Vol.31, No.03. 2020
Table of Contents
ISSN:1674-9928
CN:31-2050/P
Large spread across AeroCom Phase II models in simulating black carbon in melting snow over Arctic sea ice
Correspondence: mingkeng@nuist.edu.cn
ORCID:
Vol. 31, Issue 4, pp. 288-296 (2020) • DOI
Abstract
Basic Infomations
References
Attachments
Cited By
Abstract
Over two dozen global atmospheric chemistry models contributing to the Aerosol Comparisons between Observations and Models (AeroCom) project were used in this study to drive the Los Alamos sea ice model to simulate the black carbon (BC) concentration in melting snow on Arctic sea ice. Measurements of BC during the melting season show concentrations in the range 2.8–41.6 ng•g−1 (average: 15.3 ng•g−1) in the central Arctic Ocean and Canada Basin. Most results from models contributing to the Phase I project were within the 25th and 75th percentiles of the observations, and the multimodel mean was slightly lower than that of the observations. In contrast, there was larger divergence among the Phase II model simulations and the mean value of BC was overestimated. The multimodel mean bias was −3.1 (−11.2 to +6.7) ng•g−1 for Phase I models and +3.9 (−9.5 to +21.3) ng•g−1 for Phase II models. The differences between the models of the two phases were probably attributable to the updated aerosol scheme in the new contributions, in which removal processes are parameterized by considering the actual dimensions and chemical compositions of the particles. This means the removal mechanism acts in a way that is more selective and leads to more BC particles being transported to the Arctic. In addition, higher spatial resolution could be another important reason for overestimation of BC concentration in snow in Phase II models.
Author Address:
1 Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD),Nanjing University of Information Science & Technology, Nanjing 210044, China;
2 Key Laboratory of Meteorological Disaster, Ministry of Education (KLME),Nanjing University of Information Science & Technology, Nanjing 210044, China;
3 Joint International Research Laboratory of Climate and Environment Change (ILCEC), Nanjing University of Information Science & Technology, Nanjing 210044, China
2 Key Laboratory of Meteorological Disaster, Ministry of Education (KLME),Nanjing University of Information Science & Technology, Nanjing 210044, China;
3 Joint International Research Laboratory of Climate and Environment Change (ILCEC), Nanjing University of Information Science & Technology, Nanjing 210044, China
Ackermann I J, Hass H, Memmesheimer M, et al. 1998. Modal aerosol dynamics model for Europe: development and first applications. Atmos Environ, 32(17): 2981-2999, doi: 10.1016/s1352-2310(98) 00006-5.
Barth M C, Rasch P J, Kiehl J T, et al. 2000. Sulfur chemistry in the National Center for Atmospheric Research Community Climate Model: Description, evaluation, features, and sensitivity to aqueous chemistry. J Geophys Res, 105(D1): 1387-1415, doi: 10.1029/1999jd900773.
Bauer S E, Menon S, Koch D, et al. 2010. A global modeling study on carbonaceous aerosol microphysical characteristics and radiative effects. Atmos Chem Phys, 10(15): 7439-7456, doi: 10.5194/acp- 10-7439-2010.
Bellouin N, Rae J, Jones A, et al. 2011. Aerosol forcing in the Climate Model Intercomparison Project (CMIP5) simulations by HadGEM2-ES and the role of ammonium nitrate. J Geophys Res, 116(D20): D20206, doi: 10.1029/2011jd016074.
Bian H, Chin M, Rodriguez J M, et al. 2009. Sensitivity of aerosol optical thickness and aerosol direct radiative effect to relative humidity. Atmos Chem Phys, 9(7): 2375-2386, doi: 10.5194/acp-9-2375-2009.
Blanchard-Wrigglesworth E, Farrell S L, Newman T, et al. 2015. Snow cover on Arctic sea ice in observations and an Earth System Model. Geophys Res Lett, 42(23): 10342-10348, doi: 10.1002/2015gl066049.
Bond T C, Doherty S J, Fahey D W, et al. 2013. Bounding the role of black carbon in the climate system: A scientific assessment. J Geophys Res Atmos, 118(11): 5380-5552, doi: 10.1002/jgrd.50171.
