us CLIVARU.S. CLIVAR

U.S. CLIMATE VARIABILITY AND PREDICTABILITY (CLIVAR)

1717 Pennsylvania Avenue, NW, Suite 250, Washington, DC 20006• www.usclivar.org

November 2010, Vol. 8, No. 2

High Latitude ClimateVariability: a Role for

CLIVAR

by David M. Legler, Director

The high-latitude regions of the

earth are undergoing enor-

mous changes (see

http://www.climatescience.gov/Libra

ry/sap/sap1-2/final-report/). In the

Arctic, this past year marks the third

lowest Arctic sea ice extent in the

satellite record. The Southern

Ocean is another key region where

remarkable changes are being noted

in (just to name a few) ocean tem-

perature as well as ocean and

atmospheric circulation patterns. At

the U.S. CLIVAR Summit meeting in

July we heard about the changes in

the Polar regions as well as relevant

climate science questions, research

challenges and needs, and where

new research communities were

addressing some of them. More

importantly, we discussed some

potential research gaps and oppor-

tunities that U.S. CLIVAR could help

address with its community of

researchers.

In this issue we present two per-

spectives of the range of climate

Continued on Page Two

VARIATIONS

Seasonal Prediction of Arctic Sea IceCoverage ...................................................1New US CLIVAR Chair ...........................4Vision for Climate Variability Research inSouthern Ocean-Ice-Atmosphere Systm 5Calendar ....................................................8

IN THIS ISSUE

The decline in summer sea icein the Arctic is much in thenews and has piqued theinterest of many people on

both sides of the increasingly vocaldebate about global climate change.The dramatic minimum in the sea iceextent of 2007 and the continued verylow summer sea ice extents, includingthe near-record low of September thisyear, are easily grasped and vivid indi-cators of rapid changes in the farNorth. Such drastic changes in sea icehave large influences on naturalecosystems as well as the peoples ofthe Arctic who face considerablechanges in subsistence hunting andfishing practices, commercial fishing,shipping routes, resource extraction,tourist visits, and military presence.

As summer sea ice has declined,the year-to-year variability hasincreased markedly, leading toincreased interest in predicting the stateof the summer ice pack on seasonaland longer timescales. Anticipating thefuture of Arctic sea ice raises a set ofinteresting questions. What are theprospects for sea ice prediction on sea-sonal to decadal time scales? Whatseasons and regions show the mostpromise for accurate predictions?What are the most promising tech-niques and how can we know howaccurate they are? And if skillful pre-dictions are possible, how should theybe expressed and who would find themthe most useful? Sea ice 101

Sea ice coverage changes as aresult of both thermodynamic growth

or melt of the ice and dynamic stressesfrom the air and the ocean that pushand deform it. Variability in both typesof forcing are important in determiningthe evolution of the ice pack. The ther-modynamic forcing determines the sur-face energy balance and is dominatedby the long and short wave radiativefluxes. In the winter, the net loss ofheat through longwave radiation domi-nates, while in the summer theabsorbed solar flux does. Changes ineither of these large terms in the sur-face energy balance equation have adirect impact on the ice thickness; theyare also the root causes of two impor-tant feedbacks, the positive ice-albedofeedback in the summer and the nega-tive thin-ice-growth feedback in thewinter. The turbulent heat flux is lessimportant because the air is often innear equilibrium with the surfaceexcept over thin ice or open leadswhere the sensible and latent heat flux-es are substantial, often exceeding 200W m–2 in the winter. Changes in theabsorbed solar flux are dominated bychanges in the surface albedo as icemelts or as the date of surface meltonset advances.

In addition, dynamic forces such asthe winds and the currentsmust also beconsidered. Ocean tilt is of lesserimportance. The ice moves at a meanspeed of about 6 km / day, but some-times it can travel much faster.Changes in the winds can have largeimpacts on the ice extent as the ice ispushed from one location to anotherwithin the Arctic Ocean or as iceexport, primarily through Fram Strait,is accelerated or reduced.

Seasonal Predictions of Arctic SeaIce Coverage

Ron Lindsay, University of Washington

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U.S. CLIVARresearch challenges tied to the Polar

regions. These articles, and some fol-

low-up planning, are helping US CLI-

VAR discern what it can, and should

do, to stimulate and coordinate

research addressing knowledge gaps

on the variability (e.g. how is it

changing) and predictability (e.g.

important mechanisms?) of the cou-

pled climate system, especially with

regards to ties between the high- and

lower-latitudes.

