for Evaluating Long-term Impacts on Coral and Marine ......Trend of 1982–2014 annual maximum coral...
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Use of a Blended Satellite and in situ Sea Surface Temperature Climate Data Record for Evaluating Long-term Impacts on Coral and Marine Mammal Communities V. Banzon 1 , G. Liu 2 , K. Forney 3 , E. A. Becker 3 , L. Sun 1,4 and K. Arzayus 1 PC34A-2162 Introduction Marine Mammal Management February 2016 NOAA National Centers for Environmental Information Future SST Projections References The NOAA ¼° daily optimum interpolation (DOI) sea surface temperature (SST) dataset, described in Banzon et al. (2016): • has a ¼° (~25 km) spatial resolution suitable for examining mesoscale or greater, and • has a temporal resolution (1-day) that allows study of processes that are affected by temperature variations on a daily or greater scale. DOISST was selected by the Climate Data Records program (Bates et al., in press) as a consistent and sufficiently long satellite-based record to detect long term trends and examine climate-related processes. This foundational dataset has broad applicability, as demonstrated by three use cases shown here. Fig. 4. Trend of 1982–2014 annual maximum coral bleaching heat stress level at reef locations(a) globally, (b) the Coral Triangle, (c) Hawaiian Island chain, (d) the Caribbean [Green pixels: small (-0.2, 0.2) or insignificant (P>0.1) trends]. Fig. 1. Bleached plate corals and live sea fans on Molasses Reef, Key Largo, Florida. Photo courtesy of Matt Keiffer (Picasa). Fig. 2. A global bleaching event caused by thermal stress started in 2014 and has continued into 2015 and 2016. Heat stress is conducive to coral bleaching (Fig. 1). An ongoing global bleaching event is in its third year (Fig. 2). Has heat stress increased as a result of the recent climate change? NOAA Coral Reef Watch (CRW) examined the 34 years of DOISST data for answers, based on the algorithm used in CRW’s near- real-time satellite bleaching heat stress monitoring (Liu et al., 2014). Significant increase in severe stress is seen not only in the intensity (Fig. 3) but also spatial coverage (Fig. 4). DOISST has been used successfully in habitat-based models of the distribution and abundance of cetaceans (whales, dolphins, and porpoises) in both the central and northeastern Pacific (Fig, 5–7). For many species, SST serves as an effective proxy for unmeasured underlying ecological processes linking cetaceans to their prey. Using DOISST, the models successfully captured variability in cetacean distribution at seasonal and interannual time scales. Such models can provide a foundation for dynamic ocean management, especially in the face of long term temperature change. 1 NOAA NationalCenters for EnvironmentalInformation(NCEI),Center for Coasts,Oceans, and Geophysics n 2 NOAA Coral Reef Watch Program – GST, NOAA Center for Weather and Climate Prediction (NCWP) n 3 NOAA/NMFS/SWFSC, Marine Mammal and Turtle Division, SouthwestFisheries Science Center n 4 Cooperative Institute for Climate and Satellites-NC Banzon, V., et al., 2016: Along term record of blended satellite and in situ sea surface temperature for climate monitoring, modeling and environmental studies. Earth System Science Data Discuss., doi10.5194/essd-2015-44, in review. Bates, J.J. et al., 2015: Sustained production of multidecadal climate records - Lessons from the NOAA Climate Data Record Program. Bull. Amer. Meteor. Soc., e-View, doi:10.1175/BAMS-D-15-00015.1. Becker, E.A., et al., 2014: Predicting seasonal density patterns of California cetaceans based on habitat models. Endang Species Res, 23, 1–22, doi:10.3354/esr00548. Becker, E.A., et al., (In press): Movingtowards dynamic ocean management: How well do modeled ocean products predict species distributions? Remote Sens. Forney, K.A., et al., 2015: Habitat-based models of cetacean density and distribution in the central North Pacific. Endang Species Res, 27, 1–20, doi:10.3354/esr00632. Liu, G., et al., 2014: Reef-scale thermal stress monitoring of coral ecosystems: New 5-km global products from NOAA Coral Reef Watch. Remote Sens, 6, 11579–11606, doi:10.3390/rs61111579. Taylor, K.E., et al., 2012: An overview of CMIP5 and the experiment design. Bull Amer Meteor Soc, 93, 485–498, doi:10.1175/BAMS-D-11-00094.1. Van Vuuren, D.P. et al., 2011: The representative concentration pathways: An overview. Climatic Change, 109, 5–31, doi: 10.1007/s10584-011-0148-z. Fig. 5. Model-based estimates of density (animals km -2 ) and distribution for the tropical Bryde’s whale showing variability in the central North Pacific. Top three panels show predictions, and bottom panels show multi-year average density and confidence intervals. (Forney et al. 2015) Short-beaked common dolphin Delphinus delphis California Current Fig. 6. Densities