Sensitivity of Modeled CO2 Air–Sea Flux in a Coastal Environment to Surface Temperature Gradients, Surfactants, and Satellite Data Assimilation

emo e sensing A icle Sensi i i y o Modeled CO2Ai –Sea Flux in a Coas al En i onmen o Su ace Tempe a u e G adien s, Su ac an s, and Sa elli e Da a Assimila ion Rica do To es 1,* , Yu i A ioli 1, Vassilis Ki idis 1, S e ano Cia a a 1, Manuel Ruiz-Villa eal 2, Jamie Shu le 3, Luca Polimene 1, Vic o Ma inez 1, Clai e Widdicombe 1, E. Malcolm S. Woodwa d 1, Timo hy Smy h 1, James Fishwick 1and Ga in H. Tils one 1 1Plymou h Ma ine Labo a o y, Plymou h PL1 3DH, UK; [email p o ec ed] (Y.A.); [email p o ec ed] (V.K.); [email p o ec ed] (S.C.); [email p o ec ed] (L.P.); [email p o ec ed] (V.M.); [email p o ec ed] (C.W.); [email p o ec ed] (E.M.S.W.); [email p o ec ed] (T.S.); [email p o ec ed] (J.F.); [email p o ec ed] (G.H.T.) 2Ins i u o Español de Oceanog a ía (IEO), Cen o oceanog á ico da Co uña, Paseo Ma í imo Alcalde F ancisco Vázquez, nº10, 15001 A Co uña, Spain; [email p o ec ed] 3Exe e , Uni e si y, Pen yn Campus, Geog aphy depa men , T elie e Road, Pen yn, Co nwall TR10 9FE, UK; j.d.shu le @exe e .ac.uk *Co espondence: [email p o ec ed] Recei ed: 31 May 2020; Accep ed: 22 June 2020; Published: 25 June 2020   Abs ac : This wo k e alua es he sensi i i y o CO 2 ai –sea gas exchange in a coas al si e o ou di e en model sys em con igu a ions o he 1D coupled hyd odynamic–ecosys em model GOTM–ERSEM, owa ds iden i ying c i ical dynamics o ele ance when speci ically add essing quan i ica ion o ai –sea CO 2 exchange. The Eu opean Sea Regional Ecosys em Model (ERSEM) is a biomass and unc ional g oup-based biogeochemical model ha includes a comp ehensi e ca bona e sys em and explici ly simula es he p oduc ion o dissol ed o ganic ca bon, dissol ed ino ganic ca bon and o ganic ma e . The model was implemen ed a he coas al s a ion L4 (4 nm sou h o Plymou h, 50 ° 15.00’N, 4 ° 13.02’W, dep h o 51 m). The model pe o mance was e alua ed using mo e han 1500 hyd ological and biochemical obse a ions ou inely collec ed a L4 h ough he Wes e n Coas al Obse a o y ac i i ies o 2008—2009. In addi ion o a e e ence simula ion (A), we an h ee dis inc expe imen s o in es iga e he sensi i i y o he ca bona e sys em and modeled ai –sea luxes o (B) he sea-su ace empe a u e (SST) diu nal cycle and hus also he nea -su ace e ical g adien s, (C) biological supp ession o gas exchange and (D) da a assimila ion using sa elli e Ea h obse a ion da a. The e e ence simula ion cap u es well he physical en i onmen (simula ed SST has a co ela ion wi h obse a ions equal o 0.94 wi h a p > 0.95). O e all, he model cap u es he seasonal signal in mos biogeochemical a iables including he ai –sea lux o CO 2 and p ima y p oduc ion and can cap u e some o he in a-seasonal a iabili y and sho -li ed blooms. The model co ec ly ep oduces he seasonali y o nu ien s (co ela ion > 0.80 o silica e, ni a e and phospha e), su ace chlo ophyll-a (co ela ion >0.43) and o al biomass (co ela ion >0.7) in a wo yea un o 2008–2009. The model simula es well he concen a ion o DIC, pH and in-wa e pa ial p essu e o CO 2 (pCO 2 ) wi h co ela ions be ween 0.4–0.5. The model esul sugges ha L4 is a weak ne sou ce o CO 2 (0.3–1.8 molCm −2 yea −1 ). The esul s o he h ee sensi i i y expe imen s indica e ha bo h esol ing he empe a u e p o ile nea he su ace and assimila ion o su ace chlo ophyll-a signi ican ly impac he skill o simula ing he biogeochemis y a L4 and all o he ca bona e chemis y ela ed a iables. These esul s indica e ha ou o ecas ing abili y o CO 2 ai –sea lux in shel seas en i onmen s and hei impac in clima e modeling should conside bo h model e inemen s as means o educing unce ain ies and e o s in any u u e clima e p ojec ions. Remo e Sens. 2020,12, 2038; doi:10.3390/ s12122038 www.mdpi.com/jou nal/ emo esensing Remo e Sens. 2020,12, 2038 2 o 29 Keywo ds: da a assimila ion; 1D ecosys em model; CO2; ai –sea gas exchange 1. In oduc ion Recen esea ch e o s in ma ine and clima e disciplines highligh he e ec s ha inc easing a mosphe ic CO 2 concen a ions a e ha ing on he Ea h’s clima e and in i s con ol o e he oceanic ca bona e sys em and pH [ 1 ]. As one o he g eenhouse gases, i s ai –sea lux is a c i ical pa o he clima e sys em. A mosphe ic ca bon dioxide (CO 2 ) has been inc easing since he indus ial e olu ion due o he bu ning o ossil uels, cemen p oduc ion and land use change. While i is gene ally accep ed ha he ocean sys em annually abso bs up o 30% o he CO 2 eleased by bu ning ossil uels [ 2 ], i is no clea whe he his ocean ca bon ‘sink’ is inc easing o dec easing, and conside able empo al and spa ial in e -annual a ia ion appea s o occu [ 3 ]. The lux o CO 2 be ween he a mosphe e and he ocean (ai –sea) is p ima ily con olled by condi ions o he physical en i onmen like wind speed, sea s a e and sea-su ace empe a u e bu also by biological p ocesses aking place in he eupho ic zone. Adding CO 2 o seawa e dis up s he ca bona e sys em, leading o an inc ease in ee CO 2 /ca bonic acid and he bica bona e ion concen a ion while dec easing he ca bona e ion, pH and ca bona e sa u a ion s a es [ 4 ]. All o hese ha e implica ions o a ange o biological and chemical p ocesses like calci ica ion, p ima y p oduc ion and ep oduc ion [5,6]. In shel seas, CO 2 dynamics a e highly a iable in space and ime [ 7 , 8 ] pa ly d i en by he high p ima y p oduc i i y, leading o a la ge d aw-down o dissol ed ino ganic ca bon (DIC, he sum o CO 2 , bica bona e and ca bona e ions) and consequen ise in pH [ 9 ]. This is u he complica ed by he close link be ween ben hic p ocesses and he pelagic ca bon cycle and alkalini y [ 10 ] and i e ine inpu s o DIC and o al alkalini y (TA) in o coas al sys ems [11]. In addi ion o hese p ocesses, diapycnal mixing ac oss he he mocline d i es a con inuous change in su ace CO 2 as a esul o e ical luxes o DIC and nu ien s ha s imula e p ima y p oduc ion [ 12 ]. The exis ence o nea -su ace empe a u e a ia ions was iden i ied by Wa d e al. [13] and hei signi icance o ai –sea CO2 luxes has been ecen ly highligh ed by Wool e al. [14]. Clea ly, u he unde s anding he CO 2 pa hways, sou ces, sinks, hei espec i e budge s and hei impac s on Ea h’s clima e sys em is essen ial o moni o ing and p ojec ing u u e clima e scena ios. In his wo k we e alua e he sensi i i y o model de i ed CO 2 luxes o physical and biological d i e s in a shallow shel sea loca ion (L4) using he GOTM–ERSEM model [ 15 ]. The use o a 1D model he e is app op ia e because in shallow shel seas, he e ical mixing is mainly d i en by he balance be ween idal mixing and a mosphe ic luxes, especially sola hea ing. L4 (4 nm sou h o Plymou h, 50 ° 15.00 0 N, 4 ° 13.02 0 W, dep h o 51 m) is a coas al s a ion in he wes e n English Channel whe