Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Open Access , 7 , 11 Discussions , C. Le Quéré , I. Lima 6 2 Biogeosciences , T. Hirata 11 , E. T. Buitenhuis 5 , 1 17194 17193 , S. C. Doney 4 , 10 2 , M. R. Payne 4 , 3 , , L. Bopp 2 9 , and Y. Yamanaka * , , M. N. Aita 11 8 , T. Hashioka 1 Laboratoire du Climat et deWoods l’Environnement Hole (LSCE), Oceanographic Gif-sur-Yvette, Institution France (WHOI), Woods Hole, USA This discussion paper is/has beenPlease under refer review to for the the corresponding journal final Biogeosciences paper (BG). in BG if available. Japanese Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama, Japan Institute for Biogeochemistry and Pollutant Dynamics, Swiss Federal Institute ofGraduate School Technology, of Environmental EarthTyndall Science, Centre Hokkaido for University, Climate Sapporo, Research, Japan UniversityCore of Research East for Anglia, Evolutionary Norwich, Science UK and Technology, Japanese Science andCenter Technology for Ocean Life, National Institute of Aquatic Resources (DTU-Aqua),School Technical of Environmental Sciences, UniversityTyndall of Centre East for Climate Anglia, Research, Norwich, University UK LOG, of Université East Lille Anglia, Nord Norwich, de UK France, ULCO, CNRS, Wimereux, France now at: Plymouth Marine Laboratory, Plymouth, UK 9 10 The distribution, dominance patterns and ecological niches of plankton functional types in Dynamic Green Oceanand Models satellite estimates M. Vogt Biogeosciences Discuss., 10, 17193–17247, 2013 www.biogeosciences-discuss.net/10/17193/2013/ doi:10.5194/bgd-10-17193-2013 © Author(s) 2013. CC Attribution 3.0 License. S. Sailley 11 * Received: 10 October 2013 – Accepted: 18Correspondence October to: 2013 M. – Vogt Published: ([email protected]) 4 November 2013 Published by Copernicus Publications on behalf of the European Geosciences Union. 1 Zurich, Switzerland 2 3 4 Agency, Tokyo, Japan 5 University of Denmark,6 Charlottenlund, Denmark 7 8 S. Alvain Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , ; ; 3 − ). At 2010 Moore 2013a , ). , 2012 C), but allow ◦ , erences in the ff 10 < Trichodesmium Friedlingstein et al. ; ), such as silicification Buitenhuis et al. Le Clainche et al. 2013 Doney et al. 2002 , , ) and nitrogen cycles ( ), and the export of organic mat- erences in the representation of 2003 ff ). Several state-of- the-art DGOMs , ). Thus, marine ecosystems are influ- 2012 Anav et al. , 2008 concentrations. For annual mean diatom , 2000 3 , 2010 , 17196 17195 Bopp et al. ), to study the ocean biogeochemical response ), to simulate the response of ecosystems to iron ), the response of marine productivity to future cli- Iglesias-Rodríguez et al. ), the marine sulphur cycle ( 2006 ; 2010 2006 , , Manizza et al. , ; 2006 2005 , , erent Dynamic Green Ocean Models (DGOMs; CCSM-BEC, erent aspects of global biogeochemical cycles: models were 2012 Sigman and Boyle ff ff , ). All models consistently simulate a higher zonal mean diatom in the models, and are comparable to a satellite-derived esti- 3 − Weber and Deutsch 3 − Moore et al. ). Phytoplankton activity influences the marine cycling of micro- and ; ) or the influence of plankton biomass on the bio-optical properties of erent ranges of surface NO ff Steinacher et al. Moore et al. Aumont and Bopp 1998 2007 2010 Mouw et al. Le Quéré et al. , , , ), sea surface temperature (SST), and mixed layer depth. A general additive 3 ). DGOMs are also used in many regional studies, e.g. to simulate plankton dy- Dynamic Green Ocean Models (DGOMs) are marine ecosystem models based on Marine ecosystems are complexlevels. ensembles On the of lowest interacting trophic level, speciesand phytoplankton on contribute form the several 50 % base trophic of of the photosynthetic marine food activity web on the Earth ( of the marine ecosystem inter-comparison project (MAREMIP). 1 Introduction ter resolve phytoplankton succession and phenology in the models. This work is part model (GAM) is used to mapographic the space. probability Models of tend dominance of toand all simulate nutrient pPFTs diatom in range, dominance niche whereas and overof satellites ge- a low-intermediate confine wider annual diatom temperature mean dominancefor temperatures to niches (annual a in mean narrower di SST niche dominance, the statistically modelled probabilitythe of variance dominance in explains the theviance majority data of is (65.2–66.6 %). much For lowerdiatoms the among (44.6 models satellite % and estimates, and compared the to 32.7 %). satellite explained estimates de- The highlights the di need to bet- diatoms tend to dominate large areasarea of the of high the latitudes of surface boththan ocean hemispheres, in and dominated the the by satellite diatoms estimates.dominant pPFT is We at estimate significantly each the location higher as realizedtion in a ecological function (NO the of niches annual models filled mean surface by nitrate the concentra- annual mean surface biomassand estimates 0.77 mmolCm for diatomsmate vary (0.41 mmolCm between 0.23 mmolCmbiomass contribution in theatom high contribution latitudes varies than in substantiallyhigh the between latitudes low models (20 % latitudes, with to buttion largest 100 the % in di relative of terms di- total of biomass).locations annual We where and a investigate given phytoplankton monthly PFT distribu- contributes mean more dominance than 50 patterns, % i.e. to total the biomass. distribution In all of models, We compare thetypes (pPFTs) spatial in and fourNEMURO, di temporal PISCES and representation PlankTOM5)independent to of satellite derived estimates, phytoplankton phytoplankton with distributions a functional from particular two focus on diatom distributions. Global Abstract 2006 and Doney plankton functional types (PFTs), groupsthe of ocean plankton ( carrying out(diatoms), a calcification specific (coccolithophores) function or in nitrogenpresent, fixation several (e.g. DGOMs with varyingwidely degrees of used complexity to exist, and studyused they di to have been investigate the marine carbon ( mate change ( enced by and may feedback to climate and climate change ( are included in modern coupled climate models ( fertilization ( to dust input ( Vogt et al. the ocean ( ter generated in planktic ecosystemsinterglacial is time-scales a ( major sink of atmospheric carbon on glacial- macronutrients (e.g. Field et al. 5 5 25 15 10 20 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , , , ; , 2008 1994 Flom- ort. In Hirata Ander- ff , , ; Edwards ) and the ; Uitz et al. Buitenhuis ; Hutchinson 2005 2008 Follows et al. , ) study phyto- , 2012 Subramaniam Dupouy et al. 2012 , ). ; Beaugrand and ; , 2013 ; , 2012 ), and few on mut- ( Cropp and Norbury 2009 2013 2009 , , 2004 , 2011 , Brown and Yoder cult, as few observations , Alvain et al. ; ffi Raitsos et al. Popova et al. ; Thomas et al. ; 1996 , Peloquin et al. 2008 ) investigated the biogeography of Hashioka et al. Shutler et al. , ; 2010 Bracher et al. Hashioka et al. , Brewin et al. ; ; 2012 2005 ( 2004 ), the Arctic ( , ) study the representation and grazing be- , 2007 2006 , against observations and observation-based ). Balch et al. , erent satellite algorithms exist, with some fo- 2007 17198 17197 a ff 2013 , Beaugrand and Ibanez ( ; 2011 , ) developed a model that explicitly includes species Buitenhuis et al. Alvain et al. ), or changes in phytoplankton phenology under future ). Widely applied in terrestrial ecosystems, this con- erent DGOMs, and ; 2012 ) used this model to investigate global biodiversity pat- erences between the models in terms of the mech- Smyth et al. ect of top-down vs. bottom-up control during the spring ff ff ; , ff 2009 ( Dutkiewicz et al. 2010 2003 2008 2010 , ( , , 2002 Sailley et al. Siddorn et al. ) and pigment estimations ( , 2006 , 2011 Irwin et al. Westberry and Siegel , ; Wang and Moore ; Hashioka and Yamanaka Barton et al. 2013 ) have become available. Satellite estimates provide global coverage on 2001 , Dutkiewicz et al. ). ). Thus, there is now ample data for a systematic model evaluation e , ). In recent years, however, satellite observations ( Chase and Leibold 2013b Hirata et al. 2011 2012 ) and ) uncovered the mechanisms driving coexistence and competitive exclusion in , ; ), others on size classes (e.g. reviewed in ; ) as well as in situ observations of plankton abundance and biomass ( 2005 , , Buitenhuis et al. , Here, we build upon these results and present first results on the comparison of phy- The MARine Ecosystem Model Inter-comparison Project (MAREMIP) is an interna- For DGOMs to be able to resolve and predict changes in ecosystem structure in ma- In addition to distribution data, data on important plankton traits such as temperature In the past, the validation of DGOMs has proven to be di 2012 2007 Litchman and Klausmeier ton ( ecology. For example, models were used to investigate carbon fluxes through zooplank- namics in the North Sea ( cept has recently inspiredraphers both to marine predict ecosystem and modellers explainbaum and the et behaviour biological of al. oceanog- large scale marine ecosystems ( change. toplankton biogeography in these models.to We understand use the how concept1957 phytoplankton of the are ecological implemented niche in current DGOMs ( plankton competition and the e bloom. Both findanisms significant controlling di ecosystemfor structure the and simulation functioning, of future with marine important ecosystems implications and their responses to environmental diversity, and terns and species richness. nitrogen fixers based on the concept of resource competition, and ological controls thattion, determine its the distribution global amongcal marine size provinces, classes internal biomas and and and plankton externalthe its context fluxes, groups, of variability, organisation regional and MAREMIP, in resilience distribu- haviour biologi- to of zooplankton change. in Within four di ( complex DGOMs. tional model inter-comparison initiative thatbased aims on to foster PFTs. the MAREMIP development of attempts DGOMs to identify the main physical, chemical and bi- ( climate change ( community structure, as well asservices the related functional to diversity global requiredvestigate biogeochemical to not cycles. resolve only ecosystem Thus, questions modellers of have marine started biogeochemistry, to but in- also questions of marine tem models such as surface chlorophyll estimates, but also distributions of individual PFTs and their ecologicalrine characteristics. lower trophic level ecosystemsbiogeochemistry, models and need the to consequences be of able to such reproduce changes present for ecogeography, plankton ocean et al. et al. particular, it is now possible to evaluate not only the bulk properties of marine ecosys- Southern Ocean ( fine temporal and spatialplankton scales, communities. and A are multitude highly ofcussing promising di for on long-term single monitoring plankton of groups (e.g. sensitivities or nutrient uptake( kinetics have become available for model development of biomass or physiological rates for the planktonic community were available ( and Brown 2011 liple plankton functional groups ( et al. Iglesias-Rodríguez et al. son 2012 2006 5 5 25 20 15 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , . ), 3 2 ). The 2006 , Yamanaka Buitenhuis 2004 and particu- , 3 and particulate , and particulate 3 , and a macronu- 3 3 . As in PlankTOM5, 3 and particulate SiO degrees, and it has 25 3 ◦ Moore et al. Aumont and Bopp and NO ). Calcifiers are implicitly in- 4 fixers (diazotrophs). Calcifiers ). Autotrophic and heterotrophic 2 ), CaCO l 0.8–1.8 , NH 2006 × 4 and silicium content of pPFTs, and , ◦ 2007 a , ), PISCES ( , PO , POM 3 s , and PFT biomasses and pPFT chloro- 3 2010 , 17200 17199 ). First, we compare the distribution and dom- Kishi et al. ) comprises 7 further dissolved and abiotic com- and NO 2007 4 ) and an extended version of NEMURO ( , 2004 Aumont and Bopp degrees and 31 depth levels. , ◦ ). We briefly describe the relevant features of each model , NH 2004 4 and 31 depth levels, 10 of which are located in the upper , ◦ 2007 0.5–2 Buitenhuis et al. , , PO , and silicon content for the silicifiers. For the two size classes of × 3 a ◦ 0.5–2 × Litchman et al. ◦ ; Yamanaka et al. . All PFT carbon biomasses are output variables. The extended version Moore et al. 3 Kishi et al. ). Phytoplankton growth is limited by iron, dissolved SiO ; ). The prognostic variables for the 3 pPFTs are their total carbon biomass, 2008 4 . PISCES is coupled to the NEMO physical model version 3.2 (Madec, 2008) , ), but refer to the indicated publications for a detailed description of the four 2 contents are simulated. CCSM-BEC comprises 7 further dissolved and abiotic 1 2010 2004 a and NO , , . For MAREMIP phase 0, a simplified iron cycle was implemented in NEMURO. . CCSM-BEC is coupled to the Community Climate System Model (CCSM-3), and 2 2 4 erent models. NEMURO simulates 2 pPFTs (silicifiers and small mixed phytoplankton) and 3 zPFT: CCSM-BEC represents 3 pPTFs and 1 generic zPFT ( PISCES represents 2 pPFTs, silicifiers and nanophytoplankton and 2 zPFTs, the PlankTOM5 represents 3 phytoplankton functional types (pPFTs): silicifiers (di- ff micro- and mesozooplankton ( Helaouët of NEMURO ( calcifiers are implicitly included as amicrozooplankton, constant respectively. Phytoplankton (10 %) growth fraction is of limitedNH nanophytoplankton by and dissolved Fe, SiO partments: DIC, dissolved oxygen, alkalinity,SiO DOM and POM, CaCO micro-, meso- and macrozooplankton ( plankton. The generic zPFTing grazes preferences on adjust all to size the prey classesis concentrations. of As limited phytoplankton, in by and PISCES, phytoplankton itsphyll Fe, growth graz- SiO compartments: DIC, dissolved oxygen,SiO ALK, DOM and POM,the CaCO ocean component is the 3-DCollins Parallel et Ocean Program al., (POP; Smith 2006).depth and levels. The Gent, 2004; model resolution is 3.6 late SiO with a resolution of 2 pPFTs are silicifiers, small mixedare phytoplankton implicitly and included N as a temperature and biomass dependent fraction of nanophyto- cluded as a temperature and biomassplankton dependent growth fraction is of limited nanophytoplankton. by Phyto- Fe,PISCES dissolved SiO simulates the biomass, iron,the chlorophyll biomass for zPFTs.ments: PISCES DIC, dissolved comprises oxygen, 8 ALK, further DOM, small dissolved and and large abiotic POM, CaCO compart- PlankTOM5 is coupled toa the resolution NEMO of physical 2 100 model m version of the 2.3 water (Madec, column. 