GRENE北極気候変動研究事業研究成果報告会日時：2016年3月3日（木）10：00-17：30会場：国立国語研究所　2F講

Prabir K. Patra

National Institute of Polar Research Repository

  Modelling of greenhouse gases and related species in the Arctic environment  P. K. Patra1, T. Saeki1, K. Ishijima1, K. Ravi Kumar2, S. Taguchi3, D. Belikov4, S. Makyutov4, Y. Niwa5, T. Maki5,  D. Goto2, Y. Tohjima4, S. Ishidoya3, S. Morimoto6, S. Aoki6, K. Takata2  1Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama, Japan 2National Institute for Polar Research (NIPR), Tokyo, Japan 3National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan 4National Institute for Environmental Studies (NIES), Tsukuba, Japan 5Meteorological Research Institute (MRI), Tsukuba, Japan 6Tohoku University, Sendai, Japan   Numerical modelling of greenhouse gases (GHGs) has become an integral part for understanding amplitude and variability in their concentrations and sources/sinks, atmospheric transport and climate implication. Carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) are the three major species studied in the Arctic Green Network of Excellence (GRENE), a programme funded by the Ministry of Education, Culture, Sports, Science and Technology-Japan (MEXT). In addition some of the ozone depleting substances, e.g., methyl chloroform (CH3CCl3), have provided strong constrain on the global mean abundance of hydroxyl (OH) radical and its relative abundance in the northern and southern hemispheres (NH/SH OH ratio; Patra et al., 2014). Being the main destroyer of many of the GHGs (e.g., CH4, hydrofluorocarbons), accurate quantification of OH was needed for estimation of CH4 sink in the troposphere, and thus the sources on the Earth’s surface by inverse modelling (Patra et al., 2016). OH is also contributes to chemical production of CO2, up to ~50% of land/ocean sink. The modellers are also required to verify the accuracy of model transport using tracers of short (e.g., 222Rn with 3.8 days) and long (SF6 with 3200 yrs) lifetimes. For understanding of the carbon cycle science, analyses of oxygen (O2/N2) variability are also conducted. List of chemistry-transport models (CTMs) participating in the Arctic GRENE programme are given Table 1.       Table 1.  Summary of atmospheric models participated in the Arctic-GRENE project (✓denotes forward simulation of concentrations, and þ denotes both forward simulations and regional sources/sinks inverse modeling using the model).    Model Name* Institution Chemical species simulated for carbon, nitrogen cycle studies and atmospheric transport CO2 O2/N2 CH4 N2O SF6 CH3CCl3 222Rn ACTM JAMSTEC þ ✓ þ þ ✓ ✓ ✓ JMA-CDTM MRI/JMA þ    ✓  ✓ NICAM-TM MRI þ  ✓  ✓  ✓ NIES-TMi8 NIES þ ✓ þ  ✓  ✓ STAG AIST þ ✓   ✓  ✓ MJ98-CDTM MRI/JMA þ    ✓  ✓ *Detailed description of models can be found in Patra et al. (2014) for ACTM, Maki et al. (2013) for JMA-CDTM, Niwa et al. (2012) for NICAM-TM, Belikov et al. (2013) for NIES-TMi8, Taguchi et al. (2013) for STAG, Deushi and Shibata for MJ98-CDTM. Figure 1. Examples of recent changes in GHGs and related species concentrations; left (a): observed CO2 seasonal cycle amplitude change since the International Geophysical Year (from Graven et al., 2013; copyright Science), center (b): model - observation comparison of CH4 over the past 100 years (Ghosh et al., 2015), right (c): simulation of observed CH3CCl3 decay for estimation OH in troposphere (Patra et al., 2014; copyright Nature). The detailed modelling capabilities of different participating groups are listed in Table 1.     Figure 1 shows examples of observed and model simulated concentrations of CO2, CH4 and CH3CCl3. Using the direct CO2 measurements at the surface sites and aircrafts in the lower-middle troposphere during 1958 and 2011, an increase in seasonal amplitude of CO2 throughout the troposphere is detected (Graven et al., 2013). The detection of longterm trends in GHGs is key to track the changes in a b c   biospheric and anthropogenic activities on the Earth’s surface, and it is suggested that the CO2 seasonal amplitude increase with time is mainly caused by greater carbon assimilation in the summer months over the boreal latitudes. Using model simulations and analysis of 13C isotopic fractionation in atmospheric CH4 over the past 100 years (Fig. 1b), the fastest increase rate during 1955-1990 is attributed to enhanced biomass burning (Ghosh et al., 2015). For modelling of atmospheric CH4, the transport and chemistry in ACTM have been evaluated using SF6 and CH3CCl3 (Patra et al., 2014). We are continuing to collaborate with various observational groups in Japan for further elucidating the variations in GHGs in the Arctic region, e.g., model results are being used for analysis of the observational data collected as a part of the Arctic GRENE project; e.g., O2/N2 (Goto et al.; Ishidoya et al., in prep.), CH4 on the ship cruises (Tohjima et al., 2016).      