Biogeosciences, 12, 6955–6984, 2015 www.biogeosciences.net/12/6955/2015/ doi:10.5194/bg-12-6955-2015 © Author(s) 2015. CC Attribution 3.0 License. Drivers and uncertainties of future global marine primary production in marine ecosystem models C. Laufkötter1,16, M. Vogt1, N. Gruber1, M. Aita-Noguchi7, O. Aumont2, L. Bopp3, E. Buitenhuis4, S. C. Doney5, J. Dunne6, T. Hashioka7, J. Hauck8, T. Hirata9, J. John6, C. Le Quéré14, I. D. Lima5, H. Nakano13, R. Seferian15, I. Totterdell10, M. Vichi11,12, and C. Völker8 1Environmental Physics, Institute of Biogeochemistry and Pollutant Dynamics, ETH Zürich, Zürich, Switzerland 2Laboratoire de Physique des Oceans, Centre IRD de Bretagne, Plouzane, France 3Laboratoire des sciences du climat et de l’Environnement (LSCE), IPSL, CEA-UVSQ-CNRS,UMR8212, Gif-sur-Yvette, France 4Tyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia, Norwich, UK 5Woods Hole Oceanographic Institution, Department of Marine Chemistry & Geochemistry, Woods Hole MA, USA 6NOAA Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, USA 7Core Research for Evolutional Science and Technology, Japan Science and Technology Agency, Tokyo, Japan 8Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany 9Faculty of Environmental Earth Science, Hokkaido University, Japan 10Met Office, Exeter, UK 11Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), Bologna, Italy 12Department of Oceanography, University of Cape Town (UCT), South Africa 13Meteorological Research Institute, Tsukuba, Ibaraki, Japan 14Tyndall Centre for Climate Change Research, University of East Anglia, Norwich Research Park, Norwich, NR47TJ, UK 15CNRM-GAME, Centre National de Recherche Météorologique, Groupe d’Étude de l’Atmosphère Météorologique, Météo-France/CNRS, 42 Avenue Gaspard Coriolis, 31100 Toulouse, France 16NOAA/Geophysical Fluid Dynamics Laboratory, Princeton University, Princeton, New Jersey, USA Correspondence to: C. Laufkötter ([email protected]) Received: 19 December 2014 – Published in Biogeosciences Discuss.: 27 February 2015 Revised: 24 September 2015 – Accepted: 22 October 2015 – Published: 7 December 2015 Abstract. Past model studies have projected a global de- between −25 and C40 %. Of the seven models diagnosing a crease in marine net primary production (NPP) over the 21st net decrease in NPP in the low latitudes, only three simulate century, but these studies focused on the multi-model mean this to be a consequence of the classical interpretation, i.e., rather than on the large inter-model differences. Here, we a stronger nutrient limitation due to increased stratification analyze model-simulated changes in NPP for the 21st cen- leading to reduced phytoplankton growth. In the other four, tury under IPCC’s high-emission scenario RCP8.5. We use warming-induced increases in phytoplankton growth outbal- a suite of nine coupled carbon–climate Earth system models ance the stronger nutrient limitation. However, temperature- with embedded marine ecosystem models and focus on the driven increases in grazing and other loss processes cause spread between the different models and the underlying rea- a net decrease in phytoplankton biomass and reduce NPP sons. Globally, NPP decreases in five out of the nine models despite higher growth rates. One model projects a strong over the course of the 21st century, while three show no sig- increase in NPP in the low latitudes, caused by an inten- nificant trend and one even simulates an increase. The largest sification of the microbial loop, while NPP in the remain- model spread occurs in the low latitudes (between 30◦ S and ing model changes by less than 0.5 %. While models con- 30◦ N), with individual models simulating relative changes sistently project increases NPP in the Southern Ocean, the Published by Copernicus Publications on behalf of the European Geosciences Union. 6956 C. Laufkötter et al.: Drivers of future marine primary production regional inter-model range is also very substantial. In most The past century provides very little experimental con- models, this increase in NPP is driven by temperature, but straint on the impact of long-term climate change on ma- it is also modulated by changes in light, macronutrients and rine productivity, largely because of the lack of long-term iron as well as grazing. Overall, current projections of future (> 50 years) observations. Using a combination of in situ ob- changes in global marine NPP are subject to large uncertain- servations of chlorophyll and of ocean transparency, Boyce ties and necessitate a dedicated and sustained effort to im- et al.