- Open Access
Pacific climate variability and the possible impact on global surface CO2 flux
© Okajima and Kawamiya; licensee BioMed Central Ltd. 2011
Received: 28 August 2010
Accepted: 8 October 2011
Published: 8 October 2011
Climate variability modifies both oceanic and terrestrial surface CO2 flux. Using observed/assimilated data sets, earlier studies have shown that tropical oceanic climate variability has strong impacts on the land surface temperature and soil moisture, and that there is a negative correlation between the oceanic and terrestrial CO2 fluxes. However, these data sets only cover less than the most recent 20 years and are insufficient for identifying decadal and longer periodic variabilities. To investigate possible impacts of interannual to interdecadal climate variability on CO2 flux exchange, the last 125 years of an earth system model (ESM) control run are examined.
Global integration of the terrestrial CO2 flux anomaly shows variation much greater in amplitude and longer in periodic timescale than the oceanic flux. The terrestrial CO2 flux anomaly correlates negatively with the oceanic flux in some periods, but positively in others, as the periodic timescale is different between the two variables. To determine the spatial pattern of the variability, a series of composite analyses are performed. The results show that the oceanic CO2 flux variability peaks when the eastern tropical Pacific has a large sea surface temperature anomaly (SSTA). By contrast, the terrestrial CO2 flux variability peaks when the SSTA appears in the central tropical Pacific. The former pattern of variability resembles the ENSO-mode and the latter the ENSO-modoki1.
Our results imply that the oceanic and terrestrial CO2 flux anomalies may correlate either positively or negatively depending on the relative phase of these two modes in the tropical Pacific.
The Pacific Ocean is the largest oceanic domain on Earth and has the greatest impact of all ocean basins on climate variabilities on both a global and regional scale. One of the most dominant climatic phenomena on an interannual time scale is El Nino Southern Oscillation (ENSO). The Pacific ENSO has largest variance along the equator because it is excited by the Bjerknes feedback . For example, the enhanced zonal SST gradient makes the trade winds stronger and the thermocline tilt steeper, and hence the initial zonal SST gradient anomaly is further enhanced. Thus, the anomalous zonal SST gradient, trade winds, and thermocline tilt are closely connected at the equator in such a way that the initial perturbations grow rapidly through this feedback process. As a climatic impact, the zonal and vertical atmospheric circulation, the so-called Walker cell, is strengthened over the equatorial Pacific and brings anomalous high (low) pressure systems to the east (west) of the Pacific, resulting in Peruvian droughts and Indonesian floods. In the meridional direction, the anomalous tropical SST and trade winds also modify the atmospheric circulation on a global scale by displacing the foot of the Hadley cell and changing the stationary wave pattern [2, 3]. Therefore, the tropical SST anomaly can impact on climate not only in the tropics but also remotely at higher latitudes. The ENSO spectra has multiple peaks around the quasi-biennial or quasi-quadrennial frequency depending on the coupling parameter, shown by many model studies during the Tropical Ocean-Global Atmosphere (TOGA) program (refer to review article ).
Other than the ENSO, several Pacific variabilities have been proposed. The Pacific Decadal Oscillation (PDO) has a long-lived ENSO-like climate variability pattern in the Pacific [5, 6]. Compared to ENSO, the PDO events have maximum variance in the northeastern Pacific rather than in the tropics, with a timescale of 20 to 30 years. Some studies have shown that the PDO in the 20th century had multi-decadal modes, one with periods of 15 to 25 years, and the other of 50 to 70 years . These decadal climate variabilities were first found through Alaskan salmon production research and hence are closely related to the marine ecosystem productivity in the basin-wide North Pacific. More recent studies suggest that yet another Pacific climate variability dominates the SST anomaly around the central tropical Pacific near the date line. This phenomena is variously referred to as either ENSO-modoki [8–10], warm-pool ENSO , or central-Pacific ENSO [12, 13], and features basin-wide and decadal-scale variability in an ocean and atmosphere coupled system. Some studies further point out that global warming is related to the spatio-temporal modulation of the anomalous event [12, 13]. These decadal to interdecadal modes have been investigated in relation to the climatic regime shift in the late 1970s or recent unusual tropical variability, although their mechanism is still unclear.
