6.79 Tg). We then predicted that all of the 33 fire-related gas and aerosol emissions would increase in the future due to the enhanced amount of litter as fuel load from increasing forest biomass production under climate forcing of four Representative Concentration Pathways: RCP8.5, RCP6.0, RCP4.5, and RCP2.6. The simulation under RCP8.5 showed that the CO2, CO, PM2.5, total particulate matter (TPM), and total particulate carbon (TPC) emissions in Siberia in the present period (2000–2020) will increase relatively by 189.66±6.55, 15.18±0.52, 2.47±0.09, 1.87±0.06, and 1.30±0.04 Tg species yr−1, respectively, in the future period (2081–2100) and the number of burned trees will increase by 100 %, resulting in a 385.19±40.4 g C m−2 yr−1 loss of net primary production (NPP). Another key finding is that the higher litter moisture by higher precipitation would relatively suppress the increment of fire-related emissions; thus the simulation under RCP8.5 showed the lowest emissions among RCPs. Our study offers insights into future fire regimes and development strategies for enhancing regional resilience and for mitigating the broader environmental consequences of fire activity in Siberia.
1
Higher litter moisture from increased precipitation can relatively suppress the increase in fire-related emissions, making RCP8.5 produce the lowest emissions among RCPs in this study.
2
Integration of the SPITFIRE module into SEIB-DGVM improves fire prediction and reproduces biomass, fire intensity, and emissions compared to satellite estimates (e.g., aboveground biomass R2=0.847, RMSE=18.3 Mg ha−1).
3
Model reproduces burned fraction (R2=0.75, RMSE=0.01) and burned area (R2=0.609, RMSE=690 ha) when compared to recent satellite-based estimations.
4
Model reproduces fire-related emissions: dry-matter emissions (R2=0.624, RMSE=0.01 kg DM m−2) and CO2 emissions (R2=0.705, RMSE=6.79 Tg).
5
Under RCP8.5 the number of burned trees is projected to increase by 100%, causing a net primary production loss of 385.19±40.4 g C m−2 yr−1.
6
Under RCP8.5, projected increases from present (2000–2020) to future (2081–2100) are: CO2 +189.66±6.55 Tg yr−1, CO +15.18±0.52 Tg yr−1, PM2.5 +2.47±0.09 Tg yr−1, TPM +1.87±0.06 Tg yr−1, TPC +1.30±0.04 Tg yr−1.
7
Under all four RCP scenarios (8.5, 6.0, 4.5, 2.6), simulations predict increases in all 33 fire-related gas and aerosol emissions driven by increased litter fuel from higher forest biomass.

Future Siberian wildfire regimes and associated biomass-burning aerosol and gas emissions simulated with SEIB-DGVM coupled to the SPITFIRE module

Projected changes in fire activity, fuel loads, burned area/trees, and resulting temporal-spatial emissions of 33 fire-related gases and aerosols (e.g., CO2, CO, PM2.5, TPM, TPC) and impacts on net primary production under four RCP climate forcing scenarios

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2024-09-26
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Tomomichi Kato
Hisashi Sato
Reza Kusuma Nurrohman
Hideki Ninomiya
Lea Végh
Nicolas Delbart
Tatsuya Miyauchi
Tomohiro Shiraishi
Ryuichi Hirata
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