Based on a set of climate simulations utilizing two kinds
of Earth system models (ESMs) in which observed ocean hydrographic data are
assimilated using exactly the same data assimilation procedure, we have
clarified that the successful simulation of the observed air–sea CO
Since the industrial revolution, vast quantities of greenhouse gases (e.g.,
CO
The Paris Agreement is an agreement within the United Nations Framework
Convention on Climate Change (UNFCCC, 2015) providing the framework of
measures from 2021 to 2030 to act against climate change. The goal of the
Paris Agreement is to restrict the rise in the global mean surface air
temperature to well below 2
For future climate predictions, data assimilation procedures are incorporated into climate models in order to synchronize simulated climatic states in the model with observations, that is, the initialization of climate models. By incorporating data assimilation procedures into Earth system models (ESMs), it will be possible to reproduce and predict variations in biogeochemical properties (Brasseur et al., 2009; Tommasi et al., 2017a, b; Park et al., 2018). This includes an assessment of the predictability of CO2F on a decadal timescale for the global ocean (Li et al., 2016, 2019).
Focusing on CO2F fluctuations associated with ENSO in the equatorial
Pacific, Dong et al. (2016) analyzed the results of the Earth system models
(ESMs) that participated in the Coupled Model Intercomparison Project
Phase 5 (CMIP5; Taylor et al., 2012), which contributed to the AR5 of the Intergovernmental Panel on Climate Change
(IPCC, 2013). They showed that only some ESMs could reproduce the observed
anticorrelated relationship between SST and CO2F. This suggests that our
understanding of ENSO and associated global carbon cycle variations are
still insufficient. For reliable prediction of future CO
In this study, utilizing two kinds of ESMs in which observed ocean
hydrographic data are assimilated, we attempted to identify the key
processes to reproduce the observed historical air–sea CO
In this study, we have conducted four experiments, NEW-assim, NEW,
OLD-assim, and OLD. In NEW-assim and NEW, we used the MIROC-ES2L (Hajima et
al., 2020), and in OLD-assim and OLD, we used the MIROC-ESM (Watanabe et
al., 2011). The former is newly developed for CMIP Phase 6 (CMIP6; Eyring et
al., 2016), whereas the latter is an official model of CMIP5. The physical
core model of MIROC-ES2L is MIROC5.2, which is a minor update of MIROC5
(Watanabe et al., 2010; Tatebe et al., 2018), whereas the physical
core model of MIROC-ESM is
MIROC3m (K-1 model developers, 2004). The horizontal resolution of the
atmospheric component of MIROC-ES2L (MIROC-ESM) has T42 spectral truncation
(i.e., approximately 300 km) with 40 (80) vertical levels up to 3 hPa (0.003 hPa). The oceanic component of MIROC-ES2L has a horizontal tripolar
coordinate system. In the spherical coordinate portion south of
63
In NEW-assim and OLD-assim, we used the ESMs that incorporated the same
simple scheme for ocean data assimilation, which comprised an incremental
analysis update (IAU; Bloom et al., 1996; Huang et al., 2002). This
technique is relatively simple compared with more elaborate techniques such as
the ensemble Kalman filter and four-dimensional variational method, but it is
widely used for decadal climate predictions (e.g., Mochizuki et al., 2010;
Tatebe et al., 2012). A benefit of an IAU is its relatively low
computational cost, which enables decadal- to centennial-scale integration
and a variety of parameter sensitivity experiments. In an IAU, during the
analysis interval from
Both NEW and OLD are the exactly same as the historical simulations designated by the CMIP6 and CMIP5 protocols, respectively, with three ensemble members for each that are bifurcated from arbitrary years of the corresponding preindustrial control simulations. The ocean data assimilation experiments, NEW-assim and OLD-assim, are bifurcated from NEW and OLD at the year 1946, respectively, and they are integrated up to the year 2005. Note that the data assimilation experiments are driven with the same external forcings as in the historical simulations. In the later sections, the model results for 1961–2005 are analyzed.
CO2F depends on the difference in the CO
Seawater
To assess CO2F, ocean temperature, and wind speed of the model output, we
used observation or reanalysis datasets. We used SOM-FFN as the CO2F dataset (Landschützer et al., 2016, 2017, 2018). It is an estimate based
on the ocean surface CO
Horizontal maps of the correlation coefficients between simulated and observed
CO2F values are shown in Fig. 1. The model output data were the ensemble mean and were
linearly interpolated into the SOM-FFN grid. Note that the data were not
detrended, and a 1-year running mean filter is applied to the monthly COF2
anomalies in the 1982–2005 period before calculating the correlation coefficients in
accordance with the period for which the SOM-FFN dataset is available. CO2F
in NEW-assim shows a positive correlation with SOM-FFN in the equatorial
Pacific region (Fig. 1a) where significant interannual variations in CO2F
are found (Fig. S1). On the other hand, CO2F in OLD-assim (Fig. 1c) is
negatively correlated in the equatorial Pacific. The time series in the
Niño3 region of both the 1-year running mean SST (hereafter, NINO3-SST)
and CO2F (hereafter, NINO3-CO2F) anomalies simulated with NEW-assim
(OLD-assim) are shown in Fig. 1b (Fig. 1d). Here, the data were
detrended and monthly anomalies were calculated with respect to the
1971–2000 monthly mean climatology. The correlation coefficients between
NINO3-SST and NINO3-CO2F anomalies in NEW-assim, OLD-assim, and the observations
are
Correlation coefficients between the detrended 1-year running mean NINO3-SST and NINO3-CO2F anomalies in NEW-assim, NEW, OLD-assim, OLD, and the observations. The correlations coefficients in NEW-assim, NEW, OLD-assim, and OLD are for the period from 1961 to 2005 (Figs. 1 and S2), and the correlations coefficient in the observations is for the period from 1982 to 2005.
