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L y o n Lan er o ll e ( N O AA CSDL) and th e Estu ar in e Hy p o xia COMT team

T h e Est u ar i n e H ypo xi a Co mp on en t o f th e C oa s tal Oce an Mod e lin g Te s tb e d ( COMT ). L y o n Lan er o ll e ( N O AA CSDL) and th e Estu ar in e Hy p o xia COMT team. A C o mm unity C oa s tal and Oce an Mod e lin g Te s tb e d ( COMT ).

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L y o n Lan er o ll e ( N O AA CSDL) and th e Estu ar in e Hy p o xia COMT team

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  1. TheEstuarineHypoxiaComponentofthe Coastal OceanModeling Testbed (COMT) Lyon Lanerolle (NOAA CSDL) and the EstuarineHypoxia COMT team

  2. A Community CoastalandOceanModeling Testbed(COMT) to ImproveUnderstanding and OperaConal Forecasts of ExtremeEvents and Chronic Environmental CondiCons AffecCng theU.S. Five Teams: ChesapeakeBayEstuarineHypoxiaForecasCng IntegraCon of West Coast OperaConal Coastal & Ocean Models Puerto Rico/US Virgin Islands InundaCon & Wave ForecasCng Northern Gulf of Mexico Ecological ForecasCng Cyberinfrastructure

  3. The EstuarineHypoxia COMT Team VIMS:Marjy Friedrichs (lead PI) Carl Friedrichs (VIMS-­‐PI)IkeIrby (funded student)Aaron Bever (consultant)Jian Shen (collaborator) Cathy Feng (collaborator) NOAA-­‐CSDL:Lyon Lanerolle (NOAA-­‐PI) FrankAikman (collaborator) WHOI:Malcolm Scully (WHOI-­‐PI)UMCES:Raleigh Hood (UMCES-­‐PI) Hao Wang (funded student) Wen Long (collaborator)Jeremy Testa (collaborator)

  4. EstuarineHypoxia ObjecCve • To assess thereadiness and maturityof a suiteofexisCngcoastal ecological communitymodels fordetermining past, present and future hypoxia eventswithin the ChesapeakeBay, in order toacceleratethetransiKonof hypoxiamodelformulaKonsandproductsfrom “academic research” to“operaKonalcenters” • ChesapeakeBayEHcentersinclude: • NOAA/NOS/CO-­‐OPS • ChesapeakeBay Ecological PredicCon System (CBEPS) • EPA ChesapeakeBayProgram (CBP)

  5. EstuarineHypoxia Goal Compare mulKple modelswithin the EstuarineHypoxia Testbed in order to improveexisCng: CBOFSshort-­‐termforecasKng:by incorporaCng new oxygen and physicalmodel enhancements into theexisCng operaConal NOAA CO-­‐OPS ChesapeakeBay OperaConal Forecast System (CBOFS) forevaluaCon duringtheir next update CBEPSshort-­‐termforecasKng:by developing a 24/7 predicCvecapacity fornowcasCng/forecasCng of oxygen/hypoxicvolume as part of CBEPS at the NOAA CBPO and UMCES CBPscenario-­‐basedforecasKng:Apply the official CBP nutrientreducCon strategies to COMT models to determinewhether they perform similarly to theregulatory CBPmodel in terms of predicCng theeffect of reduced nutrients on hypoxia

  6. Model Comparisons via Chesapeake Testbed • StaCsCcallycompareoutput from four ChesapeakeBaymodels: • three ROMS models,varying biological complexity • (ChesNENA, ChesROMS-­‐BGC, ROMS-­‐RCA) • biologically sophisCcated CBPregulatorymodel • (CH3D-­‐ICM) • Howwell do theyreproduce themean and seasonalvariabilityof: • temperature, salinity, straCficaCon, dissolved oxygen (DO),chlorophyll-­‐a, and nitrate

