Present-Day PM2.5 over Asia: Simulation and Uncertainty in CMIP6 ESMs

CMIP6地球系统模式对亚洲当今PM2.5的模拟和不确定性分析

+ Author Affiliations + Find other works by these authors
  • Corresponding author: Tongwen WU, twwu@cma.gov.cn
  • Funds:

    Supported by the National Key Research and Development Program of China (2016YFA0602100) and UK–China Research & Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund

  • doi: 10.1007/s13351-022-1202-7

PDF

  • This study assesses the ability of 10 Earth System Models (ESMs) that participated in the Coupled Model Intercomparison Project Phase 6 (CMIP6) to reproduce the present-day inhalable particles with diameters less than 2.5 micrometers (PM2.5) over Asia and discusses the uncertainty. PM2.5 accounts for more than 30% of the surface total aerosol (fine and coarse) concentration over Asia, except for central Asia. The simulated spatial distributions of PM2.5 and its components, averaged from 2005 to 2020, are consistent with the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) reanalysis. They are characterized by the high PM2.5 concentrations in eastern China and northern India where anthropogenic components such as sulfate and organic aerosol dominate, and in northwestern China where the mineral dust in PM2.5 fine particles (PM2.5DU) dominates. The present-day multi-model mean (MME) PM2.5 concentrations slightly underestimate ground-based observations in the same period of 2014–2019, although observations are affected by the limited coverage of observation sites and the urban areas. Those model biases partly come from other aerosols (such as nitrate and ammonium) not involved in our analyses, and also are contributed by large uncertainty in PM2.5 simulations on local scale among ESMs. The model uncertainties over East Asia are mainly attributed to sulfate and PM2.5DU; over South Asia, they are attributed to sulfate, organic aerosol, and PM2.5DU; over Southeast Asia, they are attributed to sea salt in PM2.5 fine particles (PM2.5SS); and over central Asia, they are attributed to PM2.5DU. They are mainly caused by the different representations of aerosols within individual ESMs including the representation of aerosol size distributions, dynamic transport, and physi-cal and chemistry mechanisms.
    本文评估了第六次耦合模式比较计划(CMIP6)中十个地球系统模式(ESM)对亚洲地表PM2.5浓度的模拟并进行了不确定性分析。2005–2020年ESM模拟的PM2.5及其气溶胶组分的空间分布与现代研究与应用回顾分析第2版(MERRA-2)一致。中国东部和印度北部为PM2.5高值区,其中硫酸盐和有机气溶胶(OA)贡献较大;沙尘为主的中国西北部是第三个高值区。多模式集合平均低估了2014–2019年观测的PM2.5,模拟偏差部分来自观测站点的分布和PM2.5的估算方法,也与ESM在局部对PM2.5组分模拟的不确定性有关。东亚的不确定性归因于硫酸盐和沙尘,南亚为硫酸盐、OA和沙尘,东南亚为海盐,中亚为沙尘。这与各ESM对气溶胶的不同算法有关,如粒径分类、动力传输、物理和化学机制等,此分析可为ESM的进一步发展提供依据。
  • 加载中
  • Fig. 1.  Annual mean of near surface PM2.5DU and PM2.5SS concentrations (µg m−3) in Asia (5°–55°N, 70°–140°E) during 2005–2020 from BCC-ESM1 simulations. (a, c) PM2.5DU and (b, d) PM2.5SS, and (a, b) estimation and (c, d) original.

    Fig. 2.  Locations of observation sites in Asia (5°–55°N, 70°–140°E) from EANET (blue triangles, 25 sites), CNEMC (red circles, 348 urban sites), CMA (green circles, 2 background stations), and APAD (purple hollow squares, 2 adjacent sites). The dashed areas represent the various parts of Asia, including Central Asia (CA), East Asia (EA), South Asia (SA), and Southeast Asia (SEA).

    Fig. 3.  The 2005–2020 mean PM2.5 ratios (%) to main aerosol (including all particle sizes of dust and sea salt, sulfate, organic aerosol, and black carbon) in Asia (5°–55°N, 70°–140°E) for (a–j) the 10 ESMs, (k) their MME, and (l) MERRA-2.

    Fig. 4.  (a) Averaged annual (2014–2019) mean surface PM2.5 concentrations (μg m−3) for 373 sites from EANET (triangles, 25 sites) and CNEMC (circles, 348 urban sites) in Asia. (b–m) Scatterplots of surface PM2.5 concentrations (μg m−3) for each ESMs and their MME, and MERRA-2, separately, comparing to the observations from EANET and CNEMC sites during the same period. RMSE stands for root-mean-square error, and COR for correlation coefficient. The grey lines represent the 1 : 1, 1 : 2, and 2 : 1 lines, respectively.

    Fig. 5.  Averaged annual (2005–2020) mean surface PM2.5 concentrations (µg m−3) in Asia (5°–55°N, 70°–140°E) from (a–j) 10 ESMs, (k) their MME, and (l) MERRA-2.

    Fig. 6.  (a) Model-spread (µg m−3) among the 10 ESMs and (b) the ratio (%) of model-spread to MME for annual mean of surface PM2.5 concentration during 2005–2020.

    Fig. 7.  As in Fig. 5, but for the sulfate.

    Fig. 8.  As in Fig. 5, but for the organic aerosol.

    Fig. 9.  As in Fig. 5, but for the black carbon.

    Fig. 10.  As in Fig. 5, but for the PM2.5 fine particles of dust.

    Fig. 11.  As in Fig. 5, but for the PM2.5 fine particles of sea salt.

    Fig. 12.  The model-spread of annual mean concentrations (µg m−3) for anthropogenic aerosols during 2005−2020. (a) Sulfate, (b) OA, and (c) BC.

    Fig. 13.  As in Fig. 12, but for the natural aerosols. (a) PM2.5DU and (b) PM2.5SS.

    Fig. 14.  Taylor diagram of the annual mean surface components (sulfate, organic aerosols, black carbon, PM2.5DU, and PM2.5SS) concentrations simulated by the 10 ESMs compared with the MERRA-2 reanalysis data during 2005–2020 in Asia (5°–55°N, 70°–140°E). The radial coordinate shows the standard deviation in the spatial pattern, normalized by the observed standard deviation. The azimuthal variable shows the correlation of the modeled spatial pattern with the observed spatial pattern.

    Fig. 15.  Histograms of 2005–2020 averaged concentrations (µg m−3) of PM2.5 and their components (sulfate, OA, BC, PM2.5DU, and PM2.5SS) from 10 ESMs, their MME, and MERRA-2 for Asia (5°–55°N, 70°–140°E). The mean value in MME and model diversity for the five main PM2.5 species are 3.5 ± 1.23 µg m−3 for sulfate, 3.98 ± 0.98 µg m−3 for OA, 0.86 ± 0.15 µg m−3 for BC, 2.59 ± 1.57 µg m−3 for PM2.5DU, and 1.5 ± 0.83 µg m−3 for PM2.5SS.

    Fig. 16.  Distribution of differences for PM2.5 and their components (sulfate, OA, BC, PM2.5DU, and PM2.5SS) concentrations (µg m−3) from 10 ESMs in Asia and four subregions during 2005–2020. The box plots show the 25th and 75th percentiles as solid boxes, median values as solid lines, dots represent the concentrations from MME, and whiskers extending to the minimum and maximum.

    Fig. 17.  Time series of surface PM2.5 concentrations (µg m−3) in neighboring city and suburban from APAD, CMA, CNEMC, and MME. Red and blue lines represent observations at urban and suburban sites, respectively. Black lines represent the simulations from MME.