Since our last newsletter, there

have been some key leadership

changes in CLIVAR. A warm wel-

come to Dr Bob Molinari, new direc-

tor of the International CLIVAR

Project Office. Bob took over in

October from Howard Cattle who

ably lead CLIVAR for more than 8

years.

Closer to home, we are excited to

introduce Lisa Goddard, the new

chair of the US CLIVAR SSC (see

related article). Lisa has published

extensively on climate predictability

and forecast science and has been a

member of numerous national and

international advisory panels/com-

mittees. She is also a member of the

International CLIVAR SSG. We are

very excited to have Lisa on board!

Arctic Oscillation (AO), the NorthAtlantic Oscillation (NAO), or thePacific Decadal Oscillation (PDO), butthe correlations are much smaller thanthose based on the ice conditions.

The inherent predictability of Arcticsea ice on seasonal time scales wasinvestigated by Holland et al. (2010).Running a series of ensemble experi-ments using the Community ClimateSystem Model (CCSM) with identicalinitial ice conditions they determinedthat sea ice area exhibits predictabilityfrom January for the first summer andfor winter conditions in the next year.Comparing experiments initialized withdifferent mean ice conditions indicatesthat ice area in a thicker sea ice regime

Page 2

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Published three times per yearU.S. CLIVAR Office1717 Pennsylvania Ave., NWSuite 250, Washington, DC 20006usco@usclivar.org

Staff: Dr. David M. Legler, EditorCathy Stephens,Assistant Editor and Staff Writer

© 2010 U.S. CLIVAR Permission to use any scientific material (text and fig -ures) published in this Newsletter should be obtainedfrom the respective authors. Reference to newslettermaterials should appear as follows: AUTHORS, year.Title, U.S. CLIVAR Newsletter, No. pp. (unpublishedmanuscript).

This newsletter is supported through contributions tothe U.S. CLIVAR Office by NASA, NOAA—ClimateProgram Office, and NSF.

Continued from Page One

In addition to the thermodynamicand dynamic forcings, the initial thick-ness of the ice is a key factor for predict-ing the ice extent. The mean ice thick-ness at the end of winter is 2–4 m in theinterior of the pack and less at the edges.Some locations have much thicker ice,particularly north of the CanadianArchipelago, where it can be 6 m thickor more. But just as important as themean thickness, the relative amounts ofthin and thick ice (the thickness distribu-tion) is important because the thin ice ismore easily removed by melt than thethick ice. In recent decades strongtrends in the ice area, extent, and vol-ume have been observed. Figure 1shows how these quantities havechanged since 1980 based on retrospec-

Figure 1. Annual mean ice thickness in the Arctic

Ocean in May and September (top) and the

September fractional coverage of the Arctic Ocean

for the ice area and ice extent (bottom) from retro-

spective model simulations (Lindsay et al., 2009).

The trend lines for the period 1987–2010 are

shown as dotted lines. The trend lines account for

90% and 86% respectively of the variance for the

May and September mean ice thickness and 66%

and 58% respectively of the September ice area

and ice extent.

tive model simulations. Thedecline in ice volume appearsto be much more consistentthan for area or extent.Prediction

Because the ice respondsdirectly to the ocean andatmospheric forcing, accurateshort-term predictions of seaice extent depend on accurateestimates of the initial iceconcentration and thicknessas well as accurate weatherpredictions. On monthly toseasonal time scales, accurateweather predictions are chal-lenging, and the initial iceconcentration and thicknessare most important.

The sea ice thickness pro-vides a source of memory forthe system. Figure 2 showsthe lagged correlation of thetotal ice extent in the ArcticOcean in September with theice extent, concentration, orthickness in previous months.Much of the lagged correla-tion is due to the trend in themean thickness, so for sea-sonal ice forecasting it is dif-ficult to obtain a better skillscore than this trend line.Significant correlations alsoexist for atmospheric circula-tion indexes such as the

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generally exhibits higher predictabilityfor a longer period of time. In a thinnersea ice regime, winter ice conditionsprovide little ice area predictive capabil-ity after approximately 1 year. In allregimes, ice thickness (as opposed toarea) has high predictability for at least2 years.

