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Climatic Change

, Volume 139, Issue 2, pp 325–337 | Cite as

The transformation of Arctic clouds with warming

  • J. K. RidleyEmail author
  • M. A. Ringer
  • R. M. Sheward
Article

Abstract

The progressive loss of Arctic sea ice leads to increased surface emissions of Dimethyl Sulphide (DMS), which is the dominant local source of sulphate aerosols. We test the hypothesis that cloud condensation nuclei, derived from DMS, will increase cloud-top albedo in an earth-system global climate model. The earth-system model includes fully interactive ocean biology, DMS, atmospheric chemistry, aerosols and cloud microphysics. In an idealised warming scenario, the Arctic Ocean becomes ice-free in summer when atmospheric CO2 is increased by 1 % per year to four times the pre-industrial concentrations. The summer boundary layer near-surface inversion strengthens, increasing stratification with warming, whilst the autumn inversion weakens. We find that the dominant change in cloud albedo arises from the conversion of summer clouds from ice to liquid, reducing the solar flux at the surface by 27 W m−2. Only 1–2 W m−2 of the reduced solar flux is attributed to cloud condensation nuclei associated with sulphate aerosols derived from the 2–5 fold increase in DMS emissions that results from an ice-free ocean. We conclude that aerosol-cloud feedbacks originating from DMS production in the Arctic region are largely mitigated through increased wet deposition of sulphate aerosols by rainfall and as a result are not a significant component of changes in the surface radiation budget in our model.

Keywords

Liquid Water Content Sulphate Aerosol Cloud Condensation Nucleus International Satellite Cloud Climatology Project Cloud Liquid Water 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was supported by the Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme (GA01101). W thank the anonymous reviewers for their insightful comments.

