What Do Observed Sea Surface Temperature Anomalies and Climate Models Have In Common Over The Past 17 Years?

One word answer: NOTHING!!!!

OVERVIEW

In this post, we’ll compare satellite-based sea surface temperature anomalies (Reynolds OI.v2) for the past 17 years to the multi-model ensemble mean of the climate models that were prepared for the 2007 4th Assessment Report (AR4) of the Intergovernmental Panel on Climate Change. We’ve already showed how poorly the models simulate the warming rates of the global oceans on an individual ocean basis for the entire 30-year term of the Reynolds OI.v2 sea surface temperature data. Refer to the posts here and here, and more recently here. So the failings of the models come as no surprise. But this post does present something that will come as a surprise to many of you.

The choice of 17 years is based on the Santer et al (2011) paper, Separating Signal and Noise in Atmospheric Temperature Change: The Importance of Timescale. In the abstract, Santer et al (2011) conclude with:

Our results show that temperature records of at least 17 years in length are required for identifying human effects on global-mean tropospheric temperature.

Since sea surface temperature anomalies are not as variable as lower troposphere temperature (TLT) anomalies, we’ll assume that 17 years would also be an acceptable timescale to present sea surface temperature anomaly trends on a hemispheric, or greater, basis. This was the foundation for an earlier postthat compared models and the same sea surface temperature dataset. And we’ll also divide the oceans into their individual basins to illustrate why I’ve presented, as one combined dataset, the Indian and Pacific Oceans from pole to pole.

While the failings of the models might come as no revelation, something else might—but first a note to build the suspense. Combined, the Indian and Pacific Oceans from pole to pole (90S-90N, 20E-70W) represent about 75% of the surface area of the global oceans. See Figure 1. It’s a map of the global oceans that’s been divided into two sections: the “Indian & Pacific Ocean Plus” and “Atlantic Ocean Plus” where the “Plus” is used to note that the datasets have been extended to the South and North Poles.

Figure 1 (Revised to note which graphs present the data for the two regions.)

Why are we dividing the ocean into those two subsets? Here comes the surprise.

A Closer Look at CRUTEM4 Since 1975

With the recent release of the CRUTEM4 data came the expected two-sided discussion (argument) about the changes from the earlier version of the dataset, CRUTEM3. One side claimed the adjustments were needed, while the other side protested the increase in global surface temperature anomalies. There were also differences of opinion about where the adjustments were made. Some claimed the added Arctic surface stations were the sole contributors to the trend, and others countered that the adjustments and dataset additions impacted data globally.

Who was right about the locations of the additions and adjustments? And when did the adjustments and additions have the greatest impact in recent decades?

Polar Amplification: Observations versus IPCC Climate Models

We’ve illustrated and discussed polar amplification in a few posts in the past. See here and here. Wikipedia has a short blurb about it:

Polar amplification is the greater temperature increases in the Arctic compared to the earth as a whole as a result of the effect of feedbacks and other processes^[1] It is not observed in the Antarctic, largely because the Southern Ocean acts as a heat sink and the lack of seasonal snow cover.^[2] It is common to see it stated that “Climate models generally predict amplified warming in polar regions”, e.g. Doran et al.^[3]. However, climate models predict amplified warming for the Arctic but only modest warming for Antarctica.^[2]

Many discussions about polar amplification around the climate-related blogosphere have similar definitions, leading readers to believe polar amplification is a phenomenon that only occurs in a warming world. But if we divide the trends of the global surface temperature anomaly data since 1917 into its cooling period (1944-1976) and two warming periods (1917-1944 and 1976-2011), and present the surface temperature linear trends on a zonal-mean (latitudinal) basis, Figure 1, we can see that polar amplification works both ways. That is, during a period when global temperatures cool, like 1944-1976, there is greater cooling in the Arctic than elsewhere. Note also that, according to the GISS Land-Ocean Temperature Index (LOTI) data, the rate at which the Arctic warmed was higher during the early warming period (1917-1944) than it has been during the current warming period (1976-present).