As in many seasonal predictionproblems, there are two basic approach-es, one statistical and one based onnumerical models of the ice–ocean sys-tem. We do not yet know for surewhich approach is the best.

Statistical models use the currentstate of the ice cover or the ocean anduse past statistical relationships to proj-ect the future state of the ice. The cur-rent ice extent or concentration can bemeasured by satellites, and ice thicknessestimates from a model can be used.The age of the ice and the survivabilityof ice of different ages as well as cli-mate indexes such as the AO or thePDO may also be used. The strength ofthe statistical method is that it is readilyimplemented if a sufficiently long andrepresentative sample of the past ice orocean conditions can be obtained totrain the regression model. Neural netmodels have also been used. Thesemethods, however, rely on a statisticallystationary system in which past correla-tions hold in the present. Because theice is changing rapidly, this may not bethe case and large errors in the forecastscan occur (Lindsay et al., 2008).

Seasonal predictions can also bemade with numerical models of theice–ocean system that are initializedwith an accurate hindcast of the system.Because the weather cannot be predict-ed more than one week or two inadvance, an ensemble method isrequired. Ideally the ensemble methodgives a mean expectation and a measureof the uncertainty in the prediction.One approach for estimating futureatmospheric forcing (air temperatures,clouds, and winds) is to use data fromrecent past years to drive the model(Zhang et al., 2008). Figure 3 showsthe mean of the ice thickness inSeptember 2010 for a seven-memberensemble using the hindcast ice condi-

tions from the end of June 2010. Thepredicted ice extent is compared to theobserved extent from this year. Thismethod works well if the recent years’weather is similar to what transpires inthe current year, but if there are largedifferences, the fact that there is nointeraction between the forcing atmos-phere and the ocean or ice surface pre-cludes a large deviation in the ice con-ditions.

The weather in the Arctic respondsto global conditions, so ultimately thebest approach for seasonal predictionsmay be to use a coupled air–ice–oceanglobal model that has been initializedwith estimates of the current state of theice and ocean. In this way the memoryof the ice thickness can be exploitedand the weakness of not including aninteractive atmosphere can be avoided.But although the NCEP ClimateForecast System (CFS) is used to makeensemble forecasts of the global climateout nine months or more, it has notbeen tested and improved for polar con-ditions and the simulated sea ice is notyet a good representation of theobserved ice. CFS predictive skill inthe Arctic is not great. More workneeds to be done to know how to best

initialize a global model with observedice thickness data or ice thickness datafrom a high-resolution retrospectiveice–ocean model.

All of these methods have been usedby different investigators participatingin the SEARCH Sea Ice Outlook projectin which this year 16 groups offeredpredictions from the end of June of theSeptember mean total ice extent (fromthe Sea Ice Index). The project collectsand summarizes all of the predictions.An overview of the predictions for thisyear is found athttp://www.arcus.org/search/seaiceout-look/2010/june. This effort has shownthat a single good forecast does notprove the method, nor a single bad fore-cast disprove it, and that useful fore-casts always include an estimate of theuncertainty.

Ice thickness observations suitablefor evaluating model performance andeventually to initialize model forecastsare not yet readily available. Some pastobservations are available since 1975from submarine transects of the Arcticand from moorings with upward look-ing sonars tethered just below the ice,for which the ice drift creates a long

Figure 2. Lagged squared correlations of different quantities with the total

September ice extent. Prst is persistence (the total ice extent from the earlier

months), IC is the weighted mean of the ice concentration formed from the correla-

tion-weighted-timeseries (CWT; Lindsay et al., 2008), which weights points in the

ice concentration field by the degree to which they are correlated with the

September total ice extent. H is the CWT mean ice thickness, G0.4m is the CWT

area of water and ice less than 0.5 m, and G1.0m the CWT area with water or ice

less than 1 m thick. Persistence drops very quickly as a useful predictor. At a lead

of six months the area of water and ice less than 1 m, G1.0m, is best, but at shorter

lead times the area G0.4m is a little better. At one year lead, about half of the R2

value is due to the strong trends in all of these quantities.

