K.-T. Cheng and R.-Y. Tzeng Dept. of Atmos. Sci. National Central University Taiwan
description
Transcript of K.-T. Cheng and R.-Y. Tzeng Dept. of Atmos. Sci. National Central University Taiwan
![Page 1: K.-T. Cheng and R.-Y. Tzeng Dept. of Atmos. Sci. National Central University Taiwan](https://reader036.fdocuments.net/reader036/viewer/2022070413/56814d0c550346895dba4839/html5/thumbnails/1.jpg)
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 [email protected]
UAW 2008, July 1-3, 2008, Tokyo Japan
![Page 2: K.-T. Cheng and R.-Y. Tzeng Dept. of Atmos. Sci. National Central University Taiwan](https://reader036.fdocuments.net/reader036/viewer/2022070413/56814d0c550346895dba4839/html5/thumbnails/2.jpg)
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.
![Page 3: K.-T. Cheng and R.-Y. Tzeng Dept. of Atmos. Sci. National Central University Taiwan](https://reader036.fdocuments.net/reader036/viewer/2022070413/56814d0c550346895dba4839/html5/thumbnails/3.jpg)
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.
![Page 4: K.-T. Cheng and R.-Y. Tzeng Dept. of Atmos. Sci. National Central University Taiwan](https://reader036.fdocuments.net/reader036/viewer/2022070413/56814d0c550346895dba4839/html5/thumbnails/4.jpg)
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.
![Page 5: K.-T. Cheng and R.-Y. Tzeng Dept. of Atmos. Sci. National Central University Taiwan](https://reader036.fdocuments.net/reader036/viewer/2022070413/56814d0c550346895dba4839/html5/thumbnails/5.jpg)
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.
![Page 6: K.-T. Cheng and R.-Y. Tzeng Dept. of Atmos. Sci. National Central University Taiwan](https://reader036.fdocuments.net/reader036/viewer/2022070413/56814d0c550346895dba4839/html5/thumbnails/6.jpg)
SST spectra
• Global mean• WK > MN.
• In seasonal to annual scales, and MJO to sub-month scales.
![Page 7: K.-T. Cheng and R.-Y. Tzeng Dept. of Atmos. Sci. National Central University Taiwan](https://reader036.fdocuments.net/reader036/viewer/2022070413/56814d0c550346895dba4839/html5/thumbnails/7.jpg)
Spectra of sea level pressure
• Global mean• Scales shorter than
annual cycle are enhanced, except MJO (30-60days).
• Small differences in interannual variations.
![Page 8: K.-T. Cheng and R.-Y. Tzeng Dept. of Atmos. Sci. National Central University Taiwan](https://reader036.fdocuments.net/reader036/viewer/2022070413/56814d0c550346895dba4839/html5/thumbnails/8.jpg)
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.
![Page 9: K.-T. Cheng and R.-Y. Tzeng Dept. of Atmos. Sci. National Central University Taiwan](https://reader036.fdocuments.net/reader036/viewer/2022070413/56814d0c550346895dba4839/html5/thumbnails/9.jpg)
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.
![Page 10: K.-T. Cheng and R.-Y. Tzeng Dept. of Atmos. Sci. National Central University Taiwan](https://reader036.fdocuments.net/reader036/viewer/2022070413/56814d0c550346895dba4839/html5/thumbnails/10.jpg)
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.
![Page 11: K.-T. Cheng and R.-Y. Tzeng Dept. of Atmos. Sci. National Central University Taiwan](https://reader036.fdocuments.net/reader036/viewer/2022070413/56814d0c550346895dba4839/html5/thumbnails/11.jpg)
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.