Climate and Time Scales How do time scales affect the spatial extent of a climate signal?

1. Climate and Time ScalesHow do time scales affect the spatial extent of a climate signal? Angela Colbert, Jie He, Johnna Infanti, Hosmey Lopez April 27, 2011

2. Data • CCSM3 Model • Ocean variables (10 to choose from) have a 1 x 1 degree resolution horizontally and 40 vertical levels. • Atmospheric variables (many to choose from) have a T42 spectral (wavenumber 42 truncation) or about 2.8 degree resolution horizontally and 8 vertical levels. • The time resolution is monthly with a total time record from January 0710 to December 1543 (model years)

3. General Methodology • Identify important time scales for analysis • Subseasonal, seasonal, annual, and decadal • Compute EOFs based on time scales and regions of interest • EOF analysis use a 3D dataset, thus obtain a spatial pattern EOF (2D) and PC (time series) • Interpret the resulting spatial patterns • What is the spatial extent? • Can you find the various known climate signals? • PDO, ENSO, etc. • How does that compare with other time scales?

4. Sub-seasonal time scale forcing on large scales • The main goal here is to study the impact of sub-seasonal and synoptic scales in the large scales tropical ocean-atmosphere. • Here, we will analyze Westerly Wind Bursts (WWB) events that occurs on the western Pacific at about the equator and its interaction with the large scale Sea Surface Temperature (SST). • This WWB were introduced in CCSM3 as semi-stochastic forcing, modulated by the SST. • Here the bursts have an stochastic (random) component. • Its dependence on the large scale SST is calculated based on reanalysis wind data and observational estimates of SST

5. The analysis method:

8. Lag-lead correlation of SST along the equator and WWB parameters

10. Modes of WWB matrix • 1st EOF is dominated by the probability, zonal and eastern extents, and amplitude. This mode represent ENSO and accounts for 64% of the burst variance. • 2nd EOF is dominated by the central longitude and accounts for 33% of WWB variance. This mode reflect the seasonal cycle. • 3rd EOF, mostly dominated by the western extent, amplitude, and persistence. It accounts for only 9% of WWB variance. This mode rensemble equatorial wave activity. • 4th EOF is dominated by the persistence and zonal extent. 5% of burst variance is explained here. This mode reflects a zonal dipole in SST, but only 1.3% of the covariance is explained.

11. Seasonal SST (Skin Temperature) Results

12. Seasonal SST (Skin Temperature) Results

13. Seasonal SLP Results

14. Seasonal SLP Results

15. Annual SLP Results EOF 1 – 23.73% EOF 2 – 21.65%

16. Annual SST Results EOF 1 – 13.57% EOF 2 – 10.09%

17. Annual SST Results EOF 3 – 7.09% EOF 4 – 6.06%

18. Annual SST Results

19. Annual SST Results - Subsection EOF 1 – 27.81% EOF 2 – 12.23%

20. Data: 250 years decadal mean sst, slp and wind stress data from CCSM3. Decadal Climate Variability

21. Decadal Climate Variability over the North Pacific

22. A Possible Mechanism for PDO SST-SLP-Wind Feedback SST warmer on the East; West to East pressure gradient Intensified Westerlies Increased Latent Heat loss

23. Decadal Climate Variability over the Tropical Atlantic