Direct and semi-direct effects of aerosol climatologies on long-term climate simulations over Europe

Article

Abstract

This study compares the direct and semi-direct aerosol effects of different annual cycles of tropospheric aerosol loads for Europe from 1950 to 2009 using the regional climate model COSMO-CLM, which is laterally forced by reanalysis data and run using prescribed, climatological aerosol optical properties. These properties differ with respect to the analysis strategy and the time window, and are then used for the same multi-decadal period. Five simulations with different aerosol loads and one control simulation without any tropospheric aerosols are integrated and compared. Two common limitations of our simulation strategy, to fully assess direct and semi-direct aerosol effects, are the applied observed sea surface temperatures and sea ice conditions, and the lack of short-term variations in the aerosol load. Nevertheless, the impact of different aerosol climatologies on common regional climate model simulations can be assessed. The results of all aerosol-including simulations show a distinct reduction in solar irradiance at the surface compared with that in the control simulation. This reduction is strongest in the summer season and is balanced primarily by a weakening of turbulent heat fluxes and to a lesser extent by a decrease in longwave emissions. Consequently, the seasonal mean surface cooling is modest. The temperature profile responses are characterized by a shallow near-surface cooling and a dominant warming up to the mid-troposphere caused by aerosol absorption. The resulting stabilization of stratification leads to reduced cloud cover and less precipitation. A decrease in cloud water and ice content over Central Europe in summer possibly reinforce aerosol absorption and thus strengthen the vertical warming. The resulting radiative forcings are positive. The robustness of the results was demonstrated by performing a simulation with very strong aerosol forcing, which lead to qualitatively similar results. A distinct added value over the default aerosol setup of Tanré et al. (1984) was found in the simulations with more recent aerosol data sets for solar irradiance. The improvements are largest under low cloud conditions, while overestimated cloud cover in all setups causes a common underestimation of low and medium values of solar irradiance. In addition, the prevalent cold bias in the COSMO-CLM is reduced in winter and spring when using updated aerosol data. Our results emphasize the importance of semi-direct aerosol effects, especially over Central Europe in terms of changes in turbulent fluxes and changes in cloud properties. We also suggest to replace the default Tanré et al. (1984) aerosol climatology with more recent and realistic data sets. Thereby, a better model performance in comparison to observations can be achieved, or the masking of model shortcomings due to a too strong direct aerosol forcing thus far is prevented.

Keywords

Regional climate modelling COSMO-CLM Direct aerosol effect Semi-direct aerosol effect Added value 

References

  1. Ackerman AS, Toon OB, Stevens DE, Heymsfield AJ, Ramanathan V, Welton EJ (2000) Reduction of tropical cloudiness by soot. Science 288(5468):1042–1047. doi:10.1126/science.288.5468.1042 CrossRefGoogle Scholar
  2. Albrecht B (1989) Aerosols, cloud microphysics, and fractional cloudiness. Science 245(4923):1227–1230CrossRefGoogle Scholar