e sus ained obse a ions ha e been ca ied ou since he la e 1980s and whe e a iabili y in plank onic communi ies has been desc ibed ex ensi ely [ 16 ]. In ecen yea s, physical obse a ions ha e in ensi ied in he amewo k o he Wes e n Channel Obse a o y [ 17 ] un by he Plymou h Ma ine Labo a o y (PML) in coope a ion wi h he Ma ine Biological Associa ion (MBA). The en i onmen al and wa e condi ions a L4 ha e been sampled weekly and physico–chemical and biological obse a ions a e eely a ailable. Recen ly, a mid-ocean buoy has been ins alled a L4 [ 18 ]. As in o he shel seas, physics a L4 a e mainly he esul o a balance be ween idal mixing and a mosphe ic o cing and a seasonal empe a u e s a i ica ion appea s when summe hea ing o e comes he idal induced mixing. Nu ien dynamics and ecosys em dynamics a e s ongly dependen on his a iabili y [ 17 ]. The e a e nume ous epo s o s ong in a-seasonal and in e -annual a iabili y in he plank on p oduc ion and communi y composi ion in he wes e n English Channel and in o he shel seas e.g., [ 16 , 19 , 20 ], all o which help o modula e and al e he Remo e Sens. 2020,12, 2038 3 o 29 ai –sea lux o CO 2 . Collec i ely, hese da a p o ide a subs an ial da ase o pa ame e ize and e alua e he pe o mance o coupled hyd odynamic–ecosys em models. 2. Me hods In his wo k we e alua e he sensi i i y o modeled CO 2 luxes o h ee me hodological app oaches which can a ec he simula ion o he ca bona e sys em wi hin ecosys em models. In doing so, we ha e de ined h ee nume ical expe imen s and one e e ence simula ion ha cons i u es ou baseline da a o compa e he changes associa ed wi h each expe imen . 2.1. Physics Model Desc ip ion: Go m The Gene al Ocean Tu bulence Model, (GOTM [ 21 ]) is a 1D physical model ha is designed as a gene ic sys em o ma ine modeling. GOTM dynamically simula es he e olu ion o empe a u e, densi y and e ical mixing when o ced wi h me eo ological da a (e.g., [ 15 ]). F om all he u bulence model op ions a ailable wi hin GOTM we ha e chosen he second o de u bulence model κ – e wi h he coe icien s sugges ed in Canu o e al. [22] ( e sion A) as Umlau and Bu cha d [23] demons a ed ha i can simula e u bulence in s a i ied shea lows in a physically sound and nume ically obus way. The sensible and la en hea luxes a e calcula ed wi hin GOTM using s anda d o mulae [ 24 ]. The calcula ions o he la en and sensible hea a e dependen upon he calcula ion o hei coe icien s, which a e a unc ion o ai –sea empe a u e di e ence, wind speed and a s abili y c i e ion o he su ace wa e s [ 25 ]. The long wa e (back) adia ion is calcula ed using he May o mula ion [ 26 ]. The su ace s ess is calcula ed om he wind s ess. These o mulae we e chosen based on a sensi i i y s udy o su ace hea lux o mula ion on he model esponse [27]. The physical model supplies he empe a u e o he ecosys em p ocess desc ip ions and de e mines he luxes o ecosys em componen s be ween adjacen boxes and o laye s. The coupling o ERSEM and GOTM is desc ibed in [28]. 2.2. Biological Model Desc ip ion: E sem ERSEM is a gene ic ecosys em model based on he “ unc ional g oup app oach” [ 29 , 30 ]; he bio a in he ecosys em a e di ided in o h ee unc ional ypes: p ima y p oduce s, consume s, and decompose s, which a e subdi ided on he basis o ophic links and/o size. ERSEM can p oduce a ange o dynamics and communi y s uc u es wi h a consis en pa ame e iza ion, when applied o a wide a ie y o physical scena ios. The consis en pa ame e iza ion is only possible because o he ange o p ocess desc ip ions now included in he model. The model is capable o accu a ely simula ing he spa ial pa e n o ecological luxes h oughou he seasonal cycle and he main phy oplank on succession (cg. [ 28 , 31 ]). I includes ep esen a ions o he ben hic sys em, which a e i al o he co ec ea men o shel seas. Bo h physiological (inges ion, espi a ion, exc e ion and eges ion) and popula ion (g ow h and mo ali y) p ocesses a e desc ibed by luxes o ca bon and nu ien s be ween unc ional g oups. The associa ed decoupled ca bon and nu ien dynamics gi es a a be e app oxima ion o how nu ien limi a ion ac s on cells han ixed quo a models [ 30 ]. I can simula e bo h he classical, la ge cell dynamics and he small cell mic obial loop, he eby ep esen ing he con inuum o ophic pa hways. ERSEM s anda d pelagic compa men s include ou phy oplank on unc ional g oups; picoplank on (0.2–2 µ m), nano lagella es (2–20 µ m), dino lagella es (20–200 µ m) and dia oms (20–200 µ m) h ee zooplank on g oups; he e o ophic nano lagella es (<20 µ m), mic ozooplank on (20–200 µ m) and mesozooplank on (>200 µ m) and a bac e ia loop. F ac iona ion o dissol ed and pa icula e ma e and ins an aneous ligh a e also key aspec s o he cu en model e sion. A mo e in-dep h desc ip ion o he model can be ound in Allen e al. [28] . The model has been ex ensi ely alida ed wi h espec o he ca bon sys em [ 8 ], chl-a [ 32 , 33 ], and nu ien s and biomass [ 34 , 35 ]. The ull ERSEM model open sou ce code and manual can be ound a h p://www.pml.ac.uk/Modelling/Models/ERSEM. Remo e Sens. 2020,12, 2038 4 o 29 2.3. Obse a ions Bo h in si u and sa elli e de i ed biological measu emen s we e used o model skill assessmen and/o assimila ion. These obse a ions a e desc ibed in he ollowing sec ions. 2.3.1. In Si u Biological Measu emen s The obse a ions we e ob ained unde he weekly sampling s a egy o he Wes e n Channel Obse a o y. The wa e samples we e collec ed using Niskin bo les moun ed on he CTD ose e ame. Su ace nu ien measu emen s o silica e, phospha e, ni a e, ni i e and ammonium whe e measu ed colo ime ically using a B an and Luebbe AAIII segmen ed low au oanalyze [ 36 ]. Fo chl-a measu emen s, 1 o 2 L o seawa e om ou dep hs (0, 10, 20 and 50 m) was il e ed on o a Wha man ® GF/F glass mic o ibe il e and he il e s o ed in liquid ni ogen un il analysis. Fo he analysis s age, pigmen s we e ex ac ed om he hawed GF/F il e in o 2 mL me hanol [ 37 ] and sonica ed o 35 s. These ex ac s we e hen cen i uged o emo e il e and cell deb is (5 min a 4000 pm) and analyzed using e e sed-phase HPLC [ 38 ]. Pigmen s, including chl-a, we e iden i ied using e en ion ime and spec ally ma ched using pho o-diode a ay spec oscopy [ 39 ]. Wa e samples om 10 m dep h we e used o quan i ying he phy oplank on and mic ozooplank on communi y composi ion and abundance om mic oscopic analysis o samples p ese ed wi h Lugol’s iodine. Cell we e iden i ied o species-le el we e possible and assigned o h ee unc ional g oups (dia oms (cen ic and penna e), dino lagella es and lagella es). The con e sion om cell numbe s o biomass was based on olumes acco ding o geome ic shapes and o mulae o Olenina e al. [40] and Menden-Deue and Lessa d [41]. Pho osyn hesis–i adiance pa ame e s (P–E) o he es ima ion o in eg a ed p ima y p oduc