(2010) suggested a substantial decrease in phytoplank- prove the models and the concepts and data that guide their ton biomass over the last 50 years, implying a very strong development. response of phytoplankton to ocean warming. This result has been met with a lot of scepticism (e.g., Rykaczewski and Dunne, 2011), especially because an independent assess- ment of long-term trends in ocean color by Wernand et al. 1 Introduction (2013) implied no overall global trend. Smaller decreases in NPP (−6 % over 50 years) were suggested by a hindcast By producing organic matter, marine phytoplankton form the simulation, where a marine ecosystem model coupled to an base of the marine food web, control the amount of food ocean general circulation model was forced with observed available for higher trophic levels, and drive the majority atmospheric variability and changes over the last 50 years of the ocean’s biogeochemical cycles, particularly that of (Laufkötter et al., 2013). The satellite observations since late carbon. The net formation rate of organic carbon by phy- 1997 suggest a negative correlation between sea surface tem- toplankton, i.e., net primary production (NPP), is a key de- perature and NPP (Behrenfeld et al., 2006), but the observa- terminant for the export of organic carbon from the surface tion period is clearly too short to distinguish natural fluctu- ocean, thereby governing how ocean biology impacts the ations from an anthropogenically driven trend in global ma- ocean–atmosphere exchange of CO2 (Falkowski et al., 2003; rine NPP (Henson et al., 2011; Antoine et al., 2005; Gregg, Sarmiento and Gruber, 2006). Accurate projections of future 2003). patterns of NPP may be crucial not only to estimate the po- Far less work has been done regarding future trends in the tential impacts of climate change on marine ecosystems and biomass of specific plankton functional types (PFTs), despite fishery yields but also to properly assess the evolution of the their importance in shaping ecosystem structure and function ocean carbon sink under anthropogenic climate change. (Le Quéré et al., 2005). Experiments have revealed a nega- Several authors have analyzed trends in future NPP and the tive relationship between warmer waters and phytoplankton underlying drivers, using models of strongly varying com- cell size, suggesting that future warming may tend to favor plexity and spatial resolution with regard to both the physical small phytoplankton (Morán et al., 2010). Moreover, using and the ecosystem components and also investigating differ- year-to-year variability associated with the North Atlantic ent climate change scenarios. In the majority of these studies, Oscillation and the Southern Annular Mode, Alvain et al. global marine NPP was projected to decrease in response to (2013) found that more stagnant conditions and warmer tem- future climate change (Bopp et al., 2001; Boyd and Doney, peratures tend to disfavor diatoms, suggesting that diatoms 2002; Steinacher et al., 2010; Bopp et al., 2013; Marinov will become less prevalent in the future. The few model- et al., 2013; Cabré et al., 2014). The main mechanism sug- ing studies available support this view, i.e., they reported gested to explain this decrease in NPP was a decrease in the global decreases in the diatom fraction and a shift towards upward supply of nutrients in the low latitudes because of in- smaller size classes (Bopp et al., 2005; Marinov et al., 2010, creased vertical stratification (Bopp et al., 2001; Steinacher 2013; Dutkiewicz et al., 2013). In these models, this shift was et al., 2010) and reduced upwelling. Lower nutrient availabil- driven by increased nutrient limitation that affected diatoms ity then resulted in a decrease in phytoplankton growth and more strongly than small phytoplankton. therefore reduced NPP. While published studies emphasized the role of changes However, a few studies produced contradicting results, i.e., in bottom–up factors in explaining the changes in NPP, top– they reported increases in global NPP as climate change pro- down control by zooplankton grazing may also drive future gresses over the 21st century (Sarmiento et al., 2004; Schmit- changes in total NPP or phytoplankton composition. This tner et al., 2008). Taucher and Oschlies(2011) showed that mechanism is intriguing, since top–down control was re- in the case of the model used by Schmittner et al.(2008), the cently identified as one of the main drivers of phytoplank- simulated increase in NPP is caused by the warmer temper- ton competition during blooms in several ecosystem mod- atures enhancing phytoplankton growth and overcoming the els (Hashioka et al., 2013; Prowe et al., 2011). Further, top– suppression of their growth owing to stronger nutrient stress. down control affects the onset of the spring bloom (Behren- However, this result cannot be easily