These interannual and longer-term climate variabilities also modify both the ocean-atmosphere CO2 flux and the land-atmosphere CO2 flux by changing the oceanic and terrestrial biogeochemical cycles. Using observation and assimilated data sets, earlier studies have shown that tropical oceanic climate variability has strong impacts on the land surface temperature and soil moisture, and there is a negative correlation between oceanic and terrestrial CO2 fluxes [14, 15]. However, these data sets only cover less than the most recent 20 years and are insufficient for identifying decadal and longer periodic variabilities. Zeng et al.  performed simulations for the twentieth century by giving observed SST anomalies to an atmospheric general circulation model (AGCM) with a sophisticated land ecosystem model. They showed how ENSO impacts on the CO2 flux over tropical land regions, which accounts for a large portion of the global interannual CO2 variability. During the El Nino phase, for instance, most of the tropical land regions experience anomalous soil temperature warming with less precipitation, resulting in a large terrestrial carbon release to the atmosphere due to increased soil respiration and decreased net primary production. However, their experiments lack feedback processes between the ocean and atmosphere.
The present study examines the relations between Pacific climate variabilities and anomalies of the surface CO2 exchange by using a coupled climate-carbon cycle GCM. We conduct a simple control experiment to show that the climate variabilities in the tropical Pacific Ocean play an important role in modifying both oceanic and terrestrial CO2 flux at a global scale. We choose to focus on the tropical Pacific variabilities because of their climatic importance.
The Earth System Model (ESM) used in the present study is an ocean-atmosphere-land coupled general circulation model that includes physical and biogeochemical processes. It has been jointly developed at the Atmosphere Ocean Research Institute (formerly known as Center for Climate System Research) of the University of Tokyo, the National Institute for Environmental Studies, and the Japan Agency for Marine-Earth Science and Technology.
The atmospheric component is a global spectral model with a resolution of T42 in the horizontal and 20 sigma levels in the vertical. The land surface component, which describes heat and water exchange, has the same resolution as the atmospheric component in the horizontal and six to nine variable layers in the vertical, depending on the snow amount. The ocean component has a finer horizontal resolution: the longitudinal grid spacing is 1.4 degrees and the meridional grid intervals vary from 0.5 degrees at the equator to 1.7 degrees near the polar regions. The vertical resolution is 44 levels in sigma-z hybrid coordinate system, including eight sigma-layers near the surface and one bottom boundary layer . Both the land and ocean component feature carbon-cycle processes. The land component has five compartments of carbon storage and 20 types of vegetation . The ocean component incorporates a simple biogeochemical process: Nitrogen-Phytoplankton-Zooplankton-Detritus, which reasonably simulates the seasonal excursion of oceanic biological activities at a basin-wide scale . See Kawamiya et al.  and Yoshikawa et al.  for details. The model results are also found in an article by the Coupled Carbon Cycle Climate Model Intercomparison Project  and in the latest report by the Intergovernmental Panel on Climate Change .
In the model, the atmosphere and ocean components exchange surface fluxes every three hours. We firstly spin up the model with the observed monthly climatology as the boundary condition. During the spin-up, the atmospheric CO2 concentration is fixed to a constant preindustrial value of 285 ppmv. The globally integrated CO2 fluxes between the atmosphere and land/ocean reach a quasi-steady state after about 280 model years, and then the model run is extended for another 250 years for climate simulation. This 280-year period may be insufficient for complete spin-up for the global terrestrial and oceanic carbon cycle, but is still long enough to drive the model to a quasi-steady state, i.e., the global net atmosphere-ocean CO2 exchange becomes sufficiently small compared to its interannual variability. Due to limited computational resources, we do not perform thousands of years of spin-up. The results for the surface CO2 flux analysis should be basically the same for either 280 years or longer periods of spin-up. Immediately following the spin-up experiment, the CO2 concentration is allowed to vary and the model year count begins. Results for the last 125 years of the 250-year run are analyzed in this study. As the aim of this study is to analyze the relation between climate variability and CO2 flux anomaly, we focus on the spatio-temporal structure of simulated surface temperature and surface CO2 flux.
Overall, the ocean surface has less seasonal variance compared to the land surface because of the difference of the heat capacity between sea water and land soil. The continental seasonal variance is smaller at lower latitudes and greater at higher latitudes. In contrast, the oceanic seasonal variance is large in the sea-ice regions, equatorial Pacific, and mid-latitudes in the western boundary current regions. For a detailed description of model performance, the reader is referred to other publications (e.g., "MIROC3.2 medres" in Randall et al. ).