Figure 1

Mid-April 2012 SST Anomaly Update

GLOBAL

The weekly global sea surface temperature anomalies are wiggling their way upwards in response to ebbing of the La Niña, and they are now at about 0.12 deg C.

Global SST Anomalies – Short-Term

IPCC Models vs Observations – Land Surface Temperature Anomalies for the Last 30 Years on a Regional Basis

In two posts about a year ago and more recently in my book, we compared the satellite-based sea surface temperature anomalies and CMIP3-based climate model simulations on an ocean-basin basis. Refer to Satellite-Era Sea Surface Temperature Anomalies Versus IPCC Hindcasts/Projections Part 1 and Part 2. The models performed—I’m looking for an appropriate word—pathetically. The easiest way to portray how poorly the models matched the global satellite-based sea surface temperature data is with Figure 9from Part 1 of those posts. On a zonal-mean (latitudinal) basis, that graph compares the linear trends of the simulations of sea surface temperature (CMIP3 multi-model mean) to the observed (Reynolds OI.v2 SST anomaly data) trends. The modelers appear not to understand how or why the sea surface temperatures have warmed over the past 30 years. Refer to the discussion of that illustration under the heading of SST ANOMALY TREND COMPARISONS ON ZONAL MEAN BASES.

And when one considers that land surface temperatures are in part a product of sea surface temperatures, one wonders how climate scientists/modelers could ever attempt to simulate land surface temperatures when the oceans are modeled so poorly.

The model-simulated trends of land surface temperature are shown in Figure 1 on a zonal-mean basis. While the trends of the model-mean of land surface temperature simulations don’t match the observed rates of warming from pole to pole, they are much better than the model depiction of sea surface temperature anomaly trends. The area with the greatest divergence is the Arctic—yet we hear so much about how the models are able to simulate polar amplification.

Figure 1

Missing gif Animations

The Image Hosting site I had used for large .gif animations has folded its tent or dropped the hosting of large files. One way or the other, the following gif animations are no longer playing in the posts where they reside. So I’ve uploaded them to WordPress and replaced the ones that aren’t working. If you should come across any others, please advise me in a comment on that thread.

Thanks

Note: The following animations are large (11MB total) so they will take some time to load.

And The Current Winner Is…

The Met Office released its global HadCRUT4 land plus sea surface DATA recently. The HadCRUT4 dataset was first presented in the Morice et al (2012) paper Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 dataset.

In the race to have the highest trend since 1976, does GISS LOTI still hold its lead, or has the new HadCRUT4 data overtaken GISS?

And the current winner is…

Preview of HadCRUT4 Global Surface Temperature Anomaly Data

UPDATE:  I just discovered that the HADCRUT4 data has been released.  I’ll be posting on it shortly. See here.

HHHHHHHHHHHHHHHHHHHHHHHH

A weighted average of the two new global land surface (CruTEM4) and sea surface (HADSST3) temperature anomaly datasets was used in my recent post as a preview of the new HadCRUT4 dataset. The weighting was the same used in HadCRUT3. See note below. That post compared the approximated HadCRUT4 data to the multi-model mean of the CMIP5 models presently being prepared for the IPCC’s 5^thAssessment Report (AR5). In this post, we’ll compare the approximated HadCRUT4 data to the other global surface temperature products available from GISS, Hadley Centre, and NCDC.

The HadCRUT4 dataset was presented in the Morice et al (2012) paper Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 dataset. Unfortunately, the Hadley Centre has not yet released its new HadCRUT4 land plus sea surface temperature data through its HadCRUT4 webpagein a form that’s convenient to use. The other unfortunate fact is HADSST3 has not yet been updated from its end date of 2006. But we’ll take a look at the dataset anyway, just to get an idea of where HadCRUT4 will fit in with the other surface temperature products.

Note: The CruTEM4 data is available at the Hadley Centre’s webpage here, specifically the annual data here, and the HADSST3 data is available through the KNMI Climate Explorer here. The weighting used to approximate the HadCRUT4 data that follows was 28.92% land surface temperature and 71.08% sea surface temperature. It is assumed for this post that the HadCRUT4 dataset will have the same land to sea ratio as the HadCRUT3 dataset.

