Chevallier, Matthieu

Cruz-García, Rubén

Guemas, Virginie

Massonnet, François

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An assessment of regional sea ice predictability in the ArcticoceanRubén Cruz-García∗, Virginie Guemas, Matthieu Chevallier, François Massonnet∗Barcelona Supercomputing Center (BSC-ES), C/ Jordi Girona 29, 08034, Barcelona, Spain.Email: ruben.cruzgarcia@bsc.esAbstract—Arctic sea ice plays a central role in the Earth’s climate.Changes in the sea ice on seasonal-to-interannual timescalesimpact ecosystems, populations and a growing number of stake-holders. A prerequisite for achieving better sea ice predictions isa better understanding of the underlying mechanisms of sea icepredictability. Previous studies have shown that sea ice predictabilitydepends on the predictand (area, extent, volume), region, and theinitial and target dates. Here we investigate seasonal-to-interannualsea ice predictability in so-called "perfect model" 3-year-longexperiments run with the EC-Earth 2.3 climate model initialized inearly July. Consistent with previous studies, robust mechanisms forreemergence are highlighted, i.e. increases in the autocorrelationof sea ice properties after an initial loss. We find that Arctic regionscan be classified according to three distinct regimes. The centralArctic drives most of the pan-Arctic sea ice volume persistence. Inperipheral seas, we find trivial predictability for the sea ice areain winter but low predictability throughout the rest of the year,due to the particularly unpredictable sea ice edge location. TheLabrador Sea stands out among the considered regions, with seaice predictability extending up to 1.5 years if the oceanic conditionsupstream are known.I. METHODOLOGYWe used a 300-year long present day control experiment underperpetual 2005 forcing (ControlRun). This single-member experimentprovides the initial conditions used to perform a set of idealizedclimate prediction experiments initialized from July 1st (IdealPred).The predictions last 3 years and consist of 8 members, each ofthem with slightly different perturbations of the initial sea surfacetemperature (SST; 10−4 K magnitude).For evaluating the predictability we consider the prognostic poten-tial predictability (PPP hereafter). It compares the ensemble spreadwith an estimation of the amplitude of the natural variability of thesystem based on the standard deviation of the control simulation, andaddresses the initial value predictability. A PPP value of 1 wouldmean that we have a perfectly predictable system. Predictabilityis estimated both in a prognostic (PPP) and diagnostic (laggedControlRun properties) way. The prognostic approach suffers frominsufficient sampling, in contrast with the long control simulation,that can be used to supply that problem.Breaking down the analysis into sectors is essential since the pan-Arctic sea ice extent (SIE), area (SIA) and volume (SIV) integratea large variety of regions which are regulated by different physicalmechanisms. Thus, predictability was investigated for each Arcticsea (Fig. 1). Lagged SIE autocorrelation for each month against leadtime shows the September to September correlation reemergence(leadtime 12), consistent with Blanchard et al. (AMS, 2011) mecha-nism from one summer to the next. The melt-to-freeze mechanism ispresent in July (leadtime 3). The SIV memory regime is characterizedby its vast persistence for all start months.Figure 1: Map of the Arc-tic seas as defined in thisstudy. The black lines in-dicate the sections used forthe calculation of the Atlanticheat transport into the Arctic(Fram Strait plus Barents SeaOpening). The GIN region isformed by the Greenland, Ice-landic and Norwegian seas.Figure 2: Potentialpredictability of the totalArctic SIE (blue), SIA(red) and SIV (black)measured with the PPP ofIdealPred using the naturalvariability of ControlRun asa reference. Dots indicatesignificant values at the 95 %level, estimated by Fisher’stest. September and Marchare marked by thin grayvertical lines.II. PAN-ARCTIC SEA ICEThe melt-to-freeze mode is not only present in the lagged correla-tions (not shown), but it is also a feature in the predictions initializedin July (Fig. 2).The long SIV predictability agrees with the lagged correlations(not shown). This persistence comes almost entirely from the centralArctic SIV memory, as can be checked when comparing the laggedcorrelation of central and pan-Arctic SIV (Fig. 2; blue and blacklines).Summer-to-summer memory reemergence