INDIA and INDO-CHINA India and Indo-China are other areas where the theoretical predictability using the interactive soil moisture is superior to the fixed soil moisture integrations. In both sets of simulations India and southern China received less rainfall in the northern hemisphere fall, while southern and western Indo-China had increased precipitation in El Nino years (figure 4). During years with this precipitation pattern the interactive simulations produced improved predictability over southern India and southern Indo-China as well as increased 2 meter temperature over the same areas. During these years, southern India experienced reduced evaporation in January and February, again explaining the increased temperature in the area. However, this mechanism does not explain the improved predictability over Indo-China. It can be seen from figure 3 that the 2 meter temperature anomaly over Indo-China is quite similar between the interactive simulations and the fixed simulations. However, the mechanism leading to the increased temperatures appears to be different between the two sets of simulations. Unlike Australia or India, the cause of the warmer temperatures over Indo-China in the interactive simulations was found to be the increase in downward longwave radiation. Further study is in progress to find the relationship between soil moisture and radiation fluxes. In addition, we see that the increased predictability is partially due to more consistency between the members of the interactive ensemble (i.e. less noise). Looking at the 53 year record of the interactive integrations, all seven years with this precipitation/temperature pattern are El Nino years, however not all El Nino years have this pattern. In observations 5 out of 9 years with the temperature pattern are El Nino years. The Effects of Soil Moisture on Seasonal Predictability Jan/Feb theoretical predictability Fig 1: Jan/Feb theoretical predictability for a) average for fixed soil moisture runs b) 1983, 1988, & 1992 average for fixed runs c) average for interactive runs d) 1983, 1988, & 1992 average for interactive runs e) average for 12 members of interactive runs f) ’64, ’66, ’73, ’78, ’83, ’88, ’92, ’95, & ’98 average for interactive runs El Nino Jan/Feb 2m Temp anomaly Fig 3:’83, ’88, &’92 2m T anomaly for a) fixed runs b) interactive runs c) observations El Nino OND precipitation anomaly Fig 2: ’82, ’87 & ’91 OND precipitation anomaly for a) interactive runs b) fixed runs EL Nino ASO precipitation anomaly Fig 4: ’82, ’87 & ’91 ASO precipitation anomalies for a) interactive runs b) fixed runs PREDICTABILITY AND SOIL MOISTURE A better understanding of the role of soil moisture in seasonal predictability of numerical models is a key to the improved understanding and enhancement of the models. Here we run two sets of model integrations, one with prescribed climatological soil moisture and one with interactive soil moisture, to study the effect of soil moisture on the predictability of 2 m temperature. A total of 20 simulations were run using the Experimental Climate Prediction Center's Global to Regional Spectral Model (ECPC G-RSM) (Kanamitsu et. al 2002). The model now incorporates the Noah land surface model (Ek et al., 2003). Twelve simulations were run for 53 years ( ) with interactive soil moisture and observed SST. An additional eight simulations were run for 12 years ( ) forced with monthly climatological soil moisture computed from the other 12 members. (When comparisons are made between the two sets of integrations, a subset of the interactive ensemble is used consisting of eight members for the years ) All simulations used observed SSTs from the ERA-40 data set, created by the ECMWF. Theoretical predictability was computed according to Sardeshmukh et al. (2000) as  = s 2 /[(s 2 +1)(s 2 +1/n)] 1/2, where s is the signal to noise ratio and n is the number of ensemble members. A student’s t-test was used to test the significance of results presented here. RESULTS A comparison between the fixed and interactive runs shows improvement in predictability in some high northern latitude locations, the western United States, northern Africa, northern South America, Indo-China, India and Australia. Here we will look in depth at India and Australia. AUSTRALIA The difference in predictability between the two sets of integrations is seen most clearly in Australia. While an increase in predictability with interactive soil moisture can be seen in many months and in many years, the austral summer during El Nino years shows the improvement most consistently and clearly (fig 1). In Australia, observations show that El Nino years tend to produce less precipitation in the late spring (OND) than other years. This is a feature which both simulations of the model generally reproduce (figure 2). However, capturing the general rainfall pattern alone does not guarantee predictability. As seen in figure 1, the simulations with climatological soil moisture have little predictability over Australia in the months following the decreased precipitation. Conversely, the interactive soil moisture simulations have a large increase in predictability following the dry El Nino spring. Not surprisingly, in the months following decreased precipitation there is decreased evaporation in the interactive simulations (not shown), and consequently increased low level temperature (fig 3b). This mechanism likely explains the increased predictability with interactive soil moisture. Over the 53 year record of the interactive soil moisture simulations, the model is quite consistent in reproducing this El Nino pattern (with some variation in the exact location of the decreased precipitation and increased temperature). It is worthy to note that this predictability is not only theoretical. As seen in figure 3 the interactive simulations are more consistent with observations. These results for Australia agree with Timbal et al (2002). FUTURE WORK In addition to further study of soil moisture using the Noah land surface model, we are also interested in comparing the results of long term runs using both the Oregon State (OSU2) and the Univ. of Washington and Princeton University's Variable Infiltration Capacity (VIC) land surface models. CONTACT: Laurel De Haan, Laurel De Haan, Masao Kanamitsu, Sarah Lu*, and John Roads Experimental Climate Prediction Center Scripps Institute of Oceanography, La Jolla, CA *Environmental Modeling Center/NCEP/NWS/NOAA REFERENCES Ek, M. B., K. E. Mitchell, Y. Lin, E. Rogers, P. Grunmann, V. Koren, G. Gayno, and J. D. Tarpley, Implementation of Noah land-surface model advances in the NCEP operational mesoscale Eta model, J. Geophys. Res., Vol. 108, No. D22, 8851, doi: /2002JD003296, Kanamitsu, M., A. Kumar, H. Juang, J. Schemm, W. Wang, F. Yang, S. Hong, P. Peng, W. Chen, S. Moorthi, and M. Ji, NCEP Dynamical Seasonal Forecast System 2000, Bull. Amer. Meteor. Soc , July Sardeshmukh, P., G. Compo, and C. Penland, Changes of Probability Associated with El Nino, J of Climate, Vol 13, ,2000. Timbal, B., S. Power, R. Colman, J. Viviand, and S. Lirola, Does Soil Moisture Influence Climate Variability and Predictability over Australia?, J. of Climate, Vol 15, , 2002.