ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-10333-2018Contrasting interannual atmospheric CO2 variabilities and their
terrestrial mechanisms for two types of El NiñosContrasting interannual atmospheric CO2
variabilitiesWangJunhttps://orcid.org/0000-0001-7359-1647ZengNingzeng@umd.eduhttps://orcid.org/0000-0002-7489-7629WangMeirongJiangFeijiangf@nju.edu.cnhttps://orcid.org/0000-0003-1744-7565ChenJingmingFriedlingsteinPierrehttps://orcid.org/0000-0003-3309-4739JainAtul K.https://orcid.org/0000-0002-4051-3228JiangZiqiangJuWeiminLienertSebastianhttps://orcid.org/0000-0003-1740-918XNabelJuliahttps://orcid.org/0000-0002-8122-5206SitchStephenViovyNicolasWangHengmaoWiltshireAndrew J.International Institute for Earth System Science, Nanjing University, Nanjing, ChinaState Key Laboratory of Numerical Modelling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Beijing, ChinaDepartment of Atmospheric and Oceanic Science and Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, USAJoint Center for Data Assimilation Research and Applications/Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science & Technology, Nanjing, ChinaDepartment of Geography, University of Toronto, Toronto, Ontario M5S3G3, CanadaCollege of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, EX4 4QF, UKDepartment of Atmospheric Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USAClimate and Environmental Physics, Physics Institute, University of Bern, Bern, SwitzerlandOeschger Centre for Climate Change Research, University of Bern, Bern, SwitzerlandLand in the Earth System, Max Planck Institute for Meteorology, 20146 Hamburg, GermanyCollege of Life and Environmental Sciences, University of Exeter, Exeter, EX4 4RJ, UKLaboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL-CEA-CNRS-UVQS, 91191, Gif-sur-Yvette, FranceMet office Hadley Centre, FitzRoy Road, Exeter, EX1 3PB, UKNing Zeng (zeng@umd.edu) and Fei Jiang (jiangf@nju.edu.cn)19July20181814103331034525February201813March201819June20185July2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/18/10333/2018/acp-18-10333-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/10333/2018/acp-18-10333-2018.pdf
El Niño has two different flavors, eastern Pacific (EP) and central
Pacific (CP) El Niños, with different global teleconnections. However,
their different impacts on the interannual carbon cycle variability remain
unclear. Here we compared the behaviors of interannual atmospheric
CO2 variability and analyzed their terrestrial mechanisms during
these two types of El Niños, based on the Mauna Loa (MLO) CO2
growth rate (CGR) and the Dynamic Global Vegetation Model's (DGVM) historical
simulations. The composite analysis showed that evolution of the MLO CGR
anomaly during EP and CP El Niños had three clear differences: (1)
negative or neutral precursors in the boreal spring during an El Niño
developing year (denoted as “yr0”), (2) strong or weak amplitudes, and
(3) durations of the peak from December (yr0) to April during an El Niño
decaying year (denoted as “yr1”) compared to October (yr0) to January (yr1)
for a CP El Niño, respectively. The global land–atmosphere carbon flux
(FTA) simulated by multi-models was able to capture the
essentials of these characteristics. We further found that the gross primary
productivity (GPP) over the tropics and the extratropical Southern Hemisphere
(Trop + SH) generally dominated the global FTA variations
during both El Niño types. Regional analysis showed that during EP El
Niño events significant anomalous carbon uptake caused by increased
precipitation and colder temperatures, corresponding to the negative
precursor, occurred between 30∘ S and 20∘ N from January
(yr0) to June (yr0). The strongest anomalous carbon releases, largely due to
the reduced GPP induced by low precipitation and warm temperatures, occurred
between the equator and 20∘ N from February (yr1) to August (yr1).
In contrast, during CP El Niño events, clear carbon releases existed
between 10∘ N and 20∘ S from September (yr0) to September
(yr1), resulting from the widespread dry and warm climate conditions.
Different spatial patterns of land temperatures and precipitation in
different seasons associated with EP and CP El Niños accounted for the
evolutionary characteristics of GPP, terrestrial ecosystem respiration (TER),
and the resultant FTA. Understanding these different behaviors of
interannual atmospheric CO2 variability, along with their
terrestrial mechanisms during EP and CP El Niños, is important because
the CP El Niño occurrence rate might increase under global warming.
Introduction
The El Niño–Southern Oscillation (ENSO), a dominant year-to-year climate
variation, leads to a significant interannual variability in the atmospheric
CO2 growth rate (CGR) (Bacastow, 1976; Keeling et al., 1995). Many
studies, including measurement campaigns (Lee et al., 1998; Feely et al.,
2002), atmospheric inversions (Bousquet et al., 2000; Peylin et al., 2013),
and terrestrial carbon cycle models (Zeng et al., 2005; Wang et al., 2016),
have consistently suggested the dominant role of terrestrial ecosystems,
especially tropical ecosystems, in contributing to interannual atmospheric
CO2 variability. Recently, Ahlstrom et al. (2015) further suggested
ecosystems over the semi-arid regions played the most important role in the
interannual variability of the land CO2 sink. Moreover, this
ENSO-related interannual carbon cycle variability may be enhanced under
global warming, with approximately a 44 % increase in the sensitivity of
terrestrial carbon flux to ENSO (Kim et al., 2017).
