This contains the Jupyter notebooks used to explore and visualize sea ice variability on subseasonal timescales in S2S forecast models. We are especially interested in extreme sea ice loss on day-to-day timescales, as these daily fluctuations in sea ice loss can be very important to the people who live and work in the Arctic, and to the Arctic ecosystem. Since extreme sea ice loss on day-to-day timescales is often associated with large atmospheric circulation anomalies (e.g. Wang et al. (2020), https://doi.org/10.1175/JCLI-D-19-0528.1), we are also interested in the predictability of sea ice during and after these extreme events. This work forms the basis of a manuscript currently in preparation by M.C. McGraw, C.M. Bitz, and E. Blanchard-Wrigglesworth. The main scientific objectives of this work are the following:
- Understand the seasonal and regional variability of extreme sea ice loss on subseasonal timescales; and evaluate how well forecast models can simulate these seasonal and regional variations;
- Determine whether or not sea ice extent is MORE predictable or LESS predictable following an extreme sea ice loss event.
This research is building off of previous work detailed in Wayand et al. (2019), located at: https://doi.org/10.1029/2018GL081565. Wayand et al. evaluated the predictability of pan-Arctic sea ice concentration at subseasonal to seasonal timescales for a variety of forecast models, and found that while forecast skill varied widely from model to model, at least some models exhibit skill at predicting sea ice on subseasonal to seasonal timescales. Thus, we are extending their work to both focus on regional sea ice variability (as different regions of the Arctic can exhibit fairly different behaviors), and we also focus specifically on the predictability of sea ice during extreme sea ice loss events.
For this research, our primary observational data set is the NASA Bootstrap sea ice concentration dataset, available from the National Snow and Ice Data Center (http://nsidc.org). We use sea ice output from several forecast models that were part of the subseasonal-to-seasonal (S2S) forecasting project. All models and the observations have been regridded to the common SIPN grid, and sea ice extent is calculated by summing up the area of all grid cells with a sea ice concentration that exceeds 15%. Wayand et al. [2019] (https://doi.org/10.1029/2018GL081565) discusses the processing and standardization of the forecast model output, and most of their code is located in the ESIO github repository (https://github.com/NicWayand/ESIO). We do not currently plan to upload the sea ice concentration output of the forecast models, as this would be unwieldy, but we may include some intermediate sea ice extent data that has been partially processed and converted to csv. We also encourage interested parties to explore the Sea Ice Prediction Network Portal (https://atmos.uw.edu/sipn/index.html) for more information about sea ice forecasting.
Most of the analysis here is presented as Jupyter notebooks, so that our basic analysis can be explored in a Jupyter environment. This means that the Jupyter notebooks may only show the analysis for a single forecast model, for example. We will eventually include Python scripts that perform all calculations. This work was produced using Python 3.7.3, and most of the analysis was done using the numpy, scipy, xarray, pandas, and sklearn Python packages. Scripts that are primarily intended to plot results are styled plot*.ipynb, and our primary plotting packages are matplotlib and seaborn.