2008) with iron (Fe), chlorophyll zPFTs, only the biomassand is particulate modelled. abiotic compartments: PlankTOM5 dissolved alsogen inorganic describes carbon and (DIC), 8 alkalinity dissolved further oxy- (ALK),and dissolved semi-labile large dissolved organic (ballasted) matter sinking (DOM), particles small (organic) (POM et al. trient (PO di atoms), calcifiers (coccolithophores)and and 2 zooplankton mixed functional types phytoplankton (zPFTs): (nanophytoplankton) micro- and mesozooplankton ( (Table 2.1 Model description In phase 0 ofDGOMs: the PlankTOM5 MAREMIP ( project, we compare the representation of PFTs in four inance patterns ofWe phytoplankton then PFTs identify ecological to nichessatellite patterns for estimates the derived based dominant from on phytoplanktonent environmental satellite PFTs concentrations. conditions in estimates. such model as and temperature and nutri- 2 Methods CCSM-BEC ( et al. 5 5 25 15 20 10 20 25 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , ) ) ), . B2 2011 2005 . Both 1996 ( ( a , and The root -like and ). The al- ), and good B1 Alvain et al. Le Quéré et al. 2012 , 2012 Alvain et al. , Hirata et al. Kalnay et al. erent sets of valida- ff 2008 ) quantifies the relative , 2008 ( Synechococcus 2011 2005 ). The PHYSAT method has ( ). ) is of qualitative nature and , Prochlorococcus sp. 2008 2008 2005 , ( , erences in relative pigment concen- concentrations relevant to the niche . Note that the uncertainty in chloro- Alvain et al. ff erent plankton groups detected by the a . An evaluation of annual mean model a ff -dominated waters samples were cor- 4 Hirata et al. Alvain et al. : diatoms, dinoflagellates, green algae, prym- 17201 17202 a Alvain et al. , Table Alvain et al. Alvain et al. A and phytoplankton diagnostic pigment concentra- ) quantifies the relative contribution of seven pPFTs a ), and a complete list of the parameters correspond- ) identifies phytoplankton groups, based on empirical Prochlorococcus 2011 ( 2012 ( and 54 depth levels. 2011 ( ◦ ), and the nano- (nanoflagellates or green algae) and pico- 1 × ◦ erent pPFTs considered here is included in Appendix ff Hirata et al. and 61 % of -like picophytoplankton, eukaryotic nanoflagellates (mixed phyto- varies depending on the pPFT, with 8.0 % for diatoms, 8.3 % for micro- (DMS producers), although coccolithophore dominance is known to be Hashioka et al. Hirata et al. Phaeocystis a adds an additional error component to these estimates. ), and compared with the respective model pPFTs. Satellite-derived pPFTs com- a ) when dominant are the diatoms (silicifiers), the erent wavelengths and phytoplankton diagnostic pigment spectra from High Per- The pPFTs that can be detected using the PHYSAT method ( The method of All models use the NCEP-NCAR atmosphere reanalysis forcing ( ff 2005 plankton) and the twoPhaeocystis haptophyte plankton taxaunderestimated by of the coccolithophores PHYSAT (and (calcifiers) allthresholds other and in radiance-based) method(s), the due SeaWiFS to algorithm fixed ( Prochlorococcus has a resolution of 1 NEMURO is coupled to the COCO physical model version 3.4 (Hasumi, 2006), which satellite have been grouped into pPFTs according to the classification of relationships between chlorophyll tions. Both algorithms have beention validated data. independently Whereas with di the algorithm by phyll 2.3 Association between satellite and modelFor PFTs the investigation of ecological niches, the di We use informationspace from using two remote independent algorithms. sensing Theidentifies PHYSAT algorithm on the by the dominant distribution phytoplanktonical groups of relationships at phytoplankton between any from chlorophyll-normalizeddi given water location leaving based reflectanceformance on (nLw) Liquid empir- at Chromatographygorithm (HPLC; by to gridded monthly mean SeaWiFS chlorophyll ( prise the silicifiers (diatoms),producers calcifiers ( (coccolithophores or prymnesiophytes), DMS 2.2 Satellite-derived PFT distributions contribution of phytoplankton groupsalgorithms in were three developed for size open classes ocean to conditions total only. chlorophyll 2005 been evaluated using in situ measurements in in three size classes tonesiophytes, total chlorophyll pico-eukaryotes, pico-prokaryotes,mean and squared error (RMSE) inchlorophyll percent associated with the relative contributionphytoplankton, to 8.6 % total for nanophytoplankton,average and of 7.1 6.0 % % for for picophytoplankton, all with pPFTs. an Here, we apply the formula given in nutrients and annual andspeciation of monthly the chlorophyll di indicates the most importanttration pPFT ranges (dominance), based the on algorithm di by both in signal and regional distribution ( inance). The method isis also less reliable reliable for for diatomsSynechococcus the (73 % detection of of correct therectly identification), two identified. but However, picophytoplankton for it classes, the whereto two a picophytoplankton 57 % misattribution groups, of errors of the are signal mainly of due one group to the other, since both groups are similar agreement has been found for nanoflagellates (82 % of correct identifications of dom- ing to all pPFTs is given in Appendix are given in but each model usespossible spread its in own simulatedgeneize initialization ecosystem model dynamics, routines. structure, no parameterisation, In attempts forcingtuning order (river were inputs strategies. to made and All to dust), conserve homo- validation equations the or describing widest phytoplankton growth and loss processes 5 5 15 20 20 25 15 10 25 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ; ) is ), al- 2001 , 2011 Iglesias- ) and sil- ( ; from Sea- 3 2005 in CCSM- ( erent wave- a ). Thus, the niche. Other ff , picoeukary- ) and gridded 1994 de Boyer Mon- ), but these es- , 2011 ( 2005 , 2011 , ), but these estimates Hirata et al. grid, in order to match Alvain et al. ◦ Trichodesmium 2013 180 , Trichodesmium Prochlorococcus × Hirata et al. ◦ Garcia et al. Subramaniam and Brown Brown and Yoder , or ; Dupouy et al. ). Other satellite estimates to detect ; ), dominance was defined based on ) is based on ranges and thresholds C contains the diagnostic pigment zeax- 1996 6= , 2009 Shutler et al. P 2011 2005 exist (e.g. ( ( , ; ) is assumed to contain the coccolithophore- ), and thus cannot be compared directly with P 17204 17203 , but 2004 C Synechococcus , ⊂ ). Coccolithophores contain the diagnostic pigment 2005 Balch et al. ( P derived from the empirical relationships being larger and Trichodesmium a Alvain et al. Hirata et al. 2008 Bracher et al. , ) is assumed to contain the ; Trichodesmium Smyth et al. 2011 2006 ; is a filamentous, bloom-forming cyanobacteria, which has no ( , Alvain et al. grid. 2002 Alvain et al. ◦ ). Furthermore, we use monthly mean surface chlorophyll , 1 Prochlorococcus × and model data for the period 1996–2007 was averaged, and the monthly ◦ 2004 a ( ) measurements from World Ocean Atlas ( 3 Hirata et al. ; in mathematical terms: C Trichodesmium Nitrogen fixers (diazotrophs), are modelled to represent Calcifiers (coccolithophores) have a satellite analogue in For model validation and theicate niche (SiO calculations, we use surface nitrate (NO 2.4 Biogeochemical and environmental data otes, total picoprokaryotes, respectively). Thus, satellitewith diatoms model have been silicifiers, associated andgroups the have been contributions added by up, andniche all compared calculations, small a with distinction modelled nano- between nanophytoplankton. andhas satellite-derived For nano- the been picophytoplankton and made. picophytoplankton phytoplankton ( lengths, no absolute biomass fraction correspondingbe to indicated. this definition Coexistence of was dominance defined can as a state in which several pPFTs contribute to of relative pigment concentrations and the corresponding nLw signal at di the fraction of totalthan chlorophyll 0.5. Since dominance in the pPFT constituting more thanany 50 % given of month the or biomass year. at For any given location and during the World Ocean Atlasing grid. fields, While we models focuschlorophyll have our been analyis forced on with annualaveraged interannually climatologies and were vary- monthly used. climatologies here. SeaWiFS 2.6 Definition of dominance and coexistence Dominance patterns ofpPFT the biomass individual on pPFTs a have monthly been and calculated annual as basis, a where function dominance of was attributed to 2.5 Data treatment Modelled monthly mean surface dataoriginal for the model years grids 1996–2007 were of projected each from model the onto a regular 360 timates have not beenPFT, included and on in bloom the conditions current only. analysis due to their focus on a single iron measurements from Aumont etfrom al. the (2006). World We Ocean use Atlas seatégut (2005), surface et temperature and al. (SST) mixed layer depthWiFS (MLD) on from a 1 based. Thus, as in the caseniche of the by calcifiers, the HPLC pigment-derivedsatellite pico-prokaryote estimates to detect Westberry and Siegel have not been included inon the bloom current conditions analysis only. due to their focus onBEC. a single PFT, and satellite-analogue in anthin, on which the empirical pico-prokaryote formulation by this satellite estimate. However, niche ( coccolithophore blooms exist (e.g. Rodríguez et al. though coccolithophore dominancebased is methods ( known tomarkers hexanoyloxyfucoxanthin (Hex) be and butanoyloxyfucoxanthin underestimated (But), andincluded by are thus in reflectance- thesatellite-derived prymnesiophyte prymnesiophyte class niche ( determined by 5 5 20 15 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ; , 2012 , at a certain ) which con- ), often in the I i Geider et al. ( P T f , a nutrient lim- ( f max µ (data not shown). Iron Tagliabue et al. 3 ). The so-called realized ) to characterise the real- below). . Niches based on surface 3 3.1 1957 2006 , , ), we found that the relative biomass ), a temperature term Weber 1934 Hutchinson , was defined as a pPFT constituting more , i 1913 ect seasonal succession patterns and niche , D ff 17206 17205 , ), but due to the limited availability of observed i ) and a light harvesting function ). 1994 , concentration, MLD and SST to be the axes of a 3- 1972 2012 , 3 , ) (1) I Baas Becking ( f ) erent environmental conditions. Ecological niche space was T ff Eppley ( f ) we limit the niche analysis to macronutrient availability. In this Martin et al. nut ; L Michaelis and Menten 2012 erent PFTs are still highly uncertain in models, whereas dominance i ff , P max , which is often parameterized as the minimum of several Michaelis– Hashioka et al. have also been investigated, but the strong collinearity between these µ function ( 1990 nut 3 , = L 10 i P Q ) is the current specific growth rate (1/d) of any given pPFT t , µ Martin z erent pPFTs, despite the fact that variations in MLD also correlate with variations , ff and SiO ). Annual mean MLD serves as a proxy for the solar radiation dose received by y , 4 erent pPFTs under di Here we apply GAMs as a data exploration and simplification technique to ex- Dominance was chosen as a metric for model inter-comparison, since this allowed x ff ( 1998 natural marine ecosystems ( biomass. the inter-comparison of modelsmore, with in both most satellite models,concentrations estimates of PFTs simultaneously. all Further- are pPFTs are numerically present at prevented all from times. While going this extinct, may be and the low case also in total biomass simultaneously, but no single group contributed more than 50 % to total 2.8 Niche characterisation using a generalWe additive use model Generalized Additive Models (GAMs; plore niche-space andDGOM/satellite characterise being the dominant probability undermentioned of a above, specified an dominance set individual at of pixel pPFT environmental conditions. in As a given However, unlike traditional “straight-line” regressionthe approaches, GAMs response can using represent non-parametricsponse spline is not smoothing limited functions. totional the Furthermore, approaches: assumption the Binomial, of re- a Poisson, Gaussianobservation Negative error models, Bionomial structure, amongst and, as others, in in can this more also tradi- case, be considered. Bernoulli ferences in mortality, which couldcharacteristics also ( a ized niches of eachto pPFT within extract both broad-scale the patterns.scribe DGOMs a GAMs and response are the variable satellite a as observations, an statistical and additive modelling linear function technique of that several predictor de- variables. iron concentration data forDutkiewicz comparison et al. with theinitial model analysis, fields we ( neglect loss processes such as grazing by zooplankton and dif- itation term Menten functions ( µ where location (x, y,z, t), and it is a product of the maximal growth rate has been shown togions be ( important for phytoplankton distribution patterns in HNLC re- scribes its fundamentalecological ecological niche niche is ( from, smaller and than interactions the with,sess other fundamental a organisms niche, proxy in and of thedi realized marine is ecological environment. a niche Here, result space, wedefined based as- of on as pressure dominance the patternsgrowth. hypervolume of We spanned the chose by surfacedimensional independent NO niche factors (sub-)space, because limiting models phytoplankton parameterize pPFT growth as form of a tains a parameterisation of carbon uptake during photosynthesis (e.g. the di in nutrient, temperature, chlorophyllthe and surface biomass distribution of conditions. pPFTs, Becausebase we our we use niche only SST calculation study toPO on calculate global the concentrations temperature of niche. NO macronutrients We leads to similar patterns to those based on NO 2.7 Definition of niche space The full range of environmental conditions under which an organism prospers de- proportions of di patterns are more consistently modelled (see e.g. Sect. 5 5 15 25 20 10 20 15 10 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | (2) (3) ). In , and 2 erent i , to the ff ] ∞ ), we com- , , SST i 3 −∞ [ 2011 is the modelled ( i π ; small phytoplankton: 6 % of biomass). A di- 1 . i < − , and i π Hirata et al. ). All models show a high frac- ). PlankTOM5 simulates a high 1 2 and the satellite-derived estimates erent pPFTs to total zonal annual ) in linear-regression, which is often ff 2 )(Fig. R , and references therein) for a comparison 2011 ( erent pPFTs in ff 17208 17207 2009 , for di Trichodesmium a ). While NEMURO simulates diatom biomass to be 2
