K.-T. Cheng and R.-Y. Tzeng Dept. of Atmos. Sci. National Central University Taiwan

The impact of lower boundary forcings (sea surface temperature) on inter-annual variability of climate

K.-T. Cheng and R.-Y. TzengDept. of Atmos. Sci. National Central UniversityTaiwanmjoseph@atm.ncu.edu.tw

UAW 2008, July 1-3, 2008, Tokyo Japan

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.

Model and Data

•Model : NCAR CCM3 forced byNCEP 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.

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.

Time series of Niño 3.4 SST

℃var (wk-

mn)DJF mean

Neutral

0.112 26.5

Cold 0.161dT = -1.3

Warm 0.085 dT = 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.

SST spectra

• Global mean• WK > MN.

• In seasonal to annual scales, and MJO to sub-month scales.

Spectra of sea level pressure

• Global mean• Scales shorter than

annual cycle are enhanced, except MJO (30-60days).

• Small differences in interannual variations.

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.

Pattern correlation of stream function (850)

Phases WARM COLD

MN-ERA40 0.902 0.518

WK-ERA40 0.850 0.768

• 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.

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.

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.