ion ollowed he me hodology p esen ed in Tils one e al. [42] and Pla e al. [43] . The samples we e incuba ed o 2–3 h a e which he suspended ma e ial was il e ed h ough 25 mm Wha man GF/F il e s. The il e s we e exposed o concen a ed HCl umes o 12 h and adioca bon ac i i y on he il e s was de e mined by a Packa d T i-Ca b 2500 TR liquid scin illa ion analyze using he ex e nal s anda d and he channel a io me hods o co ec o quenching. The b oadband P–E pa ame e s we e es ima ed by i ing he da a o he model o Pla e al. [43] . The daily in eg a ed p oduc i i y was es ima ed as in Tils one e al. [42] a 1 m in e als om he sea su ace down o 0.1% su ace i adiance. Time se ies o in si u p ima y p oduc ion a L4 is gi en in Ba nes e al. [44]. The ca bona e sys em was sampled a wo dep hs; 2 m and bo om, using 250 mL glass bo les and p ese ed wi h 50 µ L o sa u a ed HgCl 2 solu ion. The samples we e analyzed wi hin 12 mon hs by i a ion ( o o al alkalini y) and coulome ic analysis ( o DIC). Full de ails o he analy ical p ocedu e and a ho ough discussion o he ca bona e sys em da a can be ound in Ki idis e al. [45]. 2.3.2. Sa elli e Obse a ions Le el 1a (calib a ed and geoloca ed adiance) 1 km spa ial esolu ion sa elli e emo e sensing Ea h obse a ion (EO) da a collec ed by he Mode a e-Resolu ion Imaging Spec ome e (MODIS, onboa d he Aqua pla o m) we e downloaded om he NASA Ocean Colo da a dis ibu ion websi e (h p://oceanda a.sci.gs c.nasa.go /) o he 2009–2010 pe iod. These we e p ocessed using he SeaWiFS Da a Analysis Sys em (SeaDAS, e sion 6.4) o gene a e 1 km chl-a da a using he h ee-band algo i hm, OC3 [ 46 ]. All p oduc s we e quali y masked using he de aul SeaDAS masks o land, cloud and high/sa u a ed adiance [47]. 2.4. Expe imen s 2.4.1. Expe imen A The e e ence simula ion o Expe imen A is desc ibed in Sec ions 2.1 and 2.2. In his wo k, he GOTM model was con igu ed wi h 50 e ical laye s wi h a ime s ep o 10 s. The e ical esolu ion is inc eased owa ds he bo om laye o be e cap u e he dynamics o he bo om bounda y laye . Remo e Sens. 2020,12, 2038 5 o 29 This esul s in a laye hickness o 30 cm a he bo om inc easing loga i hmically o 1.6 m om 15 m abo e he bo om o he su ace. Fo me eo ological o cing, we use 6 hou ly da a om he Na ional Cen e o En i onmen al P edic ion/Na ional Cen e o A mosphe ic Resea ch (NCEP/NCAR) eanalysis [ 48 ], which has a spa ial esolu ion o 1.9 ◦× 1.9 ◦ . The model is o ced wi h ai empe a u e, a mosphe ic p essu e, 10 m winds and dew empe a u e. The sho wa e adia ion is imposed sepa a ely a 5 min in e als using locally obse ed alues measu ed wi h a Li-Co py anome e (LI-200SZ) on a coas al s a ion main ained by PML (h p://www.wes e nchannelobse a o y.o g.uk/pml_wea he _s a ion). Tidal o cing was included as ime se ies o su ace ele a ion g adien s gene a ed using 15 idal ha monics ex ac ed om he esul s o a high esolu ion (1.8 × 1.8 km g id spacing) 3D ba o opic model simula ion o he UK shel [ 49 ]. The ha monics used a e all he ele an semi-diu nal and diu nal idal cons i uen s such as M2, Q1, O1, P1, S1, K1, 2N 2 , µ2 , N2, u 2 , L2, T2, S2, K2 and M4. Su ace ele a ion g adien s we e ob ained by a i icially gene a ing hou ly su ace ele a ion ime se ies on adjacen g id poin s o he L4 s a ion posi ion using he - ide MATLAB oolbox [50]. The model is ini ialized in win e when he wa e column is well-mixed wi h obse ed empe a u e and salini y p o iles (see Sec ion 2.3). The wa e column e olu ion is u he cons ained by nudging he model esul s o he obse ed empe a u e and salini y p o iles wi h a elaxa ion ime scale o wo weeks [ 15 , 21 ]. In his way, GOTM can accoun o missing 3-dimensional dynamics no esol ed wi hin 1D models like he long- e m ho izon al ad ec ion o empe a u e and salini y ha is impo an o esol ing he e ical wa e column s uc u e and hence gas luxes in coas al egions [ 51 ]. An al e na i e app oach o he in oduc ion o wa e masses wi h di e en empe a u e and salini y signa u es in o he 1D wa e column is h ough he speci ica ion o ime- a ying, e ically esol ed ho izon al empe a u e and salini y g adien s. This op ion is no employed he e o his pu pose due o he absence o such in o ma ion, bu he ea u e is used in Expe imen D as a way o a i icially in oduce e o s in he mixing dynamics as desc ibed in Sec ion 2.4.4.2. The model is ini ialized whe e possible om obse a ions aken in Decembe 2005 (plank on biomass and nu ien s) and un o wo yea s un il Decembe 2007 o c ea e a common s a ile o each o he expe imen s. The yea s 2006–2007 a e aken as a model spin-up pe iod and he model is e alua ed agains obse a ions o he pe iod 6 Janua y 2008 un il 20 Decembe 2009. The model pa ame e s used ollow Black o d e al. [31] as modi ied by A ioli e al. [8]. 2.4.2. Expe imen B Expe imen B looks in o he e ec s ha esol ing he diu nal SST cycle has on he ai –sea luxes o CO 2 . By he diu nal cycle o SST we conside he changes o he empe a u e e ical s uc u e in he op ew me e s o he ocean caused by he di ec day ime sola hea ing and nigh ime cooling. The e o e, o model hese diu nal changes, he model esolu ion is inc eased loga i hmically om 2 cm a he su ace o 1 m a 10 m deep. 2.4.3. Expe imen C Expe imen C e alua es he po en ial impac s ha biologically gene a ed su ac an s ha e on ai –sea luxes. Sea-su ace su ac an s o o ganics can in luence gas exchange in wo ways, as a monolaye physical ba ie and h ough modi ica ion o sea-su ace hyd odynamics and hence u bulen ene gy ans e [ 52 , 53 ], mani es ing as a educed gas ans e eloci y ( K ). Fo Expe imen C, we ha e chosen o implemen a p ima y p oduc i i y-based educ ion on he gas ans e eloci y. Se e al s udies ha e ied o quan i y his supp ession o K (e.g., and e e ences he ein [ 53 ]). Howe e , he exac magni ude and na u e o he supp ession is unclea as he alues ound among s udies (in bo h he na u al en i onmen and wi hin labo a o ies) a y conside ably. So he e o e we ha e chosen o in es iga e he sensi i i y o he luxes o his ype o phenomenon a he han ying o quan i y i . Remo e Sens. 2020,12, 2038 6 o 29 2.4.4. Expe imen D The hi d Expe imen (D) e alua es he impac s o using da a assimila ion o emo ely sensed chlo ophyll-a (chl-a) on he abili y o ERSEM o simula e he ca bona e sys em and i s associa ed con ol on ai –sea luxes o CO2. 