  7. Su - CON0180 ANT0366 MON0528 s CB1.0DER0015 q GUN0476 u e CON0005 ANT0203 BPC0035 han ET1.1ET2.1 n aR. GUN0258 CB1.1 MON0269 ET2.3 NPA0165 ANT0044 POT1830 ET2.2CB2.1 GUN0125 JON0184GWN0115 PAT0285 CAC0148 MON0155 WT1.1 WT2.1 ET3.1 MARYLAND CAC0031 POT1596 CB2.2 FRG0018NOM0007 MDR0028WT3.1 HOK0005 DELAWARE WT4.1PAT0176WT5.1 POT1595 MON0020 PXT0972 CB3.1 ET4.1 P POT1472 a t a CB3.2 p POT1471 s c PXT0809 o R . SEN0008 ChesterR. WT6.1 CB3.3C WT7.1CB3.3WCB3.3E CJB0005 ET5.0 ET4.2 ComparesimulaCons toobservaCons at10 main stem staCons for~16cruises in2004 and 2005 TF1.0 XGG8251 r WT8.1 POT1184 e SeeDCInset Riv WT8.2 EE1.1 WT8.3 TF1.2WXT0001 nk TF1.3 a CB4.1E pt CB4.1W o ET5.1 Ch TF1.4TF1.5 CB4.1C VIRGINIA TF2.1TF2.3TF2.2 XFB1986 CB4.2C PIS0033 EE2.1 TF1.6 CB4.2W IH1IH5 IH3 TF2.4 IH4 MAT0078MAT0016IH2 IH6 CB4.2E TF1.7 ET5.2 CB4.3E CB4.3WRET1.1 LE1.1 ET6.1 EE2.2 Pa CB4.3C XDJ9007 t u x e TRQ0146 RET2.1 n CCM0069TRQ0088 ET6.2 CB4.4 t RET2.3 R LE1.2 iv RET2.4 TF3.1WTF3.0 TF3.1D er RET2.2 R. TF3.1 WIW0141 CB5.1WCB5.1 LE1.3 TF3.1CTF3.1ATF3.1E ke LE1.4 EE3.0 o ET7.1 TF3.1B Nantic SeeSt.Mary's Inset XCI4078 EE3.1 Potomac TF3.2 LE2.2 MNK0146 Rapp CB5.2 TF3.2A a ET8.1 ha nn o River ET10.1 ET9.1 ck BXK0031 TF3.3 LE2.3 POK0087 EE3.2 RET3.1RET3.1N EE3.3 XAK7810 TF4.0M RET3.1S CB5.3 EE3.4 River CB5.4W RET3.2 CB5.4 EE3.5 TF4.0P LE3.1 CB7.1N CS-3 TF4.4 TF5.0J LE3.3LE3.3A LE3.4 LE3.6 ON-3 CB5.5 CB7.1 LE3.2 TF4.1A Jame TF4.4A s LE3.2S LE3.6N Riv CB6.1 e r RET4.2 LE3.2N TF4.2 CB7.1S TF5.2 OC-3 LE3.6S RET4.1RET4.3S RET4.3N LE3.7 CB6.2H-1A CB6.3 CB7.2 TF5.2A H-2 RET4.3 H-3 LE4.1 TF5.3 H-1 YorkR. RET5.1 C-2 TF5.4TF5.5TF5.5AN TF5.5AS TF5.5A TF5.6 WE4.1CB7.2EC-3 LE4.2N RET5.1A LE4.2LE4.2S C-1 LE4.3NWE4.2NWE4.2OP-3 TF5.0A OP-1 TF5.6A RET5.2NLE4.3 CB6.4 CB7.3EOP-2 LE5.1LE4.3SWE4.2S WE4.3 RET5.2S RET5.2 WE4.4CB7.3 LE5.2N MarylandandVirginiaWaterQualityMonitoringStations CB7.4N LE5.2 LE5.5A CB7.4CB8.1 LE5.2S LE5.5 Legend FallLine 05101520 LE5.3 LE5.4 LE5.5B CB8.1E SeeElizabethInset Miles

  8. Model Skill Assessmentvia TargetDiagrams Bias Modelskill sameasskill ofmeanofobservations OverestimatesObservedMean 1 Unbiased RMSD 1 UnderestimatesObservedMean UnderestimatesObservedStdDev OverestimatesObservedStdDev

  9. BottomTemperature BottomSalinity Stratification BottomDissolvedOxygen SurfaceChlorophyll SurfaceNitrate CH3D-ICM ChesNENA ChesROMS-BGC ROMS-RCA Bias 1 Unbiased RMSD 1 • Overallskillofallfourmodels(temporal+spatialvariability): • arehighestintermsofTemperature • aresimilartoeachotherintermsofT,S,stratificationand DO • aredifferentintermsofchlorophylland nitrate

