Global catchment based comparison of observed and HadGEM modelled precipitation, temperature and Köppen climate type Murray Peel 1, Thomas McMahon 1 & Ian Smith 2 1 Civil & Environmental Engineering, The University of Melbourne, Victoria, Australia 2 CSIRO Marine and Atmospheric Research, Aspendale, Victoria, Australia

EGU Session CL21, Peel et al Outline Background – Observed catchment data – HadGEM data Comparison – Mean annual Precipitation Temperature – Köppen climate type Conclusions

EGU Session CL21, Peel et al Background Long-term water resources management will depend upon projections of future climate change. How well do CGCMs reproduce past & present hydroclimate at the catchment scale?

EGU Session CL21, Peel et al Background Observed catchment data Globally 686 catchments.

EGU Session CL21, Peel et al Background Observed catchment data Catchment boundary, within 5% of known area using 1km DEM (HYDRO1k), Monthly streamflow data, – Unregulated for the period of record, Catchment average monthly Temperature (T) & Precipitation (P), – For the period of runoff record, – Theissen polygon area weighting using GHCN v2 P & T station data. Missing P & T infilled from nearby stations with best monthly correlation

EGU Session CL21, Peel et al Background Observed catchment data Elevation correction to Catchment P – Since station elevation is usually low relative to the catchment area, catchment average P underestimated, – Corrected using Budyko like relationship of Fu (Zhang et al., 2004, WRR 40, W02502). The 686 catchments are a subset of 900 catchments – modelled adequately with a monthly rainfall-runoff model – Some confidence in the P & T data.

EGU Session CL21, Peel et al Background HadGEM data HadGEM a coupled global climate model from the Hadley Centre, UK. – IPCC AR4 run – 20C3M scenario Monthly P & T for 1/1860 – 12/1999 – Extract catchment average P & T from raw HadGEM (no downscaling) for the concurrent period of observed streamflow Ideally an ensemble average of GCM runs would be better than the single run used here

EGU Session CL21, Peel et al Background HadGEM data Area weighted grid cell to form catchment average values

EGU Session CL21, Peel et al Comparison Compare observed and HadGEM catchment average – Mean Annual P & T – Köppen climate type

EGU Session CL21, Peel et al Global catchment mean annual temperature GCM has ~1 o C cool bias, but overall very good.

EGU Session CL21, Peel et al Hemisphere catchment mean annual temperature NH better than SH

EGU Session CL21, Peel et al Global catchment mean annual precipitation GCM not capturing the observed range.

EGU Session CL21, Peel et al Hemisphere catchment mean annual precipitation Again NH better than SH

EGU Session CL21, Peel et al Köppen climate type Köppen climate classification rules as used in Peel et al (2007, HESS, 11: )

EGU Session CL21, Peel et al Köppen climate type - Tropical Cold bias of GCM reduces the number of Tropical catchments (coldest month ≥ 18 o C) Sub- Class Obs Freq. Obs All GCM Freq. Af Am1614 Aw7258

EGU Session CL21, Peel et al Köppen climate type - Arid Errors in NH & SH GCM MAP leads to more Arid catchments Sub- Class Obs Freq. GCM Freq. BWh BWk00 BSh1321 BSk1626

EGU Session CL21, Peel et al Köppen climate type - Temperate Sub- Class Obs Freq. GCM Freq. Csa Csb2329 Csc10 Cwa2140 Cwb3611 Cfa8364 Cfb Cfc61

EGU Session CL21, Peel et al Köppen climate type - Cold Sub- Class Obs Freq. GCM Freq. Dsa Dsb110 Dsc43 Dwa35 Dwb24 Dwc49 Dwd03 Dfa322 Dfb10573 Dfc8598 Dfd32

EGU Session CL21, Peel et al Köppen climate type - Polar Sub- Class Obs Freq. Obs All GCM Freq. ET611

EGU Session CL21, Peel et al Conclusions Comparison of observed and raw HadGEM catchment average P & T. – Globally MAT good NH better than SH – Globally MAP not very good Again, NH better than SH – Köppen climate type Less Tropical & more Arid Temperate & Cold (mainly NH) good Useful assessment of GCM performance for later hydrologic analysis

EGU Session CL21, Peel et al Acknowledgements The analysis presented forms part of a paper in currently in preparation. Funded by – Australian Research Council Discovery Grant