Part 1 – Satellite-Era Sea Surface Temperature Versus IPCC Hindcast/Projections

Note:  This is a Repost of an earlier blog post.  I had problems with pdf hyperlinks to that earlier post.

GLOBAL AND PACIFIC OCEAN SEA SURFACE TEMPERATURE

NOTE: REFER TO UPDATE AT THE END OF THE POST

OVERVIEW

This series of posts examines the differences between multi-model mean of the IPCC 20C3M (Hindcast)/SRES A1B (Projection) data and the Reynolds OI.v2 Sea Surface Temperature (SST) anomalies, a satellite-based SST dataset.  In addition to times-series graphs, the linear trends of the Sea Surface Temperature anomalies are presented on a latitudinal (Zonal Mean) basis.

Part 1 looks at the Global SST anomalies and the SST anomalies of the Pacific Ocean.

The post includes discussions of the El Niño-Southern Oscillation (ENSO).  If ENSO is new to you and if you’re interested in learning more about it, refer to the post An Introduction To ENSO, AMO, and PDO – Part 1.

INTRODUCTION

Climate change-related blogs present comparisons of IPCC Model Mean hindcast and projection data to show how well the models have performed versus the observed data.  Real Climate has done this on an annual basis for the last few years.  Refer to Updates to model-data comparisons and 2010 updates to model-data comparisons for examples. And Lucia, at her blog The Blackboard, compares the models to the observations with her monthly updates per dataset.  Refer to RSS: Drop from 0.052C to -0.026C.   Global temperature data, along with other datasets, are presented in those posts.

But as we know, the globe does not warm uniformly. Land surface temperatures vary at different rates than sea surface temperature. And some parts of the globe are warming faster than others.  In fact, since the start of the Reynolds OI.v2 SST dataset, there have been decreases in SST anomalies for many portions of the global oceans. For this reason, I’ve elected to also examine the model mean hindcast/projections for Sea Surface Temperature data on an ocean-basin basis and on a zonal-mean (average temperature per 5-degree latitude band) basis.

Note:  Kevin Trenberth provided a good overview of the IPCC models in Nature’s Climate Feedback: Predictions of climate post.  It is a worthwhile read, even as a refresher.

This post is not about that post at Climate Feedback or its author so please refrain from any comments along those lines.

 

GLOBAL AND PACIFIC TIME SERIES GRAPHS

Let’s look at the time-series graphs first.

Figure 1 compares the Global (90S-90N) Reynolds OI.v2 SST anomaly data from January 1982, the start of that satellite-based dataset, and the model mean for the IPCC 20C3M (Hindcast)/SRES A1B (Projection) TOS (Sea Surface Temperature) data.   The obvious difference is, the models do not show the yearly variations caused by the El Niño-Southern Oscillation (ENSO).  This is to be expected based on the Trenberth post in Nature.  And if I’m looking at this correctly, ensemble members for each model are averaged and so are the models in turn to provide the multi-model mean.  With all of that averaging, the multi-model mean for ENSO signals would be greatly smoothed.

There is a significant difference in the linear trends.  The linear trend for the models is about 50% higher than the observed trend for Global SST anomalies.

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Figure 1

If you were to click on the Monthly scenario runs link to the KNMI Climate Explorer and scroll down to the top of the table, you’d note that both SST and TOS are shown. “TOS” is identified as “(Sea surface temperature) in K {time mean}” in the descriptions, while “SST” is identified as Sea Surface Temperature with some modifications.  I believe the difference is how they treat sea ice. But just in case you’re concerned those definitions are somehow responsible for the difference between the observed SST data and the models in Figure 1, I’ve eliminated the polar data in Figure 2.  It compares the observed and modeled Global SST data from 60S-65N.  The model trend is still about 50% higher than the observations.

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Figure 2

The linear trend of the satellite-based SST observations for the North Pacific, Figure 3, are about half that of the models.

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Figure 3

The disparity is greater in the South Pacific. Refer to Figure 4.  There the linear trend of the models is almost 2.5 times higher than the trend of the SST data.

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Figure 4

Dividing the Pacific data into their East and West components is very revealing.  As shown in Figure 5, the linear trends for the SST observations and models in the West Pacific are nearly identical.

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Figure 5

That means the major difference between the observations and the models in the Pacific exists in the East Pacific.  Refer to Figure 6.  The linear trend of the IPCC Hindcast/Projection is more than 6 times higher than the nearly flat Sea Surface Temperature observations.

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Figure 6

SST ANOMALY TREND COMPARISONS ON ZONAL MEAN BASES

Zonal Mean graphs offer a different perspective.  As you’ll note in Figure 7, the y-axis is temperature, same as the time-series graph.  But the x-axis is latitude.  The zonal mean data in the post are based on the average SST anomalies for the latitude bands of 80S to 75S, then 75S-70S, etc., from pole to pole.   And the graphs present the linear trends of the SST data for those latitude bands in Deg C/Decade.  The data starts in January 1982 and ends in February 2011.  As we can see in Figure 7, the trends of the models are higher at the equator than they are at mid-latitudes.  The trends then drop off to near zero at high latitudes.

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Figure 7

The model data basically follows this pattern in all ocean basins. Refer to Figure 8, which compares the trends for the zonal mean SST anomalies for the Atlantic, Indian, and Pacific Oceans.  Note that I’ve further divided the Indian and Pacific Oceans into their respective east and west portions.  There are some differences, primarily at the mid latitudes of both hemispheres, but in general the same overall patterns exist in all basins.

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Figure 8

-HOWEVER-

That’s not how the global SST anomalies have risen.