The approximated HadCRUT4 data, on an annual basis, is compared to the global GISS Land-Ocean Temperature Index (LOTI), HadCRUT3, and NCDC Land Plus Sea Surface Temperature datasets from 1901 to 2006 in Figure 1. Also shown are the corresponding linear trends. Since 1901, and with the HADSST3-based end year of 2006, the new HadCRUT4 data should have a slightly lower trend than the HadCRUT3 data it replaces. The HadCRUT4 data long-term trend should fall between the NCDC and GISS LOTI data.

Figure 1

The Impact of GISS Replacing Sea Surface Temperature Data With Land Surface Temperature Data

This post is a continuation of the May 31, 2010 post titled GISS Deletes Arctic And Southern Ocean Sea Surface Temperature Data. Refer also to the comments on the thread of the cross post at WattsUpWithThat. Between publishing that post and now, on its Climate Explorer, KNMI has added land and ocean masking to its features for the GISS Land-Ocean Temperature Index (LOTI) data. (GISS LOTI is listed on their Select a monthly field – Observations webpage under the TEMPERATURE field as “1880-now anomalies: GISS”, using the 1200km button.) That masking allows us to present the impacts differently. We’ll be looking at the period starting in January 1982 because GISS transitions from HADISST to Reynolds OI.v2 sea surface temperature data during 1981. The GISS LOTI data, as of this writing, only extends to October 2011 at the KNMI Climate Explorer, so that’s why the data ends then.

The topic of discussion is the impact of GISS deleting sea surface temperature data in areas of the Arctic and Southern Oceans with seasonal and permanent sea ice and their replacing that sea surface temperature data with land surface temperature data, which naturally warms at a much higher rate. Figure 1 is a graph that illustrates the linear trends of the two relevant sea surface temperature datasets for the period of January 1982 to October 2011 on a zonal mean basis. It compares Reynolds OI.v2 sea surface temperature data, which is the source sea surface temperature data for the GISS LOTI product during that period, and the GISS LOTI data with the land surface temperatures masked. That is, it shows the source sea surface temperature data (Reynolds OI.v2 – in blue) and the sea surface temperature data after GISS has deleted the sea surface temperature data in areas of sea ice and replaced it with land surface temperature data (in purple). The methods used by GISS significantly alter the sea surface temperature data north of 50N and south of 50S.

Figure 1

March 2012 Sea Surface Temperature (SST) Anomaly Update – A New Look

THE ADDITION OF CMIP3 (IPCC AR4) HINDCASTS/PROJECTIONS TO GRAPHS

I’ve added a new feature to the graphs of the monthly sea surface temperature updates on a trial basis, and it is the multi-model mean of the CMIP3 hindcasts/projections for sea surface temperatures, presenting them in comparisons to the observed data. The observed and modeled linear trends are also shown. This is done for the global, hemispheric and ocean basin sea surface temperature anomalies. As you will recall, CMIP3 is the climate model archive used by the IPCC for its 4^thAssessment Report (AR4). I haven’t decided whether to include the model simulation data and trends in each monthly update or to include them only on a quarterly basis; that is, for the monthly updates in March, June, September, and December. I’m leaning toward providing them on a quarterly basis, not only because they’re a lot of extra work, but the model data also detracts from the data update itself. On the other hand, it would likely be good to provide the monthly reminder of just how poorly the models simulate global and regional sea surface temperatures.

The multi-model mean and linear trends of the CMIP3 model simulation data definitely make the graphs busier. Refer to the Global sea surface temperature anomaly graph. We added the smoothed data (13-month running-average filter) on a trial basis a few months ago, and readers requested that we keep the smoothed data. On some occasions, the trend lines may obscure the most recent changes in the dataset. Let me know whether we should include the additional climate model data in each monthly update or if you would prefer it on a quarterly basis.

(1) Global Sea Surface Temperature Anomalies

Monthly Change = -0.016 deg C