Chin M, Diehl T, Dubovik O, et al. 2009. Light absorption by pollution, dust, and biomass burning aerosols: a global model study and evaluation with AERONET measurements. Ann Geophys, 27(9): 3439-3464, doi: 10.5194/angeo-27-3439-2009.
Doherty S J, Grenfell T C, ForsstrÖm S, et al. 2013. Observed vertical redistribution of black carbon and other insoluble light-absorbing particles in melting snow. J Geophys Res Atmos, 118: 5553-5569, doi: 10.1002/jgrd.50235.
Doherty S J, Warren S G, Grenfell T C, et al. 2010. Light-absorbing impurities in Arctic snow. Atmos Chem Phys, 10(8): 11647-11680, doi: 10.5194/acp-10-11647-2010.
Dou T, Du Z, Li S, et al. 2019. Brief communication: An alternative method for estimating the scavenging efficiency of black carbon by meltwater over sea ice. The Cryosphere Discussion, 13(12): 3309-3316, doi: 10.5194/tc-2019-147.
Dou T, Xiao C. 2013. Measurements of physical characteristics of summer snow cover on sea ice during the Third Chinese Arctic Expedition. Sciences in Cold and Arid Regions, 5(3): 309-315, doi: 10.3724/SP.J.1226.2013.00309.
Dou T, Xiao C. 2016. An overview of black carbon deposition and its radiative forcing over the Arctic. Advances in Climate Change Research, 7(3): 115-122.
Dou T, Xiao C, Du Z, et al. 2017. Sources, evolution and impacts of EC and OC in snow on sea ice: a measurement study in Barrow, Alaska. Sci Bull, 62(22): 1547-1554, doi:10.1016/j.scib.2017.10.014.
Dou T, Xiao C, Shindell D T, et al. 2012. The distribution of snow black carbon observed in the Arctic and compared to the GISS-PUCCINI model. Atmos Chem Phys, 12(17): 7995-8007, doi: 10.5194/acp-12- 7995-2012.
Flanner M G, Zender C S, Hess P G, et al. 2009. Springtime warming and reduced snow cover from carbonaceous particles. Atmos Chem Phys, 9: 2481-2497.
Flanner M G, Zender C S, Randerson J T, et al. 2007. Present-day climate forcing and response from black carbon in snow. J Geophys Res, 112: D11202, doi: 10.1029/2006jd008003.
Forsström S, Strom J, Pedersen C A, et al. 2009. Elemental carbon distribution in Svalbard snow. J Geophys Res, 114: D19112, doi: 10.1029/2008JD011480.
Gilardoni S, Vignati E, Wilson J. 2011. Using measurements for evaluation of black carbon modeling. Atmos Chem Phys, 11: 439-455, doi: 10.5194/acp-11-439-2011.
Glassmeier F, Possner A, Vogel B, et al. 2017. A comparison of two chemistry and aerosol schemes on the regional scale and the resulting impact on radiative properties and liquid and ice-phase aerosol–cloud interactions. Atmos Chem Phys, 17: 8651-8680, doi: 10.5194/acp-17- 8651-2017.
Goldenson N, Doherty S J, Bitz C M, et al. 2012. Arctic climate response to forcing from light-absorbing particles in snow and sea ice in CESM. Atmos Chem Phys, 12: 7903-7920, doi: 10.5194/acp-12-7903-2012.
Grenfell T, Light B, Sturm M. 2002. Spatial distribution and radiative effects of soot in the snow and sea ice during the SHEBA experiment. J Geophys Res, 107(C10): SHE 7-1-SHE 7-7, doi: 10.1029/2000JC000414.Grini A, Myhre G, Zender C S, et al. 2005. Model simulations of dust sources and transport in the global atmosphere: Effects of soil erodibility and wind speed variability. J Geophys Res, 110(2): 1-14, doi: 10.1029/2004JD005037.
Hansen J, Nazarenko L. 2004. Soot climate forcing via snow and ice albedos. Proc Natl Acad Sci, 101(2): 423-428, doi: 10.1073/pnas. 2237157100.
Holland M, Bailey D A, Briegleb B P, et al. 2012. Improved sea ice shortwave radiation physics in CCSM4: The impact of melt ponds and aerosols on Arctic sea ice. J Clim, 25(5): 1413-1430, doi: 10.1175/ JCLI-D-11-00078.1.
