Seasonal Arctic sea ice in the NMME Kirstin Harnos, Michelle L’Heureux, Qin Zhang, and Qinghua Ding

Current State of Sea Ice Images courtesy of National Snow Ice Data Center

Current Events: Northwest Passage Images courtesy of National Snow Ice Data Center

Previous Studies: Sea Ice Research & Prediction General Sea Ice extent is decreasing, with trends steepening Initialization Better initial conditions (including thickness) = better skill at longer leads Trend Highest skill from ability to capture trend Multi-model Multi-model ensembles better than individual models

How well does NMME predict sea ice? NMME and Sea Ice How well does NMME predict sea ice? Sea ice extent (SIE) = Total area ≥ 15% concentration Skill metrics: Model Bias Anomaly Correlation Root Mean Square Error Trend and Variability Total SIE Year-to-year SIE 1982-2010 hindcast climatology, 1 to 9 month lead Observations: NASA Bootstrap gridded sea ice concentrations

NMME Sea Ice Contributions only using 16 of the members following past CFSv2 sea ice publications complete hindcast records on the NCAR NMME archive

Climatology

Total SIE Bias Less Ice More Ice [106 km2]

Year-to-Year SIE Root Mean Square Error

Total SIE Anomaly Correlation

Year-to-Year SIE Anomaly Correlation

NMME reduces total SIE bias consequence of large opposite biases in individual models? Y2Y largest errors during fall/winter (SIE minimum) NMME slight improvement during shorter leads in fall/winter Trend dominates ACC values consistent with past studies little to no Y2Y skill beyond 5 months

Trend

The Trend Problem Comparison of linear trends in September sea ice extent for the period 1979–1996 and for 1997–2014. The smoothed nonlinear trend line is calculated using locally weighted scatterplot smoothing. Linear trends are calculated using least-squares regression. Mark C. Serreze, and Julienne Stroeve Phil. Trans. R. Soc. A 2015;373:20140159 © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Observations: Observations:

Observations: -13.4 % per decade

Observations: -13.4 % per decade NMME: -5.7% to -3.9% per decade Blue: NMME Ensemble mean spread

NMME September Root Mean Square Error Observed SIE Anomaly [106 km2] 1982 1985 1988 1991 1994 1998 2001 2004

General: Sea Ice trends non linear and steepening September NMME trends less than observed Increase in September RMSE in most recent years model inability to capture steepening trends? Increase trends in observations = increase variance = increase RMSE

Take Home NMME reduces total SIE bias High skill associated with trend SIE trends are steepening, models need to monitor and adjust to capture changing trends