Tropical climatic variations (especially in surface air temperature and
precipitation) induced by ENSO and plant and soil physiological responses can
largely account for interannual terrestrial carbon cycle variability (Zeng et
al., 2005; Wang et al., 2016; Jung et al., 2017). Multi-model simulations
involved in the TRENDY project and the Coupled Model Intercomparison Project
Phase 5 (CMIP5) have consistently suggested the biological dominance of gross
primary productivity (GPP) or net primary productivity (NPP) (Kim et al.,
2016; Wang et al., 2016; Piao et al., 2013; Ahlstrom et al., 2015). However,
debates continue regarding which is the dominant climatic mechanism
(temperature or precipitation) in the interannual variability of the
terrestrial carbon cycle (Wang et al., 2013, 2014, 2016; Cox et al., 2013; Zeng et
al., 2005; Ahlstrom et al., 2015; Qian et al., 2008; Jung
et al., 2017).
The atmospheric CGR or land–atmosphere carbon flux (FTA – if
this is positive, this indicates a flux into the atmosphere) can anomalously
increase during El Niño and decrease during La Niña episodes
(Zeng et al., 2005; Keeling et al., 1995). Cross-correlation analysis
shows that the atmospheric CGR and FTA lag the ENSO by several
months (Qian et al., 2008; Wang et al., 2013, 2016). This is due
to the period needed for surface energy and soil moisture adjustment
following ENSO-related circulation and precipitation anomalies (Gu and Adler,
2011; Qian et al., 2008). However, considering the variability inherent in
the ENSO phenomenon (Capotondi et al., 2015), the atmospheric CGR and
FTA can show different behaviors during different El Niño
events (Schwalm, 2011; Wang et al., 2018).
El Niño events can be classified into eastern Pacific El Niño (EP El
Niño, also termed as conventional El Niño) and central Pacific El
Niño (CP El Niño, also termed as El Niño Modoki) according to the
patterns of sea surface warming over the tropical Pacific (Ashok et al.,
2007; Ashok and Yamagata, 2009). These two types of El Niño have
different global climatic teleconnections, associated with contrasting
climate conditions in different seasons (Weng et al., 2007,
2009). For example, positive winter temperature anomalies are located mostly
over the northeastern US during an EP El Niño, while warm anomalies occur
in the northwestern US during a CP El Niño (Yu et al., 2012). The
contrasting summer and winter precipitation anomaly patterns associated with
these two El Niño events over China, Japan, and the US were also
discussed by Weng et al. (2007, 2009). Importantly, Ashok et al. (2007)
suggested that the occurrence of the CP El Niño had increased during
recent decades compared to the EP El Niño. This phenomenon can probably
be attributed to the anthropogenic global warming (Ashok and Yamagata, 2009;
Yeh et al., 2009).
However, the contrasting impacts of EP and CP El Niño events on carbon
cycle variability remain unclear. In this study, we attempt to reveal their
different impacts given the different regional responses of the EP and CP El
Niños. We compared the behavior of interannual atmospheric CO2
variability and analyzed their terrestrial mechanisms corresponding to these
two types of El Niños, based on Mauna Loa (MLO) long-term CGR and TRENDY
multi-model simulations.
Interannual variability in the Niño3.4 index and the carbon
cycle. (a) Niño3.4. (b) Mauna Loa (MLO) CO2
growth rate (CGR, black line), as well as TRENDY multi-model median (red
line) and Jena inversion (green line) of the global land–atmosphere carbon
flux (FTA; a positive value means flux into the atmosphere; units: Pg C yr-1), which were further smoothed by the 3-month running
average. The light red shading represents the area between the 5 and
95 % percentiles of the TRENDY simulations. The bars represent the El
Niño events selected for this study, with the EP El Niño in blue and
the CP El Niño in yellow.
This paper is organized as follows: Sect. 2 describes the datasets used,
methods, and TRENDY models selected. Section 3 reports the results regarding
the relationship between ENSO and CGR and EP and CP El Niño events, in
addition to a composite analysis on carbon cycle behaviors, and terrestrial
mechanisms. Section 4 contains a discussion of the results, and Sect. 5
presents concluding remarks.
Datasets and methodsDatasets used
Data for monthly atmospheric CO2 concentrations between 1960 and
2013 were collected from the National Oceanic and Atmospheric Administration (NOAA)
Earth System Research Laboratory (ESRL) (Thoning et al., 1989). The annual CO2
growth rate (CGR) in Pg C yr-1 was derived month by month according
to the approach described by Patra et al. (2005) and Sarmiento et
al. (2010). The calculation is as follows:
CGRt=γ⋅[pCO2t+6-pCO2t-6],
where γ=2.1276 Pg C ppm-1, pCO2 is the atmospheric
partial pressure of CO2 in ppm, and t is the time in months. The
detailed calculation of the conversion factor, γ, can be found in the
appendix of Sarmiento et al. (2010).