  3. Baldauf M, Seifert A, Förstner J, Majewski D, Raschendorfer M, Reinhardt T (2011) Operational convective-scale numerical weather prediction with the cosmo model: description and sensitivities. Mon Weather Rev 139(12):3887–3905. doi:10.1175/MWR-D-10-05013.1 CrossRefGoogle Scholar
  4. Becker N, Ulbrich U, Klein R (2015) Systematic large-scale secondary circulations in a regional climate model. Geophys Res Lett 42(10):4142–4149. doi:10.1002/2015GL063955 CrossRefGoogle Scholar
  5. Böhm U, Köcken M, Ahrens W, Block A, Hauffe D, Keuler K, Rockel B, Will A (2006) CLM - the climate version of LM: brief description and long-term applications. Tech. rep., COSMO Newsletter, vol 6. http://www.clm-community.eu/dokumente/upload/3a8e8_COSMOnewsLetter06_clm.pdf. Accessed 28 Oct 2009
  6. Blahak U (2016) Personal communicationGoogle Scholar
  7. Boucher O, Randall D, Artaxo P, Bretherton C, Feingold G, Forster P, Kerminen VM, Kondo Y, Liao H, Lohmann U, Rasch P, Satheesh S, Sherwood S, Stevens B, Zhang X (2013) Clouds and aerosols, vol 7. Cambridge University Press, Cambridge, pp 571–658. doi:10.1017/CBO9781107415324.016 Google Scholar
  8. Che H, Zhang XY, Xia X, Goloub P, Holben B, Zhao H, Wang Y, Zhang XC, Wang H, Blarel L, Damiri B, Zhang R, Deng X, Ma Y, Wang T, Geng F, Qi B, Zhu J, Yu J, Chen Q, Shi G (2015) Ground-based aerosol climatology of China: aerosol optical depths from the China aerosol remote sensing network (CARSNET) 2002–2013. Atmos Chem Phys 15(13):7619–7652. doi:10.5194/acp-15-7619-2015 CrossRefGoogle Scholar
  9. Chin M, Jacob DJ, Gardner GM, Foreman-Fowler MS, Spiro PA, Savoie DL (1996) A global three-dimensional model of tropospheric sulfate. J Geophys Res Atmos 101(D13):18,667–18,690. doi:10.1029/96JD01221 CrossRefGoogle Scholar
  10. Christensen J, Christensen O (2007) A summary of the prudence model projections of changes in European climate by the end of this century. Clim Change 81(1):7–30. doi:10.1007/s10584-006-9210-7 CrossRefGoogle Scholar
  11. Doms G, Schättler U (2002) A description of the nonhydrostatic regional model LM. Part I: Dynamics and numerics. Tech. rep., Deutscher Wetterdienst. www.cosmomodel.org/content/model/documentation/core/default.htm. Accessed 28 Oct 2009
  12. Doms G, Förstner J, Heise E, Herzog HJ, Mrionow D, Raschendorfer M, Reinhart T, Ritter B, Schrodin R, Schulz JP, Vogel G (2011) A description of the nonhydrostatic regional cosmo model. Part II: Physical parameterization. Tech. rep., Deutscher Wetterdienst. http://www.cosmo-model.org/content/model/documentation/core/cosmoPhysParamtr.pdf. Accessed 4 Sept 2014
  13. Ekman AML, Rodhe H (2003) Regional temperature response due to indirect sulfate aerosol forcing: impact of model resolution. Clim Dyn 21(1):1–10. doi:10.1007/s00382-003-0311-y CrossRefGoogle Scholar
  14. Geyer B (2014) High-resolution atmospheric reconstruction for Europe 1948–2012: coastdat2. Earth Syst Sci Data 6(1):147–164. doi:10.5194/essd-6-147-2014. http://www.earth-syst-sci-data.net/6/147/2014/. Accessed 27 May 2014
  15. Ghan SJ (2013) Technical note: estimating aerosol effects on cloud radiative forcing. Atmos Chem Phys 13(19):9971–9974. doi:10.5194/acp-13-9971-2013. https://www.atmos-chem-phys.net/13/9971/2013/. Accessed 10 July 2017
  16. Gilgen H, Ohmura A (1999) The global energy balance archive. Bull Am Meteorol Soc 80(5):831–850. doi:10.1175/1520-0477(1999)080<0831:TGEBA>2.0.CO;2 CrossRefGoogle Scholar
