(1) Arctic Sea Ice Predictability,

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1 (1) Arctic Sea Ice Predictability, (2) It s Long-term Loss and Implications for Ocean Conditions Marika Holland, NCAR With contributions from: David Bailey, Alex Jahn, Jennifer Kay, Laura Landrum, Steve Vavrus, Ed Blanchard-Wrigglesworth, Cecilia Bitz Session #3 Theoretical and Modeling Work NCAR is sponsored by the National Science Foundation

2 Workshop goals to address a number of questions including: What are the current opportunities and limitations for using Arctic sea ice predictions to assess the risk of temperature/precipitation anomalies and extreme weather events over northern continents?

3 Workshop goals to address a number of questions including: What are the current opportunities and limitations for using Arctic sea ice predictions to assess the risk of temperature/precipitation anomalies and extreme weather events over northern continents? What is the potential to predict Arctic Sea Ice (on ~Seasonal Timescales)?

4 Seasonal Ice Prediction an Initial Value Problem (Lorenz, 1963) The predictability characteristics can be dependent on the initial conditions Cold, thick ice covered Arctic could be different than a warm, thin ice covered Arctic (Figure courtesy of Branstator and Teng, 2011)

5 Climate Model Predictability Study September Extent Set 1 Set 2 Set 1 Observations NSIDC Ice Index Set 3 CCSM3 IPCC-AR4 Run Set 2 Initialize runs with identical ice-oceanland conditions from CCSM3 Atmosphere initial state differs slightly Use 3 sets of Jan 1 initial conditions Each ensemble set has ~20 members Run forward 2-years (Holland et al., 2011) Set 3

6 Assessing Predictability Examine how ensemble members diverge over time Compare to the natural variability of the system When these are indistinguishable, predictability is lost

7 Thick initial ice 1970 s ice Arctic Ice Area Potential Predictability PPP = 1-s 2 t(ens)/s 2 (cont) PPP decreases during spring, regained during summer and following winter

8 Thick initial ice January SST PPP 1970 s ice Arctic Ice Area Potential Predictability PPP = 1-s 2 t(ens)/s 2 (cont) PPP decreases during spring, regained during summer and following winter Significant winter predictability - memory in ocean heat content Memory of ice edge location associated with SST predictability Consistent with results from Blanchard- Wrigglesworth et al., 2011

9 Thick initial ice January SST PPP 1970 s ice Arctic Ice Area Potential Predictability PPP = 1-s 2 t(ens)/s 2 (cont) PPP decreases during spring, regained during summer and following winter Significant winter predictability - memory in ocean heat content

10 Thick initial ice 1970 s ice Ice Thickness Memory Correlation: Jan Ice Thickness and Sept Ice Thickness Ice Thickness/Area Coupling Correlation: Sept Ice Thickness and Sept Ice Concentration Arctic Ice Area Potential Predictability PPP = 1-s 2 t(ens)/s 2 (cont) PPP decreases during spring, regained during summer and following winter Significant winter predictability - memory in ocean heat content Significant summer predictability memory in ice thickness and strong ice areathickness coupling

11 Thinner Ice Thick initial ice Thinner initial ice Thinnest initial ice 1970 s ice Arctic Ice Area Potential Predictability PPP = 1-s 2 t(ens)/s 2 (cont) PPP decreases during spring, regained during summer and following winter Significant winter predictability - memory in ocean heat content Significant summer predictability memory in ice thickness and strong ice areathickness coupling Thinnest Ice

12 Thinner Ice Thick initial ice Thinner initial ice Thinnest initial ice 1970 s ice Arctic Ice Area Potential Predictability PPP = 1-s 2 t(ens)/s 2 (cont) PPP decreases during spring, regained during summer and following winter Significant winter predictability - memory in ocean heat content Significant summer predictability memory in ice thickness and strong ice areathickness coupling Thinnest Ice

13 Thinner Ice Thick initial ice Thinner initial ice Thinnest initial ice 1970 s ice Arctic Ice Area Potential Predictability PPP = 1-s 2 t(ens)/s 2 (cont) PPP decreases during spring, regained during summer and following winter Significant winter predictability - memory in ocean temperature Significant summer predictability memory in ice thickness and strong ice areathickness coupling Thinnest Ice Reduced summer predictability in thin ice conditions