    Table 1.  CMIP6 earth system models used in this study

    CMIP6 ESMsInstitutionResolution and vertical levels
    in atmosphere
    Aerosol component name and referenceNatural aerosol size binModel and data reference
    BCC-ESM1Beijing Climate Center, China Meteorological Administration, China2.813° × 2.813°; L26;
    top level at 2.91 hPa
    BCC-AGCM3-Chem,
    Wu et al. (2020)
    Dust (4 size bins: 0.1–1, 1–2.5, 2.5–5, 5–10 µm);
    sea salt (4 size bins: 0.2–1, 1–3, 3–10, 10–20 µm)
    Wu et al. (2020) and
    Zhang J. et al. (2018, 2019)
    CESM2-WAC
    CM
    National Center for Atmospheric Research, United States0.9° × 1.25°; L70;
    top level at 6 × 10−6 hPa
    MAM4,
    Liu et al. (2016)
    Dust and sea salt (lognormal size distribution)Danabasoglu (2019a, b) and Danabasoglu et al. (2020)
    EC-Earth3-AerChemEuropean Consortium of Meteorological Services, Research Institutes, and High-performance Computing Centers3° × 2°; L34;
    top level at 0.1 hPa
    TM5, Krol et al. (2005) and Huijnen et al. (2010)Dust and sea salt (7 size bins, lognormal size distributions)EC-Earth Consortium (2020a, b) and van Noije et al. (2021)
    GFDL-ESM4NOAA Geophysical Fluid Dynamics Laboratory, United StatesCubed-sphere (c96) grid, with ~100-km native resolution, regridded to 1.0° × 1.25°;
    L49; top level at 0.01 hPa
    GFDL AM4.1,
    Horowitz et al. (2020)
    Dust (5 size bins: 0.1–2, 2–4, 4–6, 6–12, 12–20 µm) and sea salt (5 size bins)
    Dunne et al. (2020), John et al. (2018), and Krasting et al. (2018)
    IPSL-CM5A2-INCAInstitut Pierre Simon Laplace, Paris, France3.75° × 1.875°; L39; top level at 80 kmINCA v6 NMHC-AER-S,
    Szopa et al. (2013)
    Dust and sea salt particles are partitioned into 3 size classes (< 1, 1–10, > 10 μm)Boucher et al. (2020a, b) and Sepulchre et al. (2020)
    MIROC-ES2LUniversity of Tokyo, National Institute for Environmental Studies, and Japan Agency for Marine–Earth Science and Technology, Japan2.813° × 2.813°;
    L40; top level at 3.0 hPa
    SPRINTARS,
    Takemura et al. (2000, 2005, 2009)
    Dust (10 size bins: 0.1–10 µm) and sea salt (10 size bins: 0.05–10 µm, lognormal distribution)Hajima et al. (2019, 2020)
    and Tachiiri et al. (2019)
    MPI-ESM-1-2-HAMMax Planck Institute for Meteorology, Germany1.875° × 1.875°;
    L47; top level at 0.01 hPa
    HAM2.3,
    Tegen et al. (2019)
    Dust and sea salt size distributions are represented by seven lognormal modes Neubauer et al.
    ( 2019a, b)
    MRI-ESM2-0Meteorological Research Institute, Japan1.125° × 1.125°;
    L80; top level at 0.01 hPa
    MASINGAR mk-2r4c,
    Yukimoto et al. (2019a) and Oshima et al. (2020)
    Dust and sea salt (10 size bins: 0.1–10 µm)Yukimoto et al. (2019a, b, c)
    NorESM2-LMNorwegian Climate Center, Norway1.9° × 2.5°;
    L32; top level at 3.64 hPa
    OsloAero6, Kirkevåg et al. (2018) and Seland et al. (2020)Dust and sea salt, lognormal distribution Kirkevåg et al. (2018) and Seland et al. (2019a, b)
    UKESM1-0-LLNatural Environment Research Council, and Met Office, United Kingdom1.25° × 1.875°;
    L85; top level at 85 km
    GLOMAP-Mode,
    Mulcahy et al. (2020)
    Dust (6 size bins) and sea salt (5 size bins)Good et al. (2019), Sellar et al. (2019), and Tang et al. (2019)
    Download: Download as CSV
  • [1]

    Apte, J. S., J. D. Marshall, A. J. Cohen, et al., 2015: Addressing global mortality from ambient PM2.5. Environ. Sci. Technol., 49, 8057–8066. doi: 10.1021/acs.est.5b01236.
    [2]

    Aryal, Y. N., and S. Evans, 2021: Global dust variability explained by drought sensitivity in CMIP6 models. J. Geophys. Res. Earth Surf., 126, e2021JF006073. doi: 10.1029/2021JF006073.
    [3]

    Bollasina, M. A., Y. Ming, and V. Ramaswamy, 2011: Anthropogenic aerosols and the weakening of the South Asian summer monsoon. Science, 334, 502–505. doi: 10.1126/science.1204994.
    [4]

    Bond, T. C., S. J. Doherty, D. W. Fahey, et al., 2013: Bounding the role of black carbon in the climate system: A scientific assessment. J. Geophys. Res. Atmos., 118, 5380–5552. doi: 10.1002/jgrd.50171.
    [5]

    Boucher, O., S. Denvil, G. Levavasseur, et al., 2020a: IPSL IPSL-CM5A2-INCA model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. Available online at https://doi.org/10.22033/ESGF/CMIP6.13661. Accessed on 12 May 2022.
    [6]

    Boucher, O., S. Denvil, G. Levavasseur, et al., 2020b: IPSL IPSL-CM5A2-INCA model output prepared for CMIP6 ScenarioMIP ssp370. Earth System Grid Federation. Available online at https://doi.org/10.22033/ESGF/CMIP6.15714. Acces-sed on 12May 2022.
    [7]

    Buchard, V., A. M. da Silva, C. A. Randles, et al., 2016: Evaluation of the surface PM2.5 in version 1 of the NASA MERRA aerosol reanalysis over the United States. Atmos. Environ., 125, 100–111. doi: 10.1016/j.atmosenv.2015.11.004.
    [8]

    Buchard, V., C. A. Randles, A. M. da Silva, et al., 2017: The MERRA-2 aerosol reanalysis, 1980 onward. Part II: Evaluation and case studies. J. Climate, 30, 6851–6872. doi: 10.1175/JCLI-D-16-0613.1.
    [9]

    Charlson, R. J., S. E. Schwartz, J. M. Hales, et al., 1992: Climate forcing by anthropogenic aerosols. Science, 255, 423–430. doi: 10.1126/science.255.5043.423.
    [10]

    Coakley, J. A. Jr., R. D. Cess, and F. B. Yurevich, 1983: The effect of tropospheric aerosols on the Earth’s radiation budget: A parameterization for climate models. J. Atmos. Sci., 40, 116–138. doi: 10.1175/1520-0469(1983)040<0116:TEOTAO>2.0.CO;2.
    [11]

    Cohen, D. D., and A. J. Atanacio, 2015: The IAEA/RCA Fine and Coarse Particle Ambient Air Database. ANSTO Report/E-784, 1–35.
    [12]

    Collins, W. J., J. F. Lamarque, M. Schulz, et al., 2017: AerChemMIP: Quantifying the effects of chemistry and aerosols in CMIP6. Geosci. Model Dev., 10, 585–607. doi: 10.5194/gmd-10-585-2017.
    [13]

    Danabasoglu, G., 2019a: NCAR CESM2-WACCM model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. Available online at https://doi.org/10.22033/ESGF/CMIP6.10071. Accessed on 12 May 2022.
    [14]

    Danabasoglu, G., 2019b: NCAR CESM2-WACCM model output prepared for CMIP6 ScenarioMIP ssp370. Earth System Grid Federation. Available online at https://doi.org/10.22033/ESGF/CMIP6.10102. Accessed on 12 May 2022.
    [15]

    Danabasoglu, G., J. F. Lamarque, J. Bacmeister, et al., 2020: The community earth system model version 2 (CESM2). J. Adv. Model. Earth Syst., 12, e2019MS001916. doi: 10.1029/2019MS001916.
    [16]

    Dunne, J. P., L. W. Horowitz, A. J. Adcroft, et al., 2020: The GFDL earth system model version 4.1 (GFDL-ESM 4.1): Overall coupled model description and simulation characteristics. J. Adv. Model. Earth Syst., 12, e2019MS002015. doi: 10.1029/2019MS002015.
    [17]

    EC-Earth Consortium (EC-Earth), 2020a: EC-Earth-consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. Available online at https://doi.org/10.22033/ESGF/CMIP6.4701. Acces-sed on 12May 2022.
    [18]

    EC-Earth Consortium (EC-Earth), 2020b: EC-Earth-Consortium EC-Earth3-AerChem Model Output Prepared for CMIP6 ScenarioMIP ssp370. Earth System Grid Federation. Available online at https://doi.org/10.22033/ESGF/CMIP6.4885. Accessed on 12May 2022.
    [19]

    Eyring, V., S. Bony, G. A. Meehl, et al., 2016: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev., 9, 1937–1958. doi: 10.5194/gmd-9-1937-2016.
    [20]

    Good, P., A. Sellar, Y. M. Tang, et al., 2019: MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP ssp370. Earth System Grid Federation. Available online at https://doi.org/10.22033/ESGF/CMIP6.6347. Accessed on 12May 2022.
    [21]

    Guo, J. P., H. Liu, Z. Q. Li, et al., 2018: Aerosol-induced changes in the vertical structure of precipitation: A perspective of TRMM precipitation radar. Atmos. Chem. Phys., 18, 13,329–13,343. doi: 10.5194/acp-18-13329-2018.
    [22]

    Hajima, T., M. Abe, O. Arakawa, et al., 2019: MIROC MIROC-ES2L model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. Available online at https://doi.org/10.22033/ESGF/CMIP6.5602. Accessed on 12 May 2022.
    [23]

    Hajima, T., M. Watanabe, A. Yamamoto, et al., 2020: Development of the MIROC-ES2L Earth system model and the evaluation of biogeochemical processes and feedbacks. Geosci. Model Dev., 13, 2197–2244. doi: 10.5194/gmd-13-2197-2020.
    [24]

    Haywood, J. M., N. Bellouin, A. Jones, et al., 2011: The roles of aerosol, water vapor and cloud in future global dimming/brightening. J. Geophys. Res. Atmos., 116, D20203. doi: 10.1029/2011JD016000.
    [25]

    Hoesly, R. M., S. J. Smith, L. Y. Feng, et al., 2018: Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS). Geosci. Model Dev., 11, 369–408. doi: 10.5194/gmd-11-369-2018.
    [26]

    Horowitz, L. W., V. Naik, F. Paulot, et al., 2020: The GFDL global atmospheric chemistry–climate model AM4.1: Model description and simulation characteristics. J. Adv. Model. Earth Syst., 12, e2019MS002032. doi: 10.1029/2019MS002032.
    [27]

    Huijnen, V., J. Williams, M. van Weele, et al., 2010: The global chemistry transport model TM5: Description and evaluation of the tropospheric chemistry version 3.0. Geosci. Model Dev., 3, 445–473. doi: 10.5194/gmd-3-445-2010.
    [28]

    Hwang, Y. T., D. M. W. Frierson, and S. M. Kang, 2013: Anthropogenic sulfate aerosol and the southward shift of tropical precipitation in the late 20th century. Geophys. Res. Lett., 40, 2845–2850. doi: 10.1002/grl.50502.
    [29]

