Multiyear predictability of tropical marine productivity

Significance Phytoplankton is at the base of the marine food web. Its carbon fixation, the net primary productivity (NPP), sustains most living marine resources. In regions like the tropical Pacific (30°N–30°S), natural fluctuations of NPP have large impacts on marine ecosystems including fisheries. The capacity to predict these natural variations would provide an important asset to science-based management approaches but remains unexplored yet. In this paper, we demonstrate that natural variations of NPP in the tropical Pacific can be forecasted several years in advance beyond the physical environment, whereas those of sea surface temperature are limited to 1 y. These results open previously unidentified perspectives for the future development of science-based management techniques of marine ecosystems based on multiyear forecasts of NPP. With the emergence of decadal predictability simulations, research toward forecasting variations of the climate system now covers a large range of timescales. However, assessment of the capacity to predict natural variations of relevant biogeochemical variables like carbon fluxes, pH, or marine primary productivity remains unexplored. Among these, the net primary productivity (NPP) is of particular relevance in a forecasting perspective. Indeed, in regions like the tropical Pacific (30°N–30°S), NPP exhibits natural fluctuations at interannual to decadal timescales that have large impacts on marine ecosystems and fisheries. Here, we investigate predictions of NPP variations over the last decades (i.e., from 1997 to 2011) with an Earth system model within the tropical Pacific. Results suggest a predictive skill for NPP of 3 y, which is higher than that of sea surface temperature (1 y). We attribute the higher predictability of NPP to the poleward advection of nutrient anomalies (nitrate and iron), which sustain fluctuations in phytoplankton productivity over several years. These results open previously unidentified perspectives to the development of science-based management approaches to marine resources relying on integrated physical-biogeochemical forecasting systems.

[1]  M. Hong,et al.  Decadal modes of sea surface salinity and the water cycle in the tropical Pacific Ocean: The anomalous late 1990s , 2014 .

[2]  E. Guilyardi,et al.  Decadal predictability of the Atlantic meridional overturning circulation and climate in the IPSL-CM5A-LR model , 2013, Climate Dynamics.

[3]  E. Guilyardi,et al.  Initialisation and predictability of the AMOC over the last 50 years in a climate model , 2013, Climate Dynamics.

[4]  Marie-Alice Foujols,et al.  Impact of the LMDZ atmospheric grid configuration on the climate and sensitivity of the IPSL-CM5A coupled model , 2013, Climate Dynamics.

[5]  M. Gehlen,et al.  Skill assessment of three earth system models with common marine biogeochemistry , 2013, Climate Dynamics.

[6]  G. Boer,et al.  Decadal predictability and forecast skill , 2013, Climate Dynamics.

[7]  S. Bony,et al.  Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5 , 2013, Climate Dynamics.

[8]  M. Ohman,et al.  A double-integration hypothesis to explain ocean ecosystem response to climate forcing , 2013, Proceedings of the National Academy of Sciences.

[9]  W. Gregg,et al.  Climate variability and phytoplankton composition in the Pacific Ocean , 2012 .

[10]  Francisco P. Chavez,et al.  A global analysis of ENSO synchrony: The oceans' biological response to physical forcing , 2012 .

[11]  P. Webster,et al.  Evaluation of short‐term climate change prediction in multi‐model CMIP5 decadal hindcasts , 2012 .

[12]  Tong Lee,et al.  Biological response to the 1997–98 and 2009–10 El Niño events in the equatorial Pacific Ocean , 2012 .

[13]  Karl E. Taylor,et al.  An overview of CMIP5 and the experiment design , 2012 .

[14]  Johanna Baehr,et al.  Multiyear Prediction of Monthly Mean Atlantic Meridional Overturning Circulation at 26.5°N , 2012, Science.

[15]  Francisco P Chavez,et al.  Marine primary production in relation to climate variability and change. , 2011, Annual review of marine science.

[16]  T. Delworth,et al.  Correction to “Assessing the predictability of the Atlantic meridional overturning circulation and associated fingerprints” , 2010 .

[17]  C. Moulin,et al.  Revisiting the La Niña 1998 phytoplankton blooms in the equatorial Pacific , 2010 .

[18]  F. D’Ortenzio,et al.  Climate-Driven Basin-Scale Decadal Oscillations of Oceanic Phytoplankton , 2009, Science.

[19]  R. Murtugudde,et al.  Nitrogen uptake and regeneration pathways in the equatorial Pacific: a basin scale modeling study , 2009 .

[20]  S. Henson,et al.  Decadal variability in biogeochemical models: Comparison with a 50‐year ocean colour dataset , 2009 .

[21]  Bin Wang,et al.  Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980–2004) , 2009 .

[22]  M. Behrenfeld,et al.  Spatial and temporal variations in dissolved and particulate organic nitrogen in the equatorial Pacific: biological and physical influences , 2008 .

