Climate Dynamics

, Volume 39, Issue 7–8, pp 1929–1950 | Cite as

Sources of uncertainty in future changes in local precipitation

Article

Abstract

This study considers the large uncertainty in projected changes in local precipitation. It aims to map, and begin to understand, the relative roles of uncertain modelling and natural variability, using 20-year mean data from four perturbed physics or multi-model ensembles. The largest—280-member—ensemble illustrates a rich pattern in the varying contribution of modelling uncertainty, with similar features found using a CMIP3 ensemble (despite its limited sample size, which restricts it value in this context). The contribution of modelling uncertainty to the total uncertainty in local precipitation change is found to be highest in the deep tropics, particularly over South America, Africa, the east and central Pacific, and the Atlantic. In the moist maritime tropics, the highly uncertain modelling of sea-surface temperature changes is transmitted to a large uncertain modelling of local rainfall changes. Over tropical land and summer mid-latitude continents (and to a lesser extent, the tropical oceans), uncertain modelling of atmospheric processes, land surface processes and the terrestrial carbon cycle all appear to play an additional substantial role in driving the uncertainty of local rainfall changes. In polar regions, inter-model variability of anomalous sea ice drives an uncertain precipitation response, particularly in winter. In all these regions, there is therefore the potential to reduce the uncertainty of local precipitation changes through targeted model improvements and observational constraints. In contrast, over much of the arid subtropical and mid-latitude oceans, over Australia, and over the Sahara in winter, internal atmospheric variability dominates the uncertainty in projected precipitation changes. Here, model improvements and observational constraints will have little impact on the uncertainty of time means shorter than at least 20 years. Last, a supplementary application of the metric developed here is that it can be interpreted as a measure of the agreement amongst models of their projected local precipitation change. Results differ from, but are complementary to, those of the more usual approach.