Temperature and precipitation datasets for 1960 through 2013 were obtained
from CRUNCEPv6 (Wei et al., 2014). CRUNCEP datasets are the merged product of
ground observation-based CRU data and model-based NCEP–NCAR Reanalysis data
with a 0.5∘×0.5∘ spatial resolution and 6 h temporal
resolution. These datasets are consistent with the climatic forcing used to
run dynamic global vegetation models in TRENDY v4 (Sitch et al., 2015). The
sea surface temperature anomalies (SSTAs) over the Niño3.4 region
(5∘ S–5∘ N, 120–170∘ W) were obtained from the
NOAA's Extended Reconstructed Sea Surface Temperature (ERSST) dataset,
version 4 (Huang et al., 2015).
The inversion of FTA from the Jena CarboScope was used for
comparison with the TRENDY multi-model simulations from 1981 to 2013. The
Jena CarboScope Project provided the estimates of the surface–atmosphere
carbon flux based on atmospheric measurements using an “atmospheric
transport inversion”. The inversion run used here was s81_v3.8 (Rodenbeck et al., 2003).
Schematic diagram of the two types of El Niños. (a) Sea
surface temperature anomaly (SSTA) over the tropical Pacific associated with
the anomalous Walker circulation in an EP El Niño. (b) SSTA with
two cells of the anomalous Walker circulation in a CP El Niño. Red colors
indicate warming, and blue colors indicate cooling. Vectors denote the wind
directions.
Composites of El Niño and the corresponding carbon flux anomaly
(Pg C yr-1). (a) The Niño3.4 index composite during EP El
Niño events. (b) Corresponding MLO CGR and TRENDY v4 global
FTA composite during EP El Niño events. (c) The
Nino3.4 index composite during CP El Niño events.
(d) Corresponding MLO CGR and TRENDY v4 global FTA
composite during CP El Niño events. The shaded area denotes the 95 %
confidence intervals of the variables in the composite, derived from 1000
bootstrap estimates. The bold lines indicate the significance above the
80 % level estimated by the Student's t test. The black and red dashed lines in (b) and (d) represent the thresholds of the peak
duration (75 % of the maximum CGR or FTA anomaly).
TRENDY simulations
We analyzed eight state-of-the-art dynamic global vegetation models from
TRENDY v4 for the period 1960–2013: CLM4.5 (Oleson et al., 2013), ISAM (Jain
et al., 2013), JSBACH (Reick et al., 2013), JULES (Clark et al., 2011),
LPX-Bern (Keller et al., 2017), OCN (Zaehle and Friend, 2010), VEGAS (Zeng et
al., 2005), and VISIT (Kato et al., 2013) (Table 1). Since LPX-Bern was
excluded in the analysis of TRENDY v4, due to it not fulfilling the minimum
performance requirement, the output over the same time period of a more
recent, better performing version (LPX-Bern v1.3) was used. These models
were forced using a common set of climatic datasets (CRUNCEPv6), and followed
the same experimental protocol. Models use different vegetation datasets or
internally generated vegetation. The S3 run was used in this study, in
which simulations were forced by all the drivers, including CO2,
climate, land use, and land cover change (Sitch et al., 2015).
TRENDY models used in this study.
No.ModelResolution (lat × long.)FireReferencessimulation1CLM4.50.94∘× 1.25∘yesOleson et al. (2013)2ISAM0.5∘× 0.5∘noJain et al. (2013)3JSBACH1.875∘× 1.875∘yesReick et al. (2013)4JULES1.6∘× 1.875∘noClark et al. (2011)5LPX-Bern1∘× 1∘yesKeller et al. (2017)6OCN0.5∘× 0.5∘noZaehle et al. (2010)7VEGAS0.5∘× 0.5∘yesZeng et al. (2005)8VISIT0.5∘× 0.5∘yesKato et al. (2013)
The simulated terrestrial variables (net biome productivity (NBP), GPP, terrestrial ecosystem respiration (TER), soil moisture, and
others) were interpolated into a consistent 0.5∘×0.5∘
resolution using the first-order conservative remapping scheme (Jones, 1999)
by Climate Data Operators (CDO):
F‾k=1Ak∫fdA,
where F‾k denotes the area-averaged destination quantity,
Ak is the area of cell k, and f is the quantity in an old grid which
has an overlapping area with the destination grid. Then the median, 5, and
95 % percentiles of the multi-model simulations were calculated grid by
grid to study the different effects of EP and CP El Niños on terrestrial
carbon cycle interannual variability.
Composites of anomalies in the TRENDY FTA (black lines),
gross primary productivity (GPP, green lines), terrestrial ecosystem
respiration (TER, brown lines), and the carbon flux caused by disturbances
(D, blue lines) during two types of El Niños over the extratropical
Northern Hemisphere (NH, 23–90∘ N) and the tropics and
extratropical Southern Hemisphere (Trop + SH, 60–23∘ S). The
shaded area denotes the 95 % confidence intervals of the variables in the
composite, derived from 1000 bootstrap estimates. The bold lines indicate the
significance above the 80 % level estimated by the Student's t test.