Hunke E C, Lipscomb W H, Turner A K. 2011. Sea-ice models for climate study: retrospective and new directions. J Glaciol, 56(200): 1162- 1172.
Iversen T, Seland Ø. 2002. A scheme for process-tagged SO4 and BC aerosols in NCAR CCM3: Validation and sensitivity to cloud processes. J Geophys Res, 107(D24): AAC 4-1-AAC 4-30, doi: 10.1029/2001JD000885.
Jacobi H-W, Obleitner F, Costa S D, et al. 2019. Deposition of ionic species and black carbon to the Arctic snowpack: combining snow pit observations with modeling. Atmos Chem Phys, 19(15): 10361-10377, doi: 10.5194/acp-19-10361-2019.
Jiao C, Flanner M G, Balkanski Y, et al. 2014. An AeroCom assessment of black carbon in Arctic snow and sea ice. Atmos Chem Phys, 14(5): 2399-2417, doi: 10.5194/acpd-13-26217-2013.
Kinne S, Schulz M, Textor C, et al. 2006. An AeroCom initial assessment – optical properties in aerosol component modules of global models. Atmos Chem Phys, 6: 1815-1834, doi: 10.5194/acp-6-1815-2006.
Kirkevåg A, Iversen T, Seland Ø, et al. 2013. Aerosol–climate interactions in the Norwegian Earth System Model–NorESM1-M. Geosci Model Dev, 6: 207-244, doi: 10.5194/gmd-6-207-2013.
Koch D, Bond T C, Streets D, et al. 2007. Global impacts of aerosols from particular source regions and sectors. J Geophys Res, 112: D02205, doi: 10.1029/2005JD007024.
Koch D, Schmidt G A, Field C, 2006. Sulfur, sea salt and radionuclide aerosols in GISS ModelE. J Geophys Res, 111: D06206, doi: 10.1029/2004JD005550.
Koch D, Schulz M, Kinne S, et al. 2009. Evaluation of black carbon estimations in global aerosol models. Atmos Chem Phys, 9: 9001-9026.
Krol M, Houweling S, Bregman B, 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.
Liu X, Easter R C, Ghan S J, et al. 2012. Toward a minimal representation of aerosols in climate models: description and evaluation in the Community Atmosphere Model CAM5. Geosci Model Dev, 5: 709-739, doi: 10.5194/gmd-5-709-2012.
Liu X, Penner J E. 2002. Effect of Mount Pinatubo H2SO4/H2O aerosol on ice nucleation in the upper troposphere using a global chemistry and transport model. J Geophys Res Atmos, 107: AAC 2-1-AAC 2-18, doi: 10.1029/2001JD000455.
Ma P L, Rasch P J, Fast J D, et al. 2014. Assessing the CAM5 physics suite in the WRF-Chem model: implementation, resolution sensitivity, and a first evaluation for a regional case study. Geosci Model Dev, 7(3): 755-778. doi: 10.5194/gmd-7-755-2014.
Mann G W, Carslaw K S, Reddington C L, et al. 2014. Intercomparison and evaluation of global aerosol microphysical properties among AeroCom models of a range of complexity. Atmos Chem Phys, 14: 4679-4713, doi: 10.5194/acp-14-4679-2014.
McConnell J R, Edwards R, Kok G L, et al. 2007. 20th-Century industrial black carbon emissions altered Arctic climate forcing. Science, 317: 1381-1384, doi: 10.1126/science.1144856.
Myhre G, Berglen T F, Johnsrud M, et al. 2009. Modelled radiative forcing of the direct aerosol effect with multi-observation evaluation. Atmos Chem Phys, 9: 1365-1392, doi: 10.5194/acp-9-1365-2009.
Myhre G, Samset B H, Schulz M, et al. 2013. Radiative forcing of the direct aerosol effect from AeroCom Phase II simulations. Atmos Chem Phys, 13: 1853-1877, doi: 10.5194/acp-13-1853-2013.
Perovich D K, Grenfell T C, Light B, et al. 2009. Transpolar observations of the morphological properties of Arctic sea ice. J Geophys Res, 114: C00A04, doi: 10.1029/2008JC004892.
Pitari G, Iachetti D, Mancini E, et al. 2008. Radiative forcing from particle emissions by future supersonic aircraft. Atmos Chem Phys, 8(14): 4069-4084, doi: 10.5194/acp-8-4069-2008.