Several inverse modeling schemes are also being developed using forward transport models developed in AIST, JAMSTEC, JMA, MRI and NIES. Figure 2b shows examples of CO2 fluxes estimated for the arctic region in comparison with the global total fluxes for the land and ocean regions as estimated by ACTM in JAMSTEC. Analyses of CO2 fluxes for different parts of the globe suggest that the difference between winter release and summer uptake is increasing in the boreal regions (Fig. 2c using NICAM-TM at MRI; Niwa et al., 2012, updated) and that the flux amplitude change is mainly governed by the summer uptake increase (Welp et al., 2016). A multi-model analysis of CO2 inversion fluxes using the flux tower measurements at Yakutsuk, in Siberia and biogeochemical cycle models is being conducted for understanding the trends in CO2 fluxes due to climate variations (Takata et al., 2015, also in prep.).  Figure 2. Distribution of CO2 sources and sinks around the Arctic region (a) compared to the global totals as simulated by ACTM (b; middle panel), and CO2 flux amplitude change for 4 major regions in the Northern Hemisphere by NICAM-TM (c: right panel).  References Belikov, D. A., S. Maksyutov, M. Krol, A. Fraser, M. Rigby, H. Bian, et al., Off-line algorithm for calculation of vertical tracer transport in the troposphere due to deep convection, Atmos. Chem. Phys., 13, 1093-1114, 2013.  Ghosh, A., P.K. Patra, K. Ishijima, T. Umezawa, A. Ito, D.M. Etheridge, S. Sugawara, K. Kawamura, et al., Variations in global methane sources and sinks during 1910-2010, Atmos. Chem. Phys., 2595-2612, 2015. Graven, H.D., R.F. Keeling, S.C. Piper, P.K. Patra, B.B. Stephens, S.C. Wofsy, L.R. Welp, et al., Enhanced seasonal exchange of CO2 by northern ecosystems since 1960, Science, 341, 1085-1089, 2013. Maki, T., M. Ikegami, T. Fujita, T. Hirahara, K. Yamada, et al., New technique to analyse global distributions of CO2 concentrations and fluxes from non-processed observational data, Tellus, 62B, 797–809, 2010. Niwa, Y., T. Machida, Y. Sawa, H. Matsueda, T. J. Schuck, et al., Imposing strong constraints on tropical terrestrial CO2 fluxes using passenger aircraft based measurements, J. Geophys. Res., 117, D11303, 2012. Patra, P.K., M.C. Krol, S.A. Montzka, T. Arnold, E.L. Atlas, B.R. Lintner, B.B. Stephens, B. Xiang, et al., Observational evidence for interhemispheric hydroxyl parity, Nature, 513, 219-223, 2014. Patra, P.K., T. Saeki, E.J. Dlugokencky, K. Ishijima, T. Umezawa, A. Ito, S. Aoki, S. Morimoto, et al., Regional emission and loss budgets of atmospheric methane (2002-2012), J. Meteorol. Soc. Jpn., 94, 83-105, 2016. Taguchi, S., A three-dimensional model of atmospheric CO2 transport based on analyzed winds: Model description and simulation results for TRANSCOM. J. Geophys. Res., 101, 15099-15110, 1996. Takata, K., P.K. Patra, A. Kotani, D. Belikov, J. Mori, et al., Comparison of carbon fluxes estimated by top-down and bottom-up methods - a case study at Yakutsk, Siberia, The 6th Symposium on Polar Science (IAp27), NIPR, Tokyo, Nov. 2015. Tohjima, Y., K. Katsumata, J. Matsushita, D. Sasano, N. Kosugi, S. Ishidoya, K. Ishijima and P.K. Patra, Atmospheric CH4 distributions observed during Arctic cruises of R/V Mirai in 2012-2015, This meeting, NIPR, Tokyo, Mar. 2016.  Welp, L.R., P.K. Patra, C. Rödenbeck, R. Nemani, J. Bi, S.C. Piper, and R.F. Keeling, Increasing summer net CO2 uptake in high northern ecosystems inferred from atmospheric inversions and remote sensing, Atmos. Chem. Phys. (Discuss), submitted, 2016. Deushi, M., and K. Shibata, Development of an MRI Chemistry-Climate Model ver. 2 for the Study of Tropospheric and Stratospheric Chemistry. Pap. Meteor. Geophys., 64, 1-46, 2011. Arctic and global CO2 fluxesCO2 flux [GtC/yr]−6−5−4−3−2−1012−6−5−4−3−2−1012Arctic_Land_PriorArctic_Land_PosteriorArctic_Ocean_PriorArctic_Ocean_PosteriorArctic_Total_PriorArctic_Total_PosteriorGlobal_Land_PriorGlobal_Land_PosteriorGlobal_Ocean_PriorGlobal_Ocean_PosteriorGlobal_Total_PriorGlobal_Total_Posterior 9.79 %  2.91 %  7.42 %of global of global of globalland ocean totalArctic GlobalLand Ocean Total Land Ocean Total●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●ACTM (2000−2009)●●IPCC AR5 (2000−2009)RECCAP inversionsAPN inversionsa. Arctic region: shaded b c 

Modelling of greenhouse gases and related species in the Arctic environment

Prabir, K. Patra

Institutional Repositories DataBase (IRDB)

https://nipr.repo.nii.ac.jp/?action=repository_action_common_download&item_id=12384&item_no=1&attribute_id=16&file_no=1

Modelling of greenhouse gases and related species in the Arctic environment

Abstract

Similar works

Full text

Available Versions

National Institute of Polar Research Repository

Institutional Repositories DataBase (IRDB)