1 / 17

Song Yang

Variations of the Great Plains Precipitation and Its Relationship with Tropical Central-Eastern Pacific SST. Song Yang. NOAA’s Climate Prediction Center. X. Ding and D. Zheng. Hong Kong Polytechnic University, China. Li et al. (2004): Precip and ENSO, AO, NAO, PDO, NP, …. GL. PNW. NE. MW.

rice
Download Presentation

Song Yang

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Variations of the Great Plains Precipitation and Its Relationship with Tropical Central-Eastern Pacific SST Song Yang NOAA’s Climate Prediction Center X. Ding and D. Zheng Hong Kong Polytechnic University, China

  2. Li et al. (2004): Precip and ENSO, AO, NAO, PDO, NP, … GL PNW NE MW IM GP OV MA SC SE SW GC

  3. Li et al. (2004): Precip and ENSO DJF JJA Y Y Y Y Y Y Y Y Y Y Y MAM SON Y Y Y Y Y Y Y Y Y Y Y Y

  4. Issues of Interest Time-frequency features especially the dominant time scales of the variability of GP precipitation Effect of multi-scale oscillating signals on the variability of GP precipitation Relationship between tropical Pacific SST and GP precipitation especially its time-frequency feature and seasonality

  5. Data and Methods DATA Precipitation from CRU TS 2.0 Product: 1901-2000 (Mitchell et al. 2004) NOAA Extended Reconstructed SST (Smith and Reynolds 2003) METHODS* Wavelet analysis Least squares analysis Multi-stage filtering Leap-step time series analysis Coherence and correlation analyses Multiple moving-window method Monte Carlo significance test *For details, see Ding et al. (2002) and Yang et al. (2004)

  6. May 1957 June 1988 131.9 55.8 14.3 -6.5 -4.0 7.4 35.9 25.8 8.6 0.6 -7.2 8.9 Subseasonal Semiannual Annual Biennial Interannual

  7. 8 Yrs 0.7 1.2 Yrs 4 2.5 4.5 5.3

  8. Simultaneous Relationship

  9. One-Season Lag Relationship

  10. 2.5 Yrs 10 Yrs 7.5 Yrs 10 2.2 Yrs 5 Yrs 4.5 8

  11. Summary • The variability of GP precipitation is characterized by strong annual, followed by semiannual signals. Interannual and interdecadal signals are also evident, especially the interannual signals in large precipitation anomalies. • In the case of positive (negative) precipitation anomaly, non-seasonal signals are in phase with (out of phase to) the seasonal signals. • NINO3.4 SST has a strong relationship with the GP precipitation, especially in summer. Significant SST-precipitation relationships occur on semiannual timescale in the 1950s, annual timescale in 1940s-50s, and interannaul timescales in 1910s, 1940s, and 1980s (especially the biennial timescale).

More Related