As the vertical direction of CO2F is determined mainly by
Changes in
A cross section of the monthly ocean temperature anomalies regressed onto
the standardized monthly mean NINO3-SST anomalies along the equatorial
Pacific is presented in Figs. 3 and S3 in addition to the climatological
annual mean depths of the 18, 20, and 22
The intensity and period of ENSO in NEW, OLD, and the observations calculated from the 1-year running mean NINO3-SST anomalies for the period from 1961 to 2005.
To assess the variations in zonal wind associated with ENSO, we estimated
the 10 m zonal wind anomalies over the NINO4 region (5
The wind feedback and the vertical velocity feedback in NEW-assim,
NEW, OLD, and OLD-assim. The wind feedback is computed as the monthly 10 m
zonal wind anomalies in the Niño4 region regressed onto the monthly
NINO3-SST anomalies, and the vertical velocity feedback is the monthly vertical
velocity anomalies at the depth of the 20
NA: not available.
Anomalies of equatorial ocean temperature regressed onto the
standardized NINO3-SST anomalies for NEW
Cross sections of the monthly upward water velocity and DIC concentration
anomalies along the Equator regressed onto the standardized NINO3-SST
anomalies in NEW (OLD) are shown in Fig. 4a and c (Fig. 4b and d),
respectively. By reproducing wind feedback that is consistent with the
observations, the westerly wind anomalies during El Niño periods in NEW
(Fig. S4c) are comparable to that of the JRA-55 reanalysis (Fig. S4i),
leading to a weakening of the upward vertical velocity of approximately
Anomalies of the equatorial vertical velocity
Equatorial temperature analysis increments
Next, we examined the correction term in temperature due to the data
assimilation, i.e., the temperature analysis increment; the final term on the
right-hand side of Eq. (1); and the variations in vertical velocity and DIC
concentration. Anomalies of the monthly mean temperature analysis increments,
the vertical velocity, and the DIC concentration along the Equator regressed onto
the standardized NINO3-SST anomalies are shown in Fig. 5. The maximum
absolute value of the equatorial temperature analysis increment in NEW-assim
is found at depths of 10–40 m in the eastern equatorial Pacific, which is shallower
than the depth of the thermocline (Fig. 5a). In NEW-assim, the wind
feedback is 0.92 m s
In the present study, comparing the results of two ESMs in which observed ocean hydrographic data are assimilated, we have clarified that the representation of the processes in the equatorial climate system is important to reproduce the observed anticorrelated relationship between the SST and CO2F in the equatorial Pacific. When the ocean temperature and salinity observations were assimilated into an ESM with weaker ENSO amplitude than the observations, the correction term in the governing equation of the ocean temperature, which was introduced in the data assimilation procedure, caused spurious upwelling (downwelling) anomalies along the Equator during El Niño (La Niña) periods, leading to an increased (decreased) supply of DIC-rich subsurface water to the surface layer. Due to the resultant increase (decrease) in the surface DIC concentration, the upward (downward) CO2F anomalies during El Niño (La Niña) periods were induced, which was inconsistent with observation. When the ocean temperature and salinity observations were assimilated into the other ESM with a rather realistic ENSO representation, the anticorrelated relationship between the SST and CO2F was reproduced.
Focusing on the CO2F fluctuations associated with ENSO in the equatorial Pacific, Dong et al. (2016) analyzed the results of the CMIP5 ESMs. They showed that only a portion of CMIP5 ESMs (including MIROC-ESM) could reproduce the observed anticorrelated relationship between the SST and CO2F. Bellenger et al. (2014) evaluated the reproducibility of ENSO in the CMIP5 models. They reported that most CMIP5 climate models and ESMs underestimate the amplitude of the wind stress feedback by 20 %–50 % and that only 20 % of CMIP5 models have a relative error within 25 % of the observed value. There are many ESMs where the ENSO characteristics and/or the SST–CO2F relationships are inconsistent with observations. Causes of this discrepancy should be addresses in future studies using methods such as multi-model analysis; moreover, process-based uncertainty estimation will also be required in initialized climate and carbon predictions as well as projections by ESMs.
The model outputs of MIROC-ES2L (Hajima et al., 2019) are available from the
the Earth System Grid Federation (ESGF;
The supplement related to this article is available online at:
MiW, HT, MaW, and MK contributed to the experimental design. MiW and HK embedded the ocean data assimilation system into the ESMs. MiW and TH performed the experimental simulations. MiW analyzed the model output and drafted the paper. All authors discussed the results, and commented on and edited the paper.
The authors declare that they have no conflict of interest.
We thank Benjamin Barton, Jenny Jardine, Marta Payo Payo, Chris Unsworth, and one anonymous reader for helpful comments. We also thank the three anonymous reviewers, who provided many helpful suggestions for improving the article.
This research has been supported by the Integrated Research Program for Advanced Climate Models (TOUGOU) of the Ministry of Education, Culture, Sports, Science and Technology, MEXT, Japan (grant nos. JPMXD0717935457 and JPMXD0717935715).
This paper was edited by Mario Hoppema and reviewed by three anonymous referees.