Kirstin Harnos Michelle LHeureux Qin Zhang and Qinghua Ding Images courtesy of National Snow Ice Data Center Current State of Sea Ice Images courtesy of National Snow Ice Data Center Current Events ID: 636262
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Slide1
Seasonal Arctic sea ice in the NMME
Kirstin Harnos, Michelle L’Heureux, Qin Zhang, and Qinghua DingSlide2Slide3Slide4
Images courtesy of National Snow Ice Data Center
Current State of Sea IceSlide5
Images courtesy of National Snow Ice Data Center
Current Events:
Northwest PassageSlide6
Previous Studies:Sea Ice Research & Prediction
General
Sea Ice extent is decreasing, with trends steepening
InitializationB
etter 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 modelsSlide7
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 Slide8
NMME Sea Ice Contributions
only using 16 of the members following past CFSv2 sea ice publications
complete
hindcast
records on the NCAR NMME archiveSlide9
Climatology Slide10
Total SIE
Bias
[10
6
km
2
]
Less Ice
More IceSlide11
Year-to-Year SIE
Root Mean Square ErrorSlide12
Total SIE
Anomaly CorrelationSlide13
Year-to-Year SIE
Anomaly CorrelationSlide14
NMME reduces total SIE biasconsequence of large opposite biases in individual models?
Y2Y largest errors during fall/winter (SIE minimum)NMME slight improvement during shorter leads in fall/winterTrend dominates ACC values
consistent with past studieslittle to no Y2Y skill beyond 5 monthsSlide15
TrendSlide16
The Trend
P
roblem
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.Slide17
Observations:
Observations: Slide18
Observations: -13.4 % per decade
Observations: - 2.6% per decadeSlide19
Observations: -13.4 % per decade
NMME: -5.7% to -3.9% per decade
Observations: - 2.6% per decade
NMME: -1.8% to -1.2% per decade
Blue: NMME Ensemble mean spreadSlide20
1982
1985
1988
1991
1994
1998
2001
2004
Observed SIE Anomaly
[10
6
km
2
]
NMME September
Root Mean Square Error
NMME
Root Mean Square ErrorSlide21
General: Sea Ice trends non linear and steepening
September NMME trends less than observed
Increase in September RMSE in most recent yearsmodel inability to capture steepening trends?Increase trends in observations = increase variance = increase RMSESlide22
Take Home
NMME reduces total SIE bias
High skill associated with trendSIE trends are steepening, models need to monitor and adjust to capture changing trends