i Hirata et al. cient of determination ( ,MLD i (domain [0,1]). The dominance “observations” are assumed to ffi , SST, and MLD, respectively at pixel i 3 π ,SST i Sathyendranath et al. erences between models in how biomass is partitioned into the dif- 3 ) ; ff i (Wood 2006) and a domain on the real number line i.e. 1 π ) − ( NO ... ( te is the dominance of the pPFT in question (coded as 1/0), NO i = ) D i a–d). On average, PlankTOM5 simulates the smallest, and NEMURO the high- are surface NO pPFTs 1 π Bernoulli i ( ∼ We compare the relative contribution of the di GAM models were fitted for each pPFT within each DGOM/satellite data set. In cases The quality of the GAM fit, and thereby its ability to characterise the niche, was quan- i pPFTs reveal di ferent autotrophic PFTs (Table 3.1 Global and zonal annual mean surface biomassArea-weighted global distributions annual of mean di surface biomass concentrations for the simulated 3 Results group more than 50 %)was was fitted also accordingly added to as determinefor a the each possible probability outcome outcome, of and weregroup this therefore then occurence. determined. a The evaluated Finally, GAM fitted in thegeographical GAMs niche fitted space space to dominance allow and probabilities visual the were comparisons. mapped most back likely into dominant tified based on the “explainedas deviance” an statistic: analog of this the statisticinterpreted coe can as be the best proportion understood of variance explained. where there were more than two pPFTs, cohabitation at similar biomass levels (i.e. no where MLD probability of thelinear pPFT predictor, a being three-dimensional dominant. tensor-product spline-smootherfunction, A with te a logit cubic function basis modelled is probability used toexhibit map a Bernoulli (“link”) distribution the about the expected probability then modelled as follows: D logit than 50 % of total annual mean biomass for each pixel. The observed dominance was tion of diatoms in(Fig. the high latitudes, and lower concentrations in the lower latitudes 125 gC(gChl) est fraction of diatoms in termsfraction of of coccolithophores total in biomass the (Table Tropicswhere and CCSM-BEC Subtropics (up predicts to a 40 modest %rect of fraction comparison total of between biomass), diazotrophs modelled ( larger than nanophytoplankton biomass, CCSM-BEC, PlankTOM5nanophytoplankton and to PISCES project constitute the largestthe fraction relative fractions of of autotrophic chlorophyll biomass.pute Based their on carbon equivalent usingsmall average phytoplankton carbon from conversion the factors literature for (Diatoms: diatoms 50 gC(gchl) and mean surface biomass,satellite-based as averages from simulated by the four models to the carbon-converted of the ratio between diatom andto nanophytoplankton nanophytoplankton biomass. PlankTOM5 biomass has ratio a (D diatom other : N models ratio) simulate lower than aPlankTOM5, that coccolithophore higher of biomass the D : satellite, is Nthe and of all satellite ratio equal estimates in magnitude prymnesiophyte termsbiomass. to biomass Diazotroph of diatom to biomass carbon biomass, be in biomassthe but less CCSM-BEC biomass (Table than is of one the one other fourth order two of of pPFTs. diatom magnitude smaller than 5 5 10 25 15 20 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) algorithm 2011 ( e). ) or pico- and nanophy- 1 2005 , Hirata et al. ). All models simulate diatom ), but coexistence is less wide 2011 ( ). A seasonal cycle is clearly visible 2011 ( 3 Alvain et al. ), models tend to overestimate the relative ). Thus, simulated coccolithophores tend to 17210 17209 2011 ( Hirata et al. 2011 Hirata et al. , e; areas designated by “coex”). Areas of coexistence 2 ) and Hirata et al. 2005 ( Hirata et al. e), with a high fraction of green algae and pico-prokaryotes, which 1 Alvain et al. shows the simulated dominance patterns for annual mean biomass of the 2 summarises the fraction of grid cells where a pPFT is dominant on an annual 3 In March, diatom dominated areas in the Southern Ocean decrease in all models In December, all models show a high abundance of diatom dominated grid cells In June, diatom dominated areas in the NH are shrinking in some models (NEMURO, Coccolithophores in PlankTOM5 dominate at locations where pico- and nanophyto- the winter months. We(December, March, show June here and theecosystem September), dominance dynamics to patterns in get the for a bloom four better regions selected understanding (Fig. of months seasonal Annual mean dominance patternssonality are and most good revealing coverage. in Neithercreases regions exponentially is that in the phytoplankton have case blooms alight during for low the conditions high sea- short latitude permit summer regions: growth, months biomass and when in- satellites do not observe the high latitudes during 3.3 Monthly dominance patterns satellite-based estimates of biomass areton dominated size class by (Fig. the pico- and nanophytoplank- is not possible, since they do not quantify this group explicitly. In the low latitudes, TOM5 is highest indo not March, shift but significantly overniches the the where boundaries year. satellite Coccolithophores estimates of detect in pico-toplankton PlankTOM5 coccolithophore ( tend dominance dominated to ( inhabit areas of the temperate latitudes,satellites do where not total detect biomassthose phytoplankton months is patterns due low to in duringthose the the areas. low this high light latitudes month. conditions, of we Since cannot the the validate NH model during simulationsexcept in for PlankTOM5, wherecrease diatom of diatom dominance dominated spreads.inantly areas Models detect in simulated nanophytoplankton the an dominance. NH, in- Coccolithophore where both dominance satellite in methods Plank- predom- satellite estimates. CCSM-BEC), but still expanding in others (PlankTOM5, PISCES). This indicates that in the observations and, to a lesser degree, in thein models. the Southernfor Ocean, PISCES consistent predict with diatoms to observations. dominate In total the biomass NH, in all the Arctic models Ocean except and parts inhabit niches that are dominated by plankton types of a similar or smaller size class in predicts large areas inthan the 50 % NH of where totalin biomass none (Fig. PlankTOM5 of agree the with individual those pPFTs of constitutes more However, they constitute a small fractionble of biomass in themean Tropics in and both Subtropics. numerical Ta- and satellite models. spread in the model,these also models. due Diazotrophs to do not the dominate comparatively the low annual number mean of biomass pPFTs in CCSM-BEC. included in four models, as compared todetected the by chlorophyll-based annual meandominance in dominance the patterns high latitudes,and and low mixed phytoplankton latitudes. dominance However, in modelsdiatom the appear dominance, temperate to both overestimate inthe the the two area Southern satellite of estimates. Ocean annual and mean in the Arcticplankton when dominance compared is to detectedithophore by dominance the is satellites. rare In on both the satellite annual mean. estimates, The coccol- contribution of diatoms to annual mean surface biomass (Fig. 3.2 Annual mean dominance patterns Figure constitute a fairly constant fractionsatellite of equivalent to biomass the at coccolithophores, all the12 latitudes % prymnesiophytes, (not contributes to less shown). total than The satellite-derivedsphere closest (NH). carbon Compared in to the temperate latitutes of the Northern Hemi- 5 5 15 20 10 25 20 25 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) ). 4 2008 2011 ). The Alvain , ( ). 4 2 concentra- ). ), but prym- 3 3 4 ect of MLD was , that explain the ff Alvain et al. 3 ) was constructed Hirata et al. , Fig. ). The large areas 2 2011 ( 2012 , , SST and MLD 3 concentration and SST explains ), cannot be detected in 3 a). The diatom niche is divided into Hirata et al. , the algorithm by concentration and SST (Fig. 2011 4 2 ( 3 ) to characterise the dominance niches Hashioka et al. 2.8 17212 17211 . As in Fig. 3 ) suggested that although the e Hirata et al. C ) nor the one by ), possibly also because MLD is highly correlated with 7 2008 ( ). While the cohabitation of more than 2 pPFTs with equally 3 , respectively). On annual time scales, modelled diatom dom- ), but the simulated diatom niches are constrained to a tight range 2 concentrations. 2011 , a Alvain et al. . In order to simplify visualisation and interpretation, the remainder of this 3 a–d). Models simulate diatom dominance for a wider range of environmen- ) nor simulated in PISCES and NEMURO, and is rare in PlankTOM5 and, er in the simulation of phytoplankton succession during the spring bloom, 4 ff . 1 2005 ( Hirata et al. Consistent with expectations for nanophytoplankton, the GAM predicts its dominance For PlankTOM5, the GAM predicts a narrow niche for diatom dominance at low tem- There are several general patterns that are consistent between models. For all mod- Diazotrophs in CCSM-BEC do not dominate total biomass during any given month, In September, all models show areas with diatom dominance in the Arctic, and sev- included MLD in the GAMin model was explained relatively deviance; minor for Fig. the four models (5–10 % gain statistically significant, the “added-value” (in terms of addition explained deviance) of both in terms of theof relative biomass the fraction timing attributed to of this the group, and spring also bloom in terms (see e.g. models di two sub-clusters, each describing one hemisphere.region PlankTOM5 in simulates niche an extended space to be dominated by coccolithophores, (Fig. the majority of the variancemates, (65.2–66.6 the % deviance of explained deviance is explained). much For lower the (44.6 % satellite and esti- 32.7 % for peratures and high nutrient concentrations (Fig. tal conditions (Fig. of environmental conditions forcally the modelled two probability based satellites. on For annual diatom mean NO dominance, the statisti- inance is correlated moresatellite-derived diatom strongly dominance. with annual mean environmental conditionsover a than wide range of temperatures and nutrient concentrations for all models (Fig. in niche space where nonethan of the dominance pPFTs comprises is more wide-speadet than al. 50 in % of biomass,by rather model construction, in CCSM-BEC (low total diazotroph biomass; see Fig. and In addition, satellites resolve thesize distinction between classes nano- that and are picophytoplankton, not two distiguished in the models. Coexistence, defined as the area els, the GAM predicts diatom dominancetions at (Fig. low temperatures and high NO SST and NO analysis is therefore performed withlargest proportion the of two the variables, deviance. SST and NO for a correct identification ofhigh phytoplankton chlorophyll pattern in coastal and upwelling areas with and are thus not represented in Fig. GAM explained the majoritypatterns of (typically the around variability 65–70well (deviance) % for in on the the average) two simulatedExploratory for satellite dominance analyses the estimates models, (Appendix (around but 40 % it of performs deviance less explained on average). 3.4 Ecological niches of the dominantWe pPFTs: use NO the GAMfor described each above model (Sect. and pPFT as a function of NO identifies extended regions where50 none % of of the biomass. detectedby In pPFTs any June constitutes single and pPFT more (Fig. September, than high large contributions to areas biomass of ismodels possible the both rarely in NH simulate PlankTOM5 an are and equal in not biomass CCSM-BEC, dominated distribution these among pPFTs (Fig. eral models predict increased areaswelling of areas diatom of the dominance SH. alongthe Satellite the North estimates coasts Pacific detect and and the small in Southern areasthe up- Ocean of method and diatom of the dominance continental in coasts. However, neither of diatom dominance in theoccur Southern at Hemisphere very (SH) low infraction CCSM-BEC of biomasses, biomass and as also NEMURO these inin the models Fig. SH predict winter, which diatoms contributes to to the constitute SH a biomass pattern large 5 5 10 25 15 20 15 25 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Hirata Alvain ) in the 2 ). Coccol- C), and at ◦ e). In 4 ering nutrient 20 . In the annual ff 2011 ( > 3 and concentrations. Fur- shows, that the niche 2 3 1 ), and annual mean tem- 3 − ), picophytoplankton domi- Hirata et al. 2005 ( ), diatoms dominate niche space er most in the representation of this ). In niche space, the GAM predicts ff 10 mmolm 2008 2 ( > ] c). Diatoms dominate the entire South- 3 4 17214 17213 Alvain et al. Alvain et al. and high SST niche in 3 ). Both diatom and nanophytoplankton dominate niches ). This niche is located in the North Pacific, where C), i.e. close to the Antarctic continent (Fig. 2 ◦ 2 0 concentrations. As in the other models, nanophytoplankton < 3 e and f). In 4 ). They can dominate over a wide range of temperatures and at 2 ), other factors than those investigated here control a significant frac- 7 erences in SST only in ). Models predict a high probability of diatom dominance in the high lati- ff 5 ), diatoms inhabit their largest niche at low nutrient concentrations and low ) detect nanophytoplankton dominance. , Fig. C 2011 2005 ( ( In both satellite algorithms, nanophytoplankton occupy a large area in niche space, Since the deviance explained by the GAM is relatively low for the satellites (Ap- For NEMURO’s ecosystem, the GAM derives two distinct niches for the dominance CCSM-BEC simulates a large area of diatom dominance in the Southern Ocean PISCES shows a considerable fraction of pixels dominated by diatoms on the an- concentrations (Fig. dominate at low to