  17. Hansen J, Sato M, Ruedy R (1997) Radiative forcing and climate response. J Geophys Res Atmos 102(D6):6831–6864. doi:10.1029/96JD03436 CrossRefGoogle Scholar
  18. Haylock MR, Hofstra N, Tank AMGK, Klok EJ, Jones PD, New M (2008) A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. J Geophys Res. doi:10.1029/2008JD010201
  19. Haywood J, Boucher O (2000) Estimates of the direct and indirect radiative forcing due to tropospheric aerosols: a review. Rev Geophys 38(4):513–543. doi:10.1029/1999RG000078 CrossRefGoogle Scholar
  20. Hohenegger C, Vidale PL (2005) Sensitivity of the European climate to aerosol forcing as simulated with a regional climate model. J Geophys Res Atmos. doi:10.1029/2004JD005335
  21. Jaeger EB, Anders I, Luthi D, Rockel B, Schär C, Seneviratne SI (2008) Analysis of ERA40-driven CLM simulations for Europe. Meteorol Z 17(4):349–367. doi:10.1127/0941-2948/2008/0301 CrossRefGoogle Scholar
  22. Jones P, Harris I (2011) CRU time series (TS) high resolution gridded data version 3.10. NCAS British Atmospheric Data CentreGoogle Scholar
  23. Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Leetmaa A, Reynolds R, Jenne R, Joseph D (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77(3):437–471. http://rda.ucar.edu/datasets/ds090.0/docs/bams/bams1996mar/bamspapr-bm.pdf. Accessed 29 July 2009
  24. Kaufman YJ, Tanre D, Boucher O (2002) A satellite view of aerosols in the climate system. Nature 419(6903):215–223. doi:10.1038/nature01091 CrossRefGoogle Scholar
  25. Kessler E (1969) On the distribution and continuity of water substance in atmospheric circulations. Meteor Monogr 32(10):1–84Google Scholar
  26. Kinne S, Schulz M, Textor C, Guibert S, Balkanski Y, Bauer SE, Berntsen T, Berglen TF, Boucher O, Chin M, Collins W, Dentener F, Diehl T, Easter R, Feichter J, Fillmore D, Ghan S, Ginoux P, Gong S, Grini A, Hendricks J, Herzog M, Horowitz L, Isaksen I, Iversen T, Kirkevåg A, Kloster S, Koch D, Kristjansson JE, Krol M, Lauer A, Lamarque JF, Lesins G, Liu X, Lohmann U, Montanaro V, Myhre G, Penner J, Pitari G, Reddy S, Seland O, Stier P, Takemura T, Tie X (2006) An aerocom initial assessment—optical properties in aerosol component modules of global models. Atmos Chem Phys 6(7):1815–1834. doi:10.5194/acp-6-1815-2006. http://www.atmos-chem-phys.net/6/1815/2006/. Accessed 23 Feb 2016
  27. Kinne S, O’Donnel D, Stier P, Kloster S, Zhang K, Schmidt H, Rast S, Giorgetta M, Eck TF, Stevens B (2013) Mac-v1: a new global aerosol climatology for climate studies. J Adv Model Earth Syst 5(4):704–740. doi:10.1002/jame.20035 CrossRefGoogle Scholar
  28. Kistler R, Kalnay E, Collins W, Saha S, White G, Woollen J, Chelliah M, Ebisuzaki W, Kanamitsu M, Kousky V, van den Dool H, Jenne R, Fiorino M (2001) The NCEP–NCAR 50-year reanalysis: monthly means CD-ROM and documentation. Bull Am Meteorol Soc 82:247–267CrossRefGoogle Scholar
  29. Knote C, Heinemann G, Rockel B (2010) Changes in weather extremes: assessment of return values using high resolution climate simulations at convection-resolving scale. Meteorol Z 19(1):11–23. doi:10.1127/0941-2948/2010/0424 CrossRefGoogle Scholar
  30. Koch D, Del Genio AD (2010) Black carbon semi-direct effects on cloud cover: review and synthesis. Atmos Chem Phys 10(16):7685–7696. doi:10.5194/acp-10-7685-2010 CrossRefGoogle Scholar
  31. Kothe S, Lüthi D, Ahrens B (2013) Analysis of the West African monsoon system in the regional climate model COSMO-CLM. Int J Climatol 34(2):481–493. doi:10.1002/joc.3702 CrossRefGoogle Scholar