    Jacobson, M. Z., 2001: Strong radiative heating due to the mixing state of black carbon in atmospheric aerosols. Nature, 409, 695–697. doi: 10.1038/35055518.
    [30]

    John, J. G., C. Blanton, C. McHugh, et al., 2018: NOAA-GFDL GFDL-ESM4 Model Output Prepared for CMIP6 ScenarioMIP ssp370. Earth System Grid Federation. Available online at https://doi.org/10.22033/ESGF/CMIP6.8691. Acces-sed on 12 May 2022.
    [31]

    Kirkevåg, A., A. Grini, D. Olivié, et al., 2018: A production-tagged aerosol module for earth system models, OsloAero5.3-extensions and updates for CAM5.3-Oslo. Geosci. Model Dev., 11, 3945–3982. doi: 10.5194/gmd-11-3945-2018.
    [32]

    Krasting, J. P., J. G. John, C. Blanton, et al., 2018: NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. Available online at https://doi.org/10.22033/ESGF/CMIP6.8597. Accessed on 12 May 2022.
    [33]

    Krol, M., S. Houweling, B. Bregman, et al., 2005: The two-way nested global chemistry-transport zoom model TM5: Algorithm and applications. Atmos. Chem. Phys., 5, 417–432. doi: 10.5194/acp-5-417-2005.
    [34]

    Lamarque, J. F., D. T. Shindell, B. Josse, et al., 2013: The atmospheric chemistry and climate model intercomparison Project (ACCMIP): Overview and description of models, simulations and climate diagnostics. Geosci. Model Dev., 6, 179–206. doi: 10.5194/gmd-6-179-2013.
    [35]

    Lau, K. M., M. K. Kim, and K. M. Kim, 2006: Asian summer monsoon anomalies induced by aerosol direct forcing: The role of the Tibetan Plateau. Climate Dyn., 26, 855–864. doi: 10.1007/s00382-006-0114-z.
    [36]

    Li, X., Y. W. Liu, M. H. Wang, et al., 2021: Assessment of the Coupled Model Intercomparison Project phase 6 (CMIP6) Model performance in simulating the spatial-temporal variation of aerosol optical depth over Eastern Central China. Atmos. Res., 261, 105,747. doi: 10.1016/j.atmosres.2021.105747.
    [37]

    Li, Z. Q., F. Niu, J. W. Fan, et al., 2011: Long-term impacts of aerosols on the vertical development of clouds and precipitation. Nat. Geosci., 4, 888–894. doi: 10.1038/ngeo1313.
    [38]

    Li, Z. Q., J. P. Guo, A. J. Ding, et al., 2017: Aerosol and boundary-layer interactions and impact on air quality. Natl. Sci. Rev., 4, 810–833. doi: 10.1093/nsr/nwx117.
    [39]

    Lim, S. S., T. Vos, A. D. Flaxman, et al., 2012: A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: A systematic analysis for the Global Burden of Disease Study 2010. Lancet, 380, 2224–2260. doi: 10.1016/S0140-6736(12)61766-8.
    [40]

    Liu, H. B., R. J. Yan, and J. Yang, 2021: Credibility and statistical characteristics of CAMSRA and MERRA-2 AOD reanalysis products over the Sichuan Basin during 2003–2018. Atmos. Environ., 244, 117980. doi: 10.1016/j.atmosenv.2020.117980.
    [41]

    Liu, R. J., H. Liao, W. Y. Chang, et al., 2017: Impact of climate change on aerosol concentrations in eastern China based on Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) datasets. Chinese J. Atmos. Sci., 41, 739–751. doi: 10.3878/j.issn.1006-9895.1612.16218. (in Chinese)
    [42]

    Liu, X., P. L. Ma, H. Wang, et al., 2016: Description and evaluation of a new four-mode version of the Modal Aerosol Module (MAM4) within version 5.3 of the Community Atmosphere Model. Geosci. Model Dev., 9, 505–522. doi: 10.5194/gmd-9-505-2016.
    [43]

    Menon, S., J. Hansen, L. Nazarenko, et al., 2002: Climate effects of black carbon aerosols in China and India. Science, 297, 2250–2253. doi: 10.1126/science.1075159.
    [44]

    Mulcahy, J. P., C. Johnson, C. G. Jones, et al., 2020: Description and evaluation of aerosol in UKESM1 and HadGEM3-GC3.1 CMIP6 historical simulations. Geosci. Model Dev., 13, 6383–6423. doi: 10.5194/gmd-13-6383-2020.
    [45]

    Neubauer, D., S. Ferrachat, D. C. Siegenthaler-Le, et al., 2019a: HAMMOZ-Consortium MPI-ESM1.2-HAM model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. Available online at https://doi.org/10.22033/ESGF/CMIP6.5016. Accessed on 12 May 2022.
    [46]

    Neubauer, D., S. Ferrachat, D. C. Siegenthaler-Le, et al., 2019b: HAMMOZ-Consortium MPI-ESM1.2-HAM model output prepared for CMIP6 AerChemMIP. Earth System Grid Federation. Available online at https://doi.org/10.22033/ESGF/CMIP6.1621. Accessed on 12 May 2022.
    [47]

    Oshima, N., S. Yukimoto, M. Deushi, et al., 2020: Global and Arctic effective radiative forcing of anthropogenic gases and aerosols in MRI-ESM2.0. Prog. Earth Planet. Sci., 7, 38. doi: 10.1186/s40645-020-00348-w.
    [48]

    Ramanathan, V., P. J. Crutzen, J. T. Kiehl, et al., 2001: Aerosols, climate, and the hydrological cycle. Science, 294, 2119–2124. doi: 10.1126/science.1064034.
    [49]

    Randles, C. A., A. M. da Silva, V. Buchard, et al., 2017: The MERRA-2 aerosol reanalysis, 1980 onward. Part I: System description and data assimilation evaluation. J. Climate, 30, 6823–6850. doi: 10.1175/JCLI-D-16-0609.1.
    [50]

    Seland, Ø., M. Bentsen, D. J. L. Olivié, et al., 2019a: NCC NorESM2-LM model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. Available online at https://doi.org/10.22033/ESGF/CMIP6.8036. Accessed on 12 May 2022.
    [51]

    Seland, Ø., M. Bentsen, D. J. L. Olivié, et al., 2019b: NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP ssp370. Earth System Grid Federation. Available online at https://doi.org/10.22033/ESGF/CMIP6.8268. Acces-sed on 12May 2022.
    [52]

    Seland, Ø., M. Bentsen, D. Olivié, et al., 2020: Overview of the Norwegian Earth System Model (NorESM2) and key climate response of CMIP6 DECK, historical, and scenario simulations. Geosci. Model Dev., 13, 6165–6200. doi: 10.5194/gmd-13-6165-2020.
    [53]

    Sellar, A. A., C. G. Jones, J. P. Mulcahy, et al., 2019: UKESM1: Description and evaluation of the U.K. Earth System Model. J. Adv. Model. Earth Syst., 11, 4513–4558. doi: 10.1029/2019MS001739.
    [54]

    Sepulchre, P., A. Caubel, J. B. Ladant, et al., 2020: IPSL-CM5A2—an Earth system model designed for multi-millennial climate simulations. Geosci. Model Dev., 13, 3011–3053. doi: 10.5194/gmd-13-3011-2020.
    [55]

    Shi, Y. S., T. Matsunaga, Y. Yamaguchi, et al., 2018: Long-term trends and spatial patterns of satellite-retrieved PM2.5 concentrations in South and Southeast Asia from 1999 to 2014. Sci. Total Environ., 615, 177–186. doi: 10.1016/j.scitotenv.2017.09.241.
    [56]

    Silva, R. A., J. J. West, Y. Q. Zhang, et al., 2013: Global premature mortality due to anthropogenic outdoor air pollution and the contribution of past climate change. Environ. Res. Lett., 8, 034005. doi: 10.1088/1748-9326/8/3/034005.
    [57]

    Singh, N., V. Murari, M. Kumar, et al., 2017: Fine particulates over South Asia: Review and meta-analysis of PM2.5 source apportionment through receptor model. Environ. Pollut., 223, 121–136. doi: 10.1016/j.envpol.2016.12.071.
    [58]

    Sweerts, B., S. Pfenninger, S. Yang, et al., 2019: Estimation of losses in solar energy production from air pollution in China since 1960 using surface radiation data. Nat. Energy, 4, 657–663. doi: 10.1038/s41560-019-0412-4.
    [59]

    Szopa, S., Y. Balkanski, M. Schulz, et al., 2013: Aerosol and ozone changes as forcing for climate evolution between 1850 and 2100. Climate Dyn., 40, 2223–2250. doi: 10.1007/s00382-012-1408-y.
    [60]

    Tachiiri, K., M. Abe, T. Hajima, et al., 2019: MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP ssp370. Earth System Grid Federation. Available online at https://doi.org/10.22033/ESGF/CMIP6.5751. Accessed on 12May 2022.
    [61]

    Takemura, T., H. Okamoto, Y. Maruyama, et al., 2000: Global three-dimensional simulation of aerosol optical thickness distribution of various origins. J. Geophys. Res. Atmos., 105, 17,853–17,873. doi: 10.1029/2000JD900265.
    [62]

    Takemura, T., T. Nozawa, S. Emori, et al., 2005: Simulation of climate response to aerosol direct and indirect effects with aerosol transport-radiation model. J. Geophys. Res. Atmos., 110, D02202. doi: 10.1029/2004JD005029.
    [63]