intermediate temperatures, but at variable and di PlankTOM5, coccolithophores tendphytoplankton to (Prochlorococcus inhabit and Synechococcus-like niches phytoplankton, that Fig. are characteristic of pico- nesiophyte dominance on the annual mean is rare according to the two satellites. In in the high latitudes of both hemispheres. group (Fig. mean, models and satellite estimates do not agree on the extent of diatom dominance nance is confined toithophore/Haptophyte a dominance low detected NO byGAM the does not satellites model is a rare, sizeable niche which for is this3.5 group. why the Spatial probability patterns for theWe dominance now of apply diatoms the GAMscale. to Here, determine the we probability onlysatellite of estimates, pPFT show dominance since the on models probability the and global of satellite diatom di dominancetudes, and for a both low model probabilityability in and for the dominance low of latitudes.of diatoms Satellite the estimates on statistical predict the model a annual are low mean prob- consistent almost with everywhere. our The findings results in Figs. and they arethermore, found picophytoplankton at dominate a atWheras wide high SSTs picophytoplankton range and of dominance lownance MLDs, is nutrient by concentrations. SSTs separated di and from NO nanophytoplankton domi- intermediate-high NO dominate in regions withtures. low nutrient concentrations and intermediate-high tempera- pendix where nutrient concentrations are highperatures ([NO are low (SST et al. to intermediate SSTs (Fig. et al. ern Ocean ecosystem,NH and Oceans are (Fig. abundantly dominant in the Arctic and temperate tion of the variability in the dominance patterns. However, for both satellites, diatoms and the Arctic Oceanare (Fig. characterised by a wideatoms range dominate of at nutrient lower temperatures concentrations and andnever higher dominate temperatures, nutrient but on concentrations. di- the Diazotrophs annualintermediate mean, nutrient but concentrations. occur A atrepresentation high comparison of temperatures this with ( pPFT Fig. isphytoplankton. fairly consistent with the niches of satellite-derived pico- of diatoms and nanophytoplankton (Fig. intermediate temperatures. PISCES is thedominant only in model most where upwelling diatomswhich are regions is consistently (Agulhas, explained by Westerntrations. the Boundary sub-niche Current of Systems), diatoms at intermediate-high nutrient concen- satellite estimates. nual mean, both fordiatoms to the populate NH a wide and range the of low-intermediate SH nutrient (Fig. concentrations, and low- 5 5 25 15 10 20 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , , , 2 ) and ) predict ). There- 2005 Buesseler Lima et al. , ; 2011 2012 ( , Laufkötter et al. suggests that the 2013 erential uptake of ff , 2010 3.1 , ). Since diatoms tend to Le Quéré et al. Hirata et al. ), yet diatom dominance is ), total annual export is gen- 2006 4 , – 2 2005 , Laufkötter et al. erentially on the elemental cycles of ff erences in cellular N : P ratios are crucial Weber and Deutsch ff Bopp et al. 17216 17215 Sarmiento and Gruber ; 2006 , ). If satellite estimates of diatom biomass and diatom domi- ). However, two of the models studied here already include an ). Recent model studies suggest that the di 2013 ). The validation of diatoms with monthly data showed 54 % of cor- Jin et al. , 2005 1993 ; , , , as well as between pPFT di 2008 4 , 1998 , Lima et al. Goldman ). In the satellite estimates, pPFTs pertaining to the small phytoplankton size and PO ). Globally, diatoms have been estimated to contribute around 50 % to the ex- ; ; 3 3 On the other hand, current satellite estimates may underestimate the biomass contri- On the one hand, models may overestimate diatom dominance and biomass. Such Alvain et al. Buesseler Le Quéré et al. models and satellites could be due to several factors: an overestimation of diatoming biomass of would carbon have exportcoupled consequences in to for marine patterns our systems, of understand- since relative modelled diatom export abundance ratios ( are often tighly common in the models. The discrepancy in diatom dominance and biomass between nor picophytoplankton have beenexcept associated for their with contribution a to specific background NPP biogeochemical and function export ( Current Dynamic Green Ocean Modelscommon simulate size 2–3 classes: pPFTs, andcontrast large all to diatoms have models, at satellite and least pPFTstoplankton, a two divide and the group a small varying of size number class small of into subgroups. mixed Given nano- that phytoplankton. and neither picophy- In nanophytoplankton 4 Discussion rect identification, but agreementwere with used (73 in % situ ofa correct data substantial identifications). fraction was of Similarly, much while diatoms in better the when temperate daily and high data latitudes, this fraction could bution and/or dominance of diatoms inthat the surface since ocean. diatoms Previous studies distribution havederestimated can shown in be PHYSAT when highly data variable were( averaged in on space monthly and and longer time, time they scales are un- erated throughout the year, rather thanshow during that a export few production months only. inover Preliminary the the time analyses models period of is several proportionallotte months, rather Laufkötter, to than personal NPP, during and communication, short thus 2013).the blooming generated A events inter-comparison (Char- subsequent of study plankton and should export focus seasonality. on port of particulate organic( carbon, based on a comparison with diagnosed opal fluxes 2013 nance are realistic, then aon large short proportion time scales, of ie. total duringnon-dominant annual groups. the export As spring models should bloom simulate or be diatoms duringtotal generated to episodic biomass constitute export and a events, export significant or all fraction by of year round ( dominate model export in currenttime DGOMs, scales this on suggests which that annual the2013 mechanisms export and is the generated should be revisited ( that their N : Pare ratios left are with only oftenseveral one fixed nutrients common to pPFT and the that other Redfieldsilicate acts ratio, chemical and di the compounds: contribute DGOMs significantly the analysed1998 to silicifiers high here (diatoms), latitude which productivity and use export ( NO for our understanding of the nitrogen cycle ( discrepancy between simulated and satellite-baseddiatom relative dominated extent areas of for diatom the and non- global ocean may raise important questions (Figs. 4.1 Biomass distribution and dominance patterns A critical lookand at zonal the mean annual surface and biomass monthly estimates presented mean in dominance Sect. patterns and the global fore, either a more flexibleplankton plankton group stoichiometry into or calcifiers, a( nitrogen sub-division fixers, of DMS the producers smallexplicit etc. phyto- representation would of be calcifiers desirable orrepresentation nitrogen of fixers, calcifiers and all as modelsdevelopment a in contain fraction this an of regard. implicit nanophytoplankton, thus advancing model and class appear to dominate most ocean regions (Figs. 5 5 25 20 15 10 10 15 20 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , ). erent ff 1997 , Hashioka Hashioka et al. ects. A quantita- Buitenhuis et al. ff Nelson and Brzezinski ) suggest that fucoxanthin concentra- ). The model-data misfit could thus be ). This suggests that the representation ered widely among models. In particular, bins may also lead to an underestimation ff 2013 ◦ ( 1975 2005 , 17218 17217 , suggest that the mismatch between model and 5 ). In combination with the findings of and 4 Peloquin et al. Bopp et al. 2005 erent ecological niches for the dominance of the di ). , ff , coccolithophores). If satellite estimates are reliable, a better erences between models and satellites. 2013 ff , ). Thus, errors in simulated vertical ecosystem structure are un- . Spatial averaging into 1 a Gordon and McCluney 2012 Bopp et al. erent between models. In order to better understand phytoplankton dy- , ff Phaeocystis ) for the same set of MAREMIP phase 0 models. Diatoms have been found Sailley et al. ). 2012 ), the patterns in Figs. ( Other important biogeochemical functions, such as DMS production or calcification, Last but not least, diatoms could produce the majority of export despite the fact that Alternatively, diatoms could dominate at deeper depths than the penetration depth 70 % to total biomass in three out of the four models. However, mechanisms that con- 2012 Leblanc et al. top-down and bottom-up processes has recently been discussed in detail in of diatoms and nanophytoplankton at the bloom maximum, and the relation between mean chlorophyll of patchy diatom distributions. of the satellites ( be underestimated since empirical relationships were applied to monthly and annual with observation, diatoms tend tosucceeded stay by important other throughout pPFTs the ( year rather than being satellite estimates of thedynamics diatom and niche phytoplankton may succession be ina due the variable models: to while a but models poor significant tend representation to fraction of simulate of bloom diatoms during the bloom maximum consistent of phytoplankton phenology needs to be improved in the models. ( were widely di namics and competition duringand blooms, species the interactions quantification on ofaddition, temporal it biogeochemical scales is function crucial shorter to thantal constrain one data the controls ( month of is the necessary. higher tropic In levels with experimen- than satellites ( representation of bloom dynamics andorder phytoplankton succession to may resolve be the necessaryfor in level ocean of biogeochemistry. The complexity timing and required magnitude to of the represent bloom, all relative abundance processes important et al. to be important contributors> to biomass at thetrolled bloom diatom maximum, dominance with during the a bloomthe contribution di relative of importance between top-down (grazer-mediated) and bottom-up processes 4.2 Modelled ecological niches of phytoplanktonModels PFTs clearly simulate di pPFTs that they represent.the In particular, niche the of diatom thenutrient niche smaller-sized tends concentrations. phytoplankton to by In be lower contrastfound separated temperature to to from values the be and satellite dominant higher due estimates, on to diatoms the the in annual fact models mean that at are models a simulate wide diatom range dominance of in MLDs. more This months is of mostly the year seem to be carriedare either out rare by in terms pPFTsshort of times that the ( the extension of satellite spatial models dominance, or detect dominant to only during be groups that It is conceivable thatout important by biogeochemical rare processes speciestime, are but and predominantly at not carried present few bytive DGOMs the investigation allow of for groups the the that quantification relativeof of dominate biomass biogeochemically such fractions an relevant e of ecosystem ecoystem all functionsbe at PFTs based desirable, and any on but their given observational co-located link data2013b to data would patterns is still limited on the global scale ( likely to explain the di their annual mean biomass is smallervational than evidence expected. that This is find supportedplankton that by groups some diatoms are obser- dominate quantitatively more export important also (e.g. in regions where other tions, indicative of diatom presence,at are highest almost in all the latitudes. upper( This 30 m is of confirmed the water by column in situ oberservations of diatom biomass due to models consistentlyever, simulating preliminary results diatom from dominance in too shallow waters. How- 5 5 25 10 20 15 25 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , , and 3 ) rep- 2013 , ) or during 2011 ( , a filamentous 2013 , Luo et al. Hirata et al. Iglesias-Rodríguez et al. Alvain et al. ). Hence, the study of interan- ), coccolithophore dominance Trichodesmium ) nor ), along with oxygen, solar ra- ). Thus, their (presence) niche grid. While such an aggregation ◦ 1 2011 2008 2005 ), so dominance may be rare during 2012 , 2012 ( × , ), suitable metrics to quantify model- , , ◦ ) detect coccolithophore blooms mostly 2013b 2011 2013 , , , ). Since iron is not included in this analysis, ). Thus, an investigation of daily and weekly er from the fact that in parallel to the increase 2012 ff 17220 17219 , Alvain et al. Masotti et al. Alvain et al. 2013 2008 ; , , Luo et al. Dutkiewicz et al. 2008 ), but tends to be low compared to the biomass of other Hirata et al. ; , ; Buitenhuis et al. Moore et al. 2013 2002 Luo et al. , ; 2005 , Alvain et al. , 2005 Alvain et al. , O’Brien et al. Moore et al. Alvain et al. ). A patterns only. Such studies still su Balch et al. a ; Firstly, in this initial study, we use annual and monthly mean environmental condi- Diazotrophs in CCSM-BEC have been modelled after Coccolithophores dominate in parts of the low latitudes in PlankTOM5, and their factors controlling the position and extent of their ecological niche ( observed biomass and distribution patterns for this taxon will shed more light on the modelled niche is inhabited by picophytoplanktonvestigated. in the Both satellite-based these estimates and we other in- remote sensing algorithms ( drivers of phytoplankton biogeography. However,lution for the global latter scale applications, co-locatedSimilar high nutrient, arguments reso- light holds for and the temperature aggregation data of would data onto be a required. coarse spatial grid. ENSO cycles (e.g. nual variability in dominance patterns might lead to a more robust identification of the time scales would lead to aSimilarly, better phytoplankton quantification composition of the has respective beennual phytoplankton to niches. shown decadal to time scales vary due significantly to changes on in interan- climate (e.g. tions to characterize theplankton distribution community, aggregated and onto a dominance coarsewill patterns 1 capture of north–south theacterize gradients modelled interannual phyto- and variability, large seasonal scalewith succession large patterns, patterns, spatial we bloom heterogeneiety. are eventsbiological However, data or the unable onto areas temporal to longer averaging time char- the of intervals satellite has highly estimates been variable ( shown to decrease the accuracy of data fit for ecologicalcaveats that properties need to still be need borne to in mind be when developed. interpreting our Thus, results: there are several phyll of independent sources ofused validation here ( data such as the pPFT distribution from space Brun et al., 2013). 