The black dashed lines in b and d represent the thresholds of the peak
duration.
El Niño criterion and classification methods
El Niño events are determined by the Oceanic Niño Index (ONI) (i.e., the running 3-month mean SST anomaly over the Niño3.4 region; Fig. 1a).
This NOAA criterion is that El Niño events are defined as five consecutive
overlapping 3-month periods at or above the +0.5∘ anomaly.
We classified El Niño events into EP or CP based on the consensus of
three different identification methods directly adopted from a previous study
(Yu et al., 2012). These identification methods included the El Niño
Modoki Index (EMI) (Ashok et al., 2007), the EP/CP index method (Kao and Yu,
2009), and the Niño method (Yeh et al., 2009).
Anomaly calculation and composite analysis
To calculate the anomalies, we first removed the long-term climatology for
the period from 1960 to 2013 from all of the variables used here, both
modeled and observed, in order to eliminate the seasonal cycle. We then
detrended them based on a linear regression because (1) the trend in
terrestrial carbon variables was mainly caused by long-term CO2
fertilization and climate change, and (2) the trend in CGR primarily resulted
from the anthropogenic emissions. We used these detrended monthly anomalies
to investigate the impacts of El Niño events on the interannual carbon
cycle variability.
More specifically, in terms of the composite analysis, we calculated the
averages of the carbon flux anomaly (CGR, FTA etc.) during the
selected EP and CP El Niño events, respectively. We use the bootstrap
methods (Mudelsee, 2010) to estimate the 95 % confidence intervals and the
Student's t test to estimate the significance levels in the composite
analysis. An 80 % significance level was selected, as per Weng et
al. (2007), due to the limited number of EP El Niño events.
Composites of the standardized land surface air temperature (Tas,
red lines), precipitation (green lines), and TRENDY-simulated soil moisture
content (SM, blue lines) anomalies in two types of El Niños over the NH
and over Trop + SH. The shaded area denotes the 95 % confidence intervals of the
variables in the composite, derived from 1000 bootstrap estimates. The bold
lines indicate the significance above the 80 % level estimated by the Student's t test.
ResultsThe relationship between ENSO and interannual atmospheric CO2 variability
The interannual atmospheric CO2 variability closely coupled with
ENSO (Fig. 1) with noticeable increases in CGR during El Niño and
decreases during La Niña, respectively (Bacastow, 1976; Keeling and
Revelle, 1985). The correlation coefficient between the MLO CGR and the
Niño3.4 index from 1960 to 2013 was 0.43 (p<0.01). A regression
analysis further indicated that a per unit increase in the Niño3.4 index
can lead to a 0.60 Pg C yr-1 increase in the MLO CGR.
Hovmöller diagrams of the anomalies in climate variables and the
FTA (averaged from 180∘ W to 180∘ E) during EP
and CP El Niño events. Panels (a and d) show surface air
temperature anomalies over land (units: K); panels (b and e) show precipitation anomalies
over land (units: mm d-1); panels (c and f) show TRENDY-simulated
FTA anomalies (units: g C m-2 yr-1) during EP and CP
El Niño events. The dotted areas indicate the significance above the
80 % level as estimated using the Student's t test.
The variation in the global FTA anomaly simulated by TRENDY
models resembled the MLO CGR variation, with a correlation coefficient of
0.54 (p<0.01; Fig. 1b). This was close to the correlation coefficient of
0.61 (p<0.01; Fig. 1b) between the MLO CGR and the Jena CarboScope s81 for
the time period from 1981 to 2013. This indicates that the terrestrial carbon
cycle can largely explain the interannual atmospheric CO2
variability, as suggested by previous studies (Bousquet et al., 2000; Zeng et
al., 2005; Peylin et al., 2013; Wang et al., 2016). Moreover, the correlation
coefficient of the TRENDY global FTA and the Niño3.4 index
reached 0.49 (p<0.01), and a similar regression analysis of FTA
with Niño3.4 showed a sensitivity of 0.64 Pg C yr-1 K-1.
However, owing to the diffuse light fertilization effect induced by the
eruption of Mount Pinatubo in 1991 (Mercado et al., 2009), the Jena
CarboScope s81 indicated that the terrestrial ecosystems had an anomalous
uptake during the 1991–1992 El Niño event, making the MLO CGR an anomalous
decrease. However, TRENDY models did not capture this phenomenon. This was
not only due to a lack of a corresponding process representation in some
models, but also because the TRENDY protocol did not include diffuse and
direct light forcing.
EP and CP El Niño events
Schematic diagrams of the two types of El Niños (EP and CP) are shown in
Fig. 2. During EP El Niño events (Fig. 2a), a positive sea surface
temperature anomaly (SSTA) occurs in the eastern equatorial Pacific Ocean,
showing a dipole SSTA pattern with the positive zonal SST gradient. This
condition forms a single cell of Walker circulation over the tropical
Pacific, with a dry downdraft in the western Pacific and wet updraft in the
central-eastern Pacific. In contrast, an anomalous warming in the central
Pacific, sandwiched by anomalous cooling in the east and west, is observed
during CP El Niño events (Fig. 2b). This tripole SSTA pattern makes the
positive/negative zonal SST gradient in the western/eastern tropical Pacific,
resulting in an anomalous two-cell Walker circulation over the tropical
Pacific. This alteration in atmospheric circulation produces a wet region in
the central Pacific. Moreover, apart from these differences in the equatorial
Pacific, the SSTA in other oceanic regions also differs remarkably
(Weng et al., 2007, 2009).