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sample trajectory under the ice.Aircraft measurements using electro-magnetic induction methods are alsoavailable. Satellite-based estimates ofice thickness derived from laser altime-ter measurements of the freeboard ofthe ice and snow are available fromICESat from 2003 to 2009. To continuethese measurements until the launch ofICESAT-2 in approximately 2015, aNASA program called OperationIceBridge will provide periodic airbornelaser altimeter measurements of the icethickness. A new climate data record ofa wide variety of ice thickness estimatescan be found atpsc.apl.uw.edu/sea_ice_cdr

Because the ice is pushed by unpre-dictable winds, the prediction of region-al ice area, extent, or thickness is muchless skillful than for the basin-wide totalextent, which is less sensitive to icemoving from one part of the basin toanother. Yet for field operations a pre-diction for a particular place or regionis much more useful than one for theentire basin. The prediction uncertaintyprinciple applies here: the smaller theregion the greater the uncertainty. Itwill be an additional challenge to devel-op skillful regional forecasts.

Figure 3. September 2010 sea ice thickness predicted by seven individual ensemble

members beginning with the hindcast model thickness from the end of June 2010.

The atmospheric forcings come from the summers of 2003–2009. Ensemble medi-

an ice thickness shown left and standard deviation (SD) of ice thickness shown

right. The black line is the predicted ice extent for September (4.7 x 106 km2) and

the white line is the observed ice extent from 2010 (xxxx x 106 km2). The standard

deviation shows uncertainty in the predicted ice thickness (Zhang et al. 2008).

REFERENCES

Holland, M. M., D. A. Bailey, and S.Vavrus, 2010: Inherent sea ice predictabilityin the rapidly changing Arctic environmentof the Community Climate System Model,version 3. Clim. Dyn., doi:10.1007/s00382-010-0792-4.

Lindsay, R. W., J. Zhang, A. J.Schweiger, and M. A. Steele, 2008: Seasonalpredictions of ice extent in the Arctic Ocean,J. Geophys. Res., 113,doi:10.1029/2007JC004259.

Lindsay, R. W., J. Zhang, A. J.Schweiger, M. A. Steele, and H. Stern, 2009:Arctic sea ice retreat in 2007 follows thin -ning trend. J. Clim., 22, 165-176, doi:10.1175/2008JCLI2521.

Zhang, J., M. Steele, R. Lindsay, A.Schweiger, and J. Morison, 2008: Ensemble1-Year predictions of Arctic sea ice for thespring and summer of 2008, Geophys. Res.Lett., 35, doi:10.1029/2008GL033244.

Welcome Lisa Goddard, new U.S. CLIVAR Chair

U.S. CLIVAR would like to thank Dr. Martin Hoerling (left) for serving

as the U.S. CLIVAR Scientific Steering Committee Chair for the past 3 years.

Prior to serving as chair, Marty was a key member of the U.S. CLIVAR

Predictability, Predictions, and Applications Interface Panel (PPAI). At the

2010 U.S. CLIVAR Summit, Marty handed over the reins of the U.S. CLIVAR

Steering Committee to Dr. Lisa Goddard (right). Lisa is a research scientist at

the International Research Institute for Climate and Society. Lisa has previ-

ously served as a member and co-chair of the PPAI panel from its inception

in 2005, and has been an ardent supporter of CLIVAR for many years. She is

also a member of International CLIVAR’s Scientific Steering Group. While a

membeer of the PPAI panel, Lisa developed and currently oversees a new

national post-doctoral program, the Postdocs Applying Climate Expertise

(PACE), which explicitly links recent climate PhDs with decision making insti-

tutions. Her expertise in improving the quality and content of climate predic-

tions while enhancing society’s capability to understand, anticipate and man-

age the impacts of climate, will be of great value as CLIVAR pursues new

themes of research in these directions.

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Southen Ocean RegionChanges

The variability of the SouthernOcean at various time scaleshas been documented from

observations of hydrography, sea-sur-face height, and direct measurements ofcurrents. The Argo network has dramat-ically increased the total, and important-ly, the seasonal hydrographic coveragein the upper 2000m of the water columnand has helped to provide evidence forsignificant warming and freshening(Gille 2008; Böning et al. 2008; Helmet al. 2008). Bottom water variationshave been observed as well (Aoki et al.2005; Rintoul 2007; Jacobs 2004,Jacobs 2006; Johnson et al. 2008;Fahrbach) and point to large-scalewarming.

Ocean – ice shelf interaction hasbeen linked to ice shelf collapse andfaster-than-expected dynamicalresponse of the ice sheet, with signifi-cant implications for sea-level rise(Rignot and Jacobs 2008). Recentresults suggest that ocean heat input,from upwelling warmer deep waters,will play a significant role in determin-ing the future of the Antarctic ice sheetand therefore future sea-level rise. Icesheet models within climate models arerudimentary at present, and as a result,projections of future sea-level rise arevery uncertain. In IPCC AR4 a majorsource of uncertainty in sea-level riseprediction is dynamic change to icesheets.