4.3 Caveats of this study This study attempts to quantitativelythe and qualitatively four compare DGOMs ecological participating properties in of MAREMIP phase 0 beyond the evaluation of chloro- never dominant in theand annual low nutrient mean, concentrations but ( occur at high temperatures, shallow MLDs cyanobacterium. At present, neither resent a satellite group that could be compared directly to this pPFT. Diazotrophs are is occupied bystudies the identify picophytoplankton iron as size oneoccurence class of ( in the most both important satellite explanatory variables estimates. for Recent diazotroph ithophore ecology and itsregime implications or for on ocean longerpicophytoplankton class, biogeochemistry time and outside scales. the congruence However, the ofsatellite-derived none bloom the picophytoplankton of simulated niche coccolithophore the highlights and that models the closest coccolithophores simulates in are an physiology the to explicit PFT Appendix this class, which is consistent with its parameterisation (see may be moreshows wide-spread, that and coccolithophore their biomassSubtropics niche is ( wider. highest A inpPFTs temperate recent in latitudes, the in and annual situ mean not ( non-blooming data in conditions. compilation Thus, the dominance patterns may be poor predictors of coccol- diation and temperature ( our study is unlikelyMLD to as accurately the predict only predictor the variables. diazotroph A niches direct using inter-comparison SST, between NO modelled and 2002 in temperate andoutcompeted high by latitude the environments, otherbe where pPFTs. under-estimated modelled Given by that coccolithophores the coccolithophore satellites are occurence ( is known to 5 5 25 20 15 10 20 25 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , , , ). B1 Sail- Follows Peloquin ). In partic- ). Skill met- Follows et al. ). Modelled iron Tagliabue et al. ), however, must 2009 Buitenhuis et al. 2013b , ; , 2012 2005 , , between model accuracy ff Stow et al. ; ), since surface data does not 2009 Buitenhuis et al. 2013 , , Le Quéré et al. Dutkiewicz et al. ; 2002 ). The use of suitable skill metrics developed , 17222 17221 Doney et al. 2005 , Peloquin et al. Moore et al. Anderson ) and succession patterns, the role of higher trophic levels ( 2012 , ), as well as ecosystem services specified by the biogeochemical and ). ) will allow for a more comprehensive model validation with in situ data, and 2013 , ), and the release of a global database on phytoplankton pigments ( 2007 2013 ). Thus, iron has been excluded as a predictor variable for the ecological niches of http://www.earth-syst-sci-data-discuss.net/special_issue9.html , , erent pPFTs in this study, even though iron availability has been suggested to con- At present, DGOMs do not simulate a large number of biogeochemically active In addition, future models should include a representation of mechanisms that allow Secondly, while their coverage has been improved in recent years, some key vari- Thirdly, the inter-comparison of models with pPFTs from space remains challenging ff Hashioka et al. resolved data is desirable (e.g. ocean. Beyond that, the comparison of modelled ecosystem composition with depth- ables such as iron are still unavailable for a large fraction of the globe ( role of diatoms ininvestigated terms in future of studies. biomass contribution and diatom seasonalitypPFTs needs explicitly. The to inclusion be ofgo further hand in PFTs hand ( withand a quantificative model estimate predictability of ( the trade-o Here, we presentDGOMs, a as compared first tosistent analysis two annual of mean independent patterns satellite surfacecharacteristics in estimates. (wide), pPFT nanophytoplankton Models but distribution disagree distributions simulate (abundant) withdominance con- and simulated satellite (rare) niche and estimates by the on width the four of extension the of diatom diatom niche (narrow). Our study shows that the represent plankton community structureet and al. functioning to its full extent (e.g. 5 Summary and conclusions for the validation of pPFTroles biomass is and distribution a patterns, crucial as step well in as this their ecological regard ( rics need to be expandedfunctioning. to include Such more metrics aspects should ofbiomass marine fractions, include ecosystem functional measures dynamics diversity, interannual and to( variability, phytoplankton quantify phenology relative andley absolute et al. ecological function of simulated groups. for a largerels plasticity with of self-assembling the communites PFTs may that prove useful are in modelled. this Trait-based regard models ( and mod- 2012 di trol diazotroph distributions ( marine ecosystems, such aslication global of export the production MAREDATsue, global or atlas nitrogen of fixation.2013b plankton The funtional pub- typeset (ESSD al. Special Is- due to a mismatchlites between use the a detected mixcal classes, of function. and classes Marine since defined ecosystem boththeir based modellers models definition on need and of size, to satel- functional andtheir rigorously groups, those changing and and data based iteratively inform requirements. on question thecommon Our biogeochemi- remote denominator analysis between sensing uses model community dominance andrelative about patterns satellite fraction as estimates, of smallest but the biomassestimator validation with of of ecosystem co-located the structure inular, and situ dominance functioning measurements patterns ( would are be unsuitable a to better quantify ecosystem services provided by it will assist thevations improvement and of algorithms satellite that allow algorithms.be for Improved essential more for remote direct ecosystem determination sensing monitoring of obser- and PFT the abundances detection will of future change in the surface explained to a highGAM degree is by considerably few predictor lower for variables.dictor the The variables satellite deviance increases explained estimates, the by though,for deviance most our and explained models. more a significantly larger for set satellites of than pre- fields are available, but theirHowever, here agreement we with show that existing large data scale, is latitudinal patterns poor of (see simulated Appendix pPFTs can be 5 5 15 10 20 25 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , are 3 from the 0.7, and and trace for Plank- a = 2 close to the a ρ σ concentrations ), and incorpo- 0.9. In addition, a Buitenhuis et al. ) close to the ob- D concentrations. 2013 ρ > σ , a the average concentra- × concentrations with Pear- are under- or overestimated 0.995 and a 3 C. Modelled MLDs are less are lower than ◦ σ ρ ρ > 0.1 (NEMURO), and correlations , respectively). The RMSE is low Barton et al. 1 = ) or underestimated (PlankTOM5, ; − 1 s and NO − ρ cients below. 4 ffi 4 2012 concentrations, however, is still challeng- , a 17224 17223 ). In order to simulate realistic plankton com- cients for simulated annual surface SiO ffi 2007 0.9 and standard deviations ( ) and ecosystem functioning. Only then will our mod- and 7.62 mmol L 0.43 nM). RMSEs are 1–3 , 0.4). For iron, simulated > 1 concentrations tend to be over- (PISCES, CCSM-BEC 0.7 (PISCES) to = − ) C. The RMSE is below 1 3 . The average RMSE is of the order of the annual mean ◦ Edwards et al. = ρ concentrations in the North Atlantic. The accurate simu- 2013 σ 22.24–28.68 mmol L obs ; s , σ a ρ = > ρ > σ 2012 , cients ( ffi : comparison with SeaWiFS satellite data ) by the models. The model data agreement is poorest for annual 1 a , but deviations from the observations are larger for SiO − 4 Sailley et al. 0.74–0.92), with only NEMURO and CCSM showing shows the global patterns of annual mean surface chlorophyll = and PO Thomas et al. concentrations in the subtropical gyres. Highest chlorophyll 6 of the mean). Correlation coe ρ 3 Bruggeman and Kooijman ; a ; 1 3 13.16 mmol L = Figure as NO elements such as iron.observational For value SST, of all 10.46 modelsconsistent obtain with a observations:most Correlation models coe underestimate MLD. B2 Chlorophyll σ mean surface iron concentrations: Nonethe spatial of variability the (0.20 models canby explain a more factor than of 40tions. % 2 of All or higher in ( all, models provide a consistent picture of oceanic macronutrients such 2007 model outputs range from are low because most models underestimate overall chlorophyll vations. NEMURO shows highest chlorophyll- awhich concentrations is in the due Southern to Ocean, underestimate a chlorophyll simplified iron limitationlation in of this absolute model. annual PlankTOM5 meaning and chlorophyll for CCSM-BEC models. Pixel-wise correlations between SeaWiFS observations and individual TOM5, PISCES, NEMURO, andSeaWiFS CCSM-BEC, satellite as for compared the years toin 1997–2006. chlorophyll chlorophyll All models between correctlyphyll the simulate high the gradient andare the simulated low close latitudes, toas the and in coasts, the show Southern and Ocean the in and the close lowest to North chloro- the Atlantic Antarctic continent, and in North agreement with Pacific, obser- as well lower ( the standard deviations in SiO and NEMURO; range: All models simulateson annual correlation mean coe surfaceservational PO value (0.72 mmol L (ca. 2013b rate findings from theoreticalweb dynamics ecology ( to widen our ecological understanding of food All modelCCSM-BEC parameters and NEMURO describing are given phytoplankton in Table growth in PlankTOM5,Appendix B PISCES, Model validation B1 Physical variables and nutrient concentrations Appendix A Model parameters munities, we must exploit the wealth of new data that is available ( els be able to quantifyresponse the to impacts climate of change. future changes for marine ecosystems, and their 5 5 25 15 20 10 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , , , SST , 2005. 17212 17211 3 , , 17200 , 17203 17210 , , 17199 , 10.1175/JCLI-D-12- 17202 17209 , , 17196 17222 17202 17201 , 17204 , 10.1029/2004JC002560 , , , 2006. cients (Spearman rank correla- ffi 17197 17201 17197 17203 , 17220 , are compared on a monthly basis, we , 2005. 17197 a , 2008. 17201 , 2013. , 17205 ) show large areas of high positive temporal 17226 17225 6 , 2012. 17197 erent combinations of input variables (NO ff 10.1029/2005GB002591 is high in the NH. All models tend to simulate chloro- a below. 17220 , 2005. , 7 10.1093/plankt/fbi076 17219 10.1029/2007GB003154 , M.V. acknowledges funding from ETH Zurich and EUR-OCEANS. E.T.B. 10.1016/j.rse.2013.01.014 0.42, range: 0.24–0.65) than in NH autumn (September–November; ) with the observations during the NH summer (June–August; mean: 10.1364/OE.20.001070 17196 0.8) with observations. s 17216 17220 = 17203 ρ , , > s , concentrations (see Fig. t ¯ ρ ρ 0.29, range: 0.09–0.49). Hence, the model-data agreement is better during a , 2013. = cient 17214 17219 s 17197 , , ffi seasonality better in the high latitudes (North Atlantic and Southern Ocean) ¯ ρ 10.1016/j.dsr.2005.06.015 a 0.46, range: 0.35–0.6) than0.38, range: during 0.17–0.59), the and that NH correlations winter are (December–February, stronger mean: in NH spring (March– = = When observed and simulated chlorophyll 17219 neni, R., and Zhu,cle Z.: in Evaluating the the CMIP5 land Earth00417.1 and System Models, ocean J. components Climate, of 26, the 6801–6843, doi: global carbonRes., 27, cy- 1073–1081, doi: Biogeochem. Cy., 20, GB2017, doi: bonate measurements in theSpectroradiometer surface data, global J. Geophys. ocean Res., based 110, C07001, on doi: Moderate-Resolution Imaging huis, E. T.: Rapid climatic132, driven 195–201, shifts doi: of diatoms at high latitudes, Remote Sens. Environ., of dominant phytoplankton groups inCy., 22, the GB3001, global doi: ocean: a17212 satellite view, Global Biogeochem. and implications for1070–1083, phytoplankton doi: groups detection in case 1 waters, Opt. Express, 20, Zoon, The Hague, the Netherlands, 1934. Atlantic. 2. Relating1996. optics to coccolith concentration, Limnol. Oceanogr., 41, 1684–1696, groups in case I watersdoi: from global SeaWiFS imagery, Deep-Sea17214 Res. Pt. I, 52, 1989–2004, s s ¯ ¯ Alvain, S., Moulin, C., Dandonneau, Y., and Bréon, F. M.: Remote sensing of phytoplankton References mean: the seasons where chlorophyll phyll than in the lowlow. Both latitudes, the where model average withchlorophyll concentrations the highest and (PISCES) seasonal and variability the are lowest (CCSM-BEC) absolute find that models achieve higher spatialtion correlation coe coe May; mean: Anav, A., Friedlingstein, P., Kidston, M., Bopp, L., Ciais, P., Cox, P., Jones, C., Jung, M., My- Anderson, T. R.: Plankton functional type modelling: running beforeAumont, we O. can and walk?, Bopp, L.: J. Globalizing Plankton results from ocean in situ iron fertilization studies, Global Balch, W. M., Gordon, H. R., Bowler, B. C., Drapeau, D. T., and Booth, E. S.: Calcium car- Alvain, S., Le Quéré, C., Bopp, L., Racault, M.-F., Beaugrand, G., Dessailly, D., and Buiten- Baas Becking, L. G. M.: Geobiologie of Inleidning Tot de Milieukunde, W. P. Van Stockum and Alvain, S., Loisel, H., and Dessailly, D.: Theoretical analysis of ocean color radiances anomalies Balch, W., Kilpatrick, K., and Holligan, P.: The 1991 coccolithophore bloom in the central North Alvain, S., Moulin, C., Dandonneau, Y., and Loisel, H.: Seasonal distribution and succession Acknowledgements. correlation ( Appendix C GAM sensitivity tests – model deviances explained Modelled deviance explained forand di MLD) are shown in Fig. acknowledges funding from EMBRACEedge (EU funding contract from 282672).Villum the M.R.P. would foundation. Centre like S.C.D., of to I.L.,Oceanography Ocean acknowl- Research and Life, and S.S. a Education acknowledge(EF-0424599). (C-MORE), VKR support M.V. an would center from NSF like of to Science thefor thank excellence and fruitful Center J. Technology supported discussions. Peloquin, Center for Z. by Microbial Lachkair, the D. Santaren and C. Laufkötter ρ ρ 5 5 20 15 30 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , 1994. , 2010. , 2010. 