  32. Kotlarski S, Keuler K, Christensen OB, Colette A, Déqué M, Gobiet A, Goergen K, Jacob D, Lüthi D, van Meijgaard E, Nikulin G, Schär C, Teichmann C, Vautard R, Warrach-Sagi K, Wulfmeyer V (2014) Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble. Geosci Model Dev 7(4):1297–1333. doi:10.5194/gmd-7-1297-2014 CrossRefGoogle Scholar
  33. Lohmann U, Feichter J (2005) Global indirect aerosol effects: a review. Atmos Chem Phys 5(3):715–737. doi:10.5194/acp-5-715-2005 CrossRefGoogle Scholar
  34. Makowski K, Wild M, Ohmura A (2008) Diurnal temperature range over europe between 1950 and 2005. Atmos Chem Phys 8(21):6483–6498. doi:10.5194/acp-8-6483-2008 CrossRefGoogle Scholar
  35. Müller R, Pfeifroth U, Träger-Chatterjee, Christine, Cremer R, Trentmann J, Hollmann R (2015) Surface Solar Radiation Data Set - Heliosat (SARAH) - Edition 1. Satellite Application Facility on Climate Monitoring. Tech. rep., EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF): Offenbach, Germany. doi:10.5676/EUM_SAF_CM/SARAH/V001
  36. Morcrette JJ, Boucher O, Jones L, Salmond D, Bechtold P, Beljaars A, Benedetti A, Bonet A, Kaiser JW, Razinger M, Schulz M, Serrar S, Simmons AJ, Sofiev M, Suttie M, Tompkins AM, Untch A (2009) Aerosol analysis and forecast in the European centre for medium-range weather forecasts integrated forecast system: forward modeling. J Geophys Res Atmos. doi:10.1029/2008JD011235
  37. Myhre G, Shindell D, Breon FM, Collins W, Fuglestvedt J, Huang J, Koch D, Lamarque JF, Lee D, Mendoza B, Nakajima T, Robock A, Stephens G, Takemura T, Zhang H (2013) Anthropogenic and natural radiative forcing. Cambridge University Press, Cambridge. doi:10.1017/CBO9781107415324.018 Google Scholar
  38. Nabat P, Somot S, Mallet M, Chiapello I, Morcrette JJ, Solmon F, Szopa S, Dulac F, Collins W, Ghan S, Horowitz LW, Lamarque JF, Lee YH, Naik V, Nagashima T, Shindell D, Skeie R (2013) A 4-d climatology (1979–2009) of the monthly tropospheric aerosol optical depth distribution over the Mediterranean region from a comparative evaluation and blending of remote sensing and model products. Atmos Meas Tech 6(5):1287–1314. doi:10.5194/amt-6-1287-2013 CrossRefGoogle Scholar
  39. Nabat P, Somot S, Mallet M, Sevault F, Chiacchio M, Wild M (2015) Direct and semi-direct aerosol radiative effect on the Mediterranean climate variability using a coupled regional climate system model. Clim Dyn 44(3):1127–1155. doi:10.1007/s00382-014-2205-6 CrossRefGoogle Scholar
  40. Ritter B, Geleyn JF (1992) A comprehensive radiation scheme for numerical weather prediction models with potential applications in climate simulations. Mon Weather Rev 120(2):303–325. doi:10.1175/1520-0493(1992) 120 0303:ACRSFN 2.0.CO;2 CrossRefGoogle Scholar
  41. Rockel B, Will A, Hense A (2008) The regional climate model COSMO-CLM (CCLM). Meteorol Z 17(4):347–348. doi:10.1127/0941-2948/2008/0309 CrossRefGoogle Scholar
  42. Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 63(324):1379–1389. doi:10.1080/01621459.1968.10480934 CrossRefGoogle Scholar
  43. Stier P, Feichter J, Kinne S, Kloster S, Vignati E, Wilson J, Ganzeveld L, Tegen I, Werner M, Balkanski Y, Schulz M, Boucher O, Minikin A, Petzold A (2005) The aerosol-climate model ECHAM5-HAM. Atmos Chem Phys 5(4):1125–1156. doi:10.5194/acp-5-1125-2005 CrossRefGoogle Scholar
  44. Tanré D, Geleyn JF, Slingo J (1984) First results of the introduction of an advanced aerosol-radiation interaction in the ECMWF low resolution global mode. Aerosols and their climatic effects, pp 133–177Google Scholar
