Addressing heterogeneities in climate change studies for water resources in Korea

Current Science

Volumn 98, Issue 8, 2010, Pages 1077-1083

  Kim, Young Oh ^a   Lee, Jae Kyoung ^a  

^a Seoul National University   (South Korea)

Author keywords

Climate change; Ensemble; Heterogeneities; Water resources

Indexed keywords

EID: 77953998250     PISSN: 00113891     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (18)

1. Choi, Y., Nam, K.-Y., Jung, H.-S. and Kwon, W.-T., Estimating and correcting urban bias in surface temperature time series of Korea. Int. J. Climatol., 2003, 23, 577-591.
2. Choi, Y., Trends in daily precipitation events and their extremes in the southern region of Korea. Korea Soc. Environ. Impact Assess., 2002, 11, 189-203.
3. Ministry of Construction and Transportation, A study on the Optimal Water Resources Planning (IV): Assessment of Climate Change Impacts for Water Resources Planning, Korea Water Resources Corporation, Korea, 2000.
4. Ministry of Environment, Settlement of Global and International Environmental Issue: Prediction and Impact Assessment of Water Cycle with Climate Change, Ministry of Environment, Korea, 2006
5. Ministry of Science and Technology, Technology for Climate Change Impact Assessment on Water Resources, Sustainable Water Resources Research Centre, Korea, 2007
6. Nash, J. E. and Sutcliffe, J. V., River flow forecasting through conceptual models part I - a discussion of principles. J. Hydrol., 1970, 10, 282-290.
7. Raisanen, J. and Palmer, T. N., A probability and decision-model analysis of a multimodel ensemble of climate change simulation. J. Climate, 2001, 14, 3212-3226.
8. Rajagopalan, B., Lall, U. and Zebiak, S. E., Categorical climate forecasts through regularization and optimal combination of multiple GCM ensembles. Mon. Weather Rev., 2002, 130, 1792-1811.
9. Doblas-Reyes, F. J., Hagendorn, R. and Palmer, T. N., The rationale behind the success of multi-model ensembles in seasonal forecasting - II. Calibration and combination. Tellus, 2005, A57, 234-252.
10. Allen, M. R., Stott, P. A., Mitchell, J. F. B., Schnur, R. and Delworth, T. L., Quantifying the uncertainty in forecasts of anthropogenic climate change. Nature, 2005, 407, 617-620.
11. Giorgi, F. and Mearns, L. O., Calculation of average, uncertainty range, and reliability of regional climate changes from AOGCM simulation via the 'Reliability Ensemble Averaging' (REA) method. J. Climate, 2002, 15, 1141-1158.
12. Murphy, J. M., Sexton, D. M. H., Barnett, D. N., Jones, G. S., Webb, M. J., Collins, M. and Stainforth, D. A., Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature, 2004, 430, 768-772.
13. Maurer, E. P. and Duffy, P. B., Uncertainty in projections of streamflow changes due to climate change in California. Geophys. Res. Lett., 2005, 32, L03704.
14. Wilby, R. L. and Harris, I., A framework for assessing uncertainties in climate change impacts: low-flow scenarios for the River Thames, UK. Water Resour. Res., 2006, 42, W02419.
15. Maurer, E. P., Uncertainty in hydrologic impacts of climate change in the Sierra Nevada, California, under two emission scenarios. Climatic Change, 2007, 82, 309-325.
16. Dessai, S., Lu, X. and Hulme, M., Limited sensitivity analysis of regional climate change probabilities for the 21st century. J. Geophys. Res., 2005, 110, D19108.
17. Taylor, K. E., Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res., 2001, 106, 7183-7192.
18. Raftery, A. E., Gneiting, T., Balabdaoui, F. and Polakowski, M., Using Bayesian model averaging to calibrate forecast ensembles. Mon. Weather Rev., 2005, 133, 1155-1174.