Sea-ice is a major factor in theEarth’s albedo. While evidence suggeststhat the sea-ice coverage is retreatingnear the Antarctic Peninsula, it is mar-ginally increasing in the AmundsenBasin. However, in order to link sea-icewith evolving freshwater fluxes under

climate change, it is crucial to determineice thickness changes. Recently, firstestimates of large-scale Antarctic sea icethickness (Worby et al. 2008) have beenmade. Cryospheric satellites are makingmeasurements of circumpolar sea-iceproperties for the first time, but there isa critical need for further in situ valida-tion. First estimates of sea ice forma-tion rates in the open pack, derived fromwinter salinity changes measured byelephant seals with CTD sensors(Charrassin et al. 2008) show promisefor future observational needs.

New insights into the structure,dynamics and variability of theAntarctic Circumpolar Current(ACC)have been obtained showing the ACC toconsist of multiple frontal jets, whichcan be tracked using altimetric SSH.The fact that the detailed structure ofthe ACC can be tracked in altimetryallows the variability of the ACC, andits relationship to SSH changes, to bedetermined (a; Sokolov and Rintoul2007a; Sokolov and Rintoul 2009b;Sallee et al. 2008).

A much better understanding of theformation, subduction, circulation, andvariability of Subantarctic Mode Waterand Antarctic Intermediate Water hasgrown over recent years, and in particu-lar the greater role that eddies play inthe evolution of mode water hasemerged (Talley et al.,. SAMFLOCexperiment; Sallée et al. 2006, 2008,2009; Herraiz-Borreguero et al. 2009).Analyses of IPCC AR4 models suggestthat observed changes in mode andintermediate water properties are broad-ly consistent with the “fingerprint” ofanthropogenic climate change (Meijerset al. 2007; Downes et al. 2009a). IPCCmodels suggest mode and intermediatewater migrate to lighter densities withclimate change, but the range between

models is very large (Downes et al.2009b).

Trends in the Southern AnnularMode(SAM) have been associated withozone depletion (and eventual recovery)at high latitudes and tropical ocean sur-face warming. In the southeast Pacificthese trends interact with ENSO andgive rise to the dominant low frequencyvariability. The impact is different insummer and winter and the resultingseasonal climate signal is important todistinguish. Regional processes controlthe local impact of atmospheric forcingand determine the nature of the ocean-ice response to changes in forcing,including feedbacks. Significant differ-ences exist in current SAM reconstruc-tions and any conclusions on the signif-icance, or otherwise, of recent trends setin the context of these datasets needs tobe treated with caution. Empirical andmodel efforts should go hand in hand inaddressing this question.

The Southern Ocean has beenshown to contain large amounts ofanthropogenic CO2 and the question offuture changes of this carbon sink forthe atmosphere are being debated. Air-sea CO2 fluxes may decrease in yearsto come if the SAM trends continue andmore natural carbon upwells. This satu-ration of the carbon sink (Le Quere etal. 2007) is a topic of current debate(Lovenduski and Ito 2008; Law 2008;Lovenduski et al. 2008). A related mat-ter is the rising acidity levels and thesusceptibility of certain regions tospecies decline resulting from the disso-lution of carbonate skeletal material(Orr et al. 2005; McNeil and Matear2008). Some polar regions, e.g. theRoss Sea, may be the first to sufferfrom ocean acidification.

The analysis of data and model sim-

A Vision for Climate Variability Research in theSouthern Ocean-Ice-Atmosphere System