  45. Tegen I, Hollrig P, Chin M, Fung I, Jacob D, Penner J (1997) Contribution of different aerosol species to the global aerosol extinction optical thickness: estimates from model results. J Geophys Res Atmos 102(D20):23,895–23,915. doi:10.1029/97JD01864 CrossRefGoogle Scholar
  46. Theil H (1992) A rank-invariant method of linear and polynomial regression analysis. Springer, Netherlands. doi:10.1007/978-94-011-2546-8_20 CrossRefGoogle Scholar
  47. Toll V, Gleeson E, Nielsen K, M”annik A, Masek J, Rontu L, Post P (2016) Impacts of the direct radiative effect of aerosols in numerical weather prediction over Europe using the ALADIN-HIRLAM NWP system. Atmos Res 172–173:163–173. doi:10.1016/j.atmosres.2016.01.003 CrossRefGoogle Scholar
  48. Twomey S (1977) The influence of pollution on the shortwave albedo of clouds. J Atmos Sci 34(7):1149–1152. doi:10.1175/1520-0469(1977)034<1149:TIOPOT>2.0.CO;2 CrossRefGoogle Scholar
  49. Uppala S, Kallberg P, Simmons A, Andrae U, da Costa Bechtold V, Fiorino M, Gibson J, Haseler J, Hernandez A, Kelly G, Li X, Onogi K, Saarinen S, Sokka N, Allan R, Andersson E, Arpe K, Balmaseda M, Beljaars A, van de Berg L, Bidlot J, Bormann N, Caires S, Chevallier F, Dethof A, Dragosavac M, Fisher M, Fuentes M, Hagemann S, Holm E, Hoskins B, Isaksen L, Janssen P, Jenne R, McNally A, Mahfouf JF, Morcrette JJ, Rayner N, Saunders R, Simon P, Sterl A, Trenberth K, Untch A, Vasiljevic D, Viterbo P, Woollen J (2005) The ERA-40 re-analysis. Q J R Meteor Soc 131:2961–3012. http://www3.interscience.wiley.com/journal/113522684/abstract?CRETRY=1&SRETRY=0. Accessed 14 Feb 2007
  50. Vogel B, Vogel H, Bäumer D, Bangert M, Lundgren K, Rinke R, Stanelle T (2009) The comprehensive model system COSMO-ART—radiative impact of aerosol on the state of the atmosphere on the regional scale. Atmos Chem Phys 9(22):8661–8680. doi:10.5194/acp-9-8661-2009 CrossRefGoogle Scholar
  51. von Storch H, Zwiers F (1999) Statistical analysis in climate research. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  52. Wild M, Gilgen H, Roesch A, Ohmura A, Long CN, Dutton EG, Forgan B, Kallis A, Russak V, Tsvetkov A (2005) From dimming to brightening: Decadal changes in solar radiation at earth’s surface. Science 308(5723):847–850. doi:10.1126/science.1103215. http://science.sciencemag.org/content/308/5723/847
  53. Winterfeldt J, Geyer B, Weisse R (2010) Using QuikSCAT in the added value assessment of dynamically downscaled wind speed. Int J Climatol 31(7):1028–1039. doi:10.1002/joc.2105 CrossRefGoogle Scholar
  54. World Climate Research Program (1980) Aerosols and climate, vol 12. World Meteorological Organisation, GenevaGoogle Scholar
  55. Zhang K, O’Donnell D, Kazil J, Stier P, Kinne S, Lohmann U, Ferrachat S, Croft B, Quaas J, Wan H, Rast S, Feichter J (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 CrossRefGoogle Scholar
  56. Zubler EM, Lohmann U, Lüthi D, Schär C (2011a) Intercomparison of aerosol climatologies for use in a regional climate model over Europe. Geophys Res Lett. doi:10.1029/2011GL048081
  57. Zubler EM, Lohmann U, Lüthi D, Schär C, Muhlbauer A (2011b) Statistical analysis of aerosol effects on simulated mixed-phase clouds and precipitation in the Alps. J Atmos Sci 68(7):1474–1492. doi:10.1175/2011JAS3632.1 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Institute of Coastal ResearchHelmholtz-Zentrum GeesthachtGeesthachtGermany

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