17198 , 2007. 17203 , , 2008. 17198 17196 17198 17216 , 17197 10.1007/s10021-011- 17203 17223 17204 , , , 2006. , 2012. 17215 10.1029/93JC02156 , 2009. 17197 17197 17221 , 2004. , 10.1029/2009GB003601 10.1126/science.1184961 10.4319/lo.2007.52.4.1533 , 1998. 10.3354/meps07402 , 2011. , 2011. 17219 , 17218 , 17217 ek, N., Doney, S. C., Leblanc, K., Le Quéré, C., , 17216 š 10.1029/2005GB002511 17222 17227 17228 10.5194/bg-6-751-2009 17197 , 2005. 10.1029/2004JC002378 during glacial periods, Paleoceanography, 18, 1046, 10.1029/97GB03366 17196 , 2009. 2 y, J. E., Barry, J. P., Chan, F., English, C. A., Galindo, H. M., 17196 ff 17223 , 2013b. surface blooms in the South Western Tropical Pacific, Biogeo- 10.5194/bg-8-3631-2011 10.1016/j.rse.2010.09.004 , 2003. 10.1146/annurev-marine-041911-111611 , 2013a. , 2013. 17198 17199 17198 , Trichodesmium 10.1029/2005GL023653 17203 17198 , 2012. , , 10.1002/gbc.20074 10.5194/essd-5-227-2013 10.1029/2002PA000810 10.1016/j.jmarsys.2008.05.015 10.1111/ele.12063 17197 tem modeling, Limnol.17223 Oceanogr., 52, 1533–1544, doi: Biogeochem. Cy., 12, 297–310, doi: Environ., 115, 325–339, doi: ally covered an average, J. Geophys. Res., 99, 7467–7482, doi: data, Biogeosciences, 6, 751–764, doi: vred, E., Bricaud, A., Ciotti,for detecting A., dominant and phytoplankton Gentili, size class B.: from An satellite intercomparison remote of sensing, bio-optical Remote Sens. techniques observation of cyanobacteria and diatoms from space using PhytoDOAS on SCIAMACHY primary production usingdoi: observations and models, Global Biogeochem. Cy., 27, 1–12, to global warming and potentialL19606, implications: doi: a global model study, Geophys. Res. Lett., 32, crozooplankton, Global Biogeochem. Cy.,17197 24, GB4015, doi: Towards a worlddoi: atlas of MARine Ecosystem DATa, Earth Syst. Sci. Data, 5, 227–239, Luo, Y.-W., O’Brien, C., O’Brien, T., Peloquin, J., Schiebel, R., and Swan, C.: MAREDAT: rine biotadoi: and atmospheric CO O’Brien, T., Piontkovski, S., and Straile, D.:Global Biogeochemical Biogeochem. fluxes through Cy., mesozooplankton, 20, GB2003, doi: proaches, The University of Chicago Press, Chicago and London, 2003. Grebmeier, J. M.,man, Hollowed, W. J., A. andMar. Talley, B., Sci., L. 4, D.: Knowlton, 11–37, Climate doi: N., change impacts Polovina, on J.,for marine detecting Rabalais, ecosystems, Annu. N.sciences, Rev. 8, N., 3631–3647, doi: Syde- in North Sea calanoid copepods284, in 35–47, relation 2004. to hydro-climatic variability, Mar. Ecol.-Prog. Ser., 9503-1 depth over the global ocean:J. an Geophys. Res., examination 109, of C12003, profile doi: data and a profile-based climatology, biogeochemistry models against field anddoi: remote sensing data, J. Marine Syst., 76, 95–112, complex plankton ecosystem models, Ecosystems, 15, 200–212, doi: Glover, D. M., and Takahashi, T.: Skill metrics for confronting global upper ocean ecosystem- and Ward, B.doi: A.: The biogeography of marineniche plankton of species, traits, Mar. Ecol. Ecol.-Prog. Ser., Lett., 363, 29–37, 16, doi: 522–534, in marine phytoplankton, Sciences,17198 327, 1509–1511, doi: Buesseler, K.: The decoupling of production and particulate export in the surface ocean, Global Brown, C. W. and Yoder, J. A.: Coccolithophorid blooms inBruggeman, the J. and global Kooijman, ocean S. A. blooms L. annu- M.: A biodiversity-inspired approach to aquatic ecosys- Bracher, A., Vountas, M., Dinter, T., Burrows, J. P., Röttgers, R., andBrewin, Peeken, R. I.: J., Quantitative Hardman-Mountford, N. J., Lavender, S. J., Raitsos, D. E., Hirata, T., Uitz, J., De- Bopp, L., Aumont, O., Cadule, P., Alvain, S., and Gehlen, M.: Response of diatoms distribution Buitenhuis, E. T., Hashioka, T., and Le Quéré, C.: Combined constraints on global ocean Buitenhuis, E., Rivkin, R., Sailley, S., and Le Quéré, C.: Biogeochemical fluxes through mi- Buitenhuis, E. T., Vogt, M., Moriarty, R., Bednar Bopp, L., Kohfeld, K. E., Le Quéré, C., and Aumont, O.: Dust impact on ma- Buitenhuis, E., Le Quéré, C., Aumont, O., Beaugrand, G., Bunker, A., Hirst, A., Ikeda, T., Dupouy, C., Benielli-Gary, D., Neveux, J., Dandonneau, Y., and Westberry, T. K.: An algorithm Beaugrand, G. and Ibanez, F.: Monitoring marine plankton ecosystems. II: Long-term changes Chase, J. M. and Leibold, M. A.: Ecological niches: linking classical and contemporary ap- Doney, S. C., Ruckelshaus, M., Du de Boyer Montégut, C., Madec, G., Fischer, A. S., Lazar, A., and Iudicone, D.: Mixed layer Cropp, R. A. and Norbury, J.: The mechanisms of coexistence and competitive exclusion in Doney, S., Lima, I., Moore, J. K., Lindsay, K., Behrenfeld, M. J., Westberry, T. K., Mahowald, N., Beaugrand, G. and Helaouët, P.: Simple procedures to assess and compare the ecological Barton, A. D., Pershing, A. J. E. L., Record, N. R., Edwards, K. F., Finkel, Z. V., Kiørboe, T., Barton, A. D., Dutkiewicz, S., Flierl, G., Bragg, J., and Follows, M. J.: Patterns of diversity 5 5 25 30 20 15 10 30 10 20 25 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , , , 17220 17201 17218 , 2009. , , , 17198 17223 17217 17221 , , 17197 17211 17219 , , , 2013. 17198 17197 , 1975. 17219 , , 2011. 17206 17216 17215 , , , 2012. , 2012. 17206 , 17201 17214 17198 17216 , , 1993. , 10.1029/2008GB003405 17204 17219 , 17198 17198 17212 , 2009. , 2006. , 17203 , , 2012. 10.1364/AO.14.000413 , 2012. 17211 10.5194/bg-8-311-2011 10.1073/pnas.1307701110 17198 17222 , 17196 17197 , , 17196 17230 17229 ce, 396 pp., 2005. 17205 ffi 17210 , 17198 , 2007. 10.4319/lo.2012.57.3.0787 10.4319/lo.2012.57.2.0554 , and diagnostic pigments specific to phytoplankton func- , 2006. 17196 a , 1998. 17198 17209 , 2007. , 10.1029/2009GL038912 10.1029/2005GB002532 , 2002. 10.1016/0967-0637(93)90059-C , 1957. 10.1029/2011GB004039 17208 , and opal from global nutrient and alkalinity distributions, Global Bio- , 3 10.5194/bgd-9-18083-2012 10.1175/JCLI3800.1 17204 , 17205 17205 17203 , 10.1029/2001GB001454 10.1159/000340002 10.1016/j.ecolmodel.2005.12.002 10.1126/science.1138544 10.1126/science.281.5374.237 tribution of phytoplankton functionalganic groups carbon, to CaCO thegeochem. production Cy., 20, and 1–17, export doi: of particulate or- Limnol. Oceanogr., 57, 787–797, doi: doi: Hayes, P. K.,in and ocean Falkowski, carbon P. G.: cycle Representing models: key Coccolithophorids, phytoplankton Global functional Biogeochem. groups Cy., 16, 1100, doi: 17238 Pacific spring blooms projectedphys. Res. by Lett., an 36, eddy-permitting 1–5, doi: 3-D ocean ecosystem model, Geo- 17202 Bopp, L., Hirata, T.,ing Lima, the I., spring Sailley, bloom S.,18083–18129, in and doi: four Doney, Plankton S. Functional C.: Type Phytoplankton Models, Biogeosciences competition Discuss., dur- 9, Isada, T., Howell, E.,ships Hashioka, between T., surface Noguchi-Aita, Chlorophyll- M.,tional and types, Yamanaka, Biogeosciences, Y.: 8, Synoptic 311–327, relation- doi: 17222 temperature acclimation to light,694, 1998. nutrients, and temperature, Limnol. Oceanogr., 43, 679– associated withdoi: global warming using 3D-NEMURO, Ecol. Model., 202, 95–104, rine ecosystems, in: WorldLevitus, S., Ocean US Atlas Government Printing 2005, O NOAA Atlas NESDIS 64, vol. 4, edited by: 3337–3353, doi: Res. Pt. I, 40, 159–168, doi: Eby, M., Fung, I., Bala,Lindsay, K., G., Matthews, John, H. D., J., Raddatz,G., Jones, T., Schnur, C., Rayner, R., P., Reick, Joos, Strassmann, C., F., S., Roeckner, Kato,cycle Weaver, E., A. T., Schnitzler, Kawamiya, J., feedback K.- Yoshikawa, M., C., analysis: and Knorr, Zeng, W., results N.: Climate–carbon from the C4MIP model intercomparison, J. Climate, 19, remote sensing, Appl. Optics, 14, 413–416, doi: Li, W. K. W.,Present Lomas, and M. future W.,Synechococcus, global P. Veneziano, Natl. distributions D., Acad. of Sci. Vera, USA, C. the doi: S., marine Vrugt, cyanobacteriageography J. Prochlorococcus A., and of anddoi: Martiny, microbial A. C.: communities in a model ocean, Science, 315, 1843–1846, and iron in the26, Pacific GB1012, 1–16, Ocean: doi: theory and numerical simulations, Global Biogeochem. Cy., biogeochemistry, Global Biogeochem.17198 Cy., 23, 1–15, doi: of the biosphere: integratingdoi: terrestrial and oceanic components, Science, 281, 237–240, taxonomic variation in nutrientLimnol. utilization Oceanogr., traits 57, 554–566, and doi: maximum growth rate of1085, phytoplankton, 1972. Jin, X., Gruber, N., Dunne, J. P., Sarmiento, J. L., and Armstrong, R. A.: Diagnosing the con- Irwin, A. J., Nelles, A. M., and Finkel, Z. V.: Phytoplankton niches estimated from field data, Iglesias-Rodríguez, M. D., Brown, C. W., Doney, S. C., Kleypas, J., Kolber, D., Kolber, Z., Hutchinson, E.: Concluding remarks, Cold Spring Harb. Sym., 22, 415–427, Hashioka, T., Sakamoto, T. T., and Yamanaka, Y.: Potential impact of global warming on North Hirata, T., Hardman-Mountford, N. J., Brewin, R. J. W., Aiken, J., Barlow, R., Suzuki, K., Hashioka, T., Vogt, M., Yamanaka, Y., Le Quéré, C., Buitenhuis, E. T., Aita, M. N., Alvain, S., Geider, R. J., Maclntyre, H. L., and Kana, T. M.: A dynamic regulatory model of phytoplanktonic Hashioka, T. and Yamanaka, Y.: Ecosystem change in the western North Pacific Garcia, H., Locarnini, R. A., Boyer, T. P., and Antonov, J. I.: Climate change impacts on ma- Goldman, J. C.: Potential role of large oceanic diatoms in new primary production, Deep-Sea Gordon, H. R. and McCluney, W. R.: Estimating the depth of sunlight penetration in the sea for Friedlingstein, P.,Cox, P.,Betts, R., Bopp, L., von Bloh, W., Brovkin, V., Cadule, P.,Doney, S. C., Flombaum, P., Gallegos, J. L., Gordillo, R. A., Rincón, J., Zabala, L. L., Jiao, N., Karl, D. M., Follows, M. J., Dutkiewicz, S., Grant, S., and Chisholm, S. W.: Emergent bio- Dutkiewicz, S., Ward, B. A., Monteiro, F., and Follows, M. J.: Interconnection of nitrogen fixers Dutkiewicz, S., Follows, M. J., and Bragg, J. G.: Modeling the coupling of ocean ecology and Eppley, R. W.: Temperature and phytoplankton growth inField, the C. sea, Fish. B., B.-NOAA, Behrenfeld, 70, M. 1063– J., Randerson, J. T., and Falkowski, P. G.: Primary production Edwards, K. F., Thomas, M. K., Klausmeier, C. A., and Litchman, E.: Allometric scaling and 5 5 30 25 20 10 15 30 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , , 17201 17199 17199 , 2008. , 2007. , 2007. 10.1111/j.1365- 10.5194/essd-4-149- 10.5194/bgd-10-14715-2013 17219 17222 , 2012. , 17220 , 17196 10.1016/j.ecolmodel.2006.08.021 17215 10.1111/j.1461-0248.2007.01117.x 17232 17231 , 10.1146/annurev.ecolsys.39.110707.173549 17219 17216 17206 , 2008. 17196 17202 , change: the iron hypothesis, Paleoceanography, 5, 1–13, 2 , 2013. , 2013. , 1990. , 2010. 17196 10.5194/essd-4-47-2012 , 2005. s in structuring phytoplankton communities: scaling from cellular to ecosys- ff 17217 10.1029/2007JC004478 17216 , 2012. 10.1029/PA005i001p00001 10.5194/bgd-10-7367-2013 10.1029/2009GB003721 10.5194/bgd-10-5923-2013 doi: Gibson, J., Gosselin, M.-P., Kopczynska, E.,ton, Marshall, A. H., J., Peloquin, Quéguiner, J., B., Piontkovski,Widdicombe, S., Schiebel, C., Poul- R., Shipe, and R.,biomass Yallop, Stefels, M.: J., in van A the Leeuwe, global M. world A., diatom ocean, Varela, database M., Earth – Syst. abundance, Sci. biovolume Data, and 4, 149–165, doi: tal controls ondoi: global marine nitrogen fixation, Biogeosciences Discuss.,sponse 10, to 7367–7412, phytoplankton-light feedbackC10010, in doi: a global model, J. Geophys. Res.-Oceans, 113, Hunter, C. N., Elrod, V. A.,Scoy, Nowicki, K. J. V., L., Law, Coley, C. T. L.,son, S., Barber, A., Liddicoat, R. T., Bidigare, M. Lindley, S., R., I., Watson, Ondrusek,Friederich, Ling, A. M., J., R., G., Latasa, Stanton, Sakamoto, M., T., Millero, Stockel, C.,Chisholm, F. J., Chavez, J., S. Collins, Lee, F., C., K., W., Buck, Ander- Yao, W., K., Hoge, Zhang, Kolber, J. F., Z., Z., Swift, Greene, R., R., Yungel, Falkowski, J., P., Turner, S., Nightingale, P., Hatton, A., 2012 Dore, J. E.,sen, Falcón, K., L. Hynes, A. I.,Marañón, M., Fernández, E., Karl, McGillicuddy D. A., Jr., M., D. Foster,Mulholland, Kitajima, J., R. S., Moisander, M. P. Langlois, H., R., A., R. Moore, Needoba,Rees, J., Furuya, C. J. LaRoche, M., K., A. J., Mouriño-Carballido, A., Letelier, B., Gómez, Orcutt, P., R.Varela, Riemann, K. F., M., M., Gunder- M., Villareal, L., Poulton, T. Shiozaki,diazotrophs A. A., in Webb, J., T., E. Rahav, global Subramaniam, E., A., ocean:Sci. A., Raimbault, White, Data, abundance, 4, A. P., biomass Tyrrell, 47–73, E., doi: and T., Wu, nitrogen Turk-Kubo, J., K. fixation and rates, Zehr, A., J. Earth P.: Syst. Database of ocean biogeochemistry models,2486.2005.01004.x Glob. Change Biol., 11, 2016–2040, doi: Rev. Ecol. Evol. S., 39, 615–639, doi: traits and trade-o tem level, Ecol. Lett., 10, 1170–1181, doi: net, S., Boström, K. H., Böttjer, D., Capone, D. G., Carpenter, E. J., Chen, Y. L., Church, M. J., Claustre, H., Cotrimdre, da L., Cunha, Manizza, L.,and Geider, M., Wolf-Gladrow, R., Platt, D.: Giraud, Ecosystem T., X., Rivkin, dynamics Klaas, based R. on C., B., plankton Kohfeld, Sathyendranath, functional K., S., types Legen- Uitz, for J., global Watson, A. J., ocean model, Biogeosciences Discuss., 10, 14715–14767,2013. doi: 17197 Lancelot, C., Allen, J. I., Archer,mann, S. V., D., Bopp, Simó, L., R., Deal,models C., Six, and Elliott, prospects S., K. Jin, for D., their M.,doi: use Malin, and G., in Stefels, Schoe- climate models, J.: Global A Biogeochem. first Cy., 24, appraisal GB3021, of prognostic ocean DMS tion and particledoi: export between 1960–2006, Biogeosciences Discuss., 10, 5923–5975, Tsuda, A., Yamamura, O., Yamanaka,and Y., Yokouchi, Zvalinsky, K., V. Yoshie, N., I.:ecosystem, Zhang, NEMURO Ecol. J., – Model., Zuenko, a Y. 202,17200 I., 12–25, lower doi: trophic level model for the North Pacific marine White, G., Woollen, J., Zhu,gins, Y., W., Leetmaa, Janowiak, A., J., Reynolds, Mo,NCEP/NCAR R., K. 40-year Chelliah, C., reanalysis M., Ropelewski, project, Ebisuzaki, C., B. W., Wang, Am. Hig- J., Meteorol. Jenne, Soc., R., 77, andAita, 437–471, Joseph, 1996. M., D.: Azumaya, The T.,Kantakov, Fujii, G. M., Hashimoto, A., S., Kim, Huang, D., H.