  10. Model Comparisons via Chesapeake Testbed • Regardless of complexity,models achieve similar skill scores interms of seasonal variabilityof T, S, straCficaCon and oxygen • All models reproduce DO beder than variables thataretypicallythought to be primary influences on DO (straCficaCon, chlorophyll, nitrate) • This is because seasonal DO variability is sensiCve to T (solubilityeffect),and themodels reproduce T verywell • Modeled DO simulaCons may bevery sensiCve to any future increases inBaytemperature • Oxygen forecasCng is possiblewith simple biological formulaCon

  11. EstuarineHypoxia Goal Compare mulKple modelswithin the EstuarineHypoxia Testbed in order to improveexisCng: CBOFSshort-­‐termforecasKng:by incorporaCng new oxygen and physicalmodel enhancements into theexisCng operaConal NOAA CO-­‐OPS ChesapeakeBay OperaConal Forecast System (CBOFS) forevaluaCon duringtheir next update CBEPSshort-­‐termforecasKng:by developing a 24/7 operaConal capacity fornowcasCng/forecasCng of oxygen/hypoxicvolume as part of CBEPS at the NOAA CBPO and UMCES CBPscenario-­‐basedforecasKng:Apply the official CBP nutrientreducCon strategies to COMT models to determinewhether they perform similarly to theregulatory CBPmodel in terms of predicCng theeffect of reduced nutrients on hypoxia

  12. CBOFS CBOFS Model Grid • CBOFS based on Regional Ocean ModelingSystem (ROMS) • Grid generated in segments and pasted seamlessly using Delf3D-­‐RGFGRIDgenerator • Bathymetry:NOS soundings cut-­‐off at2mdepth • Init Conds:NOAA T, S climatology for lower Bay and CBP profiles for upperBay • Rivers: discharge = USGS; T, S = CBP • OuterBdy Conds: T, S = NOAAclimatology • OuterBdy Tides:Cdal harmonic consCtuents forWL and barotropiccurrents fromADCIRC database • No sediment, precipitaCon,weing/dryingor data assimilaCon

  13. CBOFS – Model OutputArchive LocaCons Lyon – whatwould you think about pasCng a screen grab ortwo fromCBOFS to put in here, so folks can seewhat’s availablecurrentlyonline, and so you can explain that thegoal is to add oxygen to the list of othervariables currentlyavailable? ArchivewaterelevaCons,3D currents, T and S at all of the above locaCons

  14. CBOFS – Model Outputs

  15. CBOFS Goal1:CBOFSshort-­‐termforecasKngIncorporate new oxygen and physical model enhancements into theexisCng operaConal NOAA CO-­‐OPS CBOFS forevaluaCon during their next update ProgresstoDate: •Staying in touch with NOS/CO-­‐OPS on their salinity improvements •COMT colleagues haverecommended updated model opCons (advecCon scheme, TKE parameter,etc…) OngoingWork: •Re-­‐run CBOFS (2.5y) with newmodel opCons and updated code •ComparemulCple physical simulaCons; assess model skill relaCve to otherCOMT models •Incorporate“best”constant biology DO model into CBOFS (Year2?) •ComparemulCple DO simulaCons; assess model skill •Finalize CBOFS code and have itready forNOS/CO-­‐OPS next update

  16. EstuarineHypoxia Goal Compare mulKple modelswithin the EstuarineHypoxia Testbed in order to improveexisCng: CBOFSshort-­‐termforecasKng:by incorporaCng new oxygen and physicalmodel enhancements into theexisCng operaConal NOAA CO-­‐OPS ChesapeakeBay OperaConal Forecast System (CBOFS) forevaluaCon duringtheir next update CBEPSshort-­‐termforecasKng:by developing a 24/7 predicCvecapacity fornowcasCng/forecasCng of oxygen/hypoxicvolume as part of CBEPS at the NOAA CBPO and UMCES CBPscenario-­‐basedforecasKng:Apply the official CBP nutrientreducCon strategies to COMT models to determinewhether they perform similarly to theregulatory CBPmodel in terms of predicCng theeffect of reduced nutrients on hypoxia