Based on the NOAA criterion, a total of 17 El Niño events were detected
from 1960 through 2013. The events were then categorized into an EP or a CP
El Niño based on a consensus of three identification methods (EMI,
EP/CP index, and Niño methods) (Yu et al., 2012). Considering the effect
of diffuse radiation fertilization induced by volcano eruptions (Mercado et
al., 2009), we removed the 1963–1964, 1982–1983, and 1991–1992 El Niño
events, in which Mount Agung, El Chichón, and Pinatubo erupted,
respectively. In addition, we closely examined those extended El Niño
events that occurred in 1968–1970, 1976–1978, and 1986–1988. Based on the
typical responses of MLO CGR to El Niño events (anomalous increase
lasting from the El Niño developing year to El Niño decaying year;
Supplement Fig. S1), we retained 1968–1969, 1976–1977, and 1987–1988 El
Niño periods. Finally, we obtained four EP El Niño and seven CP El Niño
events in this study (Table 2; Fig. 1b and Supplement Fig. S2), with the
composite SSTA evolutions as shown in the Supplement Fig. S3.
Eastern Pacific (EP) and central Pacific (CP) El Niño events
used in this study, as identified by a majority consensus of three methods.
EP El NiñoCP El Niño1972–19731965–19661976–19771968–19691997–19981987–19882006–20071994–19952002–20032004–20052009–2010Responses of atmospheric CGR to two types of El Niños
Based on the selected EP and CP El Niño events, a composite analysis was
conducted with the non-smoothed detrended monthly anomalies of the MLO CGR
and the TRENDY global FTA to reveal the contrasting carbon cycle
responses to these two types of El Niños (Fig. 3). In addition to the
differences in the location of anomalous SST warming and the alteration of
the atmospheric circulation in EP and CP El Niños shown in Fig. 2, the
following findings were elucidated.
Different El Niño precursors. The SSTA was significantly negative in
the EP El Niño during the boreal winter (JF) and spring (MAM) in yr0
(hereafter “yr0” and “yr1” refer to the El Niño developing and decaying year,
respectively). Conversely, the SSTA was neutral in the CP El Niño.
Different tendencies of SST (∂SST/∂t).
The tendency of SST in the EP El Niño was stronger than that in the CP El
Niño.
Different El Niño amplitudes. Due to the different
tendencies of SST, the amplitude of the EP El Niño was basically stronger
than that of the CP El Niño, though they all reached maturity in November or
December of yr0 (Fig. 3a and c).
Correspondingly, behaviors of the MLO CGR during these two types of El
Niño events also displayed some differences (Fig. 3b and d). During EP El
Niño events (Fig. 3b), the MLO CGR was negative in boreal spring (yr0)
and increased quickly from boreal fall (yr0), whereas it was neutral in
boreal spring (yr0) and slowly increases from boreal summer (yr0) during the
CP El Niño episode (Fig. 3d). The amplitude of the MLO CGR anomaly during
EP El Niño events was generally larger than that during CP El Niño
events. Importantly, the duration of the MLO CGR peak during EP El Niño
was from December (yr0) to April (yr1), while the MLO CGR anomaly peaked from
October (yr0) to January (yr1) during CP El Niño. Here we simply defined
the peak duration as the period above the 75 % of the maximum CGR (or
FTA) anomaly, in which the variabilities of less than 3 months
below the threshold were also included. The positive MLO CGR anomaly ended
around September (yr1) in both cases (Fig. 3b and d). During the finalization
of this paper, we noted the publication of Chylek et al. (2018) who also
found a CGR amplitude difference in response to the two types of events.
A comparison of the MLO CGR with the TRENDY global FTA anomalies
(Fig. 3b and d) indicated that the TRENDY global FTA effectively
captured the characteristics of CGR evolution during the CP El Niño. In
contrast, the amplitude of the TRENDY global FTA anomaly was
somewhat underestimated during the EP El Niño, causing a lower
statistical significance (Fig. 3b). This underestimation of the global
FTA anomaly can, for example, be clearly seen in a comparison
between the TRENDY and the Jena CarboScope during the extreme 1997–1998 EP El
Niño (Fig. 1b). Also, other characteristics can be basically captured.
Therefore, insight into the mechanisms of these CGR evolutions during EP and
CP El Niños, based on the simulations by TRENDY models, is still
possible.