    Takemura, T., M. Egashira, K. Matsuzawa, et al., 2009: A simulation of the global distribution and radiative forcing of soil dust aerosols at the Last Glacial Maximum. Atmos. Chem. Phys., 9, 3061–3073. doi: 10.5194/acp-9-3061-2009.
    [64]

    Tang, Y. M., S. Rumbold, R. Ellis, et al., 2019: MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. Available online at https://doi.org/10.22033/ESGF/CMIP6.6113. Accessed on 12May 2022.
    [65]

    Tegen, I., D. Neubauer, S. Ferrachat, et al., 2019: The global aerosol–climate model ECHAM6.3–HAM2.3—Part 1: Aerosol evaluation. Geosci. Model Dev., 12, 1643–1677. doi: 10.5194/gmd-12-1643-2019.
    [66]

    Textor, C., M. Schulz, S. Guibert, et al., 2007: The effect of harmonized emissions on aerosol properties in global models—An AeroCom experiment. Atmos. Chem. Phys., 7, 4489–4501. doi: 10.5194/acp-7-4489-2007.
    [67]

    Tosca, M. G., J. T. Randerson, C. S. Zender, et al., 2010: Do biomass burning aerosols intensify drought in equatorial Asia during El Niño? Atmos. Chem. Phys., 10, 3515–3528. doi: 10.5194/acp-10-3515-2010.
    [68]

    Turnock, S. T., R. J. Allen, M. Andrews, et al., 2020: Historical and future changes in air pollutants from CMIP6 models. Atmos. Chem. Phys., 20, 14,547–14,579. doi: 10.5194/acp-20-14547-2020.
    [69]

    Ukhov, A., S. Mostamandi, A. da Silva, et al., 2020: Assessment of natural and anthropogenic aerosol air pollution in the Middle East using MERRA-2, CAMS data assimilation products, and high-resolution WRF-Chem model simulations. Atmos. Chem. Phys., 20, 9281–9310. doi: 10.5194/acp-20-9281-2020.
    [70]

    van Noije, T., T. Bergman, P. Le Sager, et al., 2021: EC-Earth3-AerChem: A global climate model with interactive aerosols and atmospheric chemistry participating in CMIP6. Geosci. Model Dev., 14, 5637–5668. doi: 10.5194/gmd-14-5637-2021.
    [71]

    Wang, M. X., and R. J. Zhang, 2001: Frontier of atmospheric aerosols researches. Climatic Environ. Res., 6, 119–124. doi: 10.3969/j.issn.1006-9585.2001.01.014. (in Chinese)
    [72]

    Wang, Y., Q. Wan, W. Meng, et al., 2011: Long-term impacts of aerosols on precipitation and lightning over the Pearl River Delta megacity area in China. Atmos. Chem. Phys., 11, 12,421–12,436. doi: 10.5194/acp-11-12421-2011.
    [73]

    Wang, Y., A. Khalizov, M. Levy, et al., 2013: New Directions: Light absorbing aerosols and their atmospheric impacts. Atmos. Environ., 81, 713–715. doi: 10.1016/j.atmosenv.2013.09.034.
    [74]

    Wei, J., Z. Q. Li, M. Cribb, et al., 2020: Improved 1 km resolution PM2.5 estimates across China using enhanced space-time extremely randomized trees. Atmos. Chem. Phys., 20, 3273–3289. doi: 10.5194/acp-20-3273-2020.
    [75]

    Wei, Y., X. S. Chen, H. S. Chen, et al., 2019: IAP-AACM v1.0: A global to regional evaluation of the atmospheric chemistry model in CAS-ESM. Atmos. Chem. Phys., 19, 8269–8296. doi: 10.5194/acp-19-8269-2019.
    [76]

    Wilcox, L. J., Z. Liu, B. H. Samset, et al., 2020: Accelerated increases in global and Asian summer monsoon precipitation from future aerosol reductions. Atmos. Chem. Phys., 20, 11,955–11,977. doi: 10.5194/acp-20-11955-2020.
    [77]

    Witek, M. L., P. J. Flatau, P. K. Quinn, et al., 2007: Global sea-salt modeling: Results and validation against multicampaign shipboard measurements. J. Geophys. Res. Atmos., 112, D08215. doi: 10.1029/2006JD007779.
    [78]

    Wu, G. X., Z. Q. Li, C. B. Fu, et al., 2016: Advances in studying interactions between aerosols and monsoon in China. Sci. China Earth Sci., 59, 1–16. doi: 10.1007/s11430-015-5198-z.
    [79]

    Wu, J., Y. Xu, and B. T. Zhou, 2016: The evaluation of surface PM2.5 concentration over China based on ACCMIP models. Climate Change Res., 12, 268–275. doi: 10.12006/j.issn.1673-1719.2015.188. (in Chinese)
    [80]

    Wu, T. W., F. Zhang, J. Zhang, et al., 2020: Beijing Climate Center Earth System Model version 1 (BCC-ESM1): Model description and evaluation of aerosol simulations. Geosci. Model Dev., 13, 977–1005. doi: 10.5194/gmd-13-977-2020.
    [81]

    Yan, X., Z. Zang, N. N. Luo, et al., 2020: New interpretable deep learning model to monitor real-time PM2.5 concentrations from satellite data. Environ. Int., 144, 106,060. doi: 10.1016/j.envint.2020.106060.
    [82]

    Yan, X., Z. Zang, C. Liang, et al., 2021: New global aerosol fine-mode fraction data over land derived from MODIS satellite retrievals. Environ. Pollut., 276, 116707. doi: 10.1016/j.envpol.2021.116707.
    [83]

    Yukimoto, S., H. Kawai, T. Koshiro, et al., 2019a: The meteorological research institute Earth system model version 2.0, MRI-ESM2.0: Description and basic evaluation of the physical component. J. Meteor. Soc. Japan, 97, 931–965. doi: 10.2151/jmsj.2019-051.
    [84]

    Yukimoto, S., T. Koshiro, H. Kawai, et al., 2019b: MRI MRI-ESM2.0 model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. Available online at https://doi.org/10.22033/ESGF/CMIP6.6842. Accessed on 12May 2022.
    [85]

    Yukimoto, S., T. Koshiro, H. Kawai, et al., 2019c: MRI MRI-ESM2.0 model output prepared for CMIP6 ScenarioMIP ssp370. Earth System Grid Federation. Available online at https://doi.org/10.22033/ESGF/CMIP6.6915. Accessed on 12May 2022.
    [86]

    Zhang, H., X. Y. Ma, S. Y. Zhao, et al., 2021: Advances in research on the ITCZ: Mean position, model bias, and anthropogenic aerosol influences. J. Meteor. Res., 35, 729–742. doi: 10.1007/s13351-021-0203-2.
    [87]

    Zhang, J., T. W. Wu, X. L. Shi, et al., 2018: BCC BCC-ESM1 model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. Available online at https://doi.org/10.22033/ESGF/CMIP6.2949. Accessed on 12May 2022.
    [88]

    Zhang, J., T. W. Wu, X. L. Shi, et al., 2019: BCC BCC-ESM1 model output prepared for CMIP6 AerChemMIP ssp370. Earth System Grid Federation. Available online at https://doi.org/10.22033/ESGF/CMIP6.3036. Accessed on 12May 2022.
    [89]

    Zhang, R. Y., G. H. Li, J. W. Fan, et al., 2007: Intensification of Pacific storm track linked to Asian pollution. Proc. Natl. Acad. Sci. USA, 104, 5295–5299. doi: 10.1073/pnas.0700618104.
    [90]

    Zhang, Y., Y. N. Li, J. P. Guo, et al., 2019: The climatology and trend of black carbon in China from 12-year ground observations. Climate Dyn., 53, 5881–5892. doi: 10.1007/s00382-019-04903-0.
    [91]

    Zhang, Y., J. L. Jin, P. Yan, et al., 2020: Long-term variations of major atmospheric compositions observed at the background stations in three key areas of China. Adv. Climate Change Res., 11, 370–380. doi: 10.1016/j.accre.2020.11.005.
    [92]

    Zhao, A., C. L. Ryder, and L. J. Wilcox, 2022: How well do the CMIP6 models simulate dust aerosols? Atmos. Chem. Phys., 22, 2095–2119. doi: 10.5194/acp-22-2095-2022.
    [93]

    Zhao, X. Y., R. J. Allen, and E. S. Thomson, 2021: An implicit air quality bias due to the state of pristine aerosol. Earth’s Future, 9, e2021EF001979. doi: 10.1029/2021EF001979.
  • Tongwen WU and Xiaole SU.pdf

  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Present-Day PM2.5 over Asia: Simulation and Uncertainty in CMIP6 ESMs

    Corresponding author: Tongwen WU, twwu@cma.gov.cn
  • 1. Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing 100081, China
  • 2. Beijing Climate Center, China Meteorological Administration, Beijing 100081, China
  • 3. Meteorological Observation Center, China Meteorological Administration, Beijing 100081, China
  • 4. Laboratory for Climate and Ocean–Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
  • 5. Met Office, Hadley Centre, Exeter EX1 3PB, United Kingdom
  • 6. University of Leeds Met Office Strategic (LUMOS) Research Group, School of Earth and Environment, University of Leeds, Leeds LS2 9JT, United Kingdom
Funds: Supported by the National Key Research and Development Program of China (2016YFA0602100) and UK–China Research & Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund

Abstract: This study assesses the ability of 10 Earth System Models (ESMs) that participated in the Coupled Model Intercomparison Project Phase 6 (CMIP6) to reproduce the present-day inhalable particles with diameters less than 2.5 micrometers (PM2.5) over Asia and discusses the uncertainty. PM2.5 accounts for more than 30% of the surface total aerosol (fine and coarse) concentration over Asia, except for central Asia. The simulated spatial distributions of PM2.5 and its components, averaged from 2005 to 2020, are consistent with the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) reanalysis. They are characterized by the high PM2.5 concentrations in eastern China and northern India where anthropogenic components such as sulfate and organic aerosol dominate, and in northwestern China where the mineral dust in PM2.5 fine particles (PM2.5DU) dominates. The present-day multi-model mean (MME) PM2.5 concentrations slightly underestimate ground-based observations in the same period of 2014–2019, although observations are affected by the limited coverage of observation sites and the urban areas. Those model biases partly come from other aerosols (such as nitrate and ammonium) not involved in our analyses, and also are contributed by large uncertainty in PM2.5 simulations on local scale among ESMs. The model uncertainties over East Asia are mainly attributed to sulfate and PM2.5DU; over South Asia, they are attributed to sulfate, organic aerosol, and PM2.5DU; over Southeast Asia, they are attributed to sea salt in PM2.5 fine particles (PM2.5SS); and over central Asia, they are attributed to PM2.5DU. They are mainly caused by the different representations of aerosols within individual ESMs including the representation of aerosol size distributions, dynamic transport, and physi-cal and chemistry mechanisms.

