First-authored peer-reviewed articles
Wyburn-Powell, C. & Jahn, A. Lack of Consensus Within CMIP5 and CMIP6 Large Ensembles on the Effect of the Pacific Ocean on the Timing of an Ice-Free Arctic (in prep.)
The purpose of this study is to better understand the drivers of low-frequency variability of Arctic sea ice. Teasing out the complicated relationships within the climate system takes a large number of examples. Here we use 42 of the latest generation of global climate models to construct a simple linear model based on dominant named climate features to predict regional low-frequency sea ice anomalies at a lead time of 2-20 years. In 2022, these modes of variability happen to be in the phases most conducive to low Arctic sea ice concentration anomalies. Given the context of the longer-term trend of sea ice loss due to global warming, our results suggest accelerated Arctic sea ice loss in the next decade.
Wyburn-Powell, C., Jahn, A., & England, M. (2022). Modeled Interannual Variability of Arctic Sea Ice Cover is Within Observational Uncertainty. Journal of Climate. 35 (20) 3227–3242. doi:10.1175/JCLI-D-21-0958.1
The purpose of this study is to evaluate the historical simulated internal variability of Arctic sea ice in climate models. Determining model realism is important to have confidence in the projected sea ice evolution from these models, but so far only mean state and trends are commonly assessed metrics. Here we assess internal variability with a focus on the interannual variability, which is the dominant timescale for internal variability. We find that, in general, models agree well with observations, but as no model is within observational uncertainty for all months and locations, choosing the right model for a given task is crucial. Further refinement of internal variability realism assessments will require reduced observational uncertainty.
Co-authored peer-reviewed articles
J. E. Lenetsky, A. Jahn, P. Ugrinow, C. Wyburn-Powell, R. Patel, H. Zanowski, Large future changes in the North Water Polynya are most likely if global warming exceeds 2 degrees. (Under review) in Environmental Research letters.
Responsibilities included: cleaning and aggregating data from multiple sources, formulation of definitions and metrics for polynya area, production of initial analysis figures and for a conference poster, mentoring and assisting undergraduate students in their analyses.
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, building on another graduate student's code for reprocessing data and analyses, additionally contributing to the interpretation of results during the writing of the paper.
Recent Conference Presentations
Wyburn-Powell, C., Jahn, A. (2023) Using Machine Learning to Explain Low-Frequency Drivers of Arctic Sea Ice Variability. Poster at Poster at the 22nd Conference on Artificial Intelligence for Environmental Science. Denver, Colorado. January 9th 2023.
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.