[23]  Olivier Aumont,et al.  Decadal variations in equatorial Pacific ecosystems and ferrocline/pycnocline decoupling , 2008 .

[24]  L. Kornblueh,et al.  Advancing decadal-scale climate prediction in the North Atlantic sector , 2008, Nature.

[25]  F. Joos,et al.  Climate-induced interannual variability of marine primary and export production in three global coupled climate carbon cycle models , 2008 .

[26]  David A. Siegel,et al.  Climate-driven trends in contemporary ocean productivity , 2006, Nature.

[27]  L. Bopp,et al.  Globalizing results from ocean in situ iron fertilization studies , 2006 .

[28]  Michele Scardi,et al.  A comparison of global estimates of marine primary production from ocean color , 2006 .

[29]  R. Murtugudde,et al.  Subseasonal organization of ocean chlorophyll: Prospects for prediction based on the Madden‐Julian Oscillation , 2005 .

[30]  David A. Siegel,et al.  Carbon‐based ocean productivity and phytoplankton physiology from space , 2005 .

[31]  I. C. Prentice,et al.  A dynamic global vegetation model for studies of the coupled atmosphere‐biosphere system , 2005 .

[32]  Elizabeth C. Kent,et al.  Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century , 2003 .

[33]  Stéphane Blain,et al.  An ecosystem model of the global ocean including Fe, Si, P colimitations , 2003 .

[34]  M. Collins,et al.  Predictability of decadal variations in the thermohaline circulation and climate , 2003 .

[35]  Juliette Mignot,et al.  On the interannual variability of surface salinity in the Atlantic , 2003 .

[36]  Francisco P. Chavez,et al.  From Anchovies to Sardines and Back: Multidecadal Change in the Pacific Ocean , 2003, Science.

[37]  Thomas M. Smith,et al.  An Improved In Situ and Satellite SST Analysis for Climate , 2002 .

[38]  Mathew Barlow,et al.  ENSO, Pacific Decadal Variability, and U.S. Summertime Precipitation, Drought, and Stream Flow , 2001 .

[39]  J. Randerson,et al.  Biospheric Primary Production During an ENSO Transition , 2001, Science.

[40]  Feldman,et al.  Biological and chemical response of the equatorial pacific ocean to the 1997-98 El Nino , 1999, Science.

[41]  C. Bretherton,et al.  The Effective Number of Spatial Degrees of Freedom of a Time-Varying Field , 1999 .

[42]  Wade R. McGillis,et al.  A cubic relationship between air‐sea CO2 exchange and wind speed , 1999 .

[43]  Patrick Lehodey,et al.  Predicting skipjack tuna forage distributions in the equatorial Pacific using a coupled dynamical bio-geochemical model , 1998 .

[44]  J. Randerson,et al.  Primary production of the biosphere: integrating terrestrial and oceanic components , 1998, Science.

[45]  Richard J. Geider,et al.  A dynamic regulatory model of phytoplanktonic acclimation to light, nutrients, and temperature , 1998 .

[46]  W. Sunda,et al.  Interrelated influence of iron, light and cell size on marine phytoplankton growth , 1997, Nature.

[47]  M. Maqueda,et al.  Sensitivity of a global sea ice model to the treatment of ice thermodynamics and dynamics , 1997 .

[48]  K. Coale,et al.  Iron distributions in the equatorial Pacific: Implications for new production , 1997 .

[49]  F. Zwiers,et al.  Interannual variability and predictability in an ensemble of AMIP climate simulations conducted with the CCC GCM2 , 1996 .

[50]  C. Frankignoul.  Sea surface temperature anomalies, planetary waves, and air‐sea feedback in the middle latitudes , 1985 .

[51]  James J. McCarthy,et al.  HALF‐SATURATION CONSTANTS FOR UPTAKE OF NITRATE AND AMMONIUM BY MARINE PHYTOPLANKTON1 , 1969 .

[52]  J. Bjerknes.  ATMOSPHERIC TELECONNECTIONS FROM THE EQUATORIAL PACIFIC1 , 1969 .

[53]  E. Lorenz.  A study of the predictability of a 28-variable atmospheric model , 1965 .

[54]  E. Guilyardi,et al.  Reconstructing the subsurface ocean decadal variability using surface nudging in a perfect model framework , 2014, Climate Dynamics.

[55]  Takashi T. Sakamoto,et al.  An overview of decadal climate predictability in a multi-model ensemble by climate model MIROC , 2012, Climate Dynamics.

[56]  Patrick Lehodey,et al.  On the use of IPCC-class models to assess the impact of climate on Living Marine Resources , 2011 .

[57]  P. Falkowski,et al.  A consumer's guide to phytoplankton primary productivity models , 1997 .