CMIP6地球系统模式对亚洲当今PM2.5的模拟和不确定性分析

本文评估了第六次耦合模式比较计划(CMIP6)中十个地球系统模式(ESM)对亚洲地表PM2.5浓度的模拟并进行了不确定性分析。2005–2020年ESM模拟的PM2.5及其气溶胶组分的空间分布与现代研究与应用回顾分析第2版(MERRA-2)一致。中国东部和印度北部为PM2.5高值区,其中硫酸盐和有机气溶胶(OA)贡献较大;沙尘为主的中国西北部是第三个高值区。多模式集合平均低估了2014–2019年观测的PM2.5,模拟偏差部分来自观测站点的分布和PM2.5的估算方法,也与ESM在局部对PM2.5组分模拟的不确定性有关。东亚的不确定性归因于硫酸盐和沙尘,南亚为硫酸盐、OA和沙尘,东南亚为海盐,中亚为沙尘。这与各ESM对气溶胶的不同算法有关,如粒径分类、动力传输、物理和化学机制等,此分析可为ESM的进一步发展提供依据。
    • Aerosol is a multiphase system composed of solid particles and liquid droplets, suspended in a gaseous carrier phase (e.g., air). Atmospheric aerosols can include minerals (e.g., silicates) originating from soils and rocks, carbonaceous components (black carbon and organic carbon), sulfates, nitrates, ammonium salts, sea salts, and biogenic components (Wang and Zhang, 2001; Zhang Y. et al., 2019). Through either direct (Coakley et al., 1983; Jacobson, 2001; Bond et al., 2013; Li et al., 2017) or indirect effects on atmospheric radiation (Charlson et al., 1992; Guo et al., 2018; Liu et al., 2021), aerosols are well recognized to significantly influence weather and climate at regional and global scales (Menon et al., 2002; Lau et al., 2006; Zhang et al., 2007; Tosca et al., 2010; Bollasina et al., 2011; Li et al., 2011; Wang et al., 2011, 2013; Hwang et al., 2013; Wu G. X. et al., 2016; Zhang et al., 2021). Aerosols can also cause serious environmental problems such as fog, haze, photochemical smog, and acid rain, with significant impacts on the hydrological cycle, new energy development, agricultural production, and transportation (Ramanathan et al., 2001; Haywood et al., 2011; Singh et al., 2017; Sweerts et al., 2019). Fine particulate matters with particle diameters less than 2.5 µm, commonly termed PM2.5, are generally thought of as one of the main causes of air pollution and have an adverse effect on human health. According to the Global Burden of Disease (GBD) 2010 comparative risk assessment (Lim et al., 2012), roughly 3.2 million deaths per year are attributable to ambient PM2.5. Understanding and predicting PM2.5 and its spatial and temporal variations are therefore vital for reducing mortality and other impacts on the environment (Apte et al., 2015).

      With the development of Earth System Models (ESMs), the importance of coupling between multiple components of the earth system, including atmosphere, ocean, land, and sea ice, has gradually been recognized, and increasingly improved within these ESMs. ESMs have become an important tool to simulate and forecast global aerosols (Collins et al., 2017) and can not only fill the gaps between historical observations, but also estimate the trends of aerosols in the future, and thus provide a basis for assessing the evolution of air pollution in both the past and future. The performance of ESMs to reproduce the observed aerosols is an important issue for climate modeling communities. In fact, the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) was endorsed by the Coupled Model Intercomparison Project Phase 5 (CMIP5), and tended to focus on the atmospheric chemistry (Lamarque et al., 2013), with only a few models providing the simulation results for aerosols (Collins et al., 2017). The Aerosols and Chemistry Model Intercomparison Project (AerChemMIP; Collins et al., 2017), part of the Coupled Model Intercomparison Project Phase 6 (CMIP6; Eyring et al., 2016), provides an opportunity to understand the performance of the latest ESMs in simulating aerosols. There are few relevant assessments on the performance of CMIP6 ESMs in simulating aerosols (Mulcahy et al., 2020; Wu et al., 2020). They show that most of the current generation of ESMs such as BCC-ESM1 and UKESM1 can reproduce the global spatial distributions of most aerosol components (e.g., sulfate) concentrations, although there are some model biases for certain components.

      It is important to understand the evolution of ground-level PM2.5 over Asia as it is one of the most heavily polluted regions on the globe, and has the highest mortality rate attributed to atmospheric pollution (Apte et al., 2015). Previous studies show that most of the CMIP6 ESMs can capture the spatial distributions of surface PM2.5 concentrations across the globe but underestimate the absolute magnitude (Turnock et al., 2020). However, the ability of the CMIP6 ESMs to simulate PM2.5 in Asia has not been carefully explored so far largely due to the lack of ground-based surface aerosol observations in Asia. In addition, the various components of PM2.5 have seldom been utilized in previous studies, leading to the differences among models being poorly understood.

      Here, simulations of surface PM2.5 and its component concentrations from 10 CMIP6 ESMs are evaluated in detail against observations from surface sites over Asia. Based on the ratio of PM2.5 to main aerosol mass and the relative contributions of each component to PM2.5, differences among models are revealed. The remaining parts of this manuscript are as follows. The research data and methods are presented in Section 2. In Section 3, we assess the ability of the CMIP6 ESMs to simulate the spatial distribution of PM2.5 and its main components in Asia. In Section 4, we analyze their model-spread among 10 ESMs. Uncertainties in evaluating PM2.5 concentrations are discussed in Section 5. A summary is given in Section 6.

    2.   Data and methods
    • The monthly mean PM2.5 components, including sulfate, organic aerosol (OA), black carbon (BC), dust, and sea salt, from 10 ESMs participated in CMIP6 are employed in this study. The model information is described in Table 1, and all the model data can be freely downloaded from the Earth System Grid Federation (ESGF) nodes (https://esgf-node.llnl.gov/search/cmip6/). All the models use the same anthropogenic emission inventory from the Community Emissions Data System (CEDS; Hoesly et al., 2018; http://www.globalchange.umd.edu/ceds/ceds-cmip6-data/) and their own schemes for simulating natural emissions such as dust and sea salt aerosols, which have different representations of the aerosol size distribution (Collins et al., 2017). The model data are obtained from the CMIP6 historical experiments (Eyring et al., 2016) before 2015 and from the SSP370 experiments in AerChemMIP (Collins et al., 2017) afterward.