Another reason for winter terrestrial net outgassing is that heterotrophic respiration is greater than gross primary production in winter. As the majority of land areas lie in the Northern Hemisphere, these dominate the seasonal modulation of terrestrial CO2 flux. Thus, there is a phase difference of about a quarter-period between the oceanic and terrestrial CO2 flux in the annual cycle (Figure 4). The terrestrial CO2 flux has about an order of magnitude larger amplitude than the oceanic flux, in good agreement with assimilated data .
The seasonal variation, in Figure 2 and 3, is greater over the continents than over the ocean by an order of magnitude except for areas covered by ice or snow. The oceanic CO2 variation is especially large in the eastern tropical Pacific and eastern tropical Atlantic, where the equatorial and coastal upwelling has strong variation both on seasonal and interannual time scales . The terrestrial CO2 variation strongly depends on vegetation type and is generally large in the tropical savanna regions and mid-latitude crop fields, and small over deserts and ice sheets. The horizontal distribution of net CO2 flux over the continents is rather scattered and no systematic spatial pattern is discerned.
Results and discussion
In the previous section, the model climatology has been displayed along with the reanalysis/observation to compare and validate the model performance. Now we shift our focus to the climate variability in order to examine the relations between interannual variabilities in surface temperature and surface CO2 flux.
The CO2 flux variance (PgC year-1) by region, and percentage of the global modulation from the model result.
Before concluding that the oceanic and terrestrial CO2 flux are influenced by ENSO and ENSO-modoki, respectively, we confirm that those modes in fact modify the surface CO2 flux, by taking composites in a reverse manner. Here, we use area-averaged SST anomalies to calculate the indices of each mode as follows: The Nino3 index is defined by the SST anomaly in the eastern equatorial Pacific (150W-90W, 4S-4N), and ENSO-modoki index (EMI) by the difference of central tropical Pacific (CP: 165E-140W, 10S-10N) and an average of eastern (EP: 110W-70W, 15S-5N) and western (WP: 125E-145E, 10S-20N) tropical Pacific SST anomalies (e.g. EMI = CP - (EP + WP)/ 2) .
We have carried out experiments with a climate-carbon cycle coupled GCM to investigate possible impacts of interannual to interdecadal climate variability upon surface CO2 flux. The model climatology bears features consistent with earlier studies using uncoupled GCMs or assimilated data sets. The seasonal excursion of terrestrial and oceanic CO2 flux anomalies proceeds in a correlated manner with phase difference of a quarter period. On the interannual timescale, by contrast, the CO2 flux anomalies are not always negatively correlated, as the terrestrial CO2 flux variability has a longer period than the oceanic flux. A series of composite analyses shows that the oceanic CO2 flux anomalies are associated with ENSO variability, while the terrestrial CO2 flux anomalies are associated with ENSO-modoki. Our results imply that the oceanic and terrestrial CO2 flux anomalies may correlate either positively or negatively depending on the relative phase of the two climate modes in the tropical Pacific. Earlier studies investigated either less than 20 years of assimilated data set or historical ENSO cases in an AGCM simulation. Our findings provide new insight via successful simulation and analysis of longer-term climate variabilities including ENSO-modoki. We note, however, that the model ENSO is weaker than real ENSO, allowing other modes to become important. With less ENSO interference, the model ENSO-modoki becomes more apparent than in nature. As the nature of ENSO-modoki is still not well known, further investigation of this phenomenon will also deepen our understanding of the role of climate variabilities in the global carbon cycle. While this study has focused on the most dominant climate variabilities in the tropical Pacific, we must note that other climate variabilities in higher latitudes, such as the North Atlantic Oscillation or Southern Ocean Annular Mode, likely play certain roles in the global carbon cycle. Although we use a complex climate-carbon cycle coupled model, our experiment is a simple and idealized case where anthropogenic CO2 emissions are not imposed. Since the ENSO and ENSO-like variabilities are changing in the present global warming trend, experiments with CO2 forcing under multiple scenarios is an area of future work. Efforts to continue observations of global scale CO2 distribution are also highly desirable in order to enable data analysis on the relations between long-term climate and carbon cycle variabilities.
The authors are grateful to M. Nonaka and S.-P. Xie for useful comments and suggestions, and C. Yoshikawa and H. Kawarai for conducting the model experiments and data management. All the model experiments were carried out on the Earth Simulator. This work was supported by the KAKUSHIN program, "Innovative Program of Climate Change Projection for the 21st Century", of the Ministry of Education, Culture, Sports, Science and Technology, Japan.
