Use of variability modes to evaluate AR4 climate models over the Euro-Atlantic region

Climate Dynamics

Volumn 38, Issue 1-2, 2012, Pages 225-237

  Casado, M J ^a   Pastor, M A ^a  

^a Agencia Estatal de Meteorología ^*  (Spain)

Author keywords

AR4; ERA40; Euro Atlantic region; GCMs; Variability modes

Indexed keywords

CLIMATE CHANGE; CLIMATE MODELING; COMPUTER SIMULATION; INTERGOVERNMENTAL PANEL ON CLIMATE CHANGE; NORTH ATLANTIC OSCILLATION; PROBABILITY DENSITY FUNCTION; REGIONAL CLIMATE; SPATIAL VARIATION; TEMPORAL VARIATION; VARIANCE ANALYSIS;

ATLANTIC OCEAN; ATLANTIC OCEAN (EAST);

EID: 84855317104     PISSN: 09307575     EISSN: 14320894     Source Type: Journal    
DOI: 10.1007/s00382-011-1077-2     Document Type: Article

References (58)

1. Barnston AG, Livezey RE (1987) Classification, seasonality and persistence of low-frequency atmospheric circulation patterns. Mon Weather Rev 115: 1083-1126.
2. Bell GD, Halpert MS (1995) Atlas of intraseasonal and interannual variability, 1986-1993. NOAA Atlas No. 12. Climate Prediction Center, NOAA/NWS/NMC, Washington.
3. Casado MJ, Pastor MA, Doblas-Reyes FJ (2008) Euro-Atlantic circulation types and modes of variability in winter. Theor Appl Climatol 96: 17-29. doi: 10. 1007/s00704-008-0036-2.
4. Cassou C, Guilyardi E (2007) Modes de variabilité et changement climatique. Synthèse du quatrième rapport d′évaluation du Giec. La Météorologie 59: 22-30.
5. Cheng X, Dunkerton TJ (1995) Orthogonal rotation of spatial patterns derived from singular value decomposition analysis. J Clim 8: 2631-2643.
6. Collins WD et al (2006) The community climate system model version 3 (CCSM3) CM2 global coupled climate models. Part I: formulation and simulation characteristics. J Clim 19: 2122-2143.
7. Corti S, Molteni F, Palmer T (1999) Signature of recent climate change in frequencies of natural atmospheric circulation regimes. Nature 398: 799-802.
8. Delworth TL et al (2006) GFDL's CM2 global coupled climate models. Part I: formulation and simulation characteristics. J Clim 19: 643-674.
9. Diansky NA, Volodin EM (2002) Simulation of the present day climate with a coupled atmosphere-ocean general circulation model. Izv Atmos Ocean Phys 38: 732-747.
10. Diansky NA, Bagno AV, Zalesny VB (2002) Sigma model of global ocean circulation and its sensitivity to variations in wind stress. Izv Atmos Ocean Phys 38: 477-494.
11. Errasti I, Ezcurra A, Sáenz J, Ibarra-Berastegui G (2011) Validation of IPCC AR4 models over the Iberian Peninsula. Theor Appl Climatol 103: 61-79. doi: 10. 1007/s00704-010-0282-y.
12. Feldstein SB (2000) The timescale, power spectra and climate noise properties of teleconnection patterns. J Clim 13: 4430-4440.
13. Flato GM (2005) The third generation coupled global climate model (CGCM3). Available on line at http://www. cccma. bc. ec. gc. ca/models/cgcm3. shtml.
14. Furevik T, Bentsen M, Drange H, Kindem IKT, Kvamstø NG, Sorteberg A (2003) Description and evaluation of the Bergen climate model: ARPEGE coupled with MICOM. Clim Dyn 21: 27-51.
15. Gates WL (1992) AMIP: the atmospheric model intercomparison project. Bull Am Meteorol Soc 73: 1962-1970.
16. Gates WL, Boyle JS, Covey C, Dease CG, Doutriaux CM, Drach RS, Fiorino M, Gleckler PJ, Marlais S, Phillips TJ, Potter GL, Santer BD, Sperber KR, Taylor KE, Williams DN (1999) An overview of the results of the atmospheric model intercomparison project (AMIP I). Bull Am Meteorol Soc 80: 29-55.
17. Gordon HB et al (2002) The CSIRO Mk3 climate system model. CSIRO Atm Res Tech Paper 60: pp 134. Available online at http://www. cmar. csiro. au/e-print/open/gordon_2002a. pdf.
18. Handorf D, Dethloff K (2009) Atmospheric teleconnections and flow regimes under future climate projections. Eur Phys J Special Topics 174: 237-255.
19. Hasson AEA, Mills GA, Timbal B, Walsh K (2009) Assessing the impact of climate change on extreme fire weather events over South-Eastern Australia. Clim Res 39: 159-172.
20. Hasumi H, Emori S (2004) K-1 coupled GCM (MIROC) description. K-1 Tech. Rep. 1, Center for Climate System Research, University of Tokyo, p 39. Available online at http://www. ccsr. u-tokyo. ac. jp/kyosei/hasumi/MIROC/tech-repo. pdf.
21. Huth R (1997) Continental-scale circulation in the UKHI GCM. J Clim 10: 1545-1561.
22. Johns TC et al (2006) The new Hadley centre climate model (HadGEM1): evaluation of coupled simulations. J Clim 19: 1327-1353.
23. Jolliffe IT, Stephenson DB (2003) Forecast verification. A practitioner's guide in atmospheric science. Wiley, p 240.
24. Kharin V, Zhang X (2005) Intercomparison of near surface temperature and precipitation extremes in AMIP-2 simulations. J Clim 18: 5201-5223.
25. Kharin V, Zwiers F (2000) Changes in the extremes in an ensemble of transient climate simulations with a coupled atmosphere-ocean GCM. J Clim 13: 3760-3788.
26. Knutti R (2008) Should we believe model predictions of future climate change? Philos Trans R Soc A 366: 4647-4664.
27. Knutti R, Meehl GA, Allen MR, Stainforth DA (2006) Constraining climate sensitivity from the seasonal cycle in surface temperature. J Clim 19: 4224-4233.
28. Knutti R, Furrer R, Tebaldi C, Meehl GA (2010) Challenges in combining projections from multiple climate models. J Clim 23: 2739-2758.
29. Martin GM, Ringer MA, Pope VD, Jones A, Dearden C, Hinton TJ (2006) The physical properties of the atmosphere in the new Hadley centre global environmental model (HadGEM1). Part I: model description and global climatology. J Clim 19: 1274-1301.
30. Palmer TN (1999) A nonlinear dynamical perspective on climate prediction. J Clim 12: 575-591.
31. Perkins SE, Pitman AJ (2009) Do weak AR4 models bias projections of future climate changes over Australia? Clim Change 93: 527-558.
32. Perkins SE, Pitman AJ, Holbrook NJ, McAneney J (2007) Evaluation of the AR4 climate models' simulated daily maximum temperature, minimum temperature, and precipitation over Australia using probability density functions. J Clim 20: 4356-4376.
33. Piani C, Frame DJ, Stainforth DA, Allen MR (2005) Constraints on climate change from a multi-thousand member ensemble of simulations. Geophys Res Lett 32: L23825. doi: 10. 1029/2005GL024452.
34. Pierce DW (2004) Beyond the means: validating climate models with higher order statistics. Comput Sci Eng 6(5): 22-29. Available on line at http://doi. ieeecomputersociety. org/10. 1109/MCSE. 2004. 32.
35. 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: 123-146.
36. Randall DA, Wood RA, Bony S, Coleman R, Fichefet T, Fyfe J, Kattsov V, Pitman A, Shukla J, Srinivasan J, Stouffer RJ, Sumi A, Taylor KE (2007) Climate models and their evaluation. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (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 589-662.
37. Reichler T, Kim J (2008) How well do coupled models simulate today's climate? Bull Am Meteorol Soc 89: 303-311.
38. Richman MB (1986) Rotation of principal components. J Clim 6: 293-335.