14 Predicting Arctic Sea Ice Model studies suggest limited predictability in sea ice area on 1-2 years timescales, particularly in Winter (ocean heat content) Summer (ice thickness), which may decline with thinner ice To realize even a fraction of this predictability requires an adequate observational network Currently unclear what adequate means What spatial coverage, variables, etc. are needed It also requires adequate (and compatible) models Currently unclear what adequate means What model biases impact predictability, what model complexity is required Note though some studies show some predictive skill (e.g. Wang et al., 2013; Sigmond et al., 2013)

15 Sea ice extent Initialized decadal forecasts Arctic ASO ice cover Initialized state not compatible with model climate CCSM4 Simulations exhibit considerable drift after initialization (Courtesy of Haiyan Teng, NCAR)

16 What are the current opportunities and limitations for using Arctic sea ice predictions to assess the risk of temperature/precipitation anomalies and extreme weather events over northern continents? Sea ice predictions have inherent limits and prediction system improvements are needed to realize predictability inherent in the system So even ignoring uncertainties with relation to the influence on extreme weather, there are important limitations

17 What may be the possible implications of more severe loss (and eventually, total loss) of summer Arctic sea ice upon weather patterns at lower latitudes?

18 Simulated September Arctic Sea Ice Extent CMIP3 OBS CMIP5 Stroeve et al., GRL, 2012

19 Sources of Uncertainty in Projections of Future Climate Change Intrinsic climate variability Climate models simulate the statistics of climate, not the events of any particular year Sept Extent Decadal Trends Simulated September Ice Extent Preindustrial Control Observations NSIDC Ice Index CCSM3 IPCC- AR4 Run

20 Ice Loss Influences Ocean Freshwater Budgets Freshwater sources for Arctic Ocean River Runoff Net Precipitation Bering Strait ocean inflow Freshwater sinks for Arctic Ocean Ice export Liquid ocean freshwater transport to North Atlantic

21 Ice Loss Influences Ocean Freshwater Budgets Reductions in solid (ice) transport to North Atlantic Increases in liquid transport to North Atlantic Jahn and Holland, 2013

22 Downstream Implications? Meridional Overturning Circulation (courtesy Greg Holloway)

23 Simulated Future Declines in Deep Water Formation Jahn and Holland, 2013

24 Projected Change in Surface Air Temperature Meehl et al., 2012 North Atlantic circulation changes lead to reduced ocean heat transport and reduced warming Regarding the impact of longer term ice-loss changes on midlatitude weather, these ocean changes need to be considered

25 Conclusions What are the current opportunities and limitations for using Arctic sea ice predictions to assess the risk of temperature/precipitation anomalies and extreme weather events over northern continents? What may be the possible implications of more severe loss (and eventually, total loss) of summer Arctic sea ice upon weather patterns at lower latitudes?

26 Much of the mid-21 st century North Atlantic (and high latitude) surface change can be attributed to summer sea ice loss Landrum et al., submitted

27 Change in SSS

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29 Are Ocean Changes Caused by Ice Loss? Summer Ice Free Arctic (SIFA) simulation performed with: Summer ice loss forced through albedo modifications No greenhouse-gas changes Simulated climate changes are (largely) attributable to ice loss System is internally consistent/conservative Comparison to standard 21 st century runs provides insight on ice-loss impacts Sept Ice Area SIFA Annual Ice Volume RCP8.5 Ice Volume Annual Range Landrum et al., submitted

30 Freshwater Transport

31 Extra Slides

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33 Simulated September Arctic Sea Ice Extent CMIP3 OBS CMIP5 Stroeve et al., GRL, 2012

34 State Predictability Of the First Kind: Initial value problem Of the Second Kind: Boundary value problem Total: Combination of the two Time (Adapted From Brantstator and Teng, 2011)

35 Projected sea ice change Natural variability Range in extent (for example in km 2) Sea ice change Results from a mix of natural & forced change

36 Uncertainty of Arctic sea ice change projections Sources of Uncertainty Internal Model Scenario From CMIP3 Models On <15 years intrinsic variability dominates On 20+ years, model uncertainty dominates On 50+ years, future GHG scenario uncertainty becomes important (Following Hawkins and Sutton, 2009)

37 Influence of initial values and climate forcing on sea ice predictability Ice Volume Predictability Initial values provide (limited) predictability for ~2 yrs At 5+ years, forcing provides predictability Limited predictability at 2-3 years From Blanchard-Wrigglesworth, et al, 2011, GRL

38 Changes in Arctic FW Budgets Multi-model change Increasing P-E over land and ocean Increasing ice melt, causing lower ice (solid) transport Increasing liquid transport to Atlantic Small increase in Bering Strait FW inflow Holland et al., 2007