Quinn P K, Stohl A, Arneth A, et al. 2011. The impact of black carbon on Arctic climate. Arctic Monitoring and Assessment Programme (AMAP), Oslo, 72.
Reddy M S, Boucher O A. 2004. A study of the global cycle of carbonaceous aerosols in the LMDZT general circulation model. J Geophys Res Atmos, 109(D14): D14202, doi: 10.1029/2003JD004 048.
Samset B H, Myhre G, Schulz M, et al. 2013. Black carbon vertical profiles strongly affect its radiative forcing uncertainty. Atmos Chem Phys, 13(5): 2423-2434, doi: 10.5194/acp-13-2423-2013.
Sand M, Samset B H, Balkanski Y, et al. 2017. Aerosols at the poles: an AeroCom Phase II multi-model evaluation. Atmos Chem Phys, 17(19): 1-35, doi: 10.5194/acp-2016-1120.
Schulz M, Chin M, Kinne S. 2009. The Aerosol Model Comparison Project, AeroCom, Phase II: Clearing Up Diversity, IGAC Newsletter, Issue No 41, May 2019.
Seland Ø, Iversen T, Kirkevåg A, et al. 2008. Aerosol climate interactions in the CAM-Oslo atmospheric GCM and investigation of associated basic shortcomings. Tellus A, 60: 459-491, doi: 10.1111/j.1600-0870. 2008.00318.x.
Sinha P R, Kondo Y, Koike M, et al. 2017. Evaluation of ground-based black carbon measurements by filter-based photometers at two Arctic sites. J Geophys Res Atmos, 122(6): 3544-3572, doi: 10.1002/ 2016jd025843.
Sinha P R, Kondo Y, Goto-Azuma K, et al. 2018. Seasonal progression of the deposition of black carbon by snowfall at Ny-Ålesund, Spitsbergen. J Geophys Res Atmos, 123(2): 997-1016, doi: 10.1002/2017jd028027.
Spracklen D V, Carslaw K S, Pöschl U, et al. 2011. Global cloud condensation nuclei influenced by carbonaceous combustion aerosol. Atmos Chem Phys, 11(17): 9067-9087, doi: 10.5194/acp-11-9067- 2011.
Stier P, Feichter J, Kinne S, et al. 2005. The aerosol-climate model ECHAM5-HAM. Atmos Chem Phys, 5(4): 1125-1156, doi: 10.5194/ acp-5-1125-2005.
Szopa S, Balkanski Y, Schulz M, et al. 2013. Aerosol and ozone changes as forcing for climate evolution between 1850 and 2100. Clim Dyn, 40(9-10): 2223-2250, doi: 10.1007/s00382-012-1408-y.
Takemura T, Egashira M, Matsuzawa K, 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(9): 3061-3073, doi: 10.5194/acp-9-3061-2009.
Vignati E, Karl M, Krol M, et al. 2010. Sources of uncertainties in modelling black carbon at the global scale. Atmos Chem Phys, 10(6): 2595-2611, doi: 10.5194/acp-10-2595-2010.
Wang Q, Jacob D J, Fisher J A, et al. 2011. Sources of carbonaceous aerosols and deposited black carbon in the Arctic in winter-spring: implications for radiative forcing. Atmos Chem Phys, 11(23): 12453-12473, doi: 10.5194/acp-11-12453-2011.
Wiedinmyer C, Akagi S K, Yokelson R J, et al. 2011. The Fire Inventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning. Geosci Model Dev, 4(3): 625-641, doi: 10.5194/gmd-4-625-2011.
Yun Y, Penner J E. 2012. Global model comparison of heterogeneous ice nucleation parameterizations in mixed phase clouds. J Geophys Res Atmos, 117: D07203, doi: 10.1029/2011JD016506.
Zhou C, Penner J E, Flanner M G, et al. 2012. Transport of black carbon to polar regions: Sensitivity and forcing by black carbon. Geophys Res Lett, 39(22): L22804, doi: 10.1029/2012gl053388.
Zhang K, O’Donnell D, Kazil J, et al. 2012. The global aerosol-climate model ECHAM-HAM, version 2: sensitivity to improvements in process representations. Atmos Chem Phys, 12(19): 8911-8949, doi: 10.5194/acp-12-8911-2012.
Friend Links
Related Journals
Related Links