  17. ChesapeakeBay Ecological PredicCon System (CBEPS) and Model Framework • Coupled hydrodynamic/biogeochemical model (ChesROMS) running“operaConally”at UMCES (formally supported byNOAA/NCBO) • Nowcasts = real CmeUSGS river discharge; Forecasts = assumeriver flows persist for3 days • Atmospheric forcing for3-­‐day forecasts from theNorth American Meteorological Model • Simple seasonal climatologies/flowfor biogeochemical boundarycondiCons • Baywide nowcasts & 3 day forecasts of T and S aregenerated daily and posted • Baywideecological nowcasts & 3 dayforecasts of Sea Nedles and Vibrio are generated daily, based on T, S logisCcalregression models (Vibrio not posted)

  18. CBEPS NowcasCng/ForecasCng Sea Nedles: hdp://chesapeakebay.noaa.gov/forecasCng-­‐sea-­‐nedles • Maps generated daily and posted on website • Nowcasts and 3-­‐dayforecasts • SeaSurface Temperature 20Jan 2014

  19. CBEPS NowcasCng/ForecasCng Sea Nedles: hdp://chesapeakebay.noaa.gov/forecasCng-­‐sea-­‐nedles • Maps generated daily and posted on website • Nowcasts and 3-­‐dayforecasts • Sea Surface Temperature • SeaSurface Salinity 20Jan 2014

  20. CBEPS NowcasCng/ForecasCng Sea Nedles: hdp://chesapeakebay.noaa.gov/forecasCng-­‐sea-­‐nedles • Maps generated daily and posted on website • Nowcasts and 3-­‐dayforecasts • Sea Surface Temperature • Sea Surface Salinity • SeaNeVles 20Jan 2014

  21. CBEPS NowcasCng/ForecasCng Sea Nedles: hdp://chesapeakebay.noaa.gov/forecasCng-­‐sea-­‐nedles • Maps generated daily and posted on website • Nowcasts and 3-­‐dayforecasts • Sea Surface Temperature • Sea Surface Salinity • Sea Nedles • Vibrio(notposted) 20Jan 2014

  22. ChesapeakeBay Ecological PredicCon System (CBEPS) • Goal2:CBEPSshort-­‐termforecasKngDevelop a 24/7 predicCvecapacity fornowcasCng/forecasCng of oxygen/hypoxicvolumeatNOAA CBPO & UMCES • ProgresstoDate: • •Currentlyrunning3 day forecasts “operaConally”atUMCES for Chesapeake-­‐biogeochemistry (based on “old”version of ChesROMS) • •Developed “sea nedles”forecast – transiConed to a 24/7 demonstraCve product • atNOAA through CBOFS (Success!) – sCll need to carry out skill assessment • Other organisms act similarly,e.g. Vibrio • OngoingWork: • Add simple DO formulaCon to list of variables forecasted • Update ChesROMS physics • UlCmatelymerge features of two ROMS-­‐BGC models:ChesNENA and ChesROMS

  23. EstuarineHypoxia Goal Compare mulKple modelswithin the EstuarineHypoxia Testbed in order to improveexisCng: CBOFSshort-­‐termforecasKng:by incorporaCng new oxygen and physicalmodel enhancements into theexisCng operaConal NOAA CO-­‐OPS ChesapeakeBay OperaConal Forecast System (CBOFS) forevaluaCon duringtheir next update CBEPSshort-­‐termforecasKng:by developing a 24/7 operaConal capacity fornowcasCng/forecasCng of oxygen/hypoxicvolume as part of CBEPS at the NOAA CBPO and UMCES CBPscenario-­‐basedforecasKng:Apply the official CBP nutrientreducCon strategies to COMT ROMS models to determinewhether they performsimilarly to theregulatory CBPmodel in terms of predicCng theeffect ofreduced nutrients on hypoxia (Year2)

  24. CBP Scenario-­‐based ForecasCng • ProgresstoDate: • Developed methodology to use an alternatehydrodynamic+biogeochemicalmodel to reproduceWaterQuality Standards for CBP • EPA ChesapeakeBayProgram folks areenthusiasCc about our proposed • effort to assess confidence/uncertainCes in theirregulatorymodel • FutureWork: • •Run alternatemodel(s) with CBP’s nutrientreducCon scenarios. • •Apply CBP protocol to both sets of model scenarios • •Foreach model, idenCfywhen/whereBaywill meetrequired“water qualitystandards” • •How do themodel results diverge? Where/when are thegreatest • uncertainCes in the TMDLs computed from thesemodel results?

  25. The EstuarineHypoxia COMT model skillcomparisons are improving: NOAA CBOFS nowcasts/forecasts UMCES CBEPS nowcasts/forecasts CBP scenario-­‐based forecasts

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