References

  1. Andrews T, Ringer MA (2014) Cloud feedbacks, rapid adjustments, and the forcing–response relationship in a transient CO2 reversibility scenario. J Clim 27:1799–1818. doi: 10.1175/JCLI-D-13-00421.1 CrossRefGoogle Scholar
  2. Andrews T, Ringer MA, Doutriaux-Boucher M, Webb MJ, Collins WJ (2012) Sensitivity of an earth system climate model to idealized radiative forcing. Geophys Res Lett 39:L10702. doi: 10.1029/2012GL051942 Google Scholar
  3. Beljaars ACM, Holtslag AAM (1991) Flux parameterisation over land surfaces for atmospheric models. J Appl Meteorol 30:327–341CrossRefGoogle Scholar
  4. Bellouin N, Rae J, Jones A, Johnson C, Haywood J, Boucher O (2011) Aerosol forcing in the climate model Intercomparison project (CMIP5) simulations by HadGEM2-ES and the role of ammonium nitrate. J Geophys Res 116:D20206. doi: 10.1029/2011JD016074 CrossRefGoogle Scholar
  5. Bodas-Salcedo A, Williams KD, Field PR and Lock AP (2012) The surface downwelling solar radiation surplus over the Southern Ocean in the Met Office model: the role of midlatitude cyclone clouds. J Clim, 25, 7467–7486. doi:  10.1175/JCLI-D-11-00702.1
  6. Browse J, Carslaw KS, Mann GW, Birch CE, Arnold SR, Leck C (2014) The complex response of Arctic aerosol to sea-ice retreat. Atmos Chem Phys 14:7543–7557. doi: 10.5194/acp-14-7543-2014 CrossRefGoogle Scholar
  7. Bucciarelli E, Ridame C, Sunda WG, Dimier-Hugueney C, Cheize M, Belviso S (2013) Increased intracellular concentration of DMSP and DMSO in iron-limited oceanic phytoplankton Thalassiosira oceanica and Trichodesmium erythraeum. Limnol Oceanogr 58:1667–1679. doi: 10.4319/lo.2013.58.5.1667 CrossRefGoogle Scholar
  8. Charlson RJ, Lovelock JE, Andreaei MO, Warren SG (1987) Oceanic phytoplankton, atmospheric Sulphur, cloud. Nature 326:655–661. doi: 10.1038/326655a0 CrossRefGoogle Scholar
  9. Clarke DB, Ackley SF (1984) Sea ice structure and biological activity in the Antarctic marginal ice zone. J Geophys Res 89(C2):2087–2095. doi: 10.1029/JC089iC02p02087 CrossRefGoogle Scholar
  10. Collins WJ et al. (2011) Development and evaluation of an earth-system model – HadGEM2. Geosci Model Dev 4:1051–1075. doi: 10.5194/gmd-4-1051-2011 CrossRefGoogle Scholar
  11. Curry J, Rossow W, Randall D, Schramm J (1996) Overview of Arctic cloud and radiation characteristics. J Clim 9:1731–1764CrossRefGoogle Scholar
  12. Derwent R, Collins WJ, Jenkin ME, Johnson CE, Stevenson DS (2003) The global distribution of secondary particulate matter in a 3D Lagrangian chemistry transport model. J Atmos Chem 44:57–95CrossRefGoogle Scholar
  13. Dyer AJ (1974) A review of flux-profile relationships. Bound-Layer Meteorol 7:363–372CrossRefGoogle Scholar
  14. English JM, Gettelman A, Henderson GR (2015) Arctic radiative fluxes: present-day biases and future projections in CMIP5 models. J Clim 28:6019–6038CrossRefGoogle Scholar
  15. Glasow R v, Crutzen PJ (2004) Model study of multiphase DMS oxidation with a focus on halogens. Atmos Chem Phys 14:589–608. doi: 10.5194/acp-4-589-2004 CrossRefGoogle Scholar
  16. Halloran PR, Bell TG, Totterdell IJ (2010) Can we trust empirical marine DMS parameterisations within projections of future climate? Biogeosciences 7:1645–1656. doi: 10.5194/bg-7-1645-2010 CrossRefGoogle Scholar
  17. Harada N (2016) Review: potential catastrophic reduction of sea ice in the western Arctic Ocean: its impact on biogeochemical cycles and marine ecosystems. Glob Planet Chang 136:1–17CrossRefGoogle Scholar
  18. Hobbs PV, Rangno AL (1998) Microstructures of low and middle-level clouds over the Beaufort Sea. QJR Meteorol Soc 124:2035–2071. doi: 10.1002/qj.49712455012 CrossRefGoogle Scholar
  19. Intrieri J, Fairall CW, Shupe M, Persson P, Andreas E, Guest P, Moritz R (2002) An annual cycle of Arctic surface cloud forcing at SHEBA. J Geophys Res 107:8039. doi: 10.1029/2000JC000439 CrossRefGoogle Scholar
  20. Kahl JD, Martinez DA (1996) Long-term variability in the low level inversion layer over the Arctic Ocean. Int J Climatol 16:1297–1313CrossRefGoogle Scholar
  21. Kay JE, Holland MM and Jahn A (2011) Inter-annual to multi-decadal Arctic Sea ice extent trends in a warming world, Geophys Res Lett, 38, L15708, doi: 10.1029/2011GL048008.
  22. Kettle A et al. (1999) A global database of sea surface dimethylsulfide (DMS) measurements and a procedure to predict sea surface DMS as a function of latitude, longitude, and month. Glob Biogeochem Cycles 13:399–444CrossRefGoogle Scholar
