By Bob Tisdale
Thursday, June 10, 2010
Figure 1 – Sample Animation – Not Used In Video
Recently, I began animating maps that represent 12-month averages of “noisy” datasets with good results. The weather noise and seasonal variations are gone, for the most part. The 12-month-averaged TLT anomaly maps present a much “smoother” animation, as shown in the .gif sample, Figure 2.
Figure 2 – Sample Of Animation Used In Video
In the video, I liken the effect to smoothing the data in a time-series graph with a 12-month filter, Figure 3.
Figure 3 – Smoothed Time-Series Graph
The following 2-part video series provides detailed descriptions, time-series graphs, and animations of the processes that take place during El Niño and La Niña events. It uses TLT, SST, Total Cloud Amount, Sea Level, and Downward Shortwave Radiation anomalies to help illustrate the significant differences between the 1997/98 El Niño and the 1998/99/00/01 La Niña.
The videos also help illustrate why the effects of ENSO cannot be removed from the global surface temperature record by simply subtracting scaled and lagged NINO3.4 SST anomalies (or another ENSO index) from global temperature anomalies. There are significant residuals that contribute to global temperature anomaly trends, and these residuals are not accounted for with the simple methods used in climate studies such as Thompson et al (2009). Link (with paywall) to Thompson et al (2009):
I’ve also included animations that compare global SST anomalies with the other datasets. A sample frame that compares SST and TLT anomalies is shown in Figure 4. To indicate the timing of the maps as they proceed from El Niño to La Niña, many of the animations also include time-series graphs that fill in as time progresses.
Figure 4 – Sample Frame From Animation Of Two Datasets
Please view the animations full screen and, if possible, in high definition.
SOURCES AND DATASETS
The maps were created using the map-making feature of the KNMI Climate Explorer, which was also used for the data in the time-series graphs.
The primary SST and SST anomaly data used in the animations and graphs are NOAA/Reynolds Optimum Interpolation (OI.v2) SST.
For the comparison to tropical Pacific Ocean Heat Content, a longer-term SST dataset was required, and for that graph, I used Kaplan/Reynolds (OI.v2) NINO3.4 SST anomalies from the Monthly climate indices webpage of the KNMI Climate Explorer. Link to Kaplan overview:
The other datasets used in the videos are also available through the KNMI Climate Explorer and they include:
1. International Satellite Cloud Climatology Project (ISCCP) Total Cloud Amount data. Link:
2. CAMS-OPI [Climate Anomaly Monitoring System (“CAMS”) and OLR Precipitation Index (“OPI”)] precipitation data. Link:
3. RSS MSU Lower Troposphere Temperature (TLT) anomalies. Link:
4. CLS (AVISO) Sea Level anomalies. Link:
5. NCEP/DOE Reanalysis-2 Surface Downward Shortwave Radiation Flux (dswrfsfc) anomalies. Link:
There is also an animation of the Equatorial Subsurface Temperature Cross-sections that are available through the ECMWF website:
The Trade Wind Index (5S-5N, 135W-180) Anomaly data is available through the NOAA CPC website. Scroll down to the second grouping for the anomaly data:
The first detailed posts on the multiyear aftereffects of El Nino events are:
The impacts of these El Nino events on the North Atlantic are discussed in:
The Lower Troposphere Temperature (TLT) anomaly responses are discussed in:
RSS MSU TLT Time-Latitude Plots… Show Climate Responses That Cannot Be Easily Illustrated With Time-Series Graphs Alone
The misrepresentation of ENSO in climate studies are discussed in the following (The discussions are similar but there are differences in the presentation):
Posts related to the effects of ENSO on Ocean Heat Content are here:
Detailed technical discussions can be found here:
More Detail On The Multiyear Aftereffects Of ENSO – Part 2 – La Nina Events Recharge The Heat Released By El Nino Events AND…During Major Traditional ENSO Events, Warm Water Is Redistributed Via Ocean Currents.
Posted by Bob Tisdale at 9:32 AM