ENSO Dominates NODC Ocean Heat Content Data

ENSO Dominates NODC Ocean Heat Content Data

Guest post by Bob Tisdale, BTW here is the current SST map. – Anthony

The Royal Netherlands Meteorological Institute (KNMI) recently added the National Oceanographic Data Center (NODC) Ocean Heat Content (OHC) dataset to their Climate Explorer website, allowing users to download data based on user-defined global coordinates.

http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere

This OHC dataset was presented in the Levitus et al (2009) paper “Global ocean heat content(1955-2008) in light of recent instrumentation problems” [GEOPHYSICAL RESEARCH LETTERS, VOL. 36, L07608, doi:10.1029/2008GL037155, 2009]

ftp://ftp.nodc.noaa.gov/pub/data.nodc/woa/PUBLICATIONS/grlheat08.pdf

There are differences in the presentation of the data. The NODC illustrates their OHC data in 10^22 Joules, but KNMI presents the data on an area-averaged basis, in units of Gigajoules (10^9 Joules) per square meter. The data is the same; the units in which the data is presented are different. Also, the NODC provides the data on a quarterly basis; that is, the data is grouped in three-month averages. KNMI presents the NODC OHC data on a monthly basis by listing the quarterly data for each of the three months. This is why the OHC data appears to be squared off in the graphs of monthly raw data. This can be seen in Figure 1.

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Figure 1

Figure 1 is a comparison graph of the Global OHC anomaly data (NODC), scaled NINO3.4 SST Anomalies (HADISST), and scaled Sato Index (GISS) data. This is the same format used in the graphs of the subsets illustrated in this post. The NINO3.4 SST anomalies are used to illustrate the timing of the El Nino-Southern Oscillation (ENSO) events. The Sato Index of Mean Optical Thickness at 500nm are provided to illustrate the timing of explosive volcanic eruptions. I’ve also smoothed the data for each OHC anomaly subset with a 13-month running-average filter, Figure 2. As you will see later, some of the subsets are noisy in their raw form.

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Figure 2

In the following, I’ve provided links to the graphs of the raw data, for those who are interested in seeing it in that form, but I have only posted the graphs of the data smoothed with a 13-month running-average filter. It’s much easier to see the step changes when the data is in that form.

TROPICS

The Tropical Pacific OHC anomaly data is illustrated in Figure 3. A number of things to note: The tropical Pacific OHC anomalies fall during El Nino events, but then recharge during the La Nina. For the most part, when the El Nino events occur at the same time as volcanic eruptions, the recharge does not return the OHC anomalies to the value they were at before the El Nino, but if the El Nino occurs without the influence of a volcanic eruption, the La Nina recharges the Tropical Pacific OHC anomalies to the pre-El Nino level. And it does it quickly. Note also how the 1972/73 El Nino event causes an upward step in the OHC anomalies of the Tropical Pacific. The OHC anomalies then decrease gradually, being influenced by the eruptions of El Chichon in 1982 and Mount Pinatubo in 1991, until they rise suddenly in 1995. In an earlier post, I illustrated how a shift in Tropical Pacific Total Cloud Amount may have caused the 1995 rise in Tropical Pacific OHC, providing fuel for the 1997/98 El Nino. Refer to my post Did A Decrease In Total Cloud Amount Fuel The 1997/98 El Nino?

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Figure 3

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Figure 3 Raw

However, the Tropical Indian Ocean OHC anomaly data reveals a sudden decline in 1995. Did a shift of warm water from the Tropical Indian Ocean to the Tropical Pacific also fuel the 1997/98 El Nino? I’ll investigate this in a future post. Note how the Tropical Indian Ocean OHC anomalies correlate with NINO3.4 SST anomalies over a large portion of the term of the data, but after 1995, the amplitude of the variations changes drastically.

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Figure 4

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Figure 4 Raw

In Figure 5, I’ve combined the OHC anomaly data for the Tropical Indian and Pacific Oceans. The OHC anomaly data for this subset follows the base of the NINO3.4 SST anomalies remarkably well. The OHC anomalies of the Tropical Indian and Pacific Oceans follow the rise in NINO3.4 SST anomalies after the 1972/73 and 1997/98 El Nino events. In other words, like the Tropical Pacific, there also appears to be a 25-year decay after the upward step from the 1972/73 El Nino (also influenced by the 1982 and 1991 volcanic eruptions), until the 1997/98 El Nino causes another upward step.

