climate and time scales how do time scales affect the spatial extent of a climate signal?

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

Angela Colbert, Jie He, Johnna Infanti, Hosmey LopezApril 27, 2011

DataCCSM3 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)

General MethodologyIdentify important time scales for analysis

Subseasonal, seasonal, annual, and decadalCompute EOFs based on time scales and regions of

interestEOF 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?

Sub-seasonal time scale forcing on large scalesThe 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

The analysis method:

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

1 2 3 4 5 6 7

Amplitude 43 2 -35 15 -61 53 0

Central Lon. 12 -99 -2 9 -6 2 0

East extent 42 -10 10 -40 19 7 -77

West extent -28 -4 79 10 13 43 28

Zonal extent -44 -12 -26 -59 20 -12 57

Persistence -34 -1 -34 60 64 8 0

Probability -49 -3 23 30 -33 71 0

Covariance % 59 33 4.3 1.3 0.9 0.5 0

WWB Variance %

64 13 9 5 4 5 0

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.

Seasonal SST (Skin Temperature) Results

Seasonal SST (Skin Temperature) Results

Seasonal SLP Results

Seasonal SLP Results

Annual SLP Results

EOF 1 – 23.73% EOF 2 – 21.65%

Annual SST Results

EOF 1 – 13.57% EOF 2 – 10.09%

Annual SST Results

EOF 3 – 7.09% EOF 4 – 6.06%

Annual SST Results

Annual SST Results - Subsection

EOF 1 – 27.81% EOF 2 – 12.23%

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

Decadal Climate Variability over the North Pacific

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

Decadal Climate Variability over the Tropical Atlantic