of animals km -2 (a) from summer models built using DOISST and (b) the resulting winter predictions. Black dots show actual sighting locations from summer ship surveys (a) and aerial surveys that covered a smaller study area (b). Larger sighting dots indicate more animals. (Becker et al. 2014) Dall’s porpoise Phocoenoides dalli California Current Fin whale Balaenoptera physalus California Current Fig. 7. Interannual variability of cetacean distribution. Model-based estimates of summer/fall density (animals km -2 ) and distribution for six different years, 1991–2008. Black dots are actual sighting locations during ship surveys conducted in each year. Model details: Becker et al. (In press). Fig. 3. Time series of the 1982–2015 annual percentage of (¼)° reef pixels experiencing severe heat stress levels (Alert Level 1 and 2) and the associated trends. 0 10 20 30 40 50 60 1982 1987 1992 1997 2002 2007 2012 Percentage of Reef Pixels (%) Year Percentage of Reef Pixels Experiencing Bleaching Alert Level 1 & 2 (1982-2015, OISSTv2-based) Alert Level 1 and 2 Alert Level 2 Linear (Alert Level 1 and 2) Linear (Alert Level 2) Fig. 8. Model SSTs in °C for (a) CMIP5 historical run for the period 1982–2011, (b) CMIP5 RCP6.0 simulations of the end of 21st century and (c) the magnitude of the temperature increase. Fig. 9. Caribbean region projected SSTs for 2071– 2100 from (a) CMIP5 RCP6.0 (11-Model Mean); (b) adjusted using CMIP5 historical minus DOISST has better defined ocean features. a) b) c) d) Coral Bleaching Heat Stress 2 1 Bryde’s whale Balaenoptera edeni Central North Pacific The fifth phase of the Coupled Model Intercomparison Project (CMIP5) provides estimates of future SSTs (Taylor et al. 2012) under different scenarios (called representative concentration pathways or RCPs; Van Vuuren et al., 2011). Simulations of the historic period are also done to compare model performance against current observations. For RCP6.0 (medium stabilization scenario), average SST could increase up to ~7 °C by the end of this century (Fig. 8). To match the higher resolution required by ecological applications, model SSTs can be “adjusted” using the difference between model SSTs and DOISST for a common period (e.g., 1982–2011, Fig. 9). 3 b) Winter (aerial) a) Summer (ship) Animals km –2 CDR’s can be found here: www.ncdc.noaa.gov/cdr OISST information can be found here: www.ncdc.noaa.gov/oisst
Transcript of for Evaluating Long-term Impacts on Coral and Marine ......Trend of 1982–2014 annual maximum coral...
Use of a Blended Satellite and in situ Sea Surface Temperature Climate Data Record for Evaluating Long-term Impacts on Coral and Marine Mammal Communities
V. Banzon1, G. Liu2, K. Forney3, E. A. Becker3 , L. Sun1,4 and K. Arzayus1
PC34A-2162
Introduction MarineMammalManagement
February2016
NOAANationalCentersforEnvironmentalInformation
FutureSSTProjections
References
TheNOAA¼° dailyoptimuminterpolation(DOI)seasurfacetemperature(SST)dataset,describedinBanzon etal.(2016):•hasa¼° (~25km)spatialresolutionsuitableforexaminingmesoscale orgreater,and•hasatemporalresolution(1-day)thatallowsstudyofprocessesthatareaffectedbytemperaturevariationsonadailyorgreaterscale.DOISSTwasselectedbytheClimateDataRecordsprogram(Batesetal.,inpress)asaconsistentandsufficientlylongsatellite-basedrecordtodetectlongtermtrendsandexamineclimate-relatedprocesses.
This foundationaldatasethasbroadapplicability,asdemonstratedbythreeusecasesshownhere.
Fig.4.Trendof1982–2014annualmaximumcoralbleachingheatstresslevelatreeflocations(a)globally,(b)theCoralTriangle,(c)HawaiianIslandchain,(d)theCaribbean[Greenpixels:small(-0.2,0.2)orinsignificant(P>0.1)trends].
Fig.1.BleachedplatecoralsandliveseafansonMolassesReef,KeyLargo,Florida.PhotocourtesyofMattKeiffer (Picasa).
Fig.2.Aglobalbleachingeventcausedbythermalstressstarted in2014andhascontinuedinto2015and2016.
Heatstressisconducivetocoralbleaching(Fig.1).Anongoingglobalbleachingeventisinitsthirdyear(Fig.2).Hasheatstressincreasedasaresultoftherecentclimatechange?NOAACoralReefWatch(CRW)
examinedthe34yearsofDOISSTdataforanswers,basedonthealgorithmusedinCRW’snear-real-timesatellitebleachingheatstressmonitoring(Liuetal.,2014).Significantincreaseinsevere
stressisseennotonlyintheintensity(Fig.3)butalsospatialcoverage(Fig.4).
DOISST has been used successfully in habitat-based models of the distribution and abundance of cetaceans (whales, dolphins,and porpoises) in both the central and northeastern Pacific (Fig, 5–7). For many species, SST serves as an effective proxy forunmeasured underlying ecological processes linking cetaceans to their prey.Using DOISST, the models successfully captured variability in cetacean distribution at seasonal and interannual time scales.
Such models can provide a foundation for dynamic ocean management, especially in the face of long term temperature change.