2.4.4.1. The Ensemble Kalman il e (EnKF) Expe imen D e alua es he sensi i i y o modeled CO 2 o da a assimila ion o su ace chl-a as es ima ed om sa elli e. Following p e ious wo k we ha e chosen he Ensemble Kalman Fil e [ 54 ] as implemen ed in he 1D GOTM–ERSEM model by To es e al. [15] and used by [34] o his egion. The Ensemble Kalman Fil e me hod was o mula ed o wo k wi h non-linea models and in pa icula o handle he e o co a iance e olu ion [ 54 ]. He e he analysis scheme desc ibed in E ensen [55] is implemen ed. Fi s he model is in eg a ed o wa d in ime wi h he addi ion o a s ochas ic e m), Ψ k+1= (Ψa k) + qk, (1) q ep esen ing he model e o s and ep esen s a non-linea ope a o . Ψ is a ma ix holding he model s a e a iables. The k subsc ip indica es he ime index, while he supe sc ip s and a co espond o he o ecas and analysis. The model e o s a e ep esen ed h ough he co a iances o he s a e a iable e o s. These a e de ined a p io i and a e e ol ed o wa d in ime by he Kalman il e . The in o ma ion con ained by a ull p obabili y densi y unc ion can be ep esen ed by an ensemble o model s a es. Co a iances can be de ined wi h espec o a known ue s a e o as expec a ions, ensemble co a iance ma ices a ound he ensemble mean, ψ . Hence he ensemble o model s a es will p o ide in o ma ion on s a is ical momen s o in e es . P ≃(ψ −ψ )(ψ −ψ )T(2) Pa≃(ψa−ψa)(ψa−ψa)T. (3) The s anda d EnKF analysis equa ion [ 56 ] is applied he e. The analysis s ep in ol es upda ing each o he ensemble membe s aking in o conside a ion he di e ence be ween he obse a ions and he model s a e as well as he e o s associa ed wi h he model s a e a iables and he obse a ions. As each ensemble is upda ed indi idually he e is no need o egene a e hem. The ensemble mean will be he upda ed s a e ha minimizes he model a iance. A mo e in-dep h desc ip ion o he algo i hm can be ound in Cia a a e al. [34]. The a iables included in he analysis a e log ans o med so ha hei a iabili y is educed, hei Gaussiani y is imp o ed and posi i i y o he solu ion is gua an eed. The a iables and he assimila ed obse a ions ha e been ans o med by a Gaussian anamo phosis unc ion na u al log as sugges ed in Be ino e al. [57] . O he applica ions ha e been epo ed in To es e al. [15] , Cia a a e al. [34] , Ne ge and G egg [58]. Compa ed o he o he model imp o emen s om expe imen s B and C, he EnKF equi es addi ional compu ing esou ces which scale wi h he numbe o ensembles equi ed o co ec ly app oxima e he e olu ion o he p obabili y densi y unc ion o he model s a e. In ou implemen a ion, he se up equi ed unning 100 di e en model simula ions and was he e o e 100 imes mo e compu a ionally expensi e han ei he Expe imen B o C. Fo his eason, Expe imen D ocusses on a single yea (2009). 2.4.4.2. Model E o s The ensemble o model s a es de ined in Equa ion (1) is ob ained by adding model e o s o he assimila ion scheme. They a e applied o all he pelagic a iables included in he co a iance ma ix o s a e a iables bu no o he ben hic s a e a iables. The la e a e no included in he analysis scheme and hence a e no upda ed. Remo e Sens. 2020,12, 2038 7 o 29 Two ypes o model e o s a e included in he simula ions: hose ha modi y he s a e a iables and hose ha pe u b he o cing unc ions. E o s assigned o mos ERSEM s a e a iables we e 20% Gaussian e o ; hey we e educed o 5% in he de i us ela ed a iables o limi la ge co ec ions o he la ges pool in he model and in he nu ien a iables as hey cons i u e he bes esol ed a iables in ERSEM. These e o s a e in oduced once as ini ial condi ions o gene a e he ini ial ensemble o model s a es, and a each analysis s ep be o e sol ing he Kalman il e equa ion. Addi ional e o s a e in oduced a un- ime by pe u bing he o cing unc ions ha d i e he p ima y p oduc ion and he e ical anspo o he biological s a e a iables. A each ime s ep o each ensemble simula ion, e o s a e added o he su ace i adiance alues. Addi ional e o s associa ed wi h he in e nal p essu e g adien s and backg ound sedimen concen a ion cons an a e andomly ini ialized and kep ixed du ing each assimila ion cycle so ha hey a e di e en o each ensemble membe . The sea-su ace i adiance and he backg ound suspended sedimen concen a ion a e wo o cing a iables which ha e a la ge ecological impo ance as he ligh egime de e mines in g ea measu e he sho - e m a iabili y o phy oplank on. Bo h we e assigned a 25% Gaussian e o . The in e nal p essu e g adien s e o s ac o pe u b he wa e column s uc u e and consequen ly he e ical anspo associa ed wi h mixing. The in e nal p essu e g adien s e o s we e calcula ed as pseudo- andom ields as sugges ed by E ensen [56] wi h a co ela ion leng h equal o he o al wa e dep h (51 m). 2.4.4.3. Obse a ional E o s The Gaussian measu emen e o s o he MODIS de i ed su ace chl-a we e se o 30% o he mon hs Janua y o Ma ch and Oc obe o Decembe when he EO chl-a e ie als a e expec ed o be a ec ed by high suspended sedimen s concen a ions in he wa e and high sola zeni h angle (Figu e 1) [ 59 ]. Chl-a alues we e gene ally less han 1 mgCm −3 in hose mon hs. Du ing he sp ing–summe pe iod, EO e o s we e se a 10% o e lec he dominance o chl-a o e he suspended sedimen concen a ions. We jus i y his by he good ag eemen ound by G oom e al. [60] in compa ing HPLC in si u chl-a wi h MODIS and SeaWiFS da a who ound no bias due o coas al adjacency in he ocean colou da a a L4. They concluded ha L4 could be conside ed case 2 wa e s in la e au umn and win e and case 1 wa e s o he es o he yea . Figu e 1. Su ace chl a L4 du ing 2009 as obse ed om sa elli e (blue wi h assigned e o ba s) and in si u HPLC analysis ( ed). Remo e Sens. 2020,12, 2038 8 o 29 3. Resul s The in si u obse a ions used in his wo k ha e been ex ensi ely desc ibed in nume ous pape s (e.g., [ 16 , 17 , 37 , 45 ]) bu we p o ide a b ie o e iew he e. The seasonali y o nu ien s show ypical maximum alues in Janua y ( 8µM m−3 o Ni a e , 0.5 µM m−3Phospha e , 5µM m−3Silica e ) and minima a e he sp ing bloom ( om May) un il i s a s inc easing again in Sep embe ollowing he weakening o he he mal s a i ica ion. Chl-a also shows a ma ked seasonali y a L4 (e.g., Figu e 1). Two peaks a e iden i ied yea on yea in he da a, one coinciden al wi h he sp ing bloom (Ap il–May) and one in Sep embe –Oc obe . The sp ing bloom is gene ally domina ed by dia oms bu wi h signi ican con ibu ions om coccoli opho es and phaeocys is. The analysis o 15 yea s o phy oplank on abundance a L4 [ 16 ] has shown signi ican seasonal and in e -annual changes in abundance and composi ion al hough seasonali y is he la ges de e minan o hese changes. The au ho s ound ha he win e communi y seems ela i ely s able o e he yea s. The sp ing pe iod ep esen s he la ges change e en in bo h composi ion and abundance while he summe pe iod is he mos suscep ible o in e -annual changes in composi ion [ 16 ]. O e all, lagella es a e he dominan g oup ep esen ing 87% o he o al phy oplank on cell abundance on a e age wi h he smalle cells (2–4 µ m) accoun ing o 63% o he o al abundance. In e ms o biomass, hey ep esen a small bu signi ican p opo ion o he o al biomass. 