has its origin in thesummer SIT memory (from the central Arctic). Over three continuousyears, the central SIV and SIE are synchronously correlated inSeptember (not shown).III. REGIONAL ARCTIC SEA ICEIn the Barents Sea, peaks of reemergence occur the second andthird summer (not shown). Synchronous correlation between the SSTand the SIE (Fig. 3, red line) reveals that SST is a source ofSIE predictability in December. Correlation between the gridpointSST in December and SIE from December to February confirmsthis timeseries. The SST during the previous spring also providespredictability to the December SIE (not shown).The PPP of the SIE in the interior basins (e.g. the CanadianArchipelago; Fig. 4) saturates in winter because of the extremely5th BSC Severo Ochoa Doctoral Symposium26Figure 3: The persistenceof the SIE (blue), theSST (green), OHC (0-300 mdepth, yellow) for the BarentsSea. In red, the synchronouscorrelation between the SSTand the SIE. Correlationswere calculated using theControlRun during the threesubsequent years. The dotsrepresent significant valuesat the 95% level as estimatedfrom a one-sided student-Tdistribution.Figure 4: As Fig. 2 for theCanadian Archipelago.low sea ice variability. SIA PPP differs from SIE signal because itsvariability is larger in winter, with non-fully covered ice regions. Inmost of central regions SIV is potentially predictable up to one yearbefore.Backward trajectories from the Labrador Sea reveal that the watermasses origin is the Irminger Sea, and the North Atlantic Ocean ina longer term (Fig. 5a-b). The Irminger Sea SST and ocean heatcontent (OHC) at the moment of the initialization and the LabradorSea SIE are significantly anticorrelated from February to July the twofirst years, matching exactly the time when the PPP reemergence inthe Labrador Sea occurs (Fig. 5c).IV. CONCLUSIONS• Pan–Arctic SIE experiences melt-to-freeze reemergence bothin the prognostic ensemble potential predictability and in thecontrol run lagged correlations. The SIV shows greater pre-dictability, attributable to the long-lasting persistence of the SITin the central Arctic SIT.• The summer-to-summer reemergence of the PPP of pan-ArcticSIE is due to the persistence of SIT anomalies in the centralArctic.• In the peripheral seas of the Atlantic Sector, significantly highPPP values over 1 year are driven by the persistence of localoceanic thermal anomalies (SST and OHC).• In the Labrador Sea, which is ice-free in July, the PPP peaksbetween January and April as result of the advection of oceantemperature anomalies from the Irminger Sea and the EasternNorth Atlantic Ocean.• In the interior Arctic seas, winter SIE potential predictability istrivial due to complete ice coverage. No significant predictabilitywas found for the SIA. In contrast, the SIV has a longerpredictability in this set of seas as a result of the long SITpersistence.(a)(b)(c)Figure 5: Map of the backward trajectories followed by water massestravelling from different locations in the Labrador Sea from (a) thefirst and (b) the second February until the first July. Each leadtime is marked with a dot, while the initial positions (correspondingFebruary) are marked with bigger dots. (c) Correlation betweenthe Labrador Sea SIE and the Irminger SST (in blue) and theIrminger OHC (0-300 m depth; in black) the first July for theControlRun during the three following years. The dots representthe significant values at the 95% level estimated from a one-sidedstudent-T distribution. The vertical grey lines represent the monthsof February. The SST and OHC were integrated for the correspondingarea in Fig. 1.Rubén Cruz-García. Rubén studied his bachelorin Environmental Sciences at University of Murcia,where he discovered his passion for investigating theclimate change. After that, he began a Master degreein Geophysics and Meteorology at University ofGranada. He started his PhD project in the ClimatePrediction Group at the Earth Sciences Departmentof the Barcelona Supercomputing Center in October2015. His work focuses on assessing the predictabil-ity and the prediction skill of the Arctic sea iceconditions at the regional scale using two state-of-the-art dynamical models, EC-Earth and CNRM-CM.5th BSC Severo Ochoa Doctoral Symposium27

An assessment of regional sea ice predictability in the Arctic ocean

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An assessment of regional sea ice predictability in the Arctic ocean

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