      CMIP6 ESMsInstitutionResolution and vertical levels
      in atmosphere
      Aerosol component name and referenceNatural aerosol size binModel and data reference
      BCC-ESM1Beijing Climate Center, China Meteorological Administration, China2.813° × 2.813°; L26;
      top level at 2.91 hPa
      BCC-AGCM3-Chem,
      Wu et al. (2020)
      Dust (4 size bins: 0.1–1, 1–2.5, 2.5–5, 5–10 µm);
      sea salt (4 size bins: 0.2–1, 1–3, 3–10, 10–20 µm)
      Wu et al. (2020) and
      Zhang J. et al. (2018, 2019)
      CESM2-WAC
      CM
      National Center for Atmospheric Research, United States0.9° × 1.25°; L70;
      top level at 6 × 10−6 hPa
      MAM4,
      Liu et al. (2016)
      Dust and sea salt (lognormal size distribution)Danabasoglu (2019a, b) and Danabasoglu et al. (2020)
      EC-Earth3-AerChemEuropean Consortium of Meteorological Services, Research Institutes, and High-performance Computing Centers3° × 2°; L34;
      top level at 0.1 hPa
      TM5, Krol et al. (2005) and Huijnen et al. (2010)Dust and sea salt (7 size bins, lognormal size distributions)EC-Earth Consortium (2020a, b) and van Noije et al. (2021)
      GFDL-ESM4NOAA Geophysical Fluid Dynamics Laboratory, United StatesCubed-sphere (c96) grid, with ~100-km native resolution, regridded to 1.0° × 1.25°;
      L49; top level at 0.01 hPa
      GFDL AM4.1,
      Horowitz et al. (2020)
      Dust (5 size bins: 0.1–2, 2–4, 4–6, 6–12, 12–20 µm) and sea salt (5 size bins)
      Dunne et al. (2020), John et al. (2018), and Krasting et al. (2018)
      IPSL-CM5A2-INCAInstitut Pierre Simon Laplace, Paris, France3.75° × 1.875°; L39; top level at 80 kmINCA v6 NMHC-AER-S,
      Szopa et al. (2013)
      Dust and sea salt particles are partitioned into 3 size classes (< 1, 1–10, > 10 μm)Boucher et al. (2020a, b) and Sepulchre et al. (2020)
      MIROC-ES2LUniversity of Tokyo, National Institute for Environmental Studies, and Japan Agency for Marine–Earth Science and Technology, Japan2.813° × 2.813°;
      L40; top level at 3.0 hPa
      SPRINTARS,
      Takemura et al. (2000, 2005, 2009)
      Dust (10 size bins: 0.1–10 µm) and sea salt (10 size bins: 0.05–10 µm, lognormal distribution)Hajima et al. (2019, 2020)
      and Tachiiri et al. (2019)
      MPI-ESM-1-2-HAMMax Planck Institute for Meteorology, Germany1.875° × 1.875°;
      L47; top level at 0.01 hPa
      HAM2.3,
      Tegen et al. (2019)
      Dust and sea salt size distributions are represented by seven lognormal modes Neubauer et al.
      ( 2019a, b)
      MRI-ESM2-0Meteorological Research Institute, Japan1.125° × 1.125°;
      L80; top level at 0.01 hPa
      MASINGAR mk-2r4c,
      Yukimoto et al. (2019a) and Oshima et al. (2020)
      Dust and sea salt (10 size bins: 0.1–10 µm)Yukimoto et al. (2019a, b, c)
      NorESM2-LMNorwegian Climate Center, Norway1.9° × 2.5°;
      L32; top level at 3.64 hPa
      OsloAero6, Kirkevåg et al. (2018) and Seland et al. (2020)Dust and sea salt, lognormal distribution Kirkevåg et al. (2018) and Seland et al. (2019a, b)
      UKESM1-0-LLNatural Environment Research Council, and Met Office, United Kingdom1.25° × 1.875°;
      L85; top level at 85 km
      GLOMAP-Mode,
      Mulcahy et al. (2020)
      Dust (6 size bins) and sea salt (5 size bins)Good et al. (2019), Sellar et al. (2019), and Tang et al. (2019)

      Table 1.  CMIP6 earth system models used in this study

      Not all CMIP6 ESMs provide PM2.5 concentrations, even for some ESMs with available PM2.5, they use different methods to calculate it. In order to uniformly evaluate the ability of ESMs to simulate PM2.5, it is necessary to find a consistent method to calculate PM2.5. Therefore, following the methods used in other studies (Silva et al., 2013; Turnock et al., 2020), the formula used to estimate PM2.5 mass concentrations from the ESMs data is expressed as

      $$ {\mathrm{P}\mathrm{M}}_{2.5}={\mathrm{B}\mathrm{C}+\mathrm{O}\mathrm{A}+\mathrm{S}\mathrm{O}}_{4}+(0.1\times \mathrm{D}\mathrm{U})+{(0.25\times \mathrm{S}\mathrm{S})}_{} , $$ (1)

      where BC, OA, SO4, DU, and SS represent the black carbon (CMIP6 diagnostic identifier: mmrbc), organic aerosol (mmroa), sulfate (mmrso4), dust (mmrdust), and sea salt (mmrss) mass mixing ratio (kg kg−1), respectively. All the aerosol mass concentrations in the lowest layer of each ESM are taken as the near surface values from simulations in this work. The particles for BC, OA, and SO4 aerosols are generally less than 2.5 µm in diameter.

      In Eq. (1), 10% and 25% of dust and sea salt particles are assumed to be present within the fine size fraction of less than 2.5 µm in diameter. We validated this assumption for dust and sea salt from additional BCC-ESM1 simulations, which provided output across four-size bins of dust (DST01: 0.1–1.0 µm, DST02: 1.0–2.5 µm, DST03: 2.5–5.0 µm, and DST04: 5.0–10 µm) and sea salt (SSLT01: 0.2–1.0 µm, SSLT02: 1.0–3.0 µm, SSLT03: 3.0–10 µm, and SSLT04: 10–20 µm) aerosols (Wu et al., 2020). Only the ESGF provides total aerosol mass mixing ratios so we only have access to full size resolved aerosol data from BCC-ESM1. As shown in Fig. 1, the estimated PM2.5 fine particles concentrations for dust (hereafter PM2.5DU) and sea salt (PM2.5SS) from the Eq. (1) are nearly consistent to that from the original BCC-ESM1 simulations (fine size fraction less than 2.5 µm in diameter calculated by summing by DST01, DST02, SSLT01, and SSLT02, respectively).

      Figure 1.  Annual mean of near surface PM2.5DU and PM2.5SS concentrations (µg m−3) in Asia (5°–55°N, 70°–140°E) during 2005–2020 from BCC-ESM1 simulations. (a, c) PM2.5DU and (b, d) PM2.5SS, and (a, b) estimation and (c, d) original.

      To evaluate the present-day PM2.5 climatology in ESMs, the following ground-based observations are used: monthly mean surface PM2.5 observations during 2014–2019 at 25 sites in Asia from the Acid Deposition Monitoring Network in East Asia (hereafter EANET data; http://www.eanet.asia, accessed on 16 December 2020) and 348 urban sites in China available from the Chinese National Environmental Monitoring Center (hereafter CNEMC data; http://www.cnemc.cn, accessed on 16 December 2020). The CNEMC data have been used in previous studies (Wei et al., 2019; Wei et al., 2020). In order to examine the observation uncertainty due to the impact of urban effects, monthly mean PM2.5 concentrations at two atmospheric background stations from the Meteorological Observation Center, China Meteorological Administration (hereafter CMA data; Zhang et al., 2020) are compared with the nearby urban sites from CNEMC data, as well as from a pair of urban and suburban ground-based observations in Thailand (Pathumwan and KlongHa) from the Asia–Pacific Aerosol Database (APAD; Cohen et al., 2015). The geographic distributions of all the observation sites and division of Asian subregions used in this study are shown in Fig. 2.

      Figure 2.  Locations of observation sites in Asia (5°–55°N, 70°–140°E) from EANET (blue triangles, 25 sites), CNEMC (red circles, 348 urban sites), CMA (green circles, 2 background stations), and APAD (purple hollow squares, 2 adjacent sites). The dashed areas represent the various parts of Asia, including Central Asia (CA), East Asia (EA), South Asia (SA), and Southeast Asia (SEA).

      Considering the sparsely covered and unevenly distributed ground-based observation, the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) data with a high-resolution (0.5° × 0.625°) assimilation data product (including sulfate, organic aerosols, black carbon, dust, and sea salt) developed by combining satellite observations with the Goddard Earth Observing System atmospheric model and atmosphere data assimilation system (Buchard et al., 2016; Randles et al., 2017) are further used. The MERRA-2 data are widely used by many studies in evaluation of aerosols simulations (Turnock et al., 2020; Ukhov et al., 2020; Li et al., 2021; Zhao et al., 2021). For intercomparison between ESMs and MERRA-2, we derive the monthly MERRA-2 PM2.5 data from 2005 to 2020 using Eq. (1), on the basis of the monthly sulfate, organic aerosols, black carbon, and total mass of dust and sea salt aerosols mass data that are directly downloaded from the website (https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/data_access/, accessed on 16 December 2020). In this study, all model data were interpolated to the same horizontal resolution of 0.5° × 0.625° latitude/longitude grids as in MERRA-2, and onto the site locations when compared with the ground-based observations.

    3.   Present-day climate of PM2.5 and its components in Asia
    • In this section, we will focus on the spatial features of present-day climate mean PM2.5 from 2005 to 2020. Figure 3 shows the percentage contribution of PM2.5 to the total aerosol (fine and coarse) concentration in Asia, including sulfate, OA, BC, and all particle sizes of dust and sea salt. The results from MERRA-2 (Fig. 3l) show a relatively high proportion of PM2.5 over East Asia and Southeast Asia and the contribution is up to 60%–80% over the southeastern coast of China. Central Asia is an arid or semi-arid region and has the lowest proportion (less than 30%) of PM2.5, where mineral dust is generally the main source of aerosols, and coarse particles dominate. For the multi-model mean (MME, Fig. 3k), the PM2.5 ratio is in overall a good agreement with MERRA-2, except for MIROC-ES2L (Fig. 3f). MIROC-ES2L shows the largest proportion of fine particulate matter in eastern China, which is about 20% higher than in MME and MERRA-2.

      Figure 3.  The 2005–2020 mean PM2.5 ratios (%) to main aerosol (including all particle sizes of dust and sea salt, sulfate, organic aerosol, and black carbon) in Asia (5°–55°N, 70°–140°E) for (a–j) the 10 ESMs, (k) their MME, and (l) MERRA-2.