3.1. Re e ence Simula ion; Expe imen A 3.1.1. Physics We ha e e alua ed bo h he model capaci y o ep oduce he seasonal changes in he physical en i onmen as well as sub-daily changes associa ed wi h he semi-diu nal ides. Ini ial calib a ion o he model and simula ions o e alua e he sensi i i y o he model o a ia ions in o cing ha e been pe o med o he yea s 2009–2010, when da a om he oceanog aphical buoy a e a ailable and can help o educe unce ain y in he me eo ological o cing. In Augus 2010, we deployed a bo om moun ed Teledyne RD Ins umen s 600 kHz B oadband Acous ic Dopple Cu en P o ile (ADCP) a L4 o 20 days [61]. The seasonali y in hyd og aphy is well ep oduced by he model (Figu e 2a). This a ises pa ly om he ac ha we a e using a basic nudging echnique o elax he model empe a u e (T) and salini y (S) o he obse ed CTD p o iles wi h a elaxa ion ime o wo weeks. The co ela ion o nea -su ace (1.5 m) empe a u e be ween he independen obse a ions om he L4 moni o ing buoy and he model was la ge han 0.9 and signi ican a he 99.9% con idence le el ( ull s a is ics can be ound in Table A4). The abili y o he model o ep oduce he eloci y s uc u e was e alua ed du ing a simula ion o he Augus ADCP deploymen . The esul s indica e ha he model se up ep oduces he ba o opic componen o he idal eloci y well conside ing he 1D app oach. The co ela ion be ween he dep h mean a e aged eloci y be ween 2 and 30 m was 0.7 a he 99.9% con idence le el. The dominan E–W componen is be e esol ed albei wi h a small o e es ima ion o he lood peak eloci y (co ela ion o 0.9 a 99.9% con idence le el). The N–S componen shows a la ge ampli ude in magni ude han he obse a ions which esul ed in lowe co ela ions o 0.7. The eloci ies closes o he bo om (a e aged be ween 1 and 2 m abo e he seabed, (Figu e 2b)) ha e a be e ag eemen be ween model and obse a ions wi h an o e all co ela ion o 0.8 o he eloci y magni ude. As i is a idally domina ed s a ion, his is key o ensu ing a ealis ic le el o mixing is imposed o he biological model. Remo e Sens. 2020,12, 2038 9 o 29 Figu e 2. Time e olu ion o ( a ) Six hou ly SST as simula ed wi h GOTM (solid line) and obse ed om he L4 buoy (do s) and ( b ) eloci y magni ude a e aged be ween 1 and 2 m om he sea bed o he model and obse a ions. The one o one plo wi h all da a used in calcula ing he s a is ics is included in he le op co ne . The axes ha e he same ange as he main g aph. 3.1.2. Biogeochemis y The model ou pu a six hou ly in e als in 2008–2009 a he sampling dep h o 10 m (Figu e 3a,b) shows he model co ec ly ep oduces he app oxima e iming and du a ion o he sp ing bloom. The model indica es a sho sha p sp ing bloom in 2008 (Figu e 3a) wi h subsequen sho -li ed blooms du ing June–July in ag eemen wi h he a ailable obse a ions. The sp ing bloom in 2009 is by con as longe (Figu e 3b) al hough he model o e es ima es he chl-a concen a ions. None heless, he obse a ions sugges a subs an ial o e es ima ion o dia om biomass in 2008 (Figu e 4b) bu he co ec o de o magni ude in May 2009 (Figu e 4b). The model unde es ima es o al chl-a om Janua y o mid-Ap il and om Sep embe o Decembe in 2008 and 2009. The main disc epancy wi h he obse a ions is he la e dino lagella e bloom (no shown) which he model unde es ima e in bo h yea s al hough he miss-ma ch is mos e iden in 2009 (Figu e 3b). Remo e Sens. 2020,12, 2038 16 o 29 Figu e 8. Examples o nea -su ace p o iles o empe a u e o days 18–20 May 2009 o Expe imen ( a ) A and ( b ) B. Du ing hose days, a e age nigh –day empe a u e di e ences we e o he o de o 0.5 ◦ C. Wind speed a e aged 9 ms−1 . Black lines ep esen s nigh ime p o iles (03 and 21 h) while ed lines ep esen day ime p o iles (09 and 15 h). The de elopmen o he e y nea -su ace g adien esul s in changes o he ampli ude o he daily luc ua ions o empe a u e such ha he nigh –day ange educes in Expe imen B, making nigh ime minima wa me and day ime maxima coole han in Expe imen A as he nea -su ace g adien s limi he exchange o hea wi h he a mosphe e. pCO 2 shows a simila nigh –day e olu ion o he empe a u e p o iles wi h a consis en nega i e g adien in he op 20 cm and he de elopmen o a day ime subsu ace maximum coinciden al wi h he empe a u e maximum. Expe imen B showed ha he ca bona e sys em model a iables a e sensi i e o he inc eased esolu ion nea he su ace. The compa ison be ween expe imen s A and B (Table 1) shows he la ges imp o emen s in 2008, in pa icula o pH, pCO 2 and DIC (a 10%, 19% and 18% imp o emen espec i ely, Table 1). The e ec on he ai –sea lux is la ges and i is appa en h oughou he yea sa e he mos s a i ied pe iods be ween June and Augus . The impac is impo an and esul s in a 70% o 100% inc ease in he ne CO 2 up ake a L4. The modeled in eg a ed annual CO 2 lux was 1.8 mo C/m 2 /yea in Expe imen A o 2008 inc easing o an in eg a ed annual CO 2 lux o 2.8 mo C/m 2 /yea in Expe imen B o he same yea . A simila esul is ob ained o 2009 wi h a change om ne CO2sou ce o 0.3 mo C/m2/yea in Expe imen A o ne CO2sou ce o 0.6 mo C/m2/yea in Expe imen B. 3.3. E alua ion o Su ace Slicks; Expe imen C Expe imen C in ol ed a pa ame e iza ion o he e ec s o p ima y p oduc ion on he CO 2 ans e eloci y ( KCO2 ). This was in oduced h ough a scaling KCO2 wi h he g oss p ima y p oduc ion exc e ed by he phy oplank on as a esul o i s me abolic ac i i y. In p ac ice, KCO2 was educed o a maximum o 70% o i s wind dependen alue (calcula ed ollowing Nigh ingale e al. [65] ) when he g oss p oduc i i y su passes he 30% highes alues modeled a L4 in he yea s 2008–2009. The esul s a e summa ized in Table 1and sugges a negligible impac when compa ed o Expe imen A. The in eg a ed annual CO 2 lux suppo s hese esul s wi h changes smalle han 1% o bo h 2008 and 2009 yea s. Remo e Sens. 2020,12, 2038 17 o 29 Table 1. Pea son co ela ions o simula ion expe imen s A–D, be ween obse ed and modeled a iables o 2008 and 2009 a he shallowes dep h a ailable. Signi ican co ela ions a he 95% a e indica ed by *. Values in i alic signi y an imp o emen wi h espec o Expe imen A. Yea To al Chl To al C Silica e Ni a e Phosph. Ammon. DIC pCO2Ph Exp A 2008 0.43 * 0.70 * 0.82 * 0.90 * 0.82 * −0.23 0.46 * 0.52 * 0.53 * 2009 0.45 * 0.76 * 0.86 * 0.90 * 0.77 * −0.46 * 0.47 * 0.41 * 0.38 Exp B 2008 0.41 * 0.67 * 0.83 * 0.92 * 0.87 * −0.35 * 0.51 * 0.62 * 0.63 * 2009 0.48 * 0.75 * 0.86 * 0.88 * 0.90 * −0.46 * 0.54 * 0.35 * 0.33 * Exp C 2008 0.43 * 0.70 * 0.82 * 0.90 * 0.82 * −0.23 0.46 * 0.52 * 0.53 * 2009 0.45 * 0.76 * 0.86 * 0.90 * 0.77 * −0.46 * 0.47 * 0.41 * 0.38 Exp D 2009 0.46 * 0.49 * 0.86 * 0.90 * 0.88 * −0.40 * 0.30 * −0.20 * −0.22 3.4. E alua ion o Da a Assimila ion; Expe imen D Resul s om Expe imen D show ha assimila ion o EO chl-a imp o es some o he model a iables bu i also de e io a es bulk a iables like o al biomass (Table 1and Table A1 in Appendix A). As expec ed om p e ious wo k [ 34 ] he o ecas s ep always shows a la ge a iance o ensemble sp ead han a e he analysis s ep sugges ing a educ ion in ensemble a iance as a esul . The modeled