In this post, I examine the combined impacts of Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO) and El Nino – Southern Oscillation (ENSO) on the long-term GISS Land and Ocean Temperature Anomaly (LOTA) trend.
Professor Don Easterbrook of Western Washington University has stated …
“The PDO cool mode has replaced the warm mode in the Pacific Ocean, virtually assuring us of about 30 years of global cooling, perhaps much deeper than the global cooling from about 1945 to 1977.” source
In this post I take a closer look at PDO, AMO and ENSO indexes to see how they are related to the GISS anomaly trends.
PDO, AMO, ENSO Climate Oscillation Combinations
I have retrieved the GISS, PDO , AMO and ENSO data and constructed a consolidated file (available here) of monthly values since 1900. I have assigned a PAE phase code to classify each month since 1900 based on the combined positive/negative status of each oscillation, using the codes in this table:
Since there are several ENSO phase codes, I used the Nino34 index as the ENSO index.
GISS LOTA Boxplots by PAE Phase
Figure 1 shows the GISS LOTA boxplots for each of the 8 combined PDO-AMO_ENSO phases. (click to enlarge)
These boxplots show the distribution of monthly GISS anomalies by PAE phase without considering the year of the anomaly. Three important characteristics of the monthly GISS LOTA series:
- The median temperatures of the PAE phases vary, with PAE 1 (- – -) considerably less than PAE 8 (+++).
- There is considerable overlap among the PAE phases. For example, there are PAE 1 months with GISS anomalies greater than Phase 8 months.
- Knowing the PAE phase does not provide sufficient information to assess the monthly temperature anomaly by itself.
GISS LOTA Trends by PAE Phase
Let’s take a look at the role of both the year and PAE phase on the GISS anomalies. By separating the monthly GISS data by PAE phase and plotting on the same overall trend chart we can see how the PAE phases compare over time.
The Figure 2 trend charts show monthly GISS anomalies by PAE phase over the 1900-2010 time period.
- GISS anomalies have increased during all 8 phases, from PAE 1 (- – -) to PAE 8 (+++).
- The trend rates vary by PAE phase, from a low of 0.00565 / year for Phase 1 (- – -) to a high of 0.00737 for Phase 7 (++-).
- Knowing the PAE phase and the year provides much more information than just the PAE phase. Look at the PAE 1 trend chart. PAE 1 anomalies in 1900 – 1920 were much lower than PAE 1 anomalies in 1960-1980. The same is true for the 7 other PAE phases.
GISS Anomaly Regressions Using Year, PDO, AMO, and ENSO Phases
I have developed a series of regressions to see how powerful year and the PDO, AMO and ENSO phases are in predicting actual monthly GISS LOTA anomalies in for 1900-2010. The 6 regressions are shown in Figure 3.
Figure 3 provides several additional insights into the role of year and oscillation phase on the GISS trend:
- PDO and AMO have essentially no explanatory power in predicting GISS anomalies. This is to be expected since both are detrended series. See Atmoz here for a more detailed explanation.
- The year regression follows the overall GISS trend, with fluctuations about the trend line.
- Adding the PDO-AMO-ENSO phases to the year enhances the regression’s reproduction of the month to month variability.
Based on my analysis of the 1900-2010 monthly GISS LOTA, PDO,AMO and ENSO data, I see no indication that the PDO shift to the cool phase will assure “… us of about 30 years of global cooling” as predicted by Prof. Easterbrook. While the rate of temperature increase is slightly affected by the specific PAE phase, the long term temperature anomaly trend has been upward under all PAE phases in the 1900-2010 period.
Data and RClimate Scripts
To help readers who may want to verify, extend, critique or challenge my analysis, I provide access to my R scripts and data files.