39. Richman MB (1999) Relationships between the definition of the hyperplane width to the fidelity of principal component loading patterns. J Clim 12: 1557-1576.
40. Roeckner E et al (2003) The atmospheric general circulation model ECHAM 5. Part I: Model description. Max-Planck-Institute for Meteorology Rep, p 127. Available online at http://edoc. mpg. de/175329.
41. Russell GL (2005) 4 × 3 atmosphere-ocean model documentation. Available online at http://aom. giss. nasa. gov/doc4x3. html.
42. Russell GL, Miller JR, Rind D (1995) A coupled atmosphere-ocean model for transient climate change studies. Atmos-Ocean 33(4): 683-730.
43. Salas-Melia D et al (2005) Description and validation of the CNRM-CM3 global coupled model. CNRM Working Note 103.
44. Shukla J, DelSole T, Fennessy M, Kinter J, Paolino D (2006) Climate model fidelity and projections of climate change. Geophys Res Lett 33: L07702. doi: 10. 1029/2005GL025579.
45. Silverman BW (1986) Density estimation for statistics and data analysis. Chapman and Hall, London, p 175.
46. Stephenson DB, Pavan V, Boxariu R (2000) Is the North Atlantic oscillation a random walk? Int J Climatol 20(1): 1-18.
47. Stephenson DB, Pavan V, Collins M, Junge MM, Quadrelli R (2006) North Atlantic Oscillation response to transient greenhouse gas forcing and the impact on European winter climate: a CMIP2 multi-model assessment. Clim Dyn 27: 401-420.
48. Stoner K, Hayhoe K, Wuebbles DJ (2009) Assessing general circulation model simulations of atmospheric teleconnection patterns. J Clim 22(16): 4348-4372.
49. Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res 106(7): 7183-7192.
50. Uppala SM, Kållberg PW, Simmons AJ, Andrae U, da Costa Bechtold V, Fiorino M, Gibson JK, Haseler J, Hernandez A, Kelly GA, Li X, Onogi K, Saarinen S, Sokka N, Allan RP, Andersson E, Arpe K, Balmaseda MA, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Caires S, Chevallier F, Dethof A, Dragosavac M, Fisher M, Fuentes M, Hagemann S, Hólm E, Hoskins BJ, Isaksen L, Janssen PAEM, Jenne R, McNally AP, Mahfouf JF, Morcrette JJ, Rayner NA, Saunders RW, Simon P, Sterl A, Trenberth KE, Untch A, Vasiljevic D, Viterbo P, Woollen J (2005) The ERA-40 re-analysis. Q J R Meteorolog Soc 131: 2961-3012. doi: 10. 1256/qj. 04. 176.
51. Von Storch H, Zwiers FW (1999) Statistical analysis in climate research. Cambridge University Press, Cambridge, p 484.
52. Wallace JM, Gutzler DS (1981) Teleconnections in the geopotential height field during the Northern hemisphere winter. Mon Weather Rev 109: 784-812.
53. Washington WM et al (2000) Parallel climate model (PCM) control and transient simulations. Clim Dyn 16: 755-774.
54. Watterson IG (1996) Non-dimensional measures of climate model performance. Int J Climatol 16: 379-391.
55. Wilks DS (2006) Statistical methods in the atmospheric sciences, 2nd Edition. International geophysics series vol 9, Elsevier, p 627.
56. Yukimoto S, Noda A (2003) Improvements of the meteorological research institute global ocean-atmosphere coupled GCM (MRI-GCM2) and its climate sensitivity. CGER's supercomputing activity Rep., vol 10-2001, National Institute for Environmental Studies, Ibaraki, Japan, p 37-44.
57. Yukimoto S et al (2001) The new meteorological research institute global ocean-atmosphere coupled GCM (MRICGCM2)-model climate and variability. Pap Meteor Geophys 51: 47-88.
58. Zwiers F, Zhang X (2003) Towards regional-scale climate change detection. J Clim 16: 793-797.