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Figure 5

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Figure 5 Raw

The step changes in the Tropical Atlantic OHC anomalies are obvious. The first occurred three years after the peak of 1972/73 El Nino, as the NINO3.4 SST anomalies rose from the secondary minimum of the two-year La Nina event. The same thing occurred with the next significant El Nino that was strong enough to generate a La Nina that lasted through two ENSO seasons, and that was the 1997/98 El Nino. Note also how the OHC anomalies of the Tropical Atlantic have been dropping quickly since 2005. Click on the link to the raw data (Figure 6 Raw) to see just how precipitous that drop has been in recent years.

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Figure 6

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Figure 6 Raw

MID-TO-HIGH LATITUDES

The North Pacific OHC anomalies are like no other OHC subset. In 1967, there was a sudden drop in the North Pacific OHC anomalies. Twenty plus years later North Pacific OHC anomalies rebounded. I’ll have to investigate this dataset further in a later post, to try to isolate where the majority of that variability takes place.

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Figure 7

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Figure 7 Raw

As illustrated in Figure 8, the South Pacific OHC anomalies show a sharp upward step change following the 1997/98 El Nino. Between 1971 and 1996, the OHC anomalies oscillate at or near 0 GJ/sq meter. The cause of the small rise between the 1960s and 1970 is elusive, but it’s not a significant rise compared to the upward step after the 1997/98 El Nino.

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Figure 8

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Figure 8 Raw

The South Indian Ocean OHC anomaly data, Figure 9, shows a decrease from 1955 until the late 1960s. Then the 1968/69/70 El Nino caused a minor rise in OHC anomalies. This was followed by a major upward step from the 1972/73 El Nino. OHC anomalies in the South Indian Ocean remained relatively flat until the eruption of Mount Pinatubo, when the OHC anomalies dipped. The upward step change after the 1997/98 El Nino is hard to miss. The decay until 2006 almost returned the South Indian Ocean OHC anomalies to the pre-1997/98 values, but the El Nino of 2006/07 bumped it back up again.

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Figure 9

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Figure 9 Raw

The North Atlantic OHC anomaly data, Figure 10, with its gradual climb, is clearly dominated by the Atlantic Multidecadal Oscillation. The impacts of ENSO events are visible, however. In a future post, I may detrend the North Atlantic OHC anomaly data to emphasize the ENSO impacts on this dataset.

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Figure 10

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Figure 10 Raw

There is a clear step change in the South Atlantic OHC anomaly data, Figure 11, following the 1972/73 El Nino. In this case, however, the response appears to be lagged an extra couple of years. The response is so long, it appears to result from the lesser El Nino of 1976/77. The South Atlantic OHC anomalies remain relatively flat until they appear to respond to the 1997/98 El Nino with an upward step that starts again many years after the peak of the El Nino. Why so long?

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Figure 11

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Figure 11 Raw

Could the variations in the South Atlantic OHC anomalies simply be lagged responses to the Tropical Atlantic OHC anomalies, with surface and subsurface currents transporting the waters from the tropics to the mid-to-high latitudes of the South Atlantic? Refer to Figure 12.

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Figure 12

ARCTIC AND SOUTHERN OCEANS

I’ve provided the Arctic and Southern Ocean OHC anomaly data in Figures 13 and 14, without commentary, for those who are interested in seeing what those curves look like.

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Figure 13

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Figure 13 Raw

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Figure 14

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Figure 14 Raw

CLOSING

It is clear that significant El Nino events can and do cause upward step changes in Ocean Heat Content. This indicates that ENSO events do more than simply release heat from the tropical Pacific into the atmosphere. Apparently, El Nino events also cause changes in atmospheric circulation in ways that impact Ocean Heat Content. If and when GCMs are able to recreate the variations in atmospheric circulation that cause these changes in Ocean Heat Content, GCMs may have value in predicting future climate variability. At present, they do not.

SOURCES

The NINO3.4 SST anomaly data is based on HADISST data available through the KNMI Climate Explorer:

http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere

Sato Index data is available through GISS:

http://data.giss.nasa.gov/modelforce/strataer/

Specifically:

http://data.giss.nasa.gov/modelforce/strataer/tau_line.txt