Keywords

Precipitation Climate change Uncertainty Tropics Multi-model ensemble 

References

  1. Abbot DS, Walker CC, Tziperman E (2009) Can a convective cloud feedback help to eliminate winter sea ice at high CO2 concentrations? J Clim 22:5719–5731CrossRefGoogle Scholar
  2. Allen MR, Ingram WJ (2002) Constraints on future changes in climate and the hydrological cycle. Nature 419:224–232CrossRefGoogle Scholar
  3. Alo CA, Wang G (2010) Role of dynamic vegetation in regional climate predictions over western Africa. Clim Dyn 35:907–922CrossRefGoogle Scholar
  4. Biasutti M, Held IM, Sobel AH, Giannini A (2008) SST forcings and Sahel rainfall variability in simulations of the twentieth and twenty-first centuries. J Clim 21:3471–3486CrossRefGoogle Scholar
  5. Bony S, Dufresne J-L (2005) Marine boundary layer clouds at the heart of tropical feedback uncertainties in climate models. Geophys Res Lett 32. doi:10.1029/2005GL023851
  6. Booth, BBB, Jones CD (2011) Terrestrial response of QUMPC ensemble. Hadley Centre Technical Note 89, Met Office, Exeter, UKGoogle Scholar
  7. Budikova D (2009) Role of Arctic sea ice in global atmospheric circulation: A review. Glo Plan Change 68:149–163CrossRefGoogle Scholar
  8. Chou C, Neelin JD (2004) Mechanisms of global warming impacts on regional tropical precipitation. J Clim 17:2688–2701CrossRefGoogle Scholar
  9. Christensen JH, Hewitson B, Busuioc A, Chen A, Gao X, Held I, Jones R, Kolli RK, Kwon WT, Laprise R, Magana Rueda V, Mearns L, Menendez CG, Raisanen J, Rinke A, Sarr A, Whetton P (2007): Regional climate projections. In: Solomon S, Qin D, Manning M, Marquis M, Averyt KB, Tignor M, Miller HL, Chen Z (eds) Climate change (2007). The physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, pp 847–940Google Scholar
  10. Collins M, Booth BBB, Harris GR, Murphy JM, Sexton DMH, Webb MJ (2006) Towards quantifying uncertainty in transient climate change. Clim Dyn 27:127–147CrossRefGoogle Scholar
  11. Collins M, Booth BBB, Bhaskaran B, Harris GR, Murphy JM, Sexton DMH, Webb MJ (2011) Climate model errors, feedbacks and forcings: a comparison of perturbed physics and multi-model ensembles. Clim Dyn 36:1737–1766CrossRefGoogle Scholar
  12. Cox PM, Huntingford C, Harding RJ (1998) A canopy conductance and photosynthesis model for use in a GCM land surface scheme. J Hydro 213:79–94CrossRefGoogle Scholar
  13. Deser C, Phillips A, Bourdette V, Teng H (2011) Uncertainty in climate change projections: the role of internal variability. Clim Dyn. doi:10.1007/s00382-010-0977-x
  14. Douville H (2005) Limitations of time-slice experiments for predicting regional climate change over South Asia. Clim Dyn 24:373–391CrossRefGoogle Scholar
  15. Douville H, Salas-Melia D, Tyteca S (2006) On the tropical origin of uncertainties in the global land precipitation response to global warming. Clim Dyn 26:367–385CrossRefGoogle Scholar
  16. Friedlingstein P, Cox P, Betts R, Bopp L, Von BW, Brovkin V, Cadule P, Doney S, Eby M, Fung I, Bala G, John J, Jones C, Joos F, Kato T, Kawamiya M, Knorr W, Lindsay K, Matthews HD, Raddatz T, Rayner P, Reick C, Roeckner E, Schnitzler KG, Schnur R, Strassmann K, Weaver AJ, Yoshikawa C, Zeng N (2006) Climate-carbon cycle feedback analysis: results from the C4MIP model intercomparison. J Clim 19:3337–3353CrossRefGoogle Scholar
  17. Gordon C, Cooper C, Senior CA, Banks H, Gregory JM, Johns TC, Mitchell JFB, Wood RA (2000) The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Clim Dyn 16:147–168CrossRefGoogle Scholar
  18. Hawkins E, Sutton R (2011) The potential to narrow uncertainty in projections of regional precipitation change. Clim Dyn 37:407–418CrossRefGoogle Scholar
  19. IPCC (2007) Climate change 2007: impacts, adaptation and vulnerability. In: Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE (eds) Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  20. Jones A, Roberts DL, Woodage MJ, Johnson CE (2001) Indirect sulphate aerosol forcing in a climate model with an interactive sulphur cycle. J Geophys Res 106:20293–20310. doi:10.1029/2000JD000089 CrossRefGoogle Scholar
  21. Meehl GA, Covey C, Delworth T, Latif M, McAvaney B, Mitchell JFB, Stouffer RJ, Taylor KE (2007a) The WCRP CMIP3 multimodel dataset: a new era in climate change research. Bull Am Meteor Soc 88:1383–1394CrossRefGoogle Scholar
  22. Meehl GA, Stocker TF, Collins WD, Friedlingstein P, Gaye AT, Gregory JM, Kitoh A, Knutti R, Murphy JM, Noda A, Raper SCB, Watterson IG, Weaver AJ, Zhao Z (2007b) Global climate projections. In: Solomon, S, Qin D, Manning M, Marquis M, Averyt KB, Tignor M, Miller HL, Chen Z (eds) Climate change 2007b: the physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge pp 747–845Google Scholar