mixed laye chl-a (Figu e 9a) shows an imp o ed ag eemen wi h obse a ions (Table 1) bu he model ails o cap u e he sp ing bloom co ec ly. In pa icula , he model solu ion cons uc ed as he mean o all he model ensembles o e es ima es chl-a in Ma ch and end o Ap il while he o e es ima ion du ing he obse ed sp ing bloom in May imp o es. The summe e olu ion o chl-a is be e esol ed as is he inc ease seen in Sep embe (Figu e 9a). The imp o emen seen in chl-a do no howe e ansla e o a be e i in modeled o al biomass, and he assimila ion o chl-a deg ades he model solu ion as a esul o he la ge miss- ep esen a ion o he sp ing bloom (no shown). Indeed, when he esul s be ween 15 Ma ch and 15 Ap il a e emo ed, he co ela ion o o al biomass inc eases o 0.61 a he 95% signi icance. One e ec o he assimila ion is he o e es ima ion o all he nu ien a iables du ing he sp ing bloom (no shown). This e lec he inabili y o he model o lowe he chl-a ield wi hou inc easing he nu ien alues du ing ha pe iod. The disc epancies and excess nu ien s a e howe e co ec ed du ing he summe pe iod and ha e a small impac on he o e all co ela ion wi h obse a ions (Table 1). I is, howe e , no ewo hy ha while he co ela ions wi h obse a ions o all a iables in he ca bona e sys em (pH, DIC and pCO 2 , Table 1) do no imp o e, he bias o RMSE is signi ican ly educed (Table A4). The ensemble a iance was educed in he modeled chl-a as a esul o he assimila ion bu i is appa en in Figu e 9b ha his is no he case o pCO 2 o DIC and pH (no shown), al hough a e he ini ial inc ease du ing he i s h ee mon hs o he expe imen , i is also ue ha i did no inc ease mono onically. Remo e Sens. 2020,12, 2038 18 o 29 Figu e 9. Time e olu ion o esul s om Expe imen D o ( a ) 10 m chl-a and in si u su ace chl-a obse a ions ( ed do s) and ( b ) mixed laye pCO 2 and in si u obse a ions (black). The solid black line co esponds o he model esul s om Expe imen A. The hicke blue line co esponds o he ensemble mean while he hin blue line co esponds o one s anda d de ia ion. Remo e Sens. 2020,12, 2038 19 o 29 4. Discussion 4.1. Ecosys em Models P edic i e Capabili ies o CO2 We ha e shown ha a 1D app oach using GOTM–ERSEM cap u es he seasonali y o he pelagic ecosys em a L4, including he a iables d i ing he ai –sea exchange o CO2(e.g., yea 2008 Table 1). The esul s om yea 2009 (Table 1) sugges ha he model’s abili y o simula e he o al biomass and nu ien s is no a su icien condi ion o esol e he su ace e olu ion o DIC and pCO 2 . This ollows he poo e simula ion o chl-a in 2009 and sugges s ha an adequa e simula ion o chl-a and he associa ed plank on p oduc ion and espi a ion a es is c i ical o a be e es ima ion o he CO 2 dynamics. The seasonali y and ange o simula ed DIC and pCO 2 ag ee wi h ecen obse a ions [ 45 ] and sugges bo h a physical and biological con ol. The high p oduc ion du ing he sp ing and summe pe iods (Figu e 5) p oduces unde sa u a ion o CO 2 (Figu e 10a) esul ing in a ne sink o CO 2 in o he wa e s. I is wo h no ing ha he sho - e m a iabili y in ai –sea CO 2 luxes is as high as he seasonal signal (Figu e 9a) and ha he summe blooms can d i e a simila exchange o he longe sp ing bloom. Despi e he model’s abili y o cap u e he seasonal cycle on all he a iables o he ca bona e sys em he model seems o unde es ima e he sho - e m a iabili y seen in he da a (Figu e 9b). This can be ela ed o he coa se empo al esolu ion o he wind o cing (six hou ly) (e.g., [ 64 ]) and he e ec o la e al ad ec ion which is in insic in he da a and no esol ed in he model. A L4, a ia ions in DIC d i e he obse ed a iabili y in he ca bona e sys em and hus pCO 2 as o al alkalini y has e y low a iabili y [ 45 ]. The model solu ion sugges s ha L4 can be conside ed a neu al si e wi h espec o CO 2 luxes o a small sou ce (Table 2) in con as o he esul s epo ed by Ki idis e al. [45] . Table 2. Ai -sea lux annual in eg a ed alues o each o he expe imen s. Expe imen Ai -Sea Flux o CO2in molC/m2/day 2008 2009 Exp A −1.8 −0.3 Exp B −2.8 −0.6 Exp C −1.8 −0.3 Exp D 1.3 ± 1.7 4.2. Sensi i i y o Modeled CO2 o SST Diu nal Cycle The inc eased e ical esolu ion nea he su ace in Expe imen B enabled he simula ion o he diu nal cycle in SST [ 64 ] which o e all had a small impac on he abili y o GOTM–ERSEM o simula e he bulk p ope ies o he pelagic ecosys em a L4 as obse ed a 10 m (Table 1) bu a la ge impac on he ai –sea luxes o CO 2 (Figu e 10a,b). The compa ison wi h SST measu ed a he L4 buoy yielded a small dec ease in co ela ion ( om 0.94 o 0.93 a he 99.9% con idence le el) and a small dec ease in he bias (see Table A4). Expe imen B did howe e imp o e he simula ion o pCO 2 , DIC and pH in 2008. The la ges di e ences in he ca bona e sys em be ween expe imen s A and B in 2009 concen a ed in he i s 5 mon hs (Janua y o May, Figu e 10a) and in Expe imen B esul ed in an inc eased supe sa u a ion and esul ing lux o he a mosphe e om Janua y o Ma ch. The ampli ude o he daily a ia ions in he luxes emained ela i ely unchanged om June o Decembe (Figu e 10b) d i en p ima ily by he daily luc ua ions in p oduc i i y. The di e ence was la ges du ing he low p oduc i i y mon hs o Janua y o Ma ch and i was pa ly esponsible o he change in alue o he ne lux es ima es. Remo e Sens. 2020,12, 2038 20 o 29 Figu e 10. Time e olu ion o ( a ) nea -su ace pCO 2 , ( b ) ai –sea lux o CO 2 and ( c ) wa e column a e age o G oss P oduc ion and Communi y Respi a ion a io o Expe imen s A, B and D. The black line in a 385 in (a) ep esen s he a mosphe ic pCO2 alue used in he simula ions. Remo e Sens. 2020,12, 2038 21 o 29 The changes wi h espec o he e e ence expe imen (A) a e mos ly loca ed in he op 10 m and dec ease wi h dep h in a signi ican numbe o a iables (Figu e 11). These include a e a iables like espi a ion and p oduc ion as well as s a e model a iables like biomass o plank on and zooplank on. These di e ences will be discussed in ela ion o h ee pe iods in 2009 de ined as a win e non-g owing season (Janua y o Ma ch wi h a consis en g oss p oduc ion o communi y espi a ion (G/R) a io less han one, Figu e 10c), he summe g owing season om Ap il o Augus (G/R a e aging one o abo e) and he au umn non-g owing season wi h G/R again less han one. Mos o he a iables e alua ed changed be ween 0.1% o 6% excep o g oss p oduc ion and communi y espi a ion ha showed inc eases be ween 26 and 34% in all seasons. Expe imen B displayed a small inc ease in empe a u e in he op 10m h oughou he yea (0.1%) inc easing o 0.5% du ing he g owing season. To al de i us ( he sum o he h ee classes in ERSEM, small, medium and la ge) dec eased in he win e (4%), while on a e aged did no change du ing summe and dec eased by 5% in he au umn in Expe imen B. I adiance le els also inc eased in Expe imen B (5% in all seasons) in esponse o a dec ease in biomass o 2% in