1Modoki is a Japanese word meaning "similar but something different"
- Bjerknes J: Atmospheric teleconnections from the equatorial Pacific. Mon Wea Rev 1969, 97: 163–172. 10.1175/1520-0493(1969)097<0163:ATFTEP>2.3.CO;2View ArticleGoogle Scholar
- Horel JD, Wallace JM: Planetary-Scale Atmospheric Phenomena Associated with the Southern Oscillation. Mon Wea Rev 1981, 109: 813–829. 10.1175/1520-0493(1981)109<0813:PSAPAW>2.0.CO;2View ArticleGoogle Scholar
- Nitta T, Yamada S: Recent warming of tropical sea surface temperature and its relationship to the northern hemisphere circulation. J Meteor Soc Japan 1989, 67: 375–383.Google Scholar
- Neelin JD, Battisti DS, Hirst AC, Jin FF, Wakata Y, Yamagata T, Zebiak S: ENSO theory. J Geophys Res 1998, 103: 14261–14290. 10.1029/97JC03424View ArticleGoogle Scholar
- Mantua NJ, Hare SR, Zhang Y, Wallace JM, Francis RC: A Pacific interdecadal climate oscillation with impacts on salmon production. Bull Amer Meteor Soc 1997, 78: 1069–1079. 10.1175/1520-0477(1997)078<1069:APICOW>2.0.CO;2View ArticleGoogle Scholar
- Nakamura H, Lin G, Yamagata T: Decadal climate variability in the North Pacific during the recent decades. Bull Amer Meteor Soc 1997, 78: 2215–2225. 10.1175/1520-0477(1997)078<2215:DCVITN>2.0.CO;2View ArticleGoogle Scholar
- Minobe S: Resonance in bidecadal and pentadecadal climate oscillations over the North Pacific: Role in climatic regime shifts. Geophys Res Lett 1999, 26: 855–858. 10.1029/1999GL900119View ArticleGoogle Scholar
- Ashok K, Behera SK, Rao SA, Weng H, Yamagata T: El Niño Modoki and its possible teleconnection. J Geophys Res 2007.,112 (C11007): 10.1029/2006JC003798Google Scholar
- Weng H, Ashok K, Behera SK, Rao SA, Yamagata T: Impacts of recent El Niño Modoki on dry/wet conditions in the Pacific rim during boreal summer. Cimate Dynamics 2007, 29: 113–129. 10.1007/s00382-007-0234-0View ArticleGoogle Scholar
- Ashok K, Yamagata T: Climate change: The El Niño with a difference. Nature 2009, 461: 481–484. 10.1038/461481aView ArticleGoogle Scholar
- Kug JS, Jin FF, An SI: Two types of El Niño Events: Cold tongue El Niño and warm pool El Niño. J Climate 2009, 22: 1499–1515. 10.1175/2008JCLI2624View ArticleGoogle Scholar
- Yeh SW, Kug JS, Dewitte B, Kwon MH, Kirtman BP, Jin FF: El Niño in a changing climate. Nature 2009, 461: 511–514. 10.1038/nature08316View ArticleGoogle Scholar
- Di Lorenzo E, Cobb KM, Furtado JC, Schneider N, Anderson BT, Bracco A, Alexander MA, Vimont DJ: Central Pacific El Niño and decadal climate change in the North Pacific Ocean. Nature Geoscience 2010, 3: 762–765. 10.1038/ngeo984View ArticleGoogle Scholar
- Feely R, Wanninkhof R, Takahashi T, Tans P: Influence of El Niño on the equatorial Pacific contribution to atmospheric CO 2 accumulation. Nature 1999, 398: 597–601. 10.1038/19273View ArticleGoogle Scholar
- Baker D, Law R, Gurney K, Rayner P, Peylin P, Denning A, Bousquet P, Bruhwiler L, Chen YH, Ciais P, Fung I, Heimann M, John J, Maki T, Makshyutov S, Masarie K, Prather M, Pak B, Taguchi S, Zhu Z: TransCom 3 inversion intercomparison: Impact of transport model errors on the interannual variability of regional CO 2 fluxes, 1988–2003. Global Biogeochem Cycles 1988–2003.,20(GB1002): 10.1029/2004GB002439Google Scholar
- Zeng N, Mariotti A, Wetzel P: Terrestrial mechanisms of interannual CO 2 Variability. Global Biogeochem Cycles 2005.,19(GB1016): 10.1029/2004GB002273Google Scholar