  23. Lalande C, Nöthig EM, Somavilla R, Bauerfeind E, Shevshenko V, Okolodkov Y (2014) Variability in under-ice export fluxes of biogenic matter in the Arctic Ocean. Glob Biogeochem Cycles. doi: 10.1002/2013GB004735 Google Scholar
  24. Lawrence J, Popova E, Yool A, Srokosz M (2015) On the vertical phytoplankton response to an ice-free Arctic Ocean. J Geophys Res 120:8571–8582. doi: 10.1002/2015JC011180 CrossRefGoogle Scholar
  25. Leighton A et al. (2015) The climatic importance of uncertainties in regional aerosol–cloud radiative Forcings over recent decades. J Clim 28:6589–6607. doi: 10.1175/JCLI-D-15-0127.1 CrossRefGoogle Scholar
  26. Lock AP, Brown AR, Bush MR, Martin GM, Smith RNB (2000) A New Boundary Layer Mixing Scheme. Part I: Scheme Description and Single-Column Model Tests. Mon Weather Rev 128:3187–3199CrossRefGoogle Scholar
  27. Lunden J, Svensson G, Leck C (2007) Influence of meteorological processes on the spatial and temporal variability of atmospheric dimethyl sulfide in the high Arctic summer. J Geophys Res 112:D13308. doi: 10.1029/2006JD008183 CrossRefGoogle Scholar
  28. Martin GM, Milton SF, Senior CA, Brooks ME, Ineson S, Reichler T, Kim J (2010) Analysis and reduction of systematic errors through a seamless approach to modelling weather and climate. J Clim 23:5933–5957. doi: 10.1175/2010JCLI3541.1 CrossRefGoogle Scholar
  29. Martin GM et al. (2011) The HadGEM2 family of Met Office unified model climate configurations. Geosci Model Dev 4:723–757. doi: 10.5194/gmd-4-723-2011 CrossRefGoogle Scholar
  30. Monin AS, Obukhov AM (1954) Basic regularity in turbulent mixing in the surface layer of the atmosphere, Moscow, Ak. Nauk. Geof Inst 24:163–187Google Scholar
  31. O’Dowd CD, Smith MH, Consterdine IE, Lowe JA (1997) Marine aerosol, sea-salt, and the marine Sulphur cycle: a short review. Atmos Environ 31:73–80CrossRefGoogle Scholar
  32. O'Connor FM et al. (2014) Evaluation of the new UKCA climate-composition model – part 2: the troposphere. Geosci Model Dev 7:41–91CrossRefGoogle Scholar
  33. Penner JE (2004) Climate change: the cloud conundrum. Nature 432:962–963CrossRefGoogle Scholar
  34. Quinn PK, Bates TS (2011) The case against climate regulation via oceanic phytoplankton Sulphur emissions. Nature 480:51–56. doi: 10.1038/nature10580 CrossRefGoogle Scholar
  35. Rasmussen RM, Geresdi I, Thompson G, Manning K, Karplus E (2002) Freezing drizzle formation in stably stratified layer clouds: the role of radiative cooling of cloud droplets, cloud condensation nuclei, and ice initiation. J Atmos Sci 59:837–860CrossRefGoogle Scholar
  36. Seinfeld JH, Pandis SN (2006) .: Atmospheric Chemistry and Physics. In: From Air Pollution To Climate Change. John Wiley and Sons, New Jersey, pp. 902–906Google Scholar
  37. Simo R (2001) Production of atmospheric sulfur by oceanic plankton: biogeochemical, ecological, and evolutionary links. Trends Ecol Evol 16:287–294. doi: 10.1016/S0169-5347(01)02152-8 CrossRefGoogle Scholar
  38. Simo R, Dachs J (2002) Global Ocean emission of dimethylsulfide predicted from biogeophysical data. Glob Biogeochem Cycles 16:1078. doi: 10.1029/2001GB001829 CrossRefGoogle Scholar
  39. Sunda W, Kieber DJ, Kiene RP, Huntsman S (2002) An antioxidant function for DMSP and DMS in marine algae. Nature 418:317–320. doi: 10.1038/nature00851 CrossRefGoogle Scholar
  40. Tjernström M et al. (2012) Central Arctic atmospheric summer conditions during the Arctic summer Cloud Ocean study (ASCOS): contrasting to previous expeditions. Atmos Chem Phys Discuss 12:4101–4164. doi: 10.5194/acpd12-4101-2012 CrossRefGoogle Scholar
  41. Twomey S (1974) Pollution and the planetary albedo. Atmos Environ 8:1251–1256CrossRefGoogle Scholar
  42. Twomey S, Piepgrass M, Wolfe TL (1984) An assessment of the impact on global cloud albedo. Tellus 36B:356–366CrossRefGoogle Scholar
  43. Webb M, Senior C, Bony S, Morcrette J-J (2001) Combining ERBE and ISCCP data to assess clouds in the Hadley Centre, ECMWF and LMD atmospheric climate models. Clim Dyn 17:905–922CrossRefGoogle Scholar
  44. Wilson DR, Ballard SP (1999) A microphysically based precipitation scheme for the UK meteorological office unified model. Q J R Meteorol Soc 125:1607–1636. doi: 10.1002/qj.49712555707 CrossRefGoogle Scholar
  45. Yool A, Popova EE, Coward AC (2015) Future change in ocean productivity: is the Arctic the new Atlantic? J Geophys Res 120:7771–7790. doi: 10.1002/2015JC011167 CrossRefGoogle Scholar

Copyright information

© Crown Copyright 2016

Authors and Affiliations

  1. 1.Met OfficeExeterUK
  2. 2.Institute of GeosciencesGoethe University FrankfurtFrankfurt am MainGermany

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