Kevin Speer, M at t h ew England, K ate Stansfi e l dand the

The Southern Ocean CLIVAR/CliC/SCAR Pa n e l

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Examples of recent progress in Southern Ocean Climate Researchi) OCEANS• Real-time monitoring of Drake Passage transport by sea level (Woodworth et al. 2006)• Under-ice Argo measurements in the Weddell Sea (Klatt et al. 2007)• The confirmation that the Southern Ocean is warming and that this warming is consistent with the response of the climatesystem to the anthropogenic forcing (Böning et al. 2008). Observations have resolved important new aspects of the regionalcirculation of the Southern Ocean. The first direct measurements of the Kerguelen deep western boundary current transport(Fukamachi et al. 2009), • Progress on the interaction of the oceanic mesoscale with bottom topography (and subsequent loss of geostrophic balance)and the implication for a significant physical coupling between the upper and lower cells of the Southern Ocean overturning.• Ocean acidification impacts will occur sooner than expected (McNeil and Matear 2008) • Argo data has been used to resolve the seasonal cycle of mixed layer depth evolution and to examine heat budgets quanti-tatively (Dong et al. 2008; Sallée et al. 2008), and a larger role of eddy heat fluxes has been found.• Development of a Southern Ocean State Estimate (SOSE) data synthesis, publicly available, for 2005-2007ii) ATMOSPHERE• A better description of the patterns of interannual variability of sea ice cover and a better understanding of the impact ofthe changes in atmospheric circulation (related to SAM and ENSO in particular) on the ice-ocean system• SAM relationship with temperature and precipitation across southern high latitudes is not temporally stable: this may reducethe utility of many potential SAM proxies.• Climate-chemistry models indicate that a future ozone recovery will produce a decline in the SAM (weakened circumpolarwesterlies) during austral summer. This is in contradiction to the mean model response of the IPCC AR4 models, several ofwhich do not have ozone and/or ozone recovery.• Southern Ocean response to a positive SAM trend is complex with opposing trends; natural carbon opposes anthro-pogenic; heat and freshwater opposes the winds.• Recognition of the importance of the Southern Ocean as a sink for CO2 and heat from the atmosphere and recognition thatthis sink may change as a result of a changing wind field and altered stratification

iii) ICE• Recent satellite derived estimates of the mass balance of both Greenland and Antarctica confirm the IPCC AR4 assessmentthat they are adding to sea level• Much of the increased loss from both the Greenland and Antarctic ice sheets is due to accelerated discharge from outlet gla-ciers and ice shelves – not just enhanced surface melt.• In-situ measurements for satellite validation of some sea-ice variables – especially snow thickness, that have improved globalproducts – but still require additional calibration, validation, and development

ulations are required to understand thevariability of the Southern OceanSystem, and the first high-resolutionstate estimates for Southern Ocean(Mazloff et al., 2009) and first multi-decadal coarse resolution ocean stateestimates for global ocean (e.g. ECCO,SODA) have been achieved. Thedynamics of atmospheric modes andtheir impact on the ocean-ice system,the influence of the ocean and ice onthese modes, the dynamics of the ACC,and the stability of the Southern Oceanoverturning, or upwelling circulationare key topics for Southern Ocean cli-mate research. In this framework, a bet-ter estimation of heat, moisture fluxesand wind stresses at the ocean surface is

of great importance. Model representa-tions of deep-water formation in theocean, of ocean-ice shelf interactions,and of fast ice streams should be priori-ties. These goals could in part beachieved through regional reanalyses,eventually using coupled atmosphere-ocean-sea-ice models.Research Needs

One of the main troubles whenaddressing the changes in, and behav-iors of the Southern Ocean system is thepaucity of long time series compared toother oceans. There is an absolute needto maintain the current observations sys-tem together with some expansion intounder-sampled locations in order to per-mit the analysis of long-term trends:

water masses, sea-ice concentration, seasurface elevation, and the groundingline of ice sheets.

The community should engage asynthesis of observations collected dur-ing the 20th century in the SouthernOcean, beginning with physical and bio-geochemical parameters, but extendingto ecosystems. Surface temperature(ocean and land), deep-water character-istics, carbon content, and sea ice extentare a priority. Innovative methodsshould be designed to combine observa-tions and model results to be able toestimate the magnitude and variabilityof the changes over the 20th centuryand understand their causes.

Ultimately, we should evaluate the

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quality of Earth system models in thehigh latitudes of the SouthernHemisphere and propose improvementsin order to provide better projections offuture Southern Ocean carbon uptake,water-mass trends, changes in Antarcticsea ice, the stability of the Antarctic icesheet, and the response of the ecosystemto acidification. The quality of futureSAM predictions made using currentAOGCMs without a well modeled strat-osphere and chemistry included (e.g.many AR4 models) is questionable, andsimilar criticisms can be made for theocean and ice components of climatemodels. Much work needs to be done toimprove the representations of key cli-mate physics, biology, and chemistry,and to link these together into Earth sys-tems models.

