climate and time scales how do time scales affect the spatial extent of a climate signal?
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Climate and Time Scales How do time scales affect the spatial extent of a climate signal?. Angela Colbert, Jie He, Johnna Infanti, Hosmey Lopez April 27, 2011. Data. CCSM3 Model Ocean variables (10 to choose from) have a 1 x 1 degree resolution horizontally and 40 vertical levels. - PowerPoint PPT PresentationTRANSCRIPT
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