539 Evaluating CEOP Model Performance in Semi-Arid Region of China

Wednesday, 9 January 2013
Exhibit Hall 3 (Austin Convention Center)
Weidong Guo, Insititute for Climate and Global Change Researching University, Nanjing, Jiangsu, China; and X. Ling

This study systematically evaluates simulations of near-surface temperature and precipitation using the station observations collected in the semi-arid region of China during the CEOP Enhanced Observing Period from October 2002 to 2004 (EOP3 and EOP4). The outputs being evaluated are from eight general circulation models (GCMs) archived by the Coordinated Energy and Water cycle Observations Project (CEOP), as well as, a multi-model ensemble based on these eight models. We find that the multi-model ensemble has a better performance than most of the individual models. Our results show that all individual models and the model analysis comparison (MAC) ensemble mean perform much better when simulating regionally averaged temperature than precipitation. For most models, a systematically low bias is identified in the regionally averaged simulated temperatures, while a high bias exists in the simulated precipitation except in summer. For the simulated temperatures, the lowest and largest rRMSE are found in JMA and BMRC, respectively. Furthermore, temperature is always overestimated when it is between -18°C and -10°C, while the temperature is underestimated when it is greater than 6°C; the best performance lies between -10°C and 2°C for all the models except BMRC. For the simulated precipitation, excessive rainfall is reproduced at all intervals except in ECPC-SFM, and the largest deviation is identified at the interval of 2-5 mm with a bias of 18.3%. With respect to sub-regions, the simulated temperatures are better in eastern China, but the simulated precipitation is better in the transition zone from the semi-arid region to the arid region. However, the simulation bias increases west of 100°E, which may be associated with the complex and steep topography there. We want to stress that the MAC ensemble mean is superior to any individual models.
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