The Southern Ocean Panel hasdefined certain needs or “imperatives”to emphasize the important contributionthey make, or would make to progressby the whole community of scientists asopposed to individual researchers. Theseimperatives include:

• Absolute need to maintain Argo,hydrographic (water sampling), andextend sampling or observational tech-niques to the under-ice-covered ocean,up to the grounding line

• Better assessment of the role ofeddies on transport and mixing

• Better estimates of air-sea fluxesof heat and moisture, CO2, wind stress,and boundary layer parameterizations,especially near the continent

• Broader evaluation of theimpact of acidification and the ecosys-tem response

• More accurate diagnoses of thefreshwater and moisture transfers amongthe coupled ocean-ice-atmosphere sys-tem, and associated feedbacks

In addition, as part of our responseto CLIVAR, the Panel has identified afew directions for future research thatwe think would increase our understand-ing of climate in a fundamental way,and provide a basis for a larger programincluding:• What is the future of Antarctic ice? • Improve model representation for

key Southern Ocean processes:upwelling, eddy processes, overturning,convective mixed layers, and interac-tions with the shelf.• What is the impact of acidification?How will the Southern Ocean store ofCO2 change in the future?• Carry out reanalyses using coupledmodels with biochemical representa-tions of the carbon cycle: syntheses ofocean/ice/atmosphere data and models.• How will the projected trends ingreenhouse gases and the SAM impactair-sea heat, moisture, and carbon fluxes• What is the future of the Antarcticcontinental margin? Evaluation andimprovement of Earth system models inthe high latitudes of the SouthernHemisphere, linking the cryosphere toland (e.g. basil melt), the ocean andatmosphere

In conclusion, the Panel supports theestablishment of a Southern OceanObserving System (SOOS) that encom-passes not only physical measurementsof the climate system components, butalso the chemical and biological compo-nents amenable to sustained observa-tion. In this way, the progress that hasbeen achieved will translate into anongoing set of observations, providingbenchmarks for evaluating climate vari-ability, assessing key processes, anddelivering the best information to policymakers and citizens.References

Aoki, S.,N. L. Bindoff and J. A. Church,2005: Interdecadal water mass changes inthe Southern Ocean between 30°E and160°E. Geophys. Res. Lett., 32, L07607,doi:07610.01029/02004GL022220.

Böning, C. W.,A. Dispert,M. Visbeck,S.R. Rintoul and F. U. Schwarzkopf, 2008: Theresponse of the Antarctic CircumpolarCurrent to recent climate change. NatureGeosci, 1, 864-869.

Downes, S. M.,N. L. Bindoff and S. R.Rintoul, 2009a: Impacts of climate changeon the subduction of mode and intermediatewater masses in the Southern Ocean.Journal of Climate.

Jacobs, S. S., 2004: Bottom water pro -duction and its links with the thermohalinecirculation. Antarctic Science, 16, 427-437.

Johnson, G. C.,S. G. Purkey and J. L.

Bullister, 2008: Warming and Freshening inthe Abyssal Southeastern Indian Ocean.Journal of Climate, 21, 5351-5363.

Le Quere, C.,C. Ro?denbeck,E. T.Buitenhuis,T. J. Conway,R. Langenfelds,A.Gomez,C. Labuschagne,M. Ramonet,T.Nakazawa,N. Metzl,N. Gillett and M.Heimann, 2007: Saturation of the SouthernOcean CO2 sink due to climate change.Science 316(5832), 1735-1738.

Lovenduski, N. S.,T. Ito,N. Gruber,E.Bard,R. E. Rickaby,J. Toggweiler,R. F.Anderson,S. Ali,L. Bradtmiller and M.Fleisher, 2008: Linking the Present to thePast: The Importance of the SouthernHemisphere Storm Track for SouthernOcean CO2 Uptake, Journal of MarineResearch.

McNeil, B. I. and R. J. Matear, 2008:Southern Ocean acidification: A tippingpoint at 450-ppm atmospheric CO2.Proceedings of the National Academy of theUnited States of Amercia, 105, 8860-18864.

Meijers, A. J.,N. L. Bindoff and J. L.Roberts, 2007: On the total, mean, andeddy heat and freshwater transports in thesouthern hemisphere of a vs° x 1/8 ° globalocean model. Journal of physical oceanog-raphy, 37, 277-295.

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