  23. Murphy JM, Sexton DMH, Barnett DN, Jones GS, Webb MJ, Collins M, Stainforth DA (2004) Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature 430:768–772CrossRefGoogle Scholar
  24. Murphy JM, Booth BBB, Collins M, Harris GR, Sexton DMH, Webb MJ (2007) A methodology for probabilistic predictions of regional climate change from perturbed physics ensembles. Phil Trans R Soc 365:1993–2028CrossRefGoogle Scholar
  25. Murphy JM, Sexton DMH, Jenkins GJ, Boorman PM, Booth BBB, Brown CC, Clark RT, Collins M, Harris GR, Kendon EJ, Betts RA, Brown SJ, Howard TP, Humphrey KA, McCarthy MP, McDonald RE, Stephens A, Wallace C, Warren R, Wilby R, Wood RA (2009) UK climate projections science report: climate change projections. Met Office Hadley Centre, ExeterGoogle Scholar
  26. Neelin JD, Munnich M, Meyerson JE, Holloway CE (2006) Tropical drying trends in global warming models and observations. Proc Natl Acad Sci 103:6110–6115CrossRefGoogle Scholar
  27. Paeth H, Hall NMJ, Gaertner MA, Alonso MD, Moumouni S, Polcher J, Ruti PM, Fink AH, Gosset M, Lebel T, Gaye AT, Rowell DP, Moufouma-Okia W, Jacob D, Rockel B, Giorgi F, Rummukainen M (2011) Progress in regional downscaling of West African precipitation. Atmos Sci Lett 12:75–82CrossRefGoogle Scholar
  28. Palmer JR, Totterdell IJ (2001) Production and export in a global ocean ecosystem model. Deep-Sea Res I 48:1169–1198CrossRefGoogle Scholar
  29. Patricola CM, Cook KH (2010) Northern African climate at the end of the twenty-first century: an integrated application of regional and global climate models. Clim Dyn 35:193–212CrossRefGoogle Scholar
  30. Piani C, Sanderson B, Giorgi F, Frame DJ, Christensen C, Allen MR (2007) Regional probabilistic climate forecasts from a multithousand, multimodel ensemble of simulations. J Geophys Res 112. doi:10.1029/2007JD008712
  31. Pope VD, Gallani ML, Rowntree PR, Stratton RA (2000) The impact of new physical parametrizations in the Hadley Centre climate model—HadAM3. Clim Dyn 16:123–146CrossRefGoogle Scholar
  32. Raisanen J (2001) CO2-induced climate change in CMIP2 experiments: quantification of agreement and role of internal variability. J Clim 14:2088–2104CrossRefGoogle Scholar
  33. Raisanen J, Ruokolainen L, Ylhaisi J (2010) Weighting of model results for improving best estimates of climate change. Clim Dyn 35:407–422CrossRefGoogle Scholar
  34. Rind D, Goldenberg R, Hansen J, Rosenzweig C, Ruedy R (1990) Potential evapotransiration and the likelihood of future drought. J Geophys Res 95:9983–10004CrossRefGoogle Scholar
  35. Rougier JC, Sexton DMH, Murphy JM, Stainforth D (2009) Analysing the climate sensitivity of the HadSM3 climate model using ensembles from different but related experiments. J Clim 22:3540–3557CrossRefGoogle Scholar
  36. Rowell DP (1998) Assessing potential seasonal predictability with an ensemble of multi-decadal GCM simulations. J Clim 11:109–120CrossRefGoogle Scholar
  37. Rowell DP (2009) Projected midlatitude continental summer drying: North America versus Europe. J Clim 22:2813–2833CrossRefGoogle Scholar
  38. Rowell DP, Zwiers FW (1999) The global distribution of sources of atmospheric decadal variability and mechanisms over the tropical Pacific and southern North America. Clim Dyn 15:751–772CrossRefGoogle Scholar
  39. Seneviratne SI, Pal JS, Eltahir EAB, Schär C (2002) Summer dryness in a warmer climate: a process study with a regional climate model. Clim Dyn 20:69–85CrossRefGoogle Scholar
  40. Sexton DMH, Murphy JM, Collins M, Webb M (2011) Multivariate probabilistic projections using imperfect climate models part I: outline of methodology. Clim Dyn. doi:10.1007/s00382-011-1208-9
  41. Sitch S, Huntingford C, Gedney N, Levy PE, Lomas M, Piao SL, Betts R, Ciais P, Cox P, Friedlingstein P, Jones CD, Prentice IC, Woodward FI (2008) Evaluation of the terrestrial carbon cycle, future plant geography and climate-carbon cycle feedbacks using five dynamic global vegetation models (DGVMs). Global Change Biol 14:2015–2039CrossRefGoogle Scholar
  42. Zeng N, Qian H, Roedenbeck C, Heimann M (2005) Impact of 1998–2002 midlatitude drought and warming on terrestrial ecosystem and the global carbon cycle. Geophys Res Lett 32. doi:10.1029/2005GL024607

Copyright information

© Crown Copyright 2011

Authors and Affiliations

  1. 1.Met Office Hadley CentreExeterUK

Personalised recommendations