- K-1 Model Developers: K-1 Coupled GCM (MIROC) Description. Tech rep., Center for Climate System Research (Univ. of Tokyo), National Institute for Environmental Studies, and Frontier Research Center for Global Change, Kashiwa Chiba, Japan 2004. [http://www.ccsr.u-tokyo.ac.jp/kyosei/hasumi/MIROC/tech-repo.pdf]Google Scholar
- Ito A, Oikawa T: A simulation model of the carbon cycle in land ecosystems (Sim-CYCLE): A description based on dry-matter production theory and plot-scale validation. Ecological Modelling 2002, 151: 149–179.View ArticleGoogle Scholar
- Kawamiya M, Kishi MJ, Suginohara N: An ecosystem model for the North Pacific embedded in a general circulation model Part II: Mechanisms forming seasonal variations of chlorophyll. J Marine Systems 2000, 25: 159–178. 10.1016/S0924-7963(00)00013-0View ArticleGoogle Scholar
- Kawamiya M, Yoshikawa C, Kato T, Sato H, Sudo K, Watanabe S, Matsuno T: Development of an integrated earth system model on the earth simulator. Journal of the Earth Simulator 2005, 4: 18–30. [http://www.jamstec.go.jp/esc/publication/journal/jes_vol.4/]Google Scholar
- Yoshikawa C, Kawamiya M, Kato T, Yamanaka Y, Matsuno T: Geographical distribution of the feedback between future climate and the carbon cycle. Global Biogeochem Cycles 2008.,22(GB1002): 10.1029/2007JG000570Google Scholar
- Friedlingstein P, Cox P, Betts R, Bopp L, von Bloh W, Brovkin V, Cadule P, Doney S, Eby M, Fung I, Govindasamy B, John J, Jones C, Joos F, Kato T, Kawamiya M, Knorr W, Lindsay K, Matthews HD, Raddatz T, Rayner P, Reick C, Roeckner E, Schnitzler KG, Schnur R, Strassmann K, JWeaver A, Yoshikawa C, Zeng N: Climate-carbon cycle feedback analysis, results from the C4MIP model intercomparison. J Climate 2006, 19: 3337–3353. 10.1175/JCLI3800.1View ArticleGoogle Scholar
- Randall D, Wood R, Bony S, Colman R, Fichefet T, Fyfe J, Kattsov V, Pitman A, Shukla J, Srinivasan J, Stouffer R, Sumi A, Taylor K: Cilmate Models and Their Evaluation. In Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Edited by: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt K, MTignor, Miller H. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA; 2007.Google Scholar
- Philander SG: El Niño, La Nina, and the Southern Oscillation. Academic Press, Inc; 1990.Google Scholar
- McPhaden MJ: TOGA-TAO and the 1991–93 El Niño-Southern Oscillation Event. Oceanography 1993, 6: 36–44.View ArticleGoogle Scholar
- Kanamitsu M, Ebisuzaki W, Woollen J, Yang SK, Hnilo JJ, Fiorino M, Potter GL: NCEP-DOE AMIP-II reanalysis (R-2). Bull Amer Meteorol Soc 2002, 83: 1631–1643. 10.1175/BAMS-83-11-1631View ArticleGoogle Scholar
- Takahashi T, Sutherland SC, Wanninkhof R, Sweeney C, Feely RA, Chipman DW, Hales B, Friederich G, Chavez F, Watson A, Bakker DCE, Schuster U, Metzl N, Yoshikawa-Inoue H, Ishii M, Midorikawa T, Nojiri Y, Sabine C, Olafsson J, Arnarson TS, Tilbrook B, Johannessen T, Olsen A, Bellerby R, Krtzinger A, Steinhoff T, Hoppema M, de Baar HJW, Wong CS, Delille B, Bates NR: Climatological mean and decadal changes in surface ocean pCO 2 , and net sea-air CO 2 flux over the global oceans. Deep-Sea Res II 2009, 56: 554–577. 10.1016/j.dsr2.2008.12.009View ArticleGoogle Scholar
- Fung I, Tucker CJ, Prentice KC: Application of AVHRR vegetation index to study atmosphere-biosphere exchange of CO 2 . J Geophys Res 1987, 92: 2999–3015. 10.1029/JD092iD03p02999View ArticleGoogle Scholar
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