Regional contributions, characteristics, and their mechanisms
We separated the TRENDY global FTA anomaly by major geographic
regions into two parts: the extratropical Northern Hemisphere (NH,
23∘ N–90∘ N), and the tropics plus extratropical Southern
Hemisphere (Trop + SH, 60∘ S–23∘ N) (Fig. 4). In a
comparison of the contributions from these two parts, it was found that the
FTA over Trop + SH played a more important role in the global
FTA anomaly in both cases (Fig. 4b and d), and this finding was
consistent with previous studies (Bousquet et al., 2000; Peylin et al., 2013;
Zeng et al., 2005; Wang et al., 2016; Ahlstrom et al., 2015; Jung et al.,
2017). The FTA over Trop + SH was negative in austral fall
(MAM; yr0), increased from austral spring (SON; yr0), and peaked from
December (yr0) to April (yr1) during the EP El Niño (Fig. 4b).
Conversely, it was nearly neutral in austral fall (yr0), increased from
austral winter (JJA; yr0), and peaked from November (yr0) to March (yr1)
during the CP El Niño (Fig. 4d). These evolutionary characteristics in
the FTA over the Trop + SH were generally consistent with the
global FTA and the MLO CGR (Fig. 3b and d). In contrast, the
contributions from the FTA anomaly over the NH were relatively
weaker (or nearly neutral) (Fig. 4a and c).
According to the equation
FTA=-NBP=TER-GPP+D (where D is the
carbon flux caused by disturbances such as wildfires, harvests,
grazing, and land cover change), the variation in FTA can be
explained by the variations in GPP, TER, and D. The D simulated by TRENDY
was nearly neutral during both El Niño types (Fig. 4). Therefore, GPP and
TER largely accounted for the variation in FTA.
More specifically, in Trop + SH, GPP anomalies dominated the variations in
FTA for both El Niño types, but their evolutions differed
(Fig. 4b and d). The GPP showed an anomalous positive value during austral
fall (yr0), and an anomalous negative value from austral fall (yr1) to winter
(yr1), with the minimum around April (yr1) during the EP El Niño
(Fig. 4b). Conversely, the GPP anomaly was always negative, with the minimum
occurring around October or November (yr0) during the CP El Niño
(Fig. 4d). The variation in the TER in both El Niños was relatively
weaker than that of the GPP (Fig. 4b and d). The anomalous positive TER
during austral spring (yr0) and summer (yr1) accounted for the increase in
FTA, and it partly canceled the negative GPP in austral fall
(yr1) and winter (yr1) during the EP El Niño (Fig. 4b). In contrast, the
TER had a reduction in yr0 during the CP El Niño (Fig. 4d). Over the NH,
though the FTA anomaly was relatively weaker, the behaviors of
GPP and TER differed in EP and CP El Niños. GPP and TER consistently
decreased in the growing season of yr0 and increased in the growing season of
yr1 during the EP El Niño (Fig. 4a), whereas they only showed some
increase during boreal summer (yr1) during the CP El Niño (Fig. 4c).
These evolutionary characteristics of GPP, TER, and the resultant
FTA principally resulted from their responses to the climate
variability. Figure 5 shows the standardized observed surface air
temperature, precipitation, and TRENDY-simulated soil moisture contents. Over
the Trop + SH, taking into consideration the regulation of thermodynamics and
the hydrological cycle on the surface energy balance, variations in temperature and
precipitation (soil moisture) were always opposite during the two types of El
Niños (Fig. 5b and d). Additionally, adjustments in soil moisture lagged
precipitation by approximately 2–4 months, owing to the so-called “soil
memory” of water recharge (Qian et al., 2008). The variations in GPP in both
the El Niño types were closely associated with variations in soil
moisture, namely water availability largely dominated by precipitation
(Figs. 4b, d and 5b, d), and this result was consistent with previous studies
(Zeng et al., 2005; Zhang et al., 2016). Warm temperatures during El Niño
episodes can enhance the ecosystem respiration, but dry conditions can reduce
it. These cancelations from warm and dry conditions made the amplitude of
TER variation smaller than that of GPP (Fig. 4b and d). Over the NH,
variations in temperature and precipitation were basically in the same
direction (Fig. 5a and c), as opposed to their behaviors over the
Trop + SH. This was due to the different climatic dynamics of the two
regions (Zeng et al., 2005). During the EP El Niño event, cool and dry
conditions in the boreal summer (yr0) inhibited GPP and TER, whereas warm and
wet conditions in the boreal spring and summer (yr1) enhanced them (Figs. 5a
and 4a). In contrast, only the warm and wet conditions in boreal summer (yr1)
enhanced GPP and TER during the CP El Niño event (Figs. 5c and 4c). These
different configurations of temperature and precipitation variations during
EP and CP El Niños form the different evolutionary characteristics of
GPP, TER, and the resultant FTA.
Detailed regional evolutionary characteristics can be seen from the
Hovmöller diagrams in Fig. 6 and in the Supplement Figs. S4 and S5. Obvious
large anomalies in FTA consistently occurred from 20∘ N
to 40∘ S during EP and CP El Niños (Fig. 6c and f), consistent
with the above analyses (Fig. 4b and d). Moreover, there was a clear
anomalous carbon uptake between 30∘ S and 20∘ N during the
period from January (yr0) to June (yr0) during the EP El Niño (Fig. 6c).