win e and 6% in au umn). In gene al, hough, pCO 2 inc eased in Expe imen B in bo h g owing and non-g owing season by 2%. Du ing he non-g owing season, his ansla es in o an inc ease elease o CO 2 o he a mosphe e in bo h 2008 and 2009. Du ing he g owing season, he e is no one speci ic end, as he CO 2 exchange wi h he a mosphe e depends on bo h he biological up ake o CO 2 as well as he wind. Du ing he wo yea s e alua ed, in 2009 he e was no o e all change while in 2008, he e was a la ge elease o CO2in Expe imen B. 4.3. Sensi i i y o Modeled CO2 o Su ace Su ac an s The educ ion o he gas ans e eloci y ( KCO2 ) as a unc ion o sea-su ace su ac an s had a e y small impac on he in eg a ed annual CO 2 lux (Table 2) and bulk ecosys em a iables (Table 1) in ou modeled si e compa ed o he esul s o expe imen s B and D (Sec ions 3.2 and 3.4). This is bo h a consequence o ou choice o pa ame e iza ion (Sec ion 3.3) o he p oduc ion o su ac an ma e ial and he en i onmen al cha ac e is ics o ou si e. The la ge a iabili y in phy oplank on g oss p oduc ion (simila o phy oplank on ne p oduc ion in Figu e 5) implies ha he educ ion o KCO2 is only ac i e du ing a small p opo ion o he ime ( < 5% du ing 2008–2009) be ween Ap il–Sep embe and esul s in small educ ions o KCO2 ( < 1%). The high a e age wind speed (~8 ms−1 obse ed a L4 in 2008–2009 also limi s he impac o he su ac an s on he annual in eg a ed luxes. This con as s wi h he indings o Pe ei a e al. [52] who epo ed an o e all 9% educ ion in he ne CO 2 lux in eg a ed o e he en i e A lan ic Ocean basin in 2014. None heless, hei analysis showed ha a ou la i ude (al hough no es ima es a e p esen ed o he UK shel ) he dec eased su ac an supp ession was consis en ly less han 5% excep o July–Sep embe when i inc eased o 10%. 4.4. Sensi i i y o Modeled CO2 o he Assimila ion o chl-a The assimila ion o chl-a had a signi ican impac in he ca bona e sys em bu mos ly by educing he bias in DIC, pH o pCO 2 . The co ela ions de e io a ed o all h ee a iables bu he concen a ion o he obse a ions a ound he May o Sep embe mon hs p ecludes a de ini i e assessmen o he e ec o su ace chl-a assimila ion on simula ing he seasonali y o he ca bona e sys em. Expe imen D also co ec ed he simula ion o chl-a and he nu ien s silica e and phospha e (Table 1). Remo e Sens. 2020,12, 2038 22 o 29 Figu e 11. Dis ibu ion wi h dep h o he a e aged 2008–2009 oo mean squa e e o be ween Expe imen A and B o selec ed a iables. The alues a e pe cen age change wi h espec o he maximum alue o each a iable. The highe alues in he op 10 m indica es di e ences be ween he wo models a e concen a ed he e. Simila o Expe imen B, Expe imen D had a signi ican impac on he op 10 m a e aged G and R, pa icula ly du ing he win e (G inc eased 450%, R 20%) and in au umn (G inc eased 60% R 23%). O all he a iables e alua ed, only plank on biomass had simila changes wi h a 900% inc ease in win e compa ed o 70% inc ease in he au umn. The second la ges co ec ions co esponded o he zooplank on biomass, which dec eased in win e (28%) and in summe (10%) while i inc eased in au umn (53%). Gene ally, he g owing season saw mo e modes changes ac oss all a iables be ween 3 and 12%. I is wo h emembe ing ha al hough he pe cen age inc eases a e signi ican hey ep esen a small numbe as hey co espond o he non-g owing seasons (win e in pa icula ) when biomass le els and ca bon up ake and espi ed a e e y small. This is a u he indica ion ha win e ecosys em dynamics and au umn ones a e he leas well cap u ed by his 1D model app oach. One way o isualizing how he in o ma ion associa ed wi h chl-a is sp ead ac oss all o he model a iables du ing he analysis s ep is o calcula e he ime e olu ion o he c oss-co ela ion ma ix among all o he ensemble membe s (Figu e 12). Chl-a is gene ally always co ela ed wi h model es ima es o phy oplank on p oduc ion and espi a ion and i s co ela ion wi h he biomass o he unc ional ypes (including phy oplank on and zooplank on) a ies in ime. Fo example, chl-a is highly co ela ed wi h dia oms du ing he sp ing bloom, while he co ela ion wi h he picoplank on inc eases du ing he sho -li ed blooms o his g oup du ing he summe pe iod. Equally, he co ela ions be ween chl-a and DIC, pH o pCO 2 a e ela i ely low and indica es s ong non-linea dynamics and eedback be ween chl-a o biomass and he ca bona e chemis y. This sugges ha di ec co ec ions o hese a iables we e ela i ely small in he EnKF analysis. Assimila ion o chl-a indi ec ly co ec ed hese a iables in he o ecas s ep, h ough he e-ini ializa ion o he o he model a iables in he Remo e Sens. 2020,12, 2038 23 o 29 analysis. The low co ela ions also explain why we do no see a educ ion in he ensemble sp ead o hese a iables (e.g., Figu e 9b) The co ec ions o nea -su ace pCO 2 (Figu e 10b) esul ing om he assimila ion o chl-a we e signi ican ac oss he seasonal cycle excep o June o Augus when all expe imen s ag ee in hei simula ion o chl-a and p ima y p oduc ion. The co ec ions we e as high as 100 µ a m and ended o educe he alues o nea -su ace pCO 2 esul ing in ne sink o CO 2 o 1.3 molCm −2 yea −1 in ag eemen wi h he es ima es o Ki idis e al. [45] . The la ges co ec ion wi h a signi ican impac on he annual es ima es ela es o he p ima y p oduc ion co ec ions in Sep embe o Decembe . Figu e 12. Time e olu ion o 2009 o he c oss-co ela ion ma ix ac oss ensemble membe s o Expe imen D. The colo s indica e he co ela ion o each a iable wi h he op 10 m mean o o al chl-a. 5. Conclusions We ha e shown ha he 1D GOTM–ERSEM model has skill in simula ing bo h he pelagic ecosys em and he a iables ela ed o he dynamics o CO 2 a he coas al s a ion L4, al hough he skill a ies om yea o yea . Resol ing he nea -su ace empe a u e g adien s induced by he diu nal hea ing cycle (Expe imen B) has he mos e icien (lowes compu a ional cos and skill equi emen s) impac a sho , seasonal and annual ime scales and esul s in imp o emen s ac oss he e alua ed a iables ha a e compa able o he esul s om he assimila ion o chl-a Expe imen (D). The co ela ion o ca bona e sys em a iables (DIC, pCO 2 and pH) imp o e by up o 19% while annually in eg a ed alues o CO 2 exchange wi h he a mosphe e changed by up o 50% wi h espec o he e e ence simula ion, inc easing he ne sou ce na u e o L4. A he L4 si e, he in oduc ion o a dependence o he CO 2 gas ans e eloci y on g oss p oduc ion as a p oxy o he p oduc ion o su ace slicks (Expe imen C) has a non-signi ican impac on he dynamics o CO 2 , including he ai –sea annual exchange es ima es. The assimila ion o su ace chl-a (Expe imen D) has an o e all mixed e ec on he abili y o he model o ep oduce he obse a ions. As expec ed he simula ion o chl-a imp o es and while he co ela ion o nu ien s do no signi ican ly change, hei bias dec eases. The e ec on he ca bona e sys em a iables is simila ; he bias dec eases o DIC and pH bu so do he co ela ions. The la ges impac o assimila ion is on he ne ai –sea gas exchange whe e he esul s indica e he po en ial o L4 o be a weak ne sou ce o ca bon o he A mosphe e (1.3 ± 1.7 molCm −2 yea −1 ). Tha chl-a Remo e Sens. 2020,12, 2038 24 o 29 assimila ion can deg ade he simula ion o some a iables possibly ela es o he equi emen o succinc pa ame e changes o success ully simula e he speci ic yea s conside ed in his wo k. The esul s o he h ee sensi i i y expe imen s indica e ha esol ing he empe a u e p o ile nea he su ace appea s he mos impo an aspec o hose in es iga ed he e o a skilled simula ion o he biogeochemis y a L4. This esul indica es ha his ine e ical scale should be included in u u e shel sea models used in ai –sea lux modeling s udies. Au ho Con ibu ions: Concep ualiza ion, R.T. and J.S.; me hodology, R.T. and S.C.; so wa e, R.T., M.R.