-c., Iizumi, Komatsu, H., Ishida, K., Y., Navrotsky, Kang, V. S., V., Smith, S. L., Tadokoro, K., Martin, J.: Glacial-interglacial CO Leblanc, K., Arístegui, J., Armand, L., Assmy, P., Beker, B., Bode, A., Breton, E., Cornet, V., Luo, Y.-W., Lima, I. D., Karl, D. M., and Doney, S. C.: Data-based assessment of environmen- Martin, J. H., Coale, K. H., Johnson, K. S., Fitzwater, S. E., Gordon, R. M., Tanner, S. J., Manizza, M., Le Quéré, C., Watson, A. J., and Buitenhuis, E. T.: Ocean biogeochemical re- Luo, Y.-W., Doney, S. C., Anderson, L. A., Benavides, M., Berman-Frank, I., Bode, A., Bon- Litchman, E. and Klausmeier, C. A.: Trait-based community ecology of phytoplankton, Annu. Lima, I. D., Lam, P. J., and Doney, S. C.: Dynamics of particulate organic carbon flux in a global Litchman, E., Klausmeier, C. A., Schofield, O. M., and Falkowski, P. G.: The role of functional Le Quéré, C., Harrison, S. P., Prentice, I. C., Buitenhuis, E. T., Aumont, O., Bopp, L., Le Clainche, Y., Vézina, A., Levasseur, M., Cropp, R. A., Gunson, J. R., Vallina, S. M., Vogt, M., Laufkötter, C., Vogt, M., and Gruber, N.: Long-term trends in ocean plankton produc- Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., Kishi, M. J., Kashiwai, M., Ware, D. M., Megrey, B. A., Eslinger, D. L., Werner, F. E., Noguchi- 5 5 25 20 30 25 30 15 10 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , 17196 17197 17203 , , 2006. , 2008. 17197 10.5194/essd-5-109- 17238 17206 , 17197 17223 , 10.5194/essd-5-259-2013 , 2012. 17208 17219 , 1994. ska, E., Lee, R., Poulton, A. J., 17222 , 2013. ń , , 2009. , 2012. 17218 17221 , , 17197 17196 10.4319/lo.2008.53.2.0605 17220 17198 17219 17200 , 17196 10.1038/371123a0 17233 17234 , 2007. 10.1029/2011JC007112 10.1111/j.1600-0889.2006.00209.x , 2007. from 10 years of satellite Earth observation data, Bio- , 2011. , 2013. , 2002. 17199 2 17222 , 2012. , 10.5194/bg-10-2699-2013 10.3354/meps07998 17216 , 2004. 10.1016/j.rse2001.10.001 17221 , 17217 , 10.5194/bg-8-539-2011 10.1029/2006GB002762 17197 10.1016/j.jmarsys.2006.01.018 17219 17217 17205 , 2013. 10.1016/s0967-0645(01)00109-6 10.1016/j.jmarsys.2011.08.002 10.1029/2004GB002220 10.1016/j.ecolmodel.2013.04.006 Arístegui, J., Beaufort, L.,Pritchard, Estrada, M., T., Karentz, and D., Widdicombe, Kopczy butions: coccolithophores, C.: Earth Global Syst. marine Sci. Data, plankton 5, functional 259–276, doi: type biomass distri- gyre: a 3 yr record1997. from the Sargasso Sea near Bermuda, Limnol. Oceanogr., 42, 473–486, 2013. limitation patterns indoi: surface waters of the World Ocean, Deep-Sea Res., 49, 463–507, amplifies the ocean biogeochemical response todeposition, decadal Tellus timescale B, variations 58, in 560–572, mineral doi: dust on a linkeddoi: global ocean optical and ecosystem model, J. Marine Syst., 89, 61–75, iron cycling indoi: a global three-dimensional model, Global Biogeochem. Cy., 18, GB4028, color satellite imagery: a generalizedEnviron., approach 117, for 249–263, use doi: with multiple sensors, Remote Sens. feedbacks between marine denitrification andGB2001, nitrogen doi: fixation, Global Biogeochem. Cy., 21, marine pigment measurements, Earth Syst.2013 Sci. Data, 5, 109–123, doi: feld, M., Bidigare,Goericke, R., R., Dierssen, Harding,lier, H., L., R., Ditullio, Head, Llewellyn, G., E., C.Muller-Karger, F., Fernandez, A., Holligan, Nelson, N., Lomas, E., P., O’Brien, M., Hooker, C.,Wright, Gallienne, Lucas, Prezelin, S., D., B., C., M., Karl, Stumpf, Repeta, Gibb, Mannino, D., R., D., Jr.and A., Subramaniam, S., Smith, Landry, Wright, W. Marty, A., S.: O., M., J.-C., Suzuki, Smythe- The Lete- Mitchell, K., MAREDAT global B. Trees, database C., G., of Vernet, high M., performance Wasmund, liquid N., chromatography Reid, P. C.: Identifyingproach, four Limnol. Oceanogr., phytoplankton 53, functional 605–613, doi: types from space: an ecological ap- Press, New Jersey, USA, 2006. Proctor, R., andWest Mills, European D. continental K.: shelf429, Modelling doi: for the operational hydrodynamics oceanography, J. and Marine ecosystem Syst., of 65, the 417– North- 1913. Steele, M., and Zhang,from an J.: intercomparison What of five controls generalRes.-Oceans, primary circulation 117, models C00D12, production with 1–16, biogeochemistry, in J. doi: Geophys. the Arctic Ocean? Results and Platt, T.: Carbon-to-chlorophyllEcol.-Prog. ratio Ser., and 383, growth 73–84, doi: rate of phytoplankton in the sea, Mar. in phytoplankton groups in539–550, the doi: Equatorial Pacific during ENSO cycles, Biogeosciences, 8, doi: and Blackford, J. C.:modulation of Coccolithophore the surface air-sea distributionsgeosciences, flux 10, of in 2699–2709, CO the doi: North Atlantic and their Lima, I., Leics Quéré, across C., a and suite Yamanaka, Y.: of Comparing global food marine web ecosystem structures models, and dynam- Ecol. Model., 261–262, 43–57, Liss, P., and Tindale,Pacific Ocean, N. Nature, W.: 371, 123–129, Testing doi: the iron hypothesis in ecosystems of the Equatorial Masotti, I., Moulin, C., Alvain, S., Bopp, L., Tagliabue, A., and Antoine, D.: Large-scale shifts O’Brien, C. J., Peloquin, J. A., Vogt, M., Heinle, M., Gruber, N., Ajani, P., Andruleit, H., Moore, J. K., Doney, S. C., Lindsay, K., Mahowald, N., and Michaels, A. F.: Nitrogen fixation Nelson, D. M. and Brzezinski, M. A.: Diatom growth and productivity in an oligotrophic midocean Mouw, C. B., Yoder, J. A., and Doney, S. C.: Impact of phytoplankton community size Moore, J. K., Doney, S. C., Glover, D. M., and Fung, I. Y.: Iron cycling and nutrient- Moore, J. K., Doney, S. C., and Lindsay, K.: Upper ocean ecosystemMoore, T. dynamics S., Dowell, and M. D., and Franz, B. A.: Detection of coccolithophore blooms in ocean Moore, J. K. and Doney, S. C.: Iron availability limits the ocean nitrogen inventory stabilizing Peloquin, J., Swan, C., Gruber, N., Vogt, M., Claustre, H., Ras, J., Uitz, J., Barlow, R., Behren- Siddorn, J. R., Allen, J. I., Blackford, J. C., Gilbert, F. J., Holt, J. T., Holt, M. W., Osborne, J. P., Raitsos, D. E., Lavender, S. J., Maravelias, C. D., Haralabous, J., Richardson, A. J., and Sarmiento, J. L. and Gruber, N. P.: Ocean Biogeochemical Dynamics, Princeton University Michaelis, L. and Menten, M. L.: Die Kinetik der Invertinwirkung, Biochem. Z., 49, 333–369, Popova, E. E., Yool, A., Coward, A. C., Dupont, F., Deal, C., Elliott, S., Hunke, E., Jin, M., Sathyendranath, S., Stuart, V., Nair, A., Oka, K., Nakane, T., Bouman, H., Forget, M., Maass, H., Sailley, S., Vogt, M., Doney, S. C., Aita, M. N., Bopp, L., Buitenhuis, E. T., Hashioka, T., Shutler, J. D., Land, P. E., Brown, C. W., Findlay, H. S., Donlon, C. J., Medland, M., Snooke, R., 5 5 30 25 10 15 20 15 30 20 10 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , 17222 17200 , 17197 17203 , , 2009. 17199 , 2011. 17215 17197 , 10.5194/bg-9-2333- 17206 , 2004. 17196 , 2001. 10.1029/2005GB002673 17196 , 2012. 17197 , 2010. 17215 , 10.1029/2009JC005817 , 2006. 17196 10.1016/j.jmarsys.2008.03.011 17196 17223 , 17203 , 17196 17235 17236 , 2010. 10.1038/nature11357 , 2000. 17197 17197 , 2010. 10.1016/S0967-0645(01)00096-0 10.1029/2009JC005529 , 2012. 10.1023/B:JOCE.0000038329.91976.7d , 2004. 10.1029/2005JC003207 17221 , 10.1038/nature09403 10.1038/35038000 , J., McGillicuddy, D. J., Doney, S. C., Allen, J. I., Friedrichs, M. A. M., ff 17203 17206 , 10.5194/bg-7-979-2010 17197 , 2012. 10.1126/science.1224836 10.1029/2004GL019735 467, 550–554, doi: J. Geophys. Res.-Oceans, 116, C01019, 1–18, doi: sulphide seasonality with theOceans, Dynamic 115, Green C06021, Ocean 1–21, Model doi: PlankTOM5, J. Geophys. Res.- munities in open ocean:Oceans, 111, an C08005, assessment doi: based on surface chlorophyll, J. Geophys. Res.- pattern ofdoi: thermal adaptation in marine phytoplankton, Science, 338, 1085–1088, pled with nitrogen–silicon–carbon cycles appliedOceanogr., to 60, station 227–241, A7 doi: in the Northwestern Pacific, J. circulation, Nature, 489, 418–422, doi: 2006. Gehlen, M., Lindsay, K.,century Moore, J. decrease K., in Schneider,1005, marine B., doi: productivity: and a Segschneider, J.: multi-model Projected analysis, 21st Biogeosciences, 7, 979– 2012 in the world’s oceans, Global Biogeochem. Cy., 20, GB4016, doi: doi: marine systems, J. Marine Syst., 76, 4–15, doi: Swart, S.: A globalprocesses compilation in of the dissolved Southern iron Ocean, measurements: Biogeosciences, focus 9, on 2333–2349, distributions doi: and Barents Sea, from twenty years of satellite imagery, Geophys. Res. Lett., 31, L11302, Rose, K. A., and Wallhead, P.: Skill assessment for coupled biological/physical models of 407, 859–869, doi: Sea Res. Pt. II, 49, 107–121, doi: Weber, T. and Deutsch, C.: Ocean nutrient ratios governed by plankton biogeography, Nature, Weber, S. N.: Generalized Additive Models, Chapman & Hall/CRC, 2006. Wang, S. and Moore, J. K.: Incorporating Phaeocystis into a Southern Ocean ecosystem model, Vogt, M., Vallina, S. M., Buitenhuis, E. T., Bopp, L., and Le Quéré, C.: Simulating dimethyl- Uitz, J., Claustre, H., Morel, A., and Hooker, S. B.: Vertical distribution of phytoplankton com- Thomas, M. K., Kremer, C. T., Klausmeier, C. A., and Litchman, E.: A global Yamanaka, Y., Yoshie, N., Fujii, M., Aita, M. N., and Kishi, M. J.: An ecosystem model cou- Weber, T. and Deutsch, C.: Ocean nitrogen reservoir regulated by plankton diversity and ocean Westberry, T. K. and Siegel, D. A.: Spatial and temporal distribution of Trichodesmium blooms Steinacher, M., Joos, F., Frölicher, T. L., Bopp, L., Cadule, P., Cocco, V., Doney, S. C., Tagliabue, A., Mtshali, T., Aumont, O., Bowie, A. R., Klunder, M. B., Roychoudhury, A. N., and Smyth, T. J., Tyrrell, T., and Tarrant, B.: Time series of coccolithophore activity in the Stow, C. A., Jolli Sigman, D. and Boyle, E.: Glacial/interglacial variations in atmospheric carbon dioxide, Nature, Subramaniam, A. and Brown, C.: Detecting Trichodesmium blooms in SeaWiFS imagery, Deep- 5 5 30 25 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | a 2 a 4 ◦ 0.26 0.30 0.17 0.30 1 , SiO , Fe equivalents 3 3 , NH × 3 ◦ a 1 a a a b NO CaCO Values in brack- (0.34) 0.30 macrozooplankton a b 0.59 (0.78) for nano- and picophy- for each pPFT and an- 1 a 3 − 4 ◦ Chlorophyll − 2 b – 0.12 (0.24) , PO . 4 , SiO , Fe SiO 3 3 3 0.8–1.8 − × , NH ◦ 3 CaCO 3.6 NO a for the four models, mean ratio of di- 3 (0.01) 0.21 − ––– 0.02 (0.004) – – 0.62 (0.88) 1.24 (0.47) b 0.21 (0.03) 4 2 ◦ , DOM POM, DOM POM, DOM l , PO 4 , SiO , Fe SiO 3 3 0.5–2 17238 17237 × 0.08) 0.12 , NH , POM ◦ 3 s in mg Chl m + 2 CaCO a a a b NO a ). (0.11 b 2009 Nanophytoplankton) 1.73 (0.20) 1.01 (0.16) 0.69 (0.09) 0.64 (0.15) 0.82] , + + 2 ◦ , DOM POM l (Pico- 4 , SiO , Fe SiO 3 3 0.5–2 a a a b 2010 2006 2004 2007 POM × ◦ SiO s (0.10) [1.10 CaCO b mesozooplankton mesozooplankton mesozooplankton nanophytoplankton nanophytoplankton nanophytoplankton nanophytoplankton ) were converted to/from carbon D : N using a carbon conversion ratio of 0.77 (0.07) coccolithophores (calcifiers) diazotrophs (nitrogen fixers) for diatoms and a carbon conversion of 125 gC(gChl) 2011 1 ( − Sathyendranath et al. b b Global annual mean surface average biomass in mmolCm Characteristics of the Phase-0 models: ecosystem structure, physical model, and ref- PlankTOM5PISCESCCSM-BEC 0.23NEMURO (0.05) 0.41 (0.07) 0.63 (0.14) Model/Biomass DiatomSatellite-derived Nanophytoplankton Diatoms Calcifiers Small phytoplankton Diazotrophs Prymnesiophytes D : N ratio Total Chl – D : S ratio Chl biomass Hirata et al., 2011 0.41 ModelpPFTSzPFTs diatoms PlankTOM5 (silicifiers) diatoms microzooplankton (silicifiers)Physical Model PISCES diatomsResolution (silicifiers) microzooplanktonReference diatoms generic (silicifiers) zooplankton NEMO CCSM-BEC 2 microzooplankton Buitenhuis et al., NEMO NEMURO Aumont &Bopp, Moore et al., CCSM-POP Aita et al., COCO Nutrients PO DetritusForcing POM NCEP-NCAR NCEP-NCAR NCEP-NCAR NCEP-NCAR ets indicate chlorophyll equivalentsin for NEMURO and each the PFT relative in ratio mgChlm of diatom chlorophyll : nanophytoplankton chlorophyll from atom to nanophytoplankton biomass (Dfor : N the ratio), satellite-based or diatom estimate : total (nano-plus small picophytoplankton; phytoplankton biomass D : S ratio). nual mean surface average chlorophyll Table 2. Hirata et al. Table 1. erences for further reading.tional pPFTs: types. phytoplankton functional types, zPFTs: zooplankton func- 50 gC(gchl) toplankton ( Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) ) T T · · exp(0.0693 exp(0.0693 273.15) 273.15) 273.15) + + + (30 (30 (30 − − − 10 10 10 273.16) 273.16) 273.16) + + + T T T ( ( ( 2 2 2 erent pPFTs are dominant (group ff T T 1.066 1.066 17240 17239 T T T grid cells in % where the di ◦ : 1 ) 4.0 min: 1.0 (?); max: 7.0 (?) 1.0 6.0 ) 0.04 min: 0.02; max: 0.08 0.06 0.08 ) 0.12 min: 0.1; max: 0.4 0.15 0.2 ) 0.07 0.1 ) n/a) 1.32 n/a 2.5 0.26 3.0 0.5 1.0 ) n/a 0.066) n/a 0.08 0.3 0.013 0.005 0.1 ) 50.0 4.0) 9.2 5.0 n/a 0.8 0.3125 n/a ) 0.49 5.0 1 × 1 1 1 1 1 1 1 1 1 1 ) n/a – ) n/a – − − − − − − − − 1 ◦ − − − 1 − − C) 0.6 0.66 0.375 0.8 C) 0.4C) 0.66 0.3 0.375 0.4 0.05 ◦ ◦ ◦ (µmol Si L (µmol N L (µmol N L (mol N L (nmol P L (nmol P L (µmol P L (µmol N L (µmol N L (mol N L (nmol Fe L (nmol Fe L (nmol Fe L (0 (0 (0 3 3 3 3 4 4 4 4 4 4 ) 1.066 2 2 2 2 2 2 2 2 2 2 2 2 2 / / / / / / / / / / / / / NH SiO NH Fer PO NO Fer PO NO PO NO NH Fer T 1 1 1 1 1 1 1 1 1 1 1 1 1 max max max ( K K K K Diatoms: µ K K K K K Parameterf PlankTOM5 PISCESNanophytoplankton: µ CCSM-BECf(T) NEMURO Others:µ 1.066 f(T) Coccolithophores 1.066 Diazotrophs K K K K Fraction of 1 Model properties: Parameters limiting phytoplankton growth for PlankTOM5, PISCES, pPFT/ModelSilicifiers/DiatomsNano-/picophytoplanktonCalcifiers/PrymnesiophytesNitrogen fixersCoexistence 76.8 12.3 PlankTOM5 PISCES 9.3 CCSM-BEC 77.0 NEMURO – Alvain et al. 23.0 (2008) 68.7 Hirata et – al. (2011) 1.5 – 31.0 63.3 – – 36.7 – 96.6 0.0 0.3 2.2 1.2 – – 76.6 3.9 – 0.0 – – 19.4 Table 4. CCSM-BEC, NEMURO. Table 3. constitutes more thantwo 50 % satellite estimates. of Coexistence localproportions patterns of biomass) in nano- in and Hirata picophytoplankton. et the al. annual (2011) mean are for mostly the due to four similar models and Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | diatoms, estimates = for PISCES, PHYSAT (Al- (e) (b) (e) nanophytoplankton and the formula in = a