This uptake corresponded to the negative precursor (Figs. 3b and 4b). This
anomalous carbon uptake comparably came from the three continents (Supplement
Fig. S4a–c). Biological process analyses indicated that GPP dominated
between 5 and 20∘ N and between 30 and 15∘ S (Supplement
Fig. S5a), which was related to the increased amount of precipitation
(Fig. 6b). In contrast, TER dominated between 15∘ S and
5∘ N (Supplement Fig. S5b), largely due to the colder temperatures
(Fig. 6a). Conversely, the strongest anomalous carbon releases occurred
between the equator and 20∘ N during the period from February (yr1)
to August (yr1) during the EP El Niño (Fig. 6c). The largest contribution
to these anomalous carbon releases came from South America (Supplement
Fig. S4c). Both GPP and TER showed anomalous decreases (Supplement
Fig. S5a and b), and a stronger decrease in GPP than in TER caused the anomalous
carbon releases here (Fig. 6c). Low precipitation (with a few months of
delayed dry conditions; Fig. 6b) and warm temperatures (Fig. 6a) inhibited
GPP, causing the positive FTA anomaly (Fig. 6c). In contrast,
significant carbon releases were found between 10∘ N and
20∘ S from September (yr0) to September (yr1) during the CP El
Niño (Fig. 6f). More specifically, these clear carbon releases largely
originated from South America and tropical Asia (Supplement Fig. S4d–f). TER
dominated between 15∘ S and 10∘ N during the period from
January (yr1) to September (yr1), and other regions and periods were
dominated by GPP (Supplement Fig. S5c and d). Widespread dry and warm
conditions (Fig. 6d and e) effectively explained these GPP and TER anomalies,
as well as the resultant FTA behavior. For more detailed
information on the other regions, refer to Supplement Figs. S4 and S5.
Discussion
El Niño shows large diversity in individual events (Capotondi et al.,
2015), thereby creating large uncertainties in composite analyses (Figs. 3–5).
Four EP El Niño events during the past 5 decades were selected
for this study to research their effects on interannual carbon cycle
variability (Table 1). Due to the small number of samples and large
inter-event spread (Supplement Fig. S2), the statistical significance of
the composite analyses will need to be further evaluated with upcoming EP El
Niño events occurring in the future. However, cross-correlation analyses
between the long-term CGR (or FTA) and the Niño index have
shown that the responses of the CGR (or FTA) lag ENSO by a few months
(Zeng et al., 2005; Wang et al., 2013, 2016). This phenomenon
can be clearly detected in the EP El Niño composite (Fig. 3b). Therefore,
the composite analyses in this study can still give us some insight into the
interannual variability of the global carbon cycle.
Another caveat is that the TRENDY models seemed to underestimate the
amplitude of the FTA anomaly during the extreme EP El Niño
events (Fig. 1b). This underestimation of FTA may partially
result from a bias in the estimation of carbon releases induced by wildfires.
As expected, the carbon releases induced by wildfires, such as in the 1997–1998
strong El Niño event, played an important role in global carbon variations
(van der Werf et al., 2004; Chen et al., 2017) (Supplement Fig. S6). However,
some TRENDY models (ISAM, JULES, and OCN) do not include a fire module to
explicitly simulate the carbon releases induced by wildfires (Table 1), and
those TRENDY models that do contain a fire module generally underestimate the
effects of wildfires. For instance, VISIT and JSBACH clearly underestimated
the carbon flux anomaly induced by wildfires during the 1997–1998 EP
El Niño event (Supplement Fig. S6).
The recent extreme 2015–2016 El Niño event was not included in this study
because the TRENDY v4 datasets covered the time span from 1860 to 2014. As
shown in Wang et al. (2018), the behavior of the MLO CGR in the 2015–2016 El
Niño resembled the composite result of the CP El Niño events (Fig. 3d).
But the 2015–2016 El Niño event had the extreme positive SSTA both
over the central and eastern Pacific. Its equatorial eastern Pacific SSTA
exceeded +2.0 K, comparable to the historical extreme El Niño events
(e.g., 1982–1983 and 1997–1998); the central Pacific SSTA marked the warmest event
since the modern observation (Thomalla and Boyland, 2017). Therefore, the
2015–2016 El Niño event evolved not only in a similar fashion to the EP El
Niño dynamics that rely on the basin-wide thermocline variations, but
also in a similar fashion to the CP El Niño dynamics that rely on the
subtropical forcing (Paek et al., 2017; Palmeiro et al., 2017). The 2015–2016
extreme El Niño event can be treated as the strongest mixed EP and CP El
Niño that caused different climate anomalies compared with the extreme
1997–1998 El Niño (Paek et al., 2017; Palmeiro et al., 2017), which had
contrasting terrestrial and oceanic carbon cycle responses (Wang et al., 2018;
Liu et al., 2017; Chatterjee et al., 2017).
