Do weak AR4 models bias projections of future climate changes over Australia?
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Regional climate projections using climate models commonly use an “all-model” ensemble based on data sets such as the Intergovernmental Panel on Climate Change’s (IPCC) 4th Assessment (AR4). Some regional assessments have omitted models based on specific criteria. We use a criteria based on the capacity of climate models to simulate the observed probability density function calculated using daily data, model-by-model and region-by-region for each of the AR4 models over Australia. We demonstrate that by omitting those climate models with relatively weak skill in simulating the observed probability density functions of maximum and minimum temperature and precipitation, different regional projections are obtained. Differences include: larger increases in the mean maximum and mean minimum temperatures, but smaller increases in the annual maximum and minimum temperatures. There is little impact on mean precipitation but the better models simulate a larger increase in the annual rainfall event combined with a larger decrease in the number of rain days. The weaker models bias the amount of mean warming towards lower increases, bias annual maximum temperatures to excessive warming and bias precipitation such that the amount of the annual rainfall event is under-estimated. We suggest that omitting weak models from regional scale estimates of future climate change helps clarify the nature and scale of the projected impacts of global warming.
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- Christensen JH, Hewitson B, Busuioc A, Chen A, Gao X, Held I, Jones R, Kwon W-T, Laprise R, Rueda VM, Mearns L, Menéndez CG, Räisänen J, Rinke A, Kolli RK, Sarr A, Whetton P (2007) Regional climate projections. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Climate change 2007: the scientific basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, New YorkGoogle Scholar
- Griffiths GM, Chambers LE, Haylock MR, Manton MJ, Nicholls N, Baek H-J, Choi Y, Della-Marta PM, Gosai A, Iga B, Laurent V, Maitrepierre L, Nakamigawa H, Ouprasitwong N, Solofa D, Tahani L, Thuy DT, Tibig L, Trewin B, Vediapan K, Zhai P (2005) Change in mean temperature as a predictor of extreme temperature change in the Asia-Pacific region. Int J Clim 25:1301–1330CrossRefGoogle Scholar
- Johns TC, Durman CF, Banks HT, Roberts MJ, Mclaren AJ, Ridley JK, Senior CA, Williams KD, Jones A, Rickard GJ, Cusack S, Ingram WJ, Crucifix M, Sexton DMH, Joshi MM, Dong B-W, Spencer H, Hill RSR, Gregory JM, Keen AB, Pardaens AK, Lowe JA, Bodas-Salcedo A, Stark S, Searl Y (2006) The New Hadley Centre climate model (HadGEM1): evaluation of coupled simulations. J Climate 19:1327–1353CrossRefGoogle Scholar
- Parkinson G (ed) (1986) Atlas of Australian resources, third series, vol 4. Climate, Commonwealth of Australia, Canberra, Australian, 60 ppGoogle Scholar
- 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:Art No L23825Google Scholar
- Randall D, Wood RA, Bony S, Colman R, Fichefet T, Fyfe J, Kattsov V, Pitman AJ, Shukla J, Srinivasan J, Stouffer RJ, Sumi A, Taylor K (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 scientific basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, New YorkGoogle Scholar
- Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) (2007) Climate change 2007: the scientific basis contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, New YorkGoogle Scholar