The purpose of this study is to evaluate the historical simulated internal variability of Arctic sea ice in climate models. Determining model realism is our best tool to have confidence in the projected internal variability of these models. Internal variability, and therefore the spatial and temporal distribution of sea ice conditions, is essential to a range of stakeholders. We find that, in general, models agree well with observations, but as no model is within observational uncertainty for all months and all locations, model selection is highly important. Furthermore, we highlight that model consistency is often achieved due to large observational uncertainty.
Wyburn-Powell, C., Jahn, A., England, M. (Submitted) Modeled Internal Variability of Arctic Sea Ice Cover within Observational Uncertainty
Lenaerts, J. T. M., Camron, M. D., Wyburn-Powell, C. R. & Kay, J. E. (2020). Present-day and future Greenland Ice Sheet precipitation frequency from CloudSat observations and the Community Earth System Model. The Cryosphere. 14, 2253–2265, doi:10.5194/tc-14-2253-2020.
Responsibilities included the production of figures, data analysis, and contributing to the interpretation of results.
Recent Conference Presentations
Wyburn-Powell, C., Jahn, A. and England, M. 2021. Modeled Internal Variability of Arctic Sea Ice is Within Observational Uncertainty. Poster at The University of Colorado Earth and Space Science Symposium, Boulder, Colorado. December 3rd 2021.
Wyburn-Powell, C., Jahn, A. and England, M. 2021. Realism of Simulated Internal Variability in Arctic Sea Ice. Talk at the American Meteorological Society 16th Conference on Polar Meteorology and Oceanography, Online. June 1st 2021.
An internal UK Met Office report produced during a summer internship (June-August 2018). Analysis of radiosonde descent data from 6 diverse sites and collaboration with ECMWF for background model comparisons. Findings showed a vast majority of descent data has sufficiently low biases to warrant further investigation for assimilation into operational model analyses.
Responsibilities included running Python code for heuristic model outputs and writing analyses of the data outputs.