As mentioned above, when finalizing our paper, we noted the publication of
Chylek et al. (2018) who also focused on interannual atmospheric
CO2 variability during EP and CP El Niño events. Here we simply
illustrated some differences and similarities. In the method of the
identification of EP and CP El Niño events, Chylek et al. (2018) took the
Niño1 + 2 index and Niño4 index to categorize El Niño events,
while we adopted the results of Yu et al. (2012), based on the consensus of
three different identification methods, and additionally excluded the events
that coincided with volcanic eruptions. The different methods made some
differences in the identification of EP and CP El Niño events. Chylek et
al. (2018) suggested that the CO2 rise rate had different time
delay to the tropical near surface air temperature, with the delay of about
8.5 and 4 months during EP and CP El Niños, respectively. Although we did
not find out the exactly same time delay, we suggested that MLO CGR anomaly
showed the peak duration from December (yr0) to April (yr1) in the EP El
Niño, and from October (yr0) to January (yr1) in the CP El Niño.
Additionally, we suggested the differences of MLO CGR anomaly in precursors
and amplitudes during EP and CP El Niños. Furthermore, we revealed their
terrestrial mechanisms based on the inversion results and the TRENDY
multi-model historical simulations.
Concluding remarks
In this study, we investigate the different impacts of EP and CP El Niño
events on the interannual carbon cycle variability in terms of the composite
analysis, based on the long-term MLO CGR and TRENDY multi-model simulations.
We suggest that there are three clear differences in evolutions of the MLO
CGR during EP and CP El Niños in terms of their precursor, amplitude,
and duration of the peak. Specifically, the MLO CGR anomaly was negative in
boreal spring (yr0) during EP El Niño events, while it was neutral
during CP El Niño events. Additionally, the amplitude of the CGR anomaly
was generally larger during EP El Niño events than during CP El Niño
events. Also, the duration of the MLO CGR peak during EP El Niño events
occurred from December (yr0) to April (yr1), while it peaked from October (yr0)
to January (yr1) during CP El Niño events.
The TRENDY multi-model-simulated global FTA anomalies were able
to capture these characteristics. Further analysis indicated that the
FTA anomalies over the Trop + SH made the largest contribution to
the global FTA anomalies during these two types of El Niño
events, in which GPP anomalies, rather than TER anomalies, generally
dominated the evolutions of the FTA anomalies. Regionally, during
EP El Niño events, clear anomalous carbon uptake occurred between
30∘ S and 20∘ N during the period from January (yr0) to
June (yr0), corresponding to the negative precursor. This was primarily caused by
more precipitation and colder temperatures. The strongest anomalous carbon
releases happened between the equator and 20∘ N during the period
from February (yr1) to August (yr1), largely due to the reduced GPP induced
by low precipitation and warm temperatures. In contrast, clear carbon
releases existed between 10∘ N and 20∘ S from September (yr0)
to September (yr1) during CP El Niño events, which were caused by
widespread dry and warm climate conditions.
Some studies (Yeh et al., 2009; Ashok and Yamagata, 2009) have suggested that
the CP El Niño has become or will be more frequent under global warming
compared with the EP El Niño. Because of these different behaviors of the
interannual carbon cycle variability during the two types of El Niños,
this shift of El Niño types will alter the response patterns of
interannual terrestrial carbon cycle variability. This possibility should
encourage researchers to perform further studies in the future.
The monthly atmospheric CO2 concentration is from
NOAA/ESRL (https://www.esrl.noaa.gov/gmd/ccgg/trends/index.html). The
Niño3.4 index is from ERSST4
(http://www.cpc.ncep.noaa.gov/data/indices/ersst4.nino.mth.81-10.ascii).
Temperature and precipitation are from CRUNCEPv6
(ftp://nacp.ornl.gov/synthesis/2009/frescati/temp/land_use_change/original/readme.htm).
TRENDY v4 data are available from Stephen Sitch (s.a.sitch@exeter.ac.uk) upon
your reasonable request.
The Supplement related to this article is available online at https://doi.org/10.5194/acp-18-10333-2018-supplement.
NZ and JW proposed the scientific ideas. JW, NZ, and
MRW completed the analysis and the initial draft of the manuscript. FJ,
JMC, PF, AKJ, ZQJ, WMJ, SL, JN, SS, NV, HMW, and AJW discussed the
manuscript and contributed significantly to the revisions of this
manuscript. SS provided the datasets of the TRENDY v4.
The authors declare that they have no conflict of
interest.
The 10th International Carbon Dioxide Conference (ICDC10) and
the 19th WMO/IAEA Meeting on Carbon Dioxide, other Greenhouse Gases and
Related Measurement Techniques (GGMT-2017).
Acknowledgements
We gratefully acknowledge the TRENDY DGVM community, as part of the Global
Carbon Project, for access to gridded land data and the NOAA ESRL for the use
of Mauna Loa atmospheric CO2 records. This study was supported by
the National Key R&D Program of China (grant no. 2016YFA0600204 and no.
2017YFB0504000), the Natural Science Foundation of Jiangsu Province, China
(grant no. BK20160625), and the National Natural Science Foundation of China
(grant no. 41605039). Andrew Wiltshire was supported by the Joint UK
BEIS/Defra Met Office Hadley Centre Climate Programme (GA01101). We also
would like to thank LetPub for providing linguistic
assistance. Edited by: Rachel Law
Reviewed by: two anonymous referees
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