      Figure 4a shows the spatial distribution of present-day mean of surface PM2.5 concentrations in the 373 ground-based observations from CNEMC and EANET, averaged for 2014–2019. Annual mean surface PM2.5 concentrations in most parts of eastern China can be over 40 µg m−3, and the highest values are mainly centered over the Beijing–Tianjin–Hebei region where PM2.5 concentration may be over 60 µg m−3. High annual mean PM2.5 concentrations are also present over northwestern China, mainly contributed by mineral dust. In the area south of 25°N, the annual mean PM2.5 concentrations are generally smaller, which may be caused by strong wet deposition and lower emissions. Japan and Korea are also regions with values of annual mean PM2.5 concentrations less than 20 µg m−3. Figures 4bk show the point-to-point comparisons between 10 ESMs simulations separately with 373 ground-based observations in the same period from 2014 to 2019. They illustrate that most models underestimate the observations, although all ESMs show high spatial correlations of 0.52–0.74 and 0.69 for MME (Fig. 4l). The underestimation of PM2.5 concentrations by CMIP6 models in this study partly comes from the use of the approximate method to calculate PM2.5 by Eq. (1), in which nitrate (${\rm NO}_3^- $) and ammonium (${\rm NH}_4^+ $) aerosols are not involved. Those underestimations also exist in CMIP5 models (Wu J. et al., 2016; Liu et al., 2017).

      Figure 4.  (a) Averaged annual (2014–2019) mean surface PM2.5 concentrations (μg m−3) for 373 sites from EANET (triangles, 25 sites) and CNEMC (circles, 348 urban sites) in Asia. (b–m) Scatterplots of surface PM2.5 concentrations (μg m−3) for each ESMs and their MME, and MERRA-2, separately, comparing to the observations from EANET and CNEMC sites during the same period. RMSE stands for root-mean-square error, and COR for correlation coefficient. The grey lines represent the 1 : 1, 1 : 2, and 2 : 1 lines, respectively.

      As shown in Fig. 4m, the MERRA-2 data also underestimate the observed PM2.5 concentrations at 373 sites. Nevertheless, MERRA-2 can provide the overall spatial distribution of PM2.5 in Asia with better temporal and spatial coverage and compensate for the gaps not covered by site observations. As shown in Fig. 5l, the spatial distribution of annual mean surface PM2.5 concentrations averaged for 2005 to 2020 from MERRA-2 is similar to that from ground-based observations (Fig. 4a). Except for the two regions with high surface PM2.5 concentrations in eastern China and northwestern China that can be found from ground-based observations, MERRA-2 (Fig. 5l) also shows a third region of high-concentration centered in northern India where there are high local emissions and the Himalayas plays a large role in preventing dispersal of aerosols (Shi et al., 2018). The PM2.5 concentrations are less than 5 µg m−3 over the Tibetan Plateau (about 26°–39°N, 73°–104°E) and Mongolia Plateau (about 37°–53°N, 87°–122°E), where human activities are weak.

      Figure 5.  Averaged annual (2005–2020) mean surface PM2.5 concentrations (µg m−3) in Asia (5°–55°N, 70°–140°E) from (a–j) 10 ESMs, (k) their MME, and (l) MERRA-2.

      The main spatial features of surface PM2.5 concentrations are generally well captured by the ESMs (Fig. 5ak) in comparison with MERRA-2 (Fig. 5l), except over the offshore area where MERRA-2 data overestimated sea salt as pointed out in Buchard et al. (2017). However, there exists a large diversity among models, especially over the three PM2.5 centers (eastern China, northern India, and northwestern China and Mongolia, Fig. 6a). The amplitude of model-spread (that is denoted by the standard deviation of simulated PM2.5 concentration among 10 ESMs in the study) over northwestern China and Mongolia are close to the MME regional PM2.5 concentration (Fig. 6b). Specifically, CESM2-WACCM (Fig. 5b) overestimates PM2.5 in Taklimakan desert of central Xinjiang (> 60 µg m−3), and MRI-ESM2-0 (Fig. 5h) has an abnormally high-value center in the Mongolian Plateau. The dominant species of PM2.5 vary with regions as well as the one responsible for the model-spread in PM2.5 simulation, which will be discussed in detail in Section 4.

      Figure 6.  (a) Model-spread (µg m−3) among the 10 ESMs and (b) the ratio (%) of model-spread to MME for annual mean of surface PM2.5 concentration during 2005–2020.

    • Sulfate, OA, and BC are the main PM2.5 aerosols from anthropogenic emissions in Asia and are the main PM2.5 species over eastern China and northern India (Figs. 79). In MERRA-2, sulfate (Fig. 7l) and BC (Fig. 9l) concentrations in eastern China are higher than those in northern India, whereas the spatial distribution for OA shows the opposite (Fig. 8l). The MME can generally reproduce the spatial distributions for sulfate (Fig. 7k), OA (Fig. 8k), and BC (Fig. 9k) although their magnitudes are underestimated for sulfate but overestimated for OA and BC. There are significant differences in the simulations of sulfate and OA among various ESMs. MRI-ESM2-0 (Fig. 7h) has the highest concentration of sulfate in southeastern China, while IPSL-CM5A2-INCA (Fig. 7e) has the lowest sulfate concentrations. CESM2-WACCM (Fig. 8b) and UKESM1-0-LL (Fig. 8j) have larger concentrations of OA than other ESMs, which may be caused by different volatile organic compounds (VOC) and secondary organic aerosol (SOA) formation mechanisms in the ESMs. UKESM1-0-LL also shows the largest BC concentration than the others (Fig. 9j).

      Figure 7.  As in Fig. 5, but for the sulfate.

      Figure 8.  As in Fig. 5, but for the organic aerosol.

      Figure 9.  As in Fig. 5, but for the black carbon.

      PM2.5DU and PM2.5SS are the natural components in PM2.5. As shown in Fig. 10, PM2.5DU is responsible for the PM2.5 center (Fig. 5) over northwestern China and Mongolia. The PM2.5DU concentration from MME (Fig. 10k) is similar to that from MERRA-2 (Fig. 10l). But there are large differences in PM2.5DU simulations among 10 ESMs. CESM2-WACCM (Fig. 10b) and GFDL-ESM4 (Fig. 10d) simulated larger PM2.5DU concentrations than other models. Moreover, the PM2.5DU in MIROC-ES2L (Fig. 10f) is much smaller than MERRA-2 (Fig. 10l), with PM2.5DU differences up to 20 µg m−3. In MRI-ESM2-0 (Fig. 10h), the high PM2.5DU center extends eastward to northern China and the amplitude of PM2.5DU is about twice of that in the east, which is not evident in MERRA-2 (Fig. 10l). In addition, MPI-ESM-1-2-HAM (Fig. 10g) simulated excessive amount of PM2.5DU in northern Tibetan Plateau, which is distinctive from other models. PM2.5SS is another important natural aerosol mainly distributed over oceans and coastal regions. The PM2.5SS concentration over land is lower than the other species in PM2.5, and the differences among ESMs are generally small (Fig. 11). Due to the known overestimation of sea salt in MERRA-2 (Buchard et al., 2017), there are significant differences between the MME and MERRA-2 (Figs. 11k, l).

      Figure 10.  As in Fig. 5, but for the PM2.5 fine particles of dust.

      Figure 11.  As in Fig. 5, but for the PM2.5 fine particles of sea salt.

    4.   Uncertainties in simulated PM2.5 concentrations from ESMs
    • Figure 12 shows the model-spread among 10 ESMs for main anthropogenic components of PM2.5, sulfate, OA, and BC. The regions of large model-spread are evident over eastern China, northern India, and Sichuan Basin, and the main anthropogenic emission centers in Asia. All the ESMs used the same anthropogenic emissions inventory (Hoesly et al., 2018). Large model-spread for anthropogenic aerosols in individual ESMs thus mainly comes from the different way that individual models represent chemical and physical processes relevant for aerosols including dynamic transport, dry deposition, gravitational settling, wet scavenging by clouds and precipitation, and even their chemical processes (Textor et al., 2007; Wu et al., 2020). For example, the sulfate (Fig. 12a) uncertainty is generally larger over eastern China and the Sichuan Basin than over northern India, which probably results from different gas-phase and aqueous-phase conversion from SO2 except for the above reasons. Large uncertainty over the Sichuan Basin is also caused by unique topography (Liu et al., 2021). The BC (Fig. 12c) uncertainty is relatively weaker as the results are mainly determined by the prescribed anthropogenic emissions. For OA (Fig. 12b), the concentration differences also may be caused by the way that models represent various natural biogenic VOC (SOA precursors) emissions.

      Figure 12.  The model-spread of annual mean concentrations (µg m−3) for anthropogenic aerosols during 2005−2020. (a) Sulfate, (b) OA, and (c) BC.

      Natural aerosols are important sources of uncertainty in PM2.5 simulation among ESMs. The PM2.5DU uncertainty prevails over northwestern China and Mongolia, the Mongolian Plateau, and northwestern Indian Peninsula (Fig. 13a). There are many reasons for the significant model-spread in dust simulations. In addition to the effects of dynamic transport, and wet and dry depositions, large model-spread is mainly caused by the difference in driving mechanisms of dust emissions that depend on the meteorological drivers (winds and precipitation), especially in East China and South Asia, associated with large-scale monsoonal circulations (Wilcox et al., 2020; Zhao et al., 2022), the land surface conditions (Aryal and Evans, 2021), and the representation of aerosol size distributions (Zhao et al., 2022). And the model complexities also have the influence on dust concentrations (Zhao et al., 2022). As for sea salt aerosols (Fig. 13b), it has lower concentrations than other species, and its spreads among models are less than 1 µg m−3 over land. Sea salt emissions are mainly determined by near surface wind across the ocean (Wu et al., 2020). It is possible that there is a small model-spread in surface winds across the ocean leading to less spread in sea salt emissions, although inter-model differences in advective transport, and wet or dry deposition will be similar to those for dust (Witek et al., 2007; Wu et al., 2020), which can also affect the simulation of sea salt.

      Figure 13.  As in Fig. 12, but for the natural aerosols. (a) PM2.5DU and (b) PM2.5SS.

