1. The impact of lower boundary forcings (sea surface temperature) on inter-annual variability of climate K.-T. Cheng and R.-Y. Tzeng Dept. of Atmos. Sci. National Central University Taiwan mjoseph@atm.ncu.edu.tw UAW 2008, July 1-3, 2008, Tokyo Japan

2. Introduction Kirtman et al. (2001) studied and simulated one La Niña case (88/89) with weekly SST. ▫In La Niña, the atmosphere in PNA region is sensitive to weekly SST. ▫Provide 2 possible mechanisms for the sensitivity, stochastic and deterministic effects. We simulated 20 years with different time resolution of NCEP OI-SST, i.e., weekly and monthly. The impact of different time resolution of SST on interannual variability.

3. Model and Data Model: NCAR CCM3 forced by NCEP monthly and weekly OI-SST (Reynolds et al., 2002). Duration: Nov. 1981 to Feb. 2003 Obs. data: ECMWF ERA-40 dataset Analyses : daily and monthly data.

4. Data handling 3 phases: for calculation of interannual anomalies ▫Criteria : Niño 3.4 SSTa > 1 ℃ and > 0.5 ℃ for at least 8 months (Wang et al., 2000). ▫Warm (3), (warm – neutral) ▫Cold (2) and (cold – neutral) ▫Neutral (6) phase. Correlation analysis: to quantify the model performance. ▫Spatial correlation, ▫Temporal correlation. Spectrum analysis of SST and SLP: to understand the differences of spectra of boundary forcing and the atmospheric response.

5. Time series of Niño 3.4 SST ℃ var (wk-mn)DJF mean Neutral0.11226.5 Cold0.161dT = -1.3 Warm0.085dT = 2.4 Forcing of SSTA: W (2 ℃ ) > C (1 ℃ ) Variance (WK - MN): C > N > W ENSO cold phase can not be simulated well with monthly SST, due to less of high frequency signals.

6. SST spectra Global mean WK > MN. In seasonal to annual scales, and MJO to sub-month scales.

7. Spectra of sea level pressure Global mean Scales shorter than annual cycle are enhanced, except MJO (30-60days). Small differences in interannual variations.

8. Frequency distribution of Temporal correlation of stream function (850) No much differences in normal phase. WKsst run is better in warm phase and slightly better in cold phase. The deterministic effect dominates.

9. Pattern correlation of stream function (850) PhasesWARMCOLD MN-ERA400.9020.518 WK-ERA400.8500.768 Domain: Pacific basin (60E-60W, 45S-60N) No much difference in warm anomalies, but WK is much better in cold anomalies. The stochastic effect dominates. SST variations activate atmospheric variations.

10. Conclusions Amplitudes of scales less than annual scale in weekly SST are greater than monthly SST, particularly in MJO and submonth. However annual cycle is amplified and MJO is suppressed in SLP. The temporal correlations (deterministic forcing) of warm anomalies in weekly SST are better than in monthly SST. The spatial correlations (stochastic forcing) of cold anomalies are better and more sensitive to weekly SST than those of warm phase.

11. Conclusions In ENSO warm phase (strong forcing), the atmosphere is controlled by deterministic effect. The largest temporal correlation is found in warm phase of WKsst run --- better deterministic forcing. But the stochastic effect is more important during cold phases (SST variance). Therefore weekly (SST) variations become more important. Pattern correlations in WKsst are better than MNsst.

12. Model performance:Precipitation A B C D E A B C D E JJA DJF Strong rainfall areas: A.Monsoon B.Rain forest C.ICTZ D.SPCZ E.Storm track CCM3 model can simulate annual cycle.

13. Time series of Nino3.4 SST Time Series of 3 Chosen ENSO phases (Sep. to Aug.).

14. Interannual anomalies of atmosphere: 850 hPa stream function (MN-WK) Upper chart is anomalous stream function in warm phase and bottom one is in cold phase. In both phases, the major differences locate between subtropic and extratropic at north Pacific. The difference meant that strength and activity area of Aleutian low with WKsst is quiet different from MNsst. Warm phase anomalies Cold phase anomalies

15. 90-day to 1-yr scale: 850 hPa stream function Monthly variance of filtered ψ field with 90-day to 1-year window. Winter mean (DJF). All the differences in both phases located in the mid- and high latitude, like Aleutian area. In warm phase, both could simulate the major feature. In cold phase, the WKsst was better than MNsst.

16. 30-day to 60-day scale: 850 hPa stream function In warm phase, weekly SST amplified the 30-to-60day variability at mid- to high latitude area. But in cold phase, the WKsst was better than MNsst.