Sea Ice News Volume 5 #2 – NOAA forecasts above normal Arctic ice extent for summer 2014

Nick Stokes says: May 24, 2014 at 12:59 pm
First, that’s a forecast made in July. The lead post gives their May forecast, which looks like about 5.4 M sq km; actually a closer forecast, but definitely high. Their forecast in 2012 was similar, and over by more than 2 M sq km.
I agree on both accounts, but it still invalidates the statement that CFSv2 model “prediction is biased high”. It was biased high based upon hindcasts, but is has made two sets of predictions thus far with one biased high and one biased low. It will be interesting to see what occurs this year.
Also interesting is that the CFSv2 is highest of the Nino forecasts:
[caption id="" align="alignnone" width="632"] National Oceanic & Atmospheric Administration (NOAA) – Climate Prediction Center – Click the pic to view at source[/caption]
Second, if it’s Wang’s submitted forecast, it is almost certainly bias-adjusted. Their paper explains:
“Therefore in the CFSv2, when the sea ice predictions are used for practical applications, bias correction is necessary. The bias can be obtained from the hindcast data for the period 1982– 2010, which are available from NCDC.”
They are definitely bias adjusted, I am just not sure if the “bias adjustment [is] based on 1997 ‐ 2010 hindcasts”;
http://www.arctic.noaa.gov/SIF/docs/Seasonal_Sea_Ice_Forecasting_from_CFS2-Wanqui_Wang.pdf
or “obtained from the hindcast data for the period 1982- 2010” as you and the passage below suggest:
“Sea ice Sea ice prediction is challenging and relatively new in the context of seasonal climate prediction models. Sea ice can form or melt and can move with wind and/or ocean current. Sea ice interacts with both the air above and the ocean beneath and it is influenced by, and has an impact on, the air and ocean conditions. The CFSv2 sea ice component includes a dynamic/thermodynamic sea ice model and a simple assimilation scheme, which are described in detail in Saha et al. (2010). One of the most important developments in CFSv2, compared to CFSv1, is the extension of the CFS ocean domain to the global high latitudes and the incorporation of a sea ice component.
The initial condition (IC) for ice in the CFSv2 hindcasts is from CFSR as described in Saha et al. (2010). For sea ice thickness, there are no data available for assimilation, and we suspect there is a significant bias of sea ice thickness in the CFSv2 model that causes the sea ice to be too thick in the IC. For the sea ice prediction, sea ice appears too thick and certainly too extensive in the spring and summer. Figure 10 shows themean September sea ice concentration from 1982 to 2010, and the bias in the predicted mean condition at lead times of 1 month (15 August IC), 3 months (15 June IC), and 6 months (15 March IC). The model shows a consistent high bias in its forecasts of September ice extent. The corresponding predicted model variability at the three different lead times is shown in Fig. 11. The variability from the model prediction is underestimated near the mean September ice pack and overestimated outside the observed mean September ice pack. Although the CFSv2 captured the observed seasonal cycle, long-term trend, and interannual variability to some extent, large errors exist in its representation of the observed mean state and anomalies, as shown in Figs. 10 and 11. Therefore in the CFSv2, when the sea ice predictions are used for practical applications, bias correction is necessary. The bias can be obtained from the hindcast data for the period 1982- 2010, which are available from NCDC.
In spite of the above reported shortcomings, when the model was used for the prediction of the September minimum sea ice extent organized by the Study of Environmental Arctic Change (SEARCH) during 2009 and 2011, CFSv2 (with bias correction applied) was among the best prediction models. In the future we plan to assimilate the sea ice thickness data into the CFS assuming that would reduce the bias and improve the sea ice prediction.
http://www.tmcnet.com/usubmit/2014/04/01/7755461.htm
Here’s a good visual of the CFS model;
[caption id="" align="alignnone" width="632"] National Oceanic & Atmospheric Administration (NOAA) – Climate Prediction Center – Click the pic to view at source[/caption]
which can be found here:
http://www.nws.noaa.gov/ost/climate/STIP/seaice.htm
I think this sums it up well, i.e.: “It is easy to identify some large errors in sea ice coverage and variability and it is obvious that a lot more work needs to be done in this area of sea ice modeling.”
http://www.tmcnet.com/usubmit/2014/04/01/7755461.htm

Nick Stokes says: May 24, 2014 at 5:49 pm
You are quoting, I think, bias-adjusted predictions. Yes, they should, by definition, not be biased high. But we don’t know a bias-adjusted CFSv2 prediction for 2014. We only know the raw model output, which Wang et al tell us needs bias adjustment for practical prediction, else it will be too high.
You could be right, but I interpret from “III. Bias adjustment based on 1997 ‐ 2010 hindcasts” on slide one of this pdf;
http://www.arctic.noaa.gov/SIF/docs/Seasonal_Sea_Ice_Forecasting_from_CFS2-Wanqui_Wang.pdf
that the Sea ice extent and concentration forecasts on this page;
http://www.cpc.ncep.noaa.gov/products/CFSv2/CFSv2seasonal.shtml
have already had bias adjustments applied. I don’t understand why they would post data that is not useful for “practical purposes”, since as you noted “when the sea ice predictions are used for practical applications, bias correction is necessary.”
It will be interesting to see their SEARCH prediction for 2014. I predict it will be a lot lower than 6.5 M sq km.
I have no way to guess whether their prediction will change, but I will predict that the prediction they submit to SEARCH will be the same as the prediction posted on the CFSv2 Product page:
http://www.cpc.ncep.noaa.gov/products/CFSv2/CFSv2seasonal.shtml
Also for reference, here is CFSv2’s Concentration Forecast for September 2014:
[caption id="" align="alignnone" width="632"] National Oceanic & Atmospheric Administration (NOAA) – Climate Prediction Center – Click the pic to view at source[/caption]