Figure 9 compares linear trends of the Global SST observations and IPCC hindcasts/projections on a zonal mean basis.  In the Southern Hemisphere, the SST observations are, for the most part, cooling south of 50S.  The Southern Hemisphere trends then peak in the mid latitudes before dropping significantly in the tropics.  The trends of the observations then rise again from the tropics to the high latitudes of the Northern Hemisphere.  Poleward of the peak near 60N, the observations drop quickly to near zero.  The sea surface temperature models appear to have no basis in reality.

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Figure 9

Recall in Figure 8 how, on a latitudinal basis, the trends of modeled SST anomalies were similar for all ocean basins. Figure 10 presents the same comparison with the Reynolds OI.v2 SST data. As shown, the ocean basin SST anomalies have warmed very differently since 1982. The North Atlantic has very high trends at high latitudes.  This should be a function of the Atlantic Multidecadal Oscillation (AMO) which is not visible in the IPCC model data.  (That will be presented in part 2 of this post.)   Note the significant negative trend in the Eastern Tropical Pacific.

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Figure 10

Let’s compare the observed and the modeled trends (on a zonal mean basis) for the West Pacific (125E-180). Refer to Figure 11. The models miss the significant drop in Southwest Pacific SST anomalies, south of 45S.  The model mean trends are similar to the observations from there until the mid latitudes of the Northwest Pacific.   But the models also miss the significant warming of the Northwest Pacific between 30N-45N, the location of the Kuroshio-Oyashio Extension (KOE).   I discussed the processes that cause the rise in the SST anomalies for the KOE in The ENSO-Related Variations In Kuroshio-Oyashio Extension (KOE) SST Anomalies And Their Impact On Northern Hemisphere Temperatures.  While the models may not reproduce the recent ENSO variability, should we expect to see evidence of ENSO-related processes outside of the eastern equatorial Pacific?

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Figure 11

And should we expect to see evidence of the ENSO process in the eastern Pacific zonal mean data?  That question relates to the process of ENSO, not the timing or magnitude of the modeled ENSO events.  As illustrated in Figure 12, Eastern tropical Pacific SST anomalies have dropped since the start of the Reynolds OI.v2 SST anomaly data, with the greatest cooling along the equator.  The models, on the other hand, show a high trend, one that is comparable to those of the other ocean basins (Refer back to Figure 8).

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Figure 12

Keep in mind that the eastern equatorial Pacific is only a temporary home of the warm water associated with El Niño events.  At other times, the eastern equatorial Pacific is one of the largest areas of upwelling in the global oceans.  Again, this temporary warming in the eastern Pacific happens during El Niño events, when warm water sloshes east from the surface and below the surface of the Pacific Warm Pool. An El Niño does not “exhaust” all of the warm water, and what remains has to go somewhere at the end of the El Niño.   Where does it go?  It goes back to the western Pacific during the La Niña.  Some of the warm water helps to recharge the Pacific Warm Pool for the next El Niño. The rest remains on the surface and finds its way, most noticeably, to the Kuroshio-Oyashio Extension (KOE).   For these reasons, we would expect the West Pacific trends to be considerably higher than the East Pacific in the tropics and mid latitudes of the Northern Hemisphere.  We see this in the observations, Figure 13.

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Figure 13

But we do not see it in the model mean data, Figure 14. In fact, the opposite happens in the models.  In them, the Eastern tropical Pacific rises faster than those in the Western tropical Pacific.

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Figure 14

BUT THE SST DATA STARTS WITH AN EL NIÑO AND ENDS DURING A LA NIÑA

The Reynolds OI.v2 SST dataset starts just before the significant 1982/83 El Niño and ends during the peak of the 2010/2011 La Niña.  For those who are concerned that starting the comparisons on an El Niño and ending them on a La Niña has biased this presentation, I’ve redone a few of the zonal mean comparison graphs, model versus observations.  I’ve ended the data in December 2009, at the peak of the 2009/10 El Niño, in the trend comparisons of the zonal mean data for the East and West Pacific, Figures 15 and 16, and for the global data, Figure 17.  The data now runs from El Niño to El Niño.    It does not change the results to any significant extent.

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Figure 15

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Figure 16

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Figure 17

UPDATE – April 11, 2011

I wasn’t happy with the “El Niño to El Niño” years selected under the heading of “BUT THE SST DATA STARTS WITH AN EL NIÑO AND ENDS DURING A LA NIÑA”, the heading directly above.  The 1982 El Niño was significantly stronger than the 2009/2010 El Niño.   This is very apparent if we look at annual NINO3.4 SST anomalies, Figure 18.  The annual data, however, does allow us to determine the two ENSO neutral years that are closest to the beginning and end of the Reynolds OI.v2 SST dataset.  They are 1986, with a NINO3.4 SST anomaly of +0.111 deg C, and 2005, at +0.114 deg C.  Can’t get much closer than that, but the use of those years would shorten the time span of the data considerably.

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Figure 18

Figure 19 shows the monthly NINO3.4 SST anomalies over that period.

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Figure 19

So I plotted the zonal-mean comparisons (model versus data) for the East Pacific, West Pacific and Global data once again, Figures 20, 21, and 22.  The curves have changed somewhat, but the differences are still extreme.

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Figure 20

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Figure 21

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Figure 22

CLOSING

In part 2, we’ll examine the Indian and Atlantic Oceans.

As illustrated in this post, the IPCC modeled SST data have significantly higher trends than the satellite-era SST observations for Global and Pacific Oceans.

Also illustrated, there are few similarities in the zonal mean comparisons for those datasets.   In other words, the models appear to have little basis in reality, at least since 1982.

SOURCE

The data presented in this post is available through the KNMI Climate Explorer:

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