1NOAANationalCentersforEnvironmentalInformation(NCEI),CenterforCoasts,Oceans,andGeophysicsn 2NOAACoralReefWatch Program– GST,NOAACenterforWeatherandClimatePrediction(NCWP)n 3NOAA/NMFS/SWFSC,MarineMammalandTurtleDivision,SouthwestFisheriesScienceCentern 4CooperativeInstituteforClimateandSatellites-NC
Banzon,V.,etal.,2016:Alongtermrecordofblendedsatelliteandinsituseasurfacetemperatureforclimatemonitoring,modelingandenvironmentalstudies.EarthSystemScienceDataDiscuss.,doi10.5194/essd-2015-44,inreview.
Bates,J.J.etal.,2015: Sustainedproductionofmultidecadal climaterecords-LessonsfromtheNOAAClimateDataRecordProgram. Bull.Amer.Meteor.Soc.,e-View, doi:10.1175/BAMS-D-15-00015.1.
Becker,E.A.,etal., 2014:PredictingseasonaldensitypatternsofCaliforniacetaceansbasedonhabitatmodels. Endang SpeciesRes,23,1–22,doi:10.3354/esr00548.
Becker,E.A.,etal.,(Inpress):Movingtowardsdynamicoceanmanagement:Howwelldomodeledoceanproductspredictspeciesdistributions?RemoteSens.
Forney,K.A.,etal.,2015:Habitat-basedmodelsofcetaceandensityanddistributioninthecentralNorthPacific. Endang SpeciesRes,27,1–20,doi:10.3354/esr00632.
Liu,G.,etal.,2014:Reef-scalethermalstressmonitoringofcoralecosystems:New5-kmglobalproductsfromNOAACoralReefWatch.RemoteSens,6,11579–11606,doi:10.3390/rs61111579.
Taylor,K.E.,etal.,2012:AnoverviewofCMIP5andtheexperimentdesign.BullAmer MeteorSoc, 93,485–498, doi:10.1175/BAMS-D-11-00094.1.
VanVuuren,D.P.etal.,2011:Therepresentativeconcentrationpathways:Anoverview.ClimaticChange, 109,5–31,doi:10.1007/s10584-011-0148-z.
Fig.5.Model-basedestimatesofdensity(animalskm-2) anddistributionforthetropicalBryde’swhaleshowingvariabilityinthecentralNorthPacific.Topthreepanelsshowpredictions,andbottompanelsshowmulti-yearaveragedensityandconfidenceintervals.(Forneyetal.2015)
Short-beakedcommondolphinDelphinusdelphisCaliforniaCurrent
Fig.6.Densitiesofanimalskm-2 (a)fromsummermodelsbuiltusingDOISSTand(b)theresultingwinterpredictions.Blackdotsshowactualsightinglocationsfromsummershipsurveys(a)andaerialsurveysthatcoveredasmallerstudyarea(b).Largersightingdotsindicatemoreanimals.(Beckeretal.2014)
Dall’sporpoisePhocoenoides dalliCaliforniaCurrent
FinwhaleBalaenoptera physalusCaliforniaCurrent
Fig.7.Interannual variabilityofcetaceandistribution.Model-basedestimatesofsummer/falldensity(animalskm-2)anddistributionforsixdifferentyears,1991–2008.Blackdotsareactualsightinglocationsduringshipsurveysconductedineachyear.Modeldetails:Beckeretal.(Inpress).
Fig.3.Timeseriesofthe1982–2015annualpercentageof(¼)° reefpixelsexperiencingsevereheatstresslevels(AlertLevel1and2)andtheassociatedtrends.
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Fig.8.ModelSSTsin°Cfor(a)CMIP5historicalrunfortheperiod1982–2011,(b)CMIP5RCP6.0simulationsoftheendof21st centuryand(c)themagnitudeofthetemperatureincrease.
Fig.9.CaribbeanregionprojectedSSTsfor2071–2100from(a)CMIP5RCP6.0(11-ModelMean);(b)adjustedusingCMIP5historicalminusDOISSThasbetterdefinedoceanfeatures.
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Bryde’s whaleBalaenoptera edeniCentralNorthPacific
ThefifthphaseoftheCoupledModelIntercomparison Project(CMIP5)providesestimatesoffutureSSTs(Tayloretal.2012)underdifferentscenarios(calledrepresentativeconcentrationpathwaysorRCPs;VanVuuren etal.,2011).Simulationsofthehistoricperiodarealsodonetocomparemodelperformanceagainstcurrentobservations.ForRCP6.0(mediumstabilizationscenario),averageSSTcouldincreaseupto~7°Cby
theendofthiscentury(Fig.8).Tomatchthehigherresolutionrequiredbyecologicalapplications,modelSSTscanbe“adjusted”usingthedifferencebetweenmodelSSTsandDOISSTforacommonperiod(e.g.,1982–2011,Fig.9).
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Animalskm–2
CDR’scanbefoundhere:www.ncdc.noaa.gov/cdr
OISSTinformationcanbefoundhere:www.ncdc.noaa.gov/oisst