180

180 dia nan dia nan 150 CCSM-BEC, 150 PlankTOM5, 120 (d) 120 (a) 90 90 Latitude (b) PISCES Latitude coccolithophores/prymnesiophytes,

180 (d) NEMURO 60 = 60 dia prym pico nano diazotrophs, nano 150 30 30 = 120

0 1 0

0

0.8 0.6 0.4 0.2 1 0

Fraction of Biomass of Fraction

0.8 0.6 0.4 0.2 Fraction of Biomass of Fraction NEMURO and 90 Latitude

180 180 (c) (e) Hirata et al. 2011 60 dia coc nan 17242 17241 dia diaz nan 150 150 30 based on SeaWiFS chlorophyll 120 120

0 (f) 1 0

0.8 0.6 0.4 0.2 Fraction of Biomass of Fraction diatoms, cocco-prym PISCES, picophytoplankton. “coex” denotes areas where none of the 90 90 = Latitude Latitude = (a) PlankTOM5 (c) CCSM–BEC (b) 60 60 30 30 for CCSM-BEC, as compared to observations from

0 0 1 0 1 0

0.8 0.6 0.4 0.2 0.8 0.6 0.4 0.2

Fraction of Biomass of Fraction Fraction of Biomass of Fraction (d) PlankTOM5, (a) coccolithophores/prymnesiophytes, diaz picophytoplankton. = = nanophytoplankton, pico Comparison of zonal mean surface biomass contributions of the four individual DGOMS Annual mean dominance maps for the individual pPFTs = for NEMURO and based on Hirata et al.coc/prym (2011) for all groups resolvedand by pico the four models. Legend: dia Fig. 1. Fig. 2. nano PFTs constitutes more than 50 % of biomass. (c) Hirata et al. (2011). Legend: dia to satellite estimates based onet Hirata et al. al. (2009). (2011) and conversion factors from Sathyendranath vain et al., 2005, 2006, 2008) and Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | (c) (e) dominance PISCES, (f) PHYSAT (Al- (b) x for PISCES, (a) coe pico cocco/ prym nano dia nanophytoplankton, = (d) PlankTOM5, 20 (a) 10 ata et al. 2011 PlankTOM5, c) CCSM�BEC 0 f) Hir (c) 20 concentration. 10 17244 17243 3 ain et al. 2005 SST (°C) b) PISCES 0 e) Alv O 20 OM5 dominance patterns from PHYSAT when only those pPFTs with coccolithophores/prymnesiophytes, nano 10 = (e) Hirata et al. (2011), d) NEMUR a) PlankT for CCSM-BEC for selected Months: December (upper panels), 0 (b) (f) 1.0 0.1 1.0 0.1

10.0 10.0

3 ) m (mmol. O N NEMURO,

− 3 (d) Monthly mean dominance maps for the individual pPFTs Representation of dominant pPFT in niche space as pPFT with highest probability of picophytoplankton. “coex” depicts areas where none of the pPFTs is dominant, i.e. none diatoms, cocco-prym = = patterns from Hirata etered al., (size 2011, classes, where diatoms, prymnesiophytes). onlythe Diazotrophs those annual in mean. pPFTs CCSM-BEC Habitats are with where not none modelmore dominant of equivalents than in the are pPFTs 50 % is consid- dominant, of ie. biomass, where are no designated group constitutes by “coex” Legend as in Figs. 2 and 3. CCSM-BEC, model equivalents are considered (size classes, diatoms, coccolithophores), and Fig. 4. dominance as a function of SST and NO vain etfor al., NEMURO 2008), and pico of the pPFTs reaches a biomass contribution of 50 %. March (upperdia middle), June (lower middle) and September (lower panels). Legend: Fig. 3. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | (e) NEMURO, and Alvain et al. (2008), (d) (e) erent DGOMs, as compared to ff CCSM-BEC MURO, and (e) SeaWiFS chlorophyll- ntDGOMs, as comparedto satellite ob- CCSM-BEC, (d) (c) for the di a 51 PISCES, 17246 17245 NEMURO, (b) (c) PISCES, (b) PlankTOM5, (a) . a PlankTOM5, (a) Global annual mean probability of diatom dominance for the models and the satellite Log-transformed annual mean chlorophyll Hirata et al. (2011). Log-transformedannual mean chlorophyll-afor the differe (f) a. servations: (a) PlankTOM5, (b) PISCES, (c) CCSM-BEC, (d) NE Fig. 6. Fig. 6. estimates Fig. 5. and satellite observations: SeaWiFS chlorophyll Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 3

Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | diatoms, = and MLD included in NEMURO, in compar- 3 (d) All NO3-MLD SST-MLD SST-NO3 GAM variables: GAM Hirata et al. (2011). Mod- (f) erences in the deviance explained ff and MLD, green: model with SST and 3 CCSM-BEC and (f) Hirata et al. 2011 (f) Hirata coc cox dia diz nan pic picophytoplankton. Blue dots: all 3 predictor (c) = coexistence of several pPFTs, dia 52 = 17247 (b) PISCES Alvain et al. (2005) and PISCES, . For most pPFTs, di 3 coc cox dia diz nan pic dominant PFT (b) (e) Explained model deviance Explained cox dia diz nan pic (a) PlankTOM5 (c) NEMURO (d) CCSM−BEC al. 2005 et (e) Alvain nanophytoplankton, pic coc PlankTOM5, coccolihophores, coex =

0.8 0.6 0.4 0.2 0.8 0.6 0.4 0.2 = deviance explained deviance (a) Explained deviance for the modelled dominance of all plankton functional groups with diazotrophs, nan = erent combinationsas a function of the explanatory variables SST, NO ff Fig. 7. di the GAM model for ison to theelled two satellite variables: estimates coc diz variables included in GAM,MLD violet and dots: red: model model with withare SST NO and small NO for the full model (blue), as compared to the reduced model using only SST and NO as predictor variables (red).