      The Taylor diagram in Fig. 14 statistically examines the spatial distribution as well as the spatial variability of the differences between ESMs and MERRA-2 for main species of PM2.5. The spatial distribution of BC concentrations simulated by ESMs are the best captured with spatial correlation coefficients of 0.9−0.97, followed by sulfate, OA, PM2.5DU, and PM2.5SS. For PM2.5DU, there are large differences between the individual ESMs and MERRA-2, with normalized standard deviations ranging from 0.2 to 3.5 and spatial correlation coefficients from 0.4 to 0.87. The normalized standard deviations of CESM2-WACCM and MRI-ESM2-0 are greater than 2, indicating that the spatial variability of PM2.5DU is largely overestimated in the two models. Although the spatial correlation coefficient of PM2.5SS can be 0.95 or higher, the normalized standard deviations of less than 0.6 in all ESMs, resulting from the overestimation of PM2.5SS in MERRA-2. In general, although there are differences between individual ESMs, the MME can still capture the spatial distributions of five components from PM2.5 well compared to MERRA-2. The spatial variations in ESMs are larger than MERRA-2 for OA, BC, and PM2.5DU.

      Figure 14.  Taylor diagram of the annual mean surface components (sulfate, organic aerosols, black carbon, PM2.5DU, and PM2.5SS) concentrations simulated by the 10 ESMs compared with the MERRA-2 reanalysis data during 2005–2020 in Asia (5°–55°N, 70°–140°E). The radial coordinate shows the standard deviation in the spatial pattern, normalized by the observed standard deviation. The azimuthal variable shows the correlation of the modeled spatial pattern with the observed spatial pattern.

    • Each component of PM2.5 has different contributions to the PM2.5 concentrations in various regions, and the contributions vary between the individual ESMs. Here, we analyzed four regions as illustrated in Fig. 2, Central Asia (CA), East Asia (EA), South Asia (SA), and Southeast Asia (SEA). In the whole Asian region (5°–55°N, 70°–140°E), the area-averaged MME PM2.5 is smaller than for MERRA-2 (by 3.7 µg m−3, Fig. 15), which is largely attributed to their difference in PM2.5SS. The main PM2.5 components in Asia are sulfate and OA, accounting for 28% and 32% of the PM2.5 in the MME, respectively. PM2.5DU is the third main PM2.5 components in Asia, accounting for 21% of the PM2.5 in the MME. The largest model-spread among the five main PM2.5 species comes from PM2.5DU (Fig. 16), indicating its largest contribution to the PM2.5 uncertainty over Asia.

      Figure 15.  Histograms of 2005–2020 averaged concentrations (µg m−3) of PM2.5 and their components (sulfate, OA, BC, PM2.5DU, and PM2.5SS) from 10 ESMs, their MME, and MERRA-2 for Asia (5°–55°N, 70°–140°E). The mean value in MME and model diversity for the five main PM2.5 species are 3.5 ± 1.23 µg m−3 for sulfate, 3.98 ± 0.98 µg m−3 for OA, 0.86 ± 0.15 µg m−3 for BC, 2.59 ± 1.57 µg m−3 for PM2.5DU, and 1.5 ± 0.83 µg m−3 for PM2.5SS.

      Figure 16.  Distribution of differences for PM2.5 and their components (sulfate, OA, BC, PM2.5DU, and PM2.5SS) concentrations (µg m−3) from 10 ESMs in Asia and four subregions during 2005–2020. The box plots show the 25th and 75th percentiles as solid boxes, median values as solid lines, dots represent the concentrations from MME, and whiskers extending to the minimum and maximum.

      The proportion of each PM2.5 component has large regional characteristics (Fig. 16). PM2.5DU plays a dominant role over central Asia, accounting for 70% of the PM2.5 concentration. There are also considerable differences in PM2.5DU model results over central Asia and the uncertainty range is almost 25 µg m−3. In East Asia, sulfate and OA are the main PM2.5 species, and the uncertainty is mostly attributed to PM2.5DU and sulfate. In South Asia, the uncertainty ranges are comparable for sulfate, OA, and PM2.5DU. In Southeast Asia, PM2.5SS accounts for 35% of the PM2.5 in the MME, and it has the largest contribution to the PM2.5 uncertainties. Overall, it appears that the regions of large model diversity are consistent with high concentrations areas for the five components.

    5.   Uncertainties in evaluating PM2.5 concentrations
    • The above analyses have shown that surface PM2.5 concentrations from ESMs simulations are lower than those from individual observations at CNEMC and EANET sites. One possible reason is the spatial heterogeneity of ground-based observations and the urban effect on PM2.5 concentrations. It is noticed that all the CNEMC sites are located in urban area, whereas ESMs simulate average PM2.5 concentrations across a coarse model grid larger than 100 km and are hard to identify the differences between urban and suburban areas. Figure 17a shows time series of surface PM2.5 concentrations at one city and its neighboring suburban site in Thailand from APAD data (Cohen and Atanacio, 2015). It is clear that the surface PM2.5 concentrations at the urban location are evidently higher than those at the neighboring suburban site. The urban site in Thailand is in a residential-university-shopping district containing commercial buildings and small industrial factories. The emissions mainly come from human activities (including automobile exhausts, residential cooking, and heating from buildings). By contrast, the suburban site is surrounded by residential areas with brick-timbered houses, trees, and grass. Urban observatories are more polluted than suburban ones, even when they are geographically close to each other. This is also evident in the two pairs of urban and neighboring suburban sites in China (Figs. 17b, c). Differences between downtown and suburban sites in the same city may be higher than 10 µg m−3, and the results in ESMs are closer to those at suburban sites.

      Figure 17.  Time series of surface PM2.5 concentrations (µg m−3) in neighboring city and suburban from APAD, CMA, CNEMC, and MME. Red and blue lines represent observations at urban and suburban sites, respectively. Black lines represent the simulations from MME.

      Another important reason for the uncertainty in evaluation is the method to calculate PM2.5 concentrations. Firstly, Eq. (1) used in this study does not include all the aerosol components that constitute PM2.5, such as ammonium and nitrate aerosols, which are generally included in observations but not model derived PM2.5, especially important over eastern China where nitrate aerosols may be responsible for over 20% of PM2.5 mass concentrations in winter (Liu et al., 2017). In addition, Eq. (1) assumes fixed percentages of the total mass of dust (10%) and sea salt (25%) aerosols present within the fine size fraction (i.e., less than 2.5 microns in diameter), which are not consistent among ESMs, and also is not suitable for the MERRA-2 data.

    6.   Summary
    • This study uses five main components of aerosols (i.e., sulfate, organic aerosol, black carbon, dust, and sea salt) that are simulated by 10 CMIP6 ESMs to calculate surface PM2.5 concentrations over Asia. Ground-based observation networks as well as the MERRA-2 reanalysis are used to evaluate the ability of current ESMs to simulate PM2.5 and its components. In Asia, PM2.5 accounts for more than 30% of the total aerosol (including all particle sizes), except for central Asia. The spatial distribution of PM2.5 and its main components in the MME are in a good agreement with MERRA-2 and available ground-based observations. High PM2.5 concentrations (> 40 µg m−3 in MERRA-2) are simulated in three regions, eastern China and northern India mainly consisting of anthropogenic aerosols, and northwestern China due to high concentrations of mineral dust. The contribution of each aerosol component to the MME PM2.5 across Asia are mainly from sulfate (28%), OA (32%), and PM2.5DU (21%). The proportions of components making up the MME PM2.5 are also regionally dependent. PM2.5DU accounts for more than 70% of PM2.5 in central Asia and PM2.5SS for about 35% of PM2.5 in Southeast Asia in the MME.

      Our analysis shows that PM2.5 from ESMs is biased low in the comparison with ground-based observations. It may be partly due to the unevenly distributed ground-based observations and the effect of urban areas, as well as the formula used to derive the PM2.5 concentrations in this work, which does not consider the contributions of nitrate and ammonium compounds. Compared to the MERRA-2 reanalysis data, the MME underestimates PM2.5 concentrations averaged across Asia by about 3.7 µg m−3, which is possibly due to large PM2.5SS overestimation in MERRA-2.

      There are large uncertainties in simulations of PM2.5 and its components among the 10 ESMs. Inter-model differences in PM2.5 are mainly attributed to sulfate and PM2.5DU over East Asia, and PM2.5DU over central Asia. For South Asia, the uncertainty ranges are comparable for sulfate, OA, and PM2.5DU. PM2.5SS has the largest uncertainty range in Southeast Asia. The differences in the simulation of PM2.5 and its components amongst the 10 ESMs to a large extent reflect the different algorithms used to prognose aerosol variations in the individual ESMs including the dynamic transport, dry deposition, gravitational settling, wet scavenging, chemical processes, meteorological drivers, land surface conditions, and the representation of aerosol size distributions.

      This work is the first to highlight ESM model biases in the simulation of PM2.5 concentrations across Asia using observations and a reanalysis dataset. Analyzing the individual aerosol components highlights the potential improvements to ESMs and the certain aspects of their individual aerosol schemes to target. It is noted that the ground-based observations used in this work are relatively sparse. The regional feature for PM2.5 and its components in ESMs still needs further investigations using more data with high spatial and time resolutions that retrieved from satellite observations (Wei et al., 2020; Yan et al., 2020, 2021) in the future.

      Acknowledgments. We would like to thank Meteorological Observation Center, China Meteorological Administration for providing surface PM2.5 data at atmospheric background stations.

Reference (93)

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return