Model-Data Comparison: Hemispheric Sea Ice Area

I discovered this climate model failure a while ago, but haven’t published a post about it because, if I were to compare the modeled and observed sea ice area for each hemisphere, I would need to make too many approximations and assumptions. The reasons: The NSIDC sea ice area data through the KNMI Climate Explorer is presented in millions of square kilometers, while the CMIP5-archived model outputs there are presented in the fraction of sea ice area—assumedly a fraction of the ocean area for the input coordinates.

I decided to take a simpler approach with this post—to show whether the models simulate a gain or loss in each hemisphere.

That is, we know the oceans have been losing sea ice in the Arctic since November 1978, but gaining it around Antarctica. See Figure 1.

Figure 1

Then there are the oodles of climate models stored in the CMIP5 archive. They’re the models being used by the IPCC for the upcoming 5^th Assessment Report. Would you like to guess whether they show the Northern and Southern Hemispheres should have gained or lost sea ice area over the same time period?

The multi-model ensemble mean of their outputs indicate, if sea ice area were dependent on the increased emissions of manmade greenhouse gases, the Southern Ocean surrounding Antarctica should have lost sea ice from November 1978 to May 2013. See Figure 2.

Figure 2

Well at least the models were right about the sea ice loss in the Northern Hemisphere. Too bad for the modelers that our planet also has a Southern Hemsiphere.

We could have guessed the models simulated a loss of sea ice around Antarctica based on their simulation of the sea surface temperatures in the Southern Ocean. As illustrated in the most recent model-data comparison of sea surface temperatures, here, sea surface temperatures in the Southern Ocean have cooled, Figure 3, while the models say they should have warmed.

Figure 3

STANDARD BLURB ABOUT THE USE OF THE MODEL MEAN

We’ve published numerous posts that include model-data comparisons. If history repeats itself, proponents of manmade global warming will complain in comments that I’ve only presented the model mean in the above graphs and not the full ensemble. In an effort to suppress their need to complain once again, I’ve borrowed parts of the discussion from the post Blog Memo to John Hockenberry Regarding PBS Report “Climate of Doubt”.

The model mean provides the best representation of the manmade greenhouse gas-driven scenario—not the individual model runs, which contain noise created by the models. For this, I’ll provide two references:

The first is a comment made by Gavin Schmidt (climatologist and climate modeler at the NASA Goddard Institute for Space Studies—GISS). He is one of the contributors to the website RealClimate. The following quotes are from the thread of the RealClimate post Decadal predictions. At comment 49, dated 30 Sep 2009 at 6:18 AM, a blogger posed this question:

If a single simulation is not a good predictor of reality how can the average of many simulations, each of which is a poor predictor of reality, be a better predictor, or indeed claim to have any residual of reality?

Gavin Schmidt replied with a general discussion of models:

Any single realisation can be thought of as being made up of two components – a forced signal and a random realisation of the internal variability (‘noise’). By definition the random component will uncorrelated across different realisations and when you average together many examples you get the forced component (i.e. the ensemble mean).

To paraphrase Gavin Schmidt, we’re not interested in the random component (noise) inherent in the individual simulations; we’re interested in the forced component, which represents the modeler’s best guess of the effects of manmade greenhouse gases on the variable being simulated.

The quote by Gavin Schmidt is supported by a similar statement from the National Center for Atmospheric Research (NCAR). I’ve quoted the following in numerous blog posts and in my recently published ebook. Sometime over the past few months, NCAR elected to remove that educational webpage from its website. Luckily the Wayback Machine has a copy. NCAR wrote on that FAQ webpage that had been part of an introductory discussion about climate models (my boldface):

Averaging over a multi-member ensemble of model climate runs gives a measure of the average model response to the forcings imposed on the model. Unless you are interested in a particular ensemble member where the initial conditions make a difference in your work, averaging of several ensemble members will give you best representation of a scenario.

In summary, we are definitely not interested in the models’ internally created noise, and we are not interested in the results of individual responses of ensemble members to initial conditions. So, in the graphs, we exclude the visual noise of the individual ensemble members and present only the model mean, because the model mean is the best representation of how the models are programmed and tuned to respond to manmade greenhouse gases.

CLOSING

Just add sea ice onto the growing list of variables that are simulated poorly by the IPCC’s climate models. Over the past few months, we’ve illustrated and discussed that the climate models stored in the CMIP5 archive for the upcoming 5^th Assessment Report (AR5) cannot simulate observed:

Global Precipitation

Satellite-Era Sea Surface Temperatures

Global Surface Temperatures (Land+Ocean) Since 1880

And in an upcoming post, we’ll illustrate how poorly the models simulate daily maximum and minimum temperatures and the difference between them, the diurnal temperature range. I should be publishing that post within the next week.