-V., Y.A., L.P.; alida ion, V.K., E.M.S.W., T.S., J.F., G.T. and V.M.; o mal analysis, R.T.; in es iga ion, R.T., M.R.-V.; esou ces, R.T., V.K., E.M.S.W., C.W., J.F., T.S., V.M.; da a cu a ion, R.T., V.K., E.M.S.W., C.W., J.F., M.R.-V.; w i ing—o iginal d a p epa a ion, R.T.; w i ing— e iew and edi ing, R.T.; p ojec adminis a ion, J.S.; unding acquisi ion, J.S. and R.T. All au ho s ha e ead and ag eed o he published e sion o he manusc ip . Funding: This wo k was unded by he Eu opean Space Agency (ESA) h ough he OceanFlux G eenhouse Gases p ojec (con ac numbe 4000104762/11/I-AM), NERC Na ional Capabili y Modeling and he UK Na ional Cen e o Ea h Obse a ion. GT was suppo ed by he Eu opean Union unded con ac In o ma ion Sys em on he Eu ophica ion o ou Coas al Seas (ISECA) (Con ac no. 07-027-FR-ISECA) unded by INTERREG IVA 2 Me s Seas Zeeen C oss-bo de Coope a ion P og amme 2007–2013. Acknowledgmen s: We would like o hank he c ew o he RV Plymou h Ques o hei assis ance du ing ieldwo k and all o hose ha con ibu e o he ou ine analysis o da a om he Wes e n Channel Obse a o y. Thanks o Ca olyn Ha is o he nu ien analysis o L4 samples. All in si u da a a e a ailable om he B i ish Oceanog aphic Da a Cen e (www.bodc.ac.uk). Sa elli e ocean colo and sea-su ace empe a u e (SST) we e ob ained om he NERC NEODAAS se ice hos ed a PML. Con lic s o In e es : The au ho s decla e no con lic o in e es . Appendix A Table o all s a is ics pe o med in he expe imen s Table A1. Pea son co ela ions o expe imen s A–D, be ween obse ed and modeled a iables o 2008–2009 a he shallowes dep h a ailable. Signi ican co ela ions a he 95% a e indica ed by *. Exp Yea Va iable Dep h Co pValue RMSE Mean S d N Exp A 2008 Biomass 10 0.70 * 0.000 99.59 21.91 42.81 40 Exp A 2009 Biomass 10 0.76 * 0.000 119.83 17.85 33.54 41 Exp B 2008 Biomass 10 0.67 * 0.000 98.91 19.95 37.18 40 Exp B 2009 Biomass 10 0.75 * 0.000 120.41 17.36 34.76 41 Exp C 2008 Biomass 10 0.70 * 0.000 99.71 21.99 43.05 40 Exp C 2009 Biomass 10 0.76 * 0.000 119.83 17.85 33.54 41 Exp D 2009 Biomass 10 0.49 * 0.001 119.58 23.32 37.84 42 Exp A 2009 Chl-a 0 0.47 * 0.003 1.28 0.78 0.95 38 Exp B 2009 Chl-a 0 0.50 * 0.002 1.29 0.81 1.00 38 Exp C 2009 Chl-a 0 0.47 * 0.003 1.28 0.78 0.95 38 Exp D 2009 Chl-a 0 0.46 * 0.003 1.39 0.95 1.15 40 Exp A 2008 Chl-a 10 0.43 * 0.005 2.42 0.65 0.87 41 Exp B 2008 Chl-a 10 0.41 * 0.008 2.42 0.66 0.88 41 Exp C 2008 Chl-a 10 0.43 * 0.005 2.42 0.66 0.88 41 Exp A 2009 Chl-a 10 0.45 * 0.004 1.27 0.76 0.95 39 Exp B 2009 Chl-a 10 0.48 * 0.002 1.28 0.79 1.00 39 Exp C 2009 Chl-a 10 0.45 * 0.004 1.27 0.76 0.95 39 Exp D 2009 Chl-a 10 0.50 * 0.002 1.29 0.95 1.09 37 Exp A 2009 Chl-a 50 0.63 * 0.000 0.45 0.29 0.46 33 Exp B 2009 Chl-a 50 0.63 * 0.000 0.45 0.28 0.45 33 Exp C 2009 Chl-a 50 0.63 * 0.000 0.45 0.29 0.46 33 Exp D 2009 Chl-a 50 0.32 0.054 0.46 0.30 0.21 36 Exp A 2009 P ima y p oduc ion 10 0.40 * 0.015 23.51 17.50 23.95 36 Exp B 2009 P ima y p oduc ion 10 0.39 * 0.018 17.22 10.34 15.05 36 Exp C 2009 P ima y p oduc ion 10 0.40 * 0.015 23.51 17.50 23.95 36 Exp D 2009 P ima y p oduc ion 10 0.42 * 0.010 32.16 23.81 32.09 38 Remo e Sens. 2020,12, 2038 25 o 29 Table A2. Pea son co ela ions o expe imen s A–D, be ween obse ed and modeled a iables o 2008–2009 a he shallowes dep h a ailable. Signi ican co ela ions a he 95% a e indica ed by *. Exp Yea Va iable Dep h Co pValue RMSE Mean S d N Exp A 2008 Dia oms 10 0.66 * 0.000 53.38 16.11 38.63 40 Exp A 2009 Dia oms 10 0.62 * 0.000 27.65 12.21 30.43 41 Exp B 2008 Dia oms 10 0.64 * 0.000 49.85 14.37 33.20 40 Exp B 2009 Dia oms 10 0.61 * 0.000 29.48 12.18 32.00 41 Exp C 2008 Dia oms 10 0.66 * 0.000 53.55 16.18 38.84 40 Exp C 2009 Dia oms 10 0.62 * 0.000 27.64 12.21 30.43 41 Exp D 2009 Dia oms 10 0.13 0.415 33.21 14.93 32.09 42 Exp A 2008 Flagella es 10 0.29 0.067 6.34 3.49 5.06 40 Exp A 2009 Flagella es 10 0.76 * 0.000 7.23 3.02 3.81 41 Exp B 2008 Flagella es 10 0.29 0.068 6.18 3.22 4.54 40 Exp B 2009 Flagella es 10 0.76 * 0.000 7.38 2.83 3.56 41 Exp C 2008 Flagella es 10 0.29 0.071 6.35 3.51 5.09 40 Exp C 2009 Flagella es 10 0.76 * 0.000 7.23 3.02 3.81 41 Exp D 2009 Flagella es 10 0.48 * 0.001 7.34 5.12 7.45 42 Table A3. Pea son co ela ions o expe imen s A–D, be ween obse ed and modeled a iables o 2008–2009 a he shallowes dep h a ailable. Signi ican co ela ions a he 95% a e indica ed by *. Exp Yea Va iable Dep h Co pValue RMSE Mean S d N Exp A 2008 Phospha e 1 0.84 * 0.000 0.17 0.39 0.16 39 Exp A 2009 Phospha e 1 0.90 * 0.000 0.19 0.39 0.16 41 Exp B 2008 Phospha e 1 0.87 * 0.000 0.19 0.42 0.16 39 Exp B 2009 Phospha e 1 0.90 * 0.000 0.21 0.42 0.16 41 Exp C 2008 Phospha e 1 0.84 * 0.000 0.17 0.39 0.16 39 Exp C 2009 Phospha e 1 0.90 * 0.000 0.19 0.39 0.16 41 Exp D 2009 Phospha e 1 0.88 * 0.000 0.24 0.47 0.18 43 Exp A 2008 Ni a e 1 0.90 * 0.000 2.29 5.04 2.68 40 Exp A 2009 Ni a e 1 0.90 * 0.000 2.54 4.99 2.79 42 Exp B 2008 Ni a e 1 0.92 * 0.000 2.66 5.50 2.65 40 Exp B 2009 Ni a e 1 0.88 * 0.000 2.89 5.37 2.75 42 Exp C 2008 Ni a e 1 0.90 * 0.000 2.29 5.04 2.68 40 Exp C 2009 Ni a e 1 0.90 * 0.000 2.54 4.99 2.79 42 Exp D 2009 Ni a e 1 0.90 * 0.000 2.40 4.98 3.00 44 Exp A 2008 Ammonium 1 −0.23 0.156 0.59 0.46 0.35 39 Exp A 2009 Ammonium 1 −0.46 * 0.002 0.63 0.46 0.38 41 Exp B 2008 Ammonium 1 −0.35 * 0.031 0.70 0.51 0.45 39 Exp B 2009 Ammonium 1 −0.46 * 0.003 0.71 0.51 0.48 41 Exp C 2008 Ammonium 1 −0.23 0.156 0.59 0.47 0.36 39 Exp C 2009 Ammonium 1 −0.46 * 0.002 0.63 0.46 0.38 41 Exp D 2009 Ammonium 1 −0.40 * 0.008 0.67 0.61 0.40 43 Exp A 2008 Silica e 1 0.82 * 0.000 1.95 4.42 2.04 37 Exp A 2009 Silica e 1 0.86 * 0.000 2.17 4.47 1.97 41 Exp B 2008 Silica e 1 0.83 * 0.000 1.99 4.49 1.94 37 Exp B 2009 Silica e 1 0.86 * 0.000 2.15 4.45 1.98 41 Exp C 2008 Silica e 1 0.82 * 0.000 1.95 4.42 2.03 37 Exp C 2009 Silica e 1 0.86 * 0.000 2.17 4.47 1.97 41 Exp D 2009 Silica e 1 0.86 * 0.000 1.73 4.15 2.04 43