I’ve moved to WordPress. This post can now be found at Is The Difference Between NINO3.4 SST Anomalies And The PDO A Function Of Sea Level Pressure?###################
In Misunderstandings about the PDO – REVISED, I showed that the Pacific Decadal Oscillation (PDO) does not represent the Sea Surface Temperature (SST) anomalies of the North Pacific (North of 20N), and that the PDO is not detrended SST anomalies of the North Pacific like the Atlantic Multidecadal Oscillation (AMO), and that the PDO does not represent the difference between the North Pacific SST anomalies and Global temperature anomalies. I also provided links to Zhang et al (1997) “ENSO-like interdecadal variability: 1900-93”…
…and Newman et al (2003) “ENSO-Forced Variability of the Pacific Decadal Oscillation”…
…both of which showed that the PDO lags ENSO. In fact, Newman et al state, “The PDO is dependent upon ENSO on all timescales.”
That earlier post was co-posted at WattsUpWithThat with the similar title of Misunderstandings about the Pacific Decadal Oscillation.
This post is not intended to resurrect the arguments presented in the previous post, but it will show a possible cause for the difference between NINO3.4 SST anomalies and the PDO.
THE DIFFERENCE BETWEEN THE PDO AND NINO3.4 SST ANOMALIES
Figure 1 is a comparison graph of monthly PDO Index Monthly Values from the JISAO PDO website and standardized NINO3.4 SST anomalies. I used standardized NINO3.4 SST anomalies in this post because the PDO data is standardized and I was going to subtract one from the other. (But there really was little visual difference in the results if the NINO3.4 SST anomalies were not standardized.) And both datasets have been smoothed with 13-month running-average filters to remove some of the noise. The variances between the two datasets lead to speculation and debate about which dataset drives the other (even though the papers linked above show the PDO lags ENSO).
The calculated difference between the two datasets (Standardized NINO3.4 SST anomalies MINUS PDO data) is shown in Figure 2.
The difference between the two datasets is noisy so I’ve smoothed it with an 85-month filter in Figure 3.
THE NORTH PACIFIC INDEX
The North Pacific (NP) Index is “the area-weighted sea level pressure over the region 30N-65N, 160E-140W, available since 1899. It was introduced in Trenberth and Hurrell (1994) “Decadal atmosphere-ocean variations in the Pacific”:
Figure 4 illustrates a time-series graph of the North Pacific Index, smoothed with a 13-month filter. Even with the filter it’s a noisy dataset.
For those noting the spikes and wondering if they correlate with ENSO, I’ve prepared Figure 5. NINO3.4 SST anomalies and the North Pacific Index are negatively correlated but poorly. There are times when the North Pacific Index falls (rises) during an El Nino (La Nina), and other times when it does not.
Let’s compare the North Pacific Index to the data created by subtracting the PDO data from the NINO3.4 SST anomalies. Refer to Figure 6. While they do diverge from time to time, the curves do follow one another quite well as far back as the mid-1940s. Prior to then, they diverge significantly. But when one considers these datasets are based on reconstructions of data with periods when and areas where there were few measurements, the divergence is not surprising.
THE SAME GRAPH WITHOUT STANDARDIZED NINO3.4 DATA
Earlier I noted that standardizing the NINO3.4 SST anomalies made little difference in this visual comparison. Figure 7 is the same as Figure 6, except that the NINO3.4 SST anomalies in Figure 7 have not been standardized.
Regarding the question asked in the title of this post, Is The Difference Between NINO3.4 SST Anomalies And The PDO A Function Of Sea Level Pressure?, the answer appears to be yes.
The HADISST NINO3.4 SST anomaly data is available through the KNMI Climate Explorer Observations webpage:
The North Pacific Index data is available through the KNMI Climate Explorer Climate Indices webpage (as is the HADISST NINO3.4 data):
The PDO data from JISAO is also available through the KNMI Climate Explorer Climate Indices webpage, but I used the data directly from the JISAO website for this post: