ESDEarth System DynamicsESDEarth Syst. Dynam.2190-4987Copernicus GmbHGöttingen, Germany10.5194/esd-5-423-2014Continued increase in atmospheric CO2 seasonal
amplitude in the 21st century projected by the CMIP5 Earth system modelsZhaoF.ZengN.zeng@atmos.umd.eduhttps://orcid.org/0000-0002-7489-7629Department of Atmospheric and Oceanic Science, University
of Maryland, USAEarth System Science Interdisciplinary Center, University
of Maryland, USAN. Zeng (zeng@atmos.umd.edu)1December2014524234391June201423June201429September201428October2014This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://www.earth-syst-dynam.net/5/423/2014/esd-5-423-2014.htmlThe full text article is available as a PDF file from https://www.earth-syst-dynam.net/5/423/2014/esd-5-423-2014.pdf
In the Northern Hemisphere, atmospheric CO2 concentration declines in
spring and summer, and rises in fall and winter. Ground-based and
aircraft-based observation records indicate that the amplitude of this
seasonal cycle has increased in the past. Will this trend continue in the
future? In this paper, we analyzed simulations for historical (1850–2005)
and future (RCP8.5, 2006–2100) periods produced by 10 Earth system models
participating in the fifth phase of the Coupled Model Intercomparison
Project (CMIP5). Our results present a model consensus that the increase of
CO2 seasonal amplitude continues throughout the 21st century.
Multi-model ensemble relative amplitude of detrended global mean CO2
seasonal cycle increases by 62±19 % in 2081–2090, compared to
1961–1970. This amplitude increase corresponds to a 68±25 %
increase in net biosphere production (NBP). The results show that the
increase of NBP amplitude mainly comes from enhanced ecosystem uptake during
Northern Hemisphere growing season under future CO2 and temperature
conditions. Separate analyses on net primary production (NPP) and
respiration reveal that enhanced ecosystem carbon uptake contributes about
75 % of the amplitude increase. Stimulated by higher CO2
concentration and high-latitude warming, enhanced NPP likely outcompetes
increased respiration at higher temperature, resulting in a higher net
uptake during the northern growing season. The zonal distribution and
spatial pattern of NBP change suggest that regions north of 45∘ N
dominate the amplitude increase. Models that simulate a stronger carbon
uptake also tend to show a larger increase of NBP seasonal amplitude, and
the cross-model correlation is significant (R=0.73, p<0.05).
Introduction
Modern measurements at Mauna Loa, Hawaii (19.5∘ N,
155.6∘ W, 3400 m altitude) have shown an increase in atmospheric
CO2 concentration from < 320 ppm in 1958 to 400 ppm in 2013.
There is also a mean seasonal cycle that is characterized with a 5-month
decrease (minimum in October) and a 7-month increase (maximum in May). The
peak-to-trough amplitude of this seasonal cycle is approximately 6.5 ppm,
which represents a close average of a large portion of the Northern
Hemisphere (NH) biosphere (Kaminski et al., 1996) where the amplitude ranges
from about 3 ppm near the Equator to 17 ppm at Point Barrow, Alaska
(71∘ N). The seasonal variation of Mauna Loa (MLO) CO2
reflects the imbalance of growth and decay of the NH biosphere. Early
studies have speculated that global primary production would decrease
because of global changes such as acid rain and deforestation (Reiners,
1973; Whittaker and Likens, 1973). If this is the case, assuming changes in
respiration are similar at peak and trough of the CO2 seasonal cycle,
we might observe a reduction of CO2 seasonal amplitude. However, Hall
et al. (1975) found no evidence of long-term amplitude change from 15 years
of MLO CO2 record (1958–1972). They concluded that either the biosphere
is too big to be affected yet or the degradation of biosphere is balanced by
enhanced CO2 fertilization and increased use of fertilizers in
agriculture.
In the 1970s through the 1980s, the metabolic activity of the biosphere seems
to be getting stronger, as indicated by rapid increase in MLO CO2 amplitude
(Pearman and Hyson, 1981; Cleveland et al., 1983; Bacastow et al., 1985).
Enhanced CO2 fertilization was considered as a major factor, and
climate change a possible cause (Bacastow et al., 1985). Keeling et al.
(1996) linked the amplitude increase with climate change by showing the
2-year phase lag relationship between trends of CO2 amplitude and
30–80∘ N mean land temperature. Unlike CO2 fertilization, the
combined effect of climate (temperature, precipitation, etc.) introduces
strong interannual variability to the CO2 amplitude change. In the
early 1990s, despite of the continuing rise of 30–80∘ N mean land
temperature, CO2 seasonal amplitude at MLO has declined. Buermann et
al. (2007) attributed this decline to the severe drought in North America
during 1998–2003.
In late 1990s, the increasing trend resumed at MLO. The latest analysis
shows a 0.32 % yr-1 increase in MLO amplitude and a 0.60 % yr-1
increase in Point Barrow (BRW) amplitude (Fig. 1a, Graven et
al., 2013). This trend (over 50 years) corresponds to an increase of 16 %
in MLO, and 30 % in BRW CO2 seasonal amplitude, respectively. Graven
et al. (2013) also compared aircraft measurements taken at 500 and 700 hPa
heights in 1958–1961 and 2009–2011, suggesting an even larger
(∼50 %) increase of CO2 seasonal amplitude north of
45∘ N. Furthermore, to infer the model-simulated CO2
amplitude increase at 500 hPa, they applied two atmospheric transport models
to monthly net ecosystem production (NEP) from the historical simulation
(Exp3.2) results of eight CMIP5 models. Compared with aircraft data, they
found the CMIP5 models simulated a much lower amplitude increase.
Surface CO2 monitoring stations have two major limitations. First, they
are sparse. For several decades, the Global Monitoring Division of
NOAA/Earth System Research Laboratory (ESRL) has measured CO2 from
more than 100 surface monitoring sites (Conway et al., 1994). Only some
have over 30 years of record. Similarly, Randerson et al. (1997) determined
the CO2 amplitude trend north of 55∘ N by averaging flask
data from five stations. Second, the surface CO2 stations do not
measure carbon exchange between the land/ocean and atmosphere directly.
Atmospheric inversion models are capable of providing surface carbon fluxes
with global coverage. However, the resolution and accuracy of these models
are inherently limited due to a small number of stations used, and errors in
atmospheric transport (Peylin et al., 2013).
Process-based terrestrial biosphere models (TBMs) can generate surface
fluxes over the past for longer period, usually with a spatial resolution of
half to three degrees. Thus, they offer opportunities to understand the
mechanisms of CO2 amplitude increase better. McGuire et al. (2001)
calculated amplitude trends of total land–atmosphere carbon flux (north of
30∘ N) from four TBMs. Compared to Mauna Loa CO2, they found
the trend was overestimated by one of the four models and underestimated by
the other three. They suggest the observed trend may be a consequence of the
combined effects of rising CO2, climate variability and land use
changes, which have also been recognized in previous studies (Kohlmaier et
al., 1989; Keeling et al., 1995, 1996; Randerson et al., 1997, 1999; Zimov
et al., 1999). Models show varied extent of amplitude increase, possibly due
to their different sensitivities to CO2 concentration and climate.
Interestingly, Graven et al. (2013) found that CMIP5 models underestimate
the CO2 amplitude increase in the mid-troposphere at latitudes north of
45∘ N. However, previous observations indicated that the models
might overestimate CO2 fertilization effect (Piao et al., 2013),
suggesting that our understanding of the amplitude trend is still limited.
In the future, we do not know if the CO2 amplitude will increase or
decrease. With temperature rise and CO2 increase, we may see a further
increase of CO2 amplitude. On the other hand, the frequency and/or
duration of heat waves are very likely to increase over most land areas, and
the increases in intensity and/or duration of drought and flood are likely
(IPCC, 2013). As a result, the ecosystem
productivity may decrease, which may reduce the CO2 amplitude. In this
study, we analyzed the fully coupled CMIP5 Earth system model runs as part
of the Fifth Assessment Report (AR5) of the United Nations' Intergovernmental Panel on Climate Change (IPCC). Specifically, we looked
into the emission-driven simulations, which include many of the
aforementioned processes and feedbacks. Our specific questions are the following. (1) How
do the CMIP5 models predict the amplitude and phase changes of CO2
seasonal cycle in the future? (2) Are the changes mostly driven by changes
in ecosystem production or respiration? (3) Where do the models predict the
largest CO2 amplitude changes will occur?
Section 2 describes the CMIP5 experiments, models used and our analyzing
method; Sect. 3 presents the major results of our multi-model analyses;
Sect. 4 discusses amplitude increases at individual stations, physical
mechanisms and uncertainties; and Sect. 5 concludes our main findings.
List of Models used and their characteristics.
LandResolutionModelsModeling CenterComponent(Lon × Lat)ReferenceBNU-ESMBeijing Normal University, ChinaCoLM32.8125∘× 2.8125∘Ji et al. (2014)CanESM2Canadian Centre for Climate Modeling and Analysis, CanadaCTEM 2.8125∘× 2.8125∘Arora et al. (2011)CESM1-BGCCommunity Earth System Model Contributors, NSF-DOE-NCAR, USACLM41.25∘× 0.9∘Long et al. (2013) GFDL-ESM2mNOAA Geophysical Fluid Dynamics Laboratory, USALM32.5∘× 2∘Dunne et al. (2013)INM-CM4Institute for Numerical Mathematics,Russia2∘× 1.5∘Volodin et al. (2010)IPSL-CM5A-LRInstitut Pierre-Simon Laplace, FranceORCHIDEE3.75∘× 1.875∘Dufresne et al. (2013)MIROC-ESMJapan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute (University of Tokyo), and National Institute for Environmental Studies, JapanMATSIRO + SEIB-DGVM 2.8125∘× 2.8125∘Watanabe et al. (2011)MPI-ESM-LRMax Planck Institute for Meteorology, GermanyJSBACH2.8125∘× 2.8125∘Ilyina et al. (2013)MRI-ESM1Meteorological Research Institute, JapanHAL1.125∘× 1.125∘Yukimoto et al. (2011)NorESM1-MENorwegian Climate Centre, NorwayCLM42.5∘× 1.875∘Tjiputra et al. (2013)Method Model descriptions
We analyzed historical and future emission-driven simulation results from 10
CMIP5 Earth System Models (ESMs). The historical simulations, referred to as experiment 5.2 or ESM
historical 1850–2005 run (Taylor et al., 2012), were forced with gridded
CO2 emissions reconstructed from fossil fuel consumption estimates
(Andres et al., 2011). The future simulations, referred to as experiment 5.3
or ESM RCP8.5 2006–2100 run, were forced with projected CO2 emissions,
following only one scenario–RCP8.5 (Moss et al., 2010). We chose the
emission-driven runs because the fully coupled ESMs in these runs have
interactive carbon cycle component. Global atmospheric CO2
concentrations are simulated prognostically, therefore they reflect the
total effect of all the physical, chemical, and biological processes on
Earth, and their interactions and feedbacks with the climate system. We
obtained model output primarily from the Earth System Grid Federation
(ESGF), an international network of distributed climate data servers
(Williams et al., 2011). For the GFDL model, we retrieved results from its data portal (http://nomads.gfdl.noaa.gov:8080/DataPortal/cmip5.jsp). The
main characteristics of the 10 models are listed in Table 1.
Analysis procedure
We first analyzed the monthly output of prognostic atmospheric CO2
concentrations to evaluate the change of CO2 seasonal amplitude
(defined as maximum minus minimum of detrended seasonal cycle) from 1961 to
2099. Atmospheric CO2 was obtained primarily as the area- and
pressure-weighted mean of CO2 across all vertical levels – a better
representation of atmospheric carbon content than surface CO2. The
INM-CM4 model does not provide CO2 concentration, so we converted its
total atmospheric mass of CO2 to mole fraction. We excluded the IPSL
model from analyses in Sects. 3.1 and 3.2 because its CO2 output is
not available. Only CanESM2 provides three different realizations for both
historical and future runs, and we simply use its first realization in our
comparison. We believe this choice would lead to a more representative
result than including all realizations of CanESM2 in multi-model averaging.
To extract the CO2 seasonal cycle from the monthly records, we applied
the curve-fitting procedures using the CCGCRV software developed at the
National Oceanic and Atmospheric Administration Climate Monitoring and
Diagnostics Laboratory (Thoning et al., 1989; http://www.esrl.noaa.gov/gmd/ccgg/mbl/crvfit/crvfit.html). This algorithm
first fits the long-term variations and the seasonal component in the
monthly CO2 record with a combination of a trend function and a series
of annual harmonics. Then the residuals are filtered with fast Fourier
transform and transformed back to the real domain. Specifically, we followed
the default setup of a quadratic polynomial for the trend function, 4-yearly harmonics for the seasonal component, and long/short-term cutoff
values of 667 days/80 days for the filtering in our analyses. We examined
the phase change of CO2 detrended seasonal cycle by counting how
frequent the maxima and minima occur in different months. We used two
definitions of seasonal amplitude in our analyses that yield similar
results: one directly comes from the CCGCRV package, and another definition
is simply maximum minus minimum of detrended seasonal cycle in each year.
For each model's monthly global mean CO2, we first computed the
detrended CO2 seasonal cycle as the annual harmonic part plus the
filtered residue using the short-term cutoff value. Then we started to
investigate the global carbon budget in each model:
dCO2dt=FFE-NBP+FOA.
Nine-model (excluding IPSL) averaged monthly detrended: (a) global
mean CO2 (ppm, column average); (b) global mean CO2 growth rate
(PgC month-1, using a conversion factor of 1 ppm =2.12 PgC month-1); and (c) global total -NBP (PgC month-1) from 1961 to
2099. (d) Presents eight-model (excluding IPSL and INM) averaged
monthly detrended global mean CO2 (ppm) at lowest model level and
ESRL's global mean detrended surface CO2 observation (shown in green).
The left term is the change of CO2 concentration (or CO2 growth
rate), which we simply computed as the difference between the current month
and previous month's concentration – this leads to a half-month shift
earlier than the results indicate. The right hand side (RHS) comprises of
fossil fuel emission (FFE), net biosphere production (NBP, or net
terrestrial–atmosphere carbon exchange, positive if land is a carbon sink),
and net ocean–atmosphere flux (FOA, positive if ocean releases carbon). For
each model, we checked and ensured that the sum of individual flux terms on
the RHS of Eq. (1) equals to the CO2 growth rate.
Previous studies have limited the impact of FFE and FOA on trends in
CO2 amplitude to less than a few percent change (Graven et al., 2013).
Therefore we focused on examining the seasonal cycle of NBP in this study.
To investigate whether the NBP amplitude change is mostly due to enhanced
production or respiration, we inspected the seasonal cycle of NPP and
respiration separately. The INM model does not provide NPP output, so it is
excluded in this part of analyses. For respiration, one complication is
that even though NBP represents the net terrestrial–atmosphere carbon
exchange in all models (thus allowing model comparison), its further
breakdown varies. For example, the GFDL-ESM2M model's NBP has component
fluxes including NPP, heterotrophic respiration (Rh), land use change
(fLuc), fire (fFire), harvest (fHarvest) and grazing (fGrazing). In
contrast, NBP approximately equals to NPP minus Rh in CanESM2. Instead
of directly adding all flux components such as Rh and fLuc for each
model (which would be unnecessary and difficult since not all fluxes are
provided), we defined Rh∗ (dominated by Rh) such that
Rh∗=NPP-NBP.
Additionally, we analyzed the spatial patterns of NBP change between future
(2081–2090) and historical (1961–1970) period. We approximated NBP amplitude
change as the difference between the peak seasons of carbon uptake and
release by the biosphere, namely May–July and October–December averages,
respectively. We chose 3-month averages for multi-model ensemble,
because not all models simulate peak uptake in June and peak release in
October. Monthly output of NBP, NPP and Rh∗ (derived from
NBP and NPP) from all models were first resampled to 2×2∘ grids. Then the spatial and zonal means for both May–July and
October–December were computed.
Amplitude (maximum minus minimum) of global mean column atmospheric
CO2, CO2 growth rate (CO2g) and global total NBP, averaged
over 1961–1970 and 2081–2090 for the nine models, and their multi-model
ensemble (MME) and standard deviation (SD).
∗ The multi-model ensemble (MME) here is a simple average over the
nine models in the table. The values are slightly larger than given in text
because of averaging method (in the main text, multi-model averaging of
detrended variables are done first, then their amplitude are computed and
mean amplitude changes are derived).
Amplitude increase (ppm) and trends of maxima/minima of surface
CO2 from eight models, their multi-model ensemble (MME), and ESRL's
Global mean CO2 (CO2GL).
1981–19852001–2005PercentTrend of MinimaTrend of MaximaModels(ppm)(ppm)Change(ppm 10yr-1)(ppm 10yr-1)BNU-ESM2.713.114.39 %-0.0990.096CanESM23.043.246.58 %-0.0640.02CESM1-BGC2.052.186.34 %-0.0320.044GFDL-ESM2m3.713.761.35 %-0.0330.095MIROC-ESM3.393.616.49 %-0.0780.045MPI-ESM-LR6.197.0213.41 %-0.250.171MRI-ESM13.693.854.34 %-0.0950.031NorESM1-ME2.372.474.22 %-0.0240.016MME3.13.378.71 %-0.0840.065CO2GL4.114.47.06 %-0.1020.024
Time series of the relative seasonal amplitude (relative to
1961–1970 mean) of (a) global mean atmospheric CO2; and (b) global
total NBP from 1961 to 2099. Thick black line represents multi-model
ensemble, and one standard deviation model spread is indicated by light grey
shade.
ResultsChanges of CO2 and NBP seasonal amplitude
The CMIP5 models project that the increase of CO2 seasonal amplitude
continues in the future. Figure 1a shows detrended and globally averaged
monthly column atmospheric CO2 from 1961 to 2099, averaged over nine
models (no IPSL). The models project an increase of CO2 seasonal
amplitude (defined as maximum minus minimum in each year) by about 70 %
over 120 years, from 1.6 ppm during 1961–1970 to 2.7 ppm in 2081–2090. The
increase is faster in the future than in the historical period. Another
feature is that the trend of minima (-0.63 ppm century-1) has a
larger magnitude than the trend of maxima (0.41 ppm century-1),
suggesting that enhanced vegetation growth contributes more to the amplitude
increase than respiration increase. Gurney and Eckels (2011) found the trend
of net flux in dormant season is larger than that of growing season.
However, they applied a very different definition for amplitude, considering
all months instead of maxima and minima, to analyze the atmospheric CO2
inversion results from 1980–2008. Specifically, they defined growing season
net flux (dormant season net flux) as the total of any month for which the
net carbon flux is negative (positive), and amplitude as the difference of
the two net fluxes. It is no surprise they reached a conclusion that seems
to contradict ours, since growing season is much shorter than dormant season
at global scale. Figure 1b and c present detrended global mean CO2
growth rate (1 ppm =2.12 PgC month-1 for unit conversion) and global
total -NBP, two quantities showing very similar characteristics as
expected. All models simulate an increase in amplitude, although
considerable model spread is found (Table 2).
In addition, we notice a phase advance of maxima and minima by counting their time of occurrence (data not
shown). Excluding models above one standard deviation from the ensemble mean
yields similar results.
To illustrate how well the models reproduce the seasonal variations of
CO2, we compared the multi-model ensemble global CO2 at the lowest
model level – not equivalent to the height of surface CO2 measurement,
but relatively close – with ESRL's global mean CO2 over 1981–2005
(Fig. 1d). The surface CO2 seasonal amplitude estimated by the model
ensemble is lower than that of ESRL's global CO2 estimate (Ed
Dlugokencky and Pieter Tans, NOAA/ESRL, www.esrl.noaa.gov/gmd/ccgg/trends/), however the amplitude increases are
similar (Table 3). This surface station-based global CO2 estimate also
indicates that the amplitude increase is dominated by the trend of minima.
We further calculated the change of relative amplitude (relative to
1961–1970) for each model. The amplitude here is computed by the CCGCRV
package. As illustrated in Fig. 2, all nine models show an increase in
both global mean CO2 and total NBP seasonal amplitude. CO2
seasonal amplitude has increased by 62±19 % in 2081–2090, compared
to 1961–1970; whereas NBP seasonal amplitude has increased by 68±25 % over the same period (see Table 4 for details of individual models).
The trend of increase is much higher in the future (CO2/NBP:
0.70 %/0.73 % yr-1 during 2006–2099) than in the historical period
(0.25 % and 0.28 % yr-1 during 1961–2005 for CO2 and NBP),
albeit the model spread also becomes larger in the future. When we applied
the same procedure to the Northern Hemisphere (25–90∘ N) mean
CO2 and total NBP for the eight models (excluding INM-CM4 which only
has global CO2 mass), we saw a higher amplitude increase and larger
model spread: 81±46 and 77±43 % for CO2 and NBP,
respectively.
Production vs. respiration
Our next major question is whether the amplitude increase of NBP is largely
driven by NPP or respiration. We computed the mean seasonal cycle of
detrended CO2 growth rate, -NBP, -NPP (reverse signs so that
negative values always indicate carbon uptake) and Rh∗ in two
periods: 1961–1970 (black) and 2081–2090 (red), for the nine models (for
this and following analyses, we excluded INM which does not provide NPP, and
included the IPSL model except for CO2 growth rate). The seasonal cycle
of -NBP resembles that of detrended CO2 growth rate (Fig. 3a–d),
confirming that the activities of land ecosystem dominate the CO2
seasonal cycle and its amplitude increase in the model simulations. Except
for CanESM2 (also noted in Anav et al., 2013), and BNU-ESM (which simulates
a second peak carbon uptake around November) to some extent, most models can
reproduce the net uptake of carbon during spring and summer (when increasing
NPP overcomes respiration) and the net carbon release during fall and winter
at global scale: net carbon uptake peaks in June (five models) or July
(three models) for the historical period, and exclusively in June for the
future period. However, the model spread on amplitude is large: CESM1-BGC
and NorESM1-ME, which has the same land model (CLM4) that features an
interactive nitrogen cycle, are characterized by a small seasonal amplitude
of -NBP – merely 30 % of those on the high end of the models
(IPSL-CM5A-LR and MPI-ESM-LR). The seasonal amplitude of multi-model
ensemble NBP, computed as maximum minus minimum (June–October), has
increased from 2.7 to 4.7 PgC month-1 (Fig. 3d).
Column atmospheric CO2 and NBP amplitude (computed by CCGCRV,
slightly different from max minus min) Increases of nine models by 2081–2090
relative to their 1961–1970 values and their multi-model ensemble (MME).
Seasonal cycle of detrended global mean CO2 growth rate
(a and b), global total -NBP (c and d), global total -NPP (e and f), and global total
Rh∗ (g and h, computed as NPP minus NBP), averaged over 1961–1970
and 2081–2090 for the CMIP5 models (excluding INM, also excluding IPSL for
CO2 growth rate). Seasonal cycles of individual models are presented in
the left panel (dashed for 1961–1970, and solid for 2081–2090). Ensemble
mean and one standard deviation model spread (black/grey for 1961–1970,
red/pink for 2081–2090) are displayed in the right panels. Blue arrows mark
the changes in June and October (NBP maxima and minima), except for CO2
growth rate and -NPP, where arrows also indicate phase shifts of minima
between the two periods. We show -NBP and -NPP so that the negative
values represent carbon uptake by the biosphere, and positive values
indicate carbon release from the biosphere. Note that -NBP and its two
components -NPP and Rh∗ are not detrended, so that the sum of
(f) and (h) equals to (d). Detrended -NBP seasonal cycle (not
shown) looks very similar to (d), as its trend is small compared to the
magnitude of seasonal cycle.
This 2 PgC month-1 amplitude increase is the sum of enhanced net carbon
uptake in June and higher net release in October, and the enhancement in
uptake (1.4 PgC month-1) is nearly 3 times as large as the release
increase (0.5 PgC month-1).
Spatial patterns and latitudinal distributions of 10-model mean
-NBP (gC m-2 day-1) changes between 2081–2090 and 1961–1970,
during mean (a) peak growing season (May–July) and (b) dormant season
(October–December). (c) Aggregates the spatial patterns in (a) and
(b) zonally, where the black curve corresponds to the -NBP changes in
May–July (a), and the red curve corresponds to the -NBP changes in
October–December (b). Further reduction of -NBP in peak growing
season – where the black curve falls on the left of the zero line, and
increase of -NBP in dormant season – where the red curve is on the right
of the zero line, both contribute to amplitude increase. We shade those
instances in green, and shade the reversed case (contribute negatively to
global total -NBP amplitude increase) in yellow. It is clear that the
amplitude increase is dominated by the boreal regions, and by changes in
peak growing season.
We then investigate the June and October changes of -NPP and
Rh∗, respectively. By definition, their sum should equal to
the amplitude change of -NBP. NPP has increased in all months (Fig. 3e and
f), with much larger changes during the NH growing season. The amplitude of
multi-model ensemble NPP has increased from 4.8 to 7.1 PgC month-1, and an increase from 2.7 to 4.3 PgC month-1 is found for
Rh∗. In June, NPP increase (4.5 PgC month-1) is larger
than that of Rh∗ (3.1 PgC month-1), resulting in enhanced
net uptake. In October, NPP increase (1.9 PgC month-1) is smaller than
that of Rh∗ (2.4 PgC month-1), leading to enhanced net
release. These results are consistent with trends of maxima and minima in
Fig. 1. The models also indicate a shift in peak NPP from July to June,
consistent with the shift of CO2 minima.
Spatial and latitudinal contributions
To further investigate the regional contribution to NBP amplitude increase,
we plotted the 10-model mean -NBP changes (Fig. 4) over peak NH growing
season (May–July, Fig. 4a) and dormant season (October–December, Fig. 4b).
Because the models disagree on the time of maximum and minimum NBP (Fig. 3), our choice of doing seasonal averages would be more representative of
the models than averaging over 1 month. Note that the difference between
the two seasonal averages is smaller than the peak-to-trough amplitude, but
here we are only concerned with the spatial pattern. We saw a stronger net
carbon uptake in May–July almost everywhere north of 45∘ N, and
also over the Tibetan Plateau and some places near the equator. Net carbon
uptake weakens over western United States and Central America, South and
Southeast Asia and central South America. The change of net carbon release
in October–December generally shows an opposite spatial pattern, with a
noticeably smaller magnitude north of 45∘ N.
In addition, we calculated the corresponding zonal averages (Fig. 3c). The
area-weighted totals of the zonal mean curves correspond to the future minus
historical averages of global total -NBP (Fig. 3d), averaged over
May–July and October–December, respectively. These two curves do not account
for phase difference; instead, they approximate latitudinal contribution to
the amplitude increase of global total -NBP. It is apparent that this
increase is dominated by regions north of 45∘ N with a weak
contribution from the Southern Hemisphere tropics (0–25∘ S).
The northern subtropical region and Southern Hemisphere
(10–30∘ N, 35–55∘ S) partly offset the amplitude
increase. It is also clear that the amplitude increase is dominated by
changes in peak growing season (the green shade is larger on the left of the
zero line than on its right), consistent with our findings in the previous
section.
Analogous to the cold–warm seasonality in the temperate/boreal region, the
tropics has distinctive dry and wet seasons, and recently Wang et al. (2014)
suggested the tropical ecosystem is becoming more sensitive to climate
change. In our analyses on the multi-model ensemble patterns, the tropical
region exhibits a small negative contribution to the seasonal amplitude
increase of global total -NBP. This does not mean the net carbon flux in
the tropics, which has a different seasonal cycle phase, would experience an
amplitude decrease in the future. To illustrate the seasonal amplitude
change at different latitudes, we show the zonal amplitude of NBP in the
historical (black) and future (red) periods for all models (Fig. 5). At
every 2∘ band, we first calculated a 10-year mean seasonal cycle,
then compute its amplitude (maximum minus minimum). Most models predict an
increase in NBP seasonal amplitude at almost every latitude under the RCP85
emission scenario. Only two of the models, CanESM2 and MIROC-ESM, predict
decreased seasonality for parts of the tropics and subtropics. Unlike in
Fig. 4c, an area-weighted integral cannot be performed due to different
phases zonally. The Southern Hemisphere has an opposite phase from its
northern counterpart, but its magnitude is small due to its small land area.
The two subtropical maxima around 10∘ N and 10–15∘ S
reflect the wet–dry seasonal shift in the Intertropical Convergence Zone
(ITCZ) and monsoon movement. They are comparable to the NH maxima in terms
of both amplitude and amplitude increase for about a third of the models,
however they are out of phase and largely cancel each other out.
To further illustrate this cancelation effect, we aggregated monthly -NBP
over six large regions: the globe (90∘ S–90∘ N),
northern boreal (50–90∘ N), northern temperate (25–50∘ N),
northern tropics (0–25∘ N), southern tropics (25∘ S–0∘)
and Southern Hemisphere (90–25∘ S; Fig. S1).
It is clear that the changes of global -NBP seasonal cycle mostly come
from the northern boreal region; it partly comes from the northern
temperature region in a few models. The seasonal cycle of the northern
tropics is characterized by spring maxima and fall minima, and prominent
increases of its seasonal amplitude are found for BNU-ESM, GFDL-ESM2M and
IPSL-CM5A-LR. However, they are largely counterbalanced by the southern
tropics. For GFDL-ESM2M, changes in the southern tropics are larger than its
northern counterpart, but even so, the net contribution of tropical regions
to its global -NBP seasonal amplitude (September maxima minus June minima)
increase is limited to about 25 %, the largest of all models.
Zonal amplitude of NBP from the 10 CMIP5 models (PgC month-1
per 2-degree band), averaged over 1961–1970 (black) and 2081–2090 (red). For
each model, NBP is first regridded to a 2×2∘ common
grid. Monthly zonal totals are then computed for every 2-degree band, which
determine the amplitude (maximum minus minimum) at every band. The Southern
Hemisphere has an opposite phase from its northern counterpart, but its
magnitude is small due to its small land area. The two subtropical maxima
around 10∘ N and 10–15∘ S reflect the wet–dry seasonal
shift in the Intertropical Convergence Zone (ITCZ) and monsoon movement.
They have similar magnitude as the Northern Hemisphere maxima in about a
third of the models, however their net contribution to global total NBP
seasonal amplitude is small, because they are out of phase and largely
cancel each other out.
Spatial patterns of GFDL-ESM2M -NBP (gC m-2 day-1)
changes from 2081 to 2090 and from 1961 to 1970, during mean peak growing season
(May–July, first row) and dormant season (October–December, second row) for
the esmFdbk2 (first column, constant CO2 fertilization and changing
climate) and esmFixClim2 (second column, constant climate and rising
CO2) experiments. The northern high latitude regions show mixed
response to climate change during peak growing season (a), and most of
the northern temperate and boreal regions see enhanced carbon uptake under
elevated CO2(b). Net carbon release is increased both under
climate change (c) and elevated CO2 conditions (d), however
they have different spatial patterns.
Same as Fig. 6, but for IPSL-CM5A-LR. Both the carbon uptake in
peak growing season and net carbon release in dormant season are clearly
dominated by changes in atmospheric CO2 rather than climate for this
model.
Mechanisms for amplitude increase
As discussed in Sect. 1, two major mechanisms for amplitude increase
identified in previous literature are CO2 fertilization effect and high
latitudes “greening” in a warmer climate. Both mechanisms lead to enhanced
ecosystem productivity during peak growing season, and consequently more
biomass to decompose in dormant season, therefore increasing the amplitude
of NBP seasonal cycle. Because models have different climate and CO2
sensitivity (Arora et al., 2013), their relative importance may vary. In the
case of CMIP5 ESMs, two additional sensitivity experiments are recommended:
fixed feedback 2 (esmFdbk2) and fixed climate 2 (esmFixClim2). The former
keeps CO2 concentration fixed but allows physical climate change
responding to increasing historical and future (RCP4.5) concentrations; the
latter keeps climate fixed under preindustrial CO2 condition but allows
the carbon cycle to respond to historical and future (RCP4.5) CO2
increase. This setup does not permit quantifying the contribution of
CO2 increase and climate change to NBP amplitude increase: one major
difference is the use of RCP4.5 concentrations instead of RCP8.5 emissions.
However, we can still make qualitative assessments by examining the spatial
patterns. We will focus on the high latitude regions, which contribute most
to amplitude increase of global total NBP.
Of the 10 models we studied, only CanESM2, GFDL-ESM2M and IPSL-CM5A-LR have
submitted NBP output for these two experiments (MIROC submitted output for
esmFixClim2 only). Here we display the spatial patterns of -NBP changes
for GFDL-ESM2M (Fig. 6) and IPSL-CM5A-LR (Fig. 7). CanESM2 results are
not shown because it does not correctly reproduce the phase of global total
NBP seasonal cycle. The changes of -NBP for both models during peak
growing season are clearly dominated by CO2 fertilization effect (right
panels). In contrast, climate change under fixed CO2 fertilization
conditions has mixed effects on high latitude regions. Northern high
latitude net carbon release in October–December is increased both under
climate change (Fig. 6c) and elevated CO2 conditions (Fig. 6d) for
GFDL-ESM2M, but over different regions. For IPSL-CM5A-LR however, net carbon
release increase in regions north of 45∘ N is only obvious under
elevated CO2 condition.
Our results only indicate CO2 fertilization effect is the dominant
factor for NBP seasonal amplitude increase in some models. For models with
strong carbon-climate feedbacks and weak/moderate water constraints in
northern high latitude regions, climate change may be more important.
However, we cannot find a clear example due to data availability. MIROC-ESM
is known to have strong carbon-climate feedback (Arora et al., 2013). From
its simulation under fixed climate (figure not shown), we found no obvious
patterns of widespread net carbon release increase in dormant season,
suggesting climate change may play a bigger role for this model. The HadGEM
model is another possible candidate; it is also a particularly interesting
model to analyze since one of its historical simulations represented the
largest increase in CO2 amplitude in Graven et al. (2013).
Unfortunately, for the ESM simulations, both CO2 and NBP from HadGEM
are not available on the ESGF servers.
Relationship with mean carbon sink
Our analyses above suggest CO2 fertilization effect is a major
mechanism causing the amplitude increase in some models. If it is important
in most models, we expect to see models with a larger change in mean carbon
sink simulate a higher increase in seasonal amplitude. By plotting the
-NBP change against NBP seasonal amplitude increase for all 10 models
(Fig. 8), we found there is indeed a negative cross-model correlation
(R=-0.73, p<0.05), indicating models with a stronger net carbon
uptake are likely to simulate a larger increase in NBP seasonal amplitude.
Note that this result is based on the 10 models we analyzed; it is subject
to large uncertainty and may change substantially with inclusion or
exclusion of certain model(s). Again all models show an increase in NBP
seasonal amplitude, even though they disagree on the direction of future NBP
change. While our study hint at a possible relationship between mean carbon
sink and NBP seasonal amplitude, it is beyond our scope to discuss further,
or comment on why models show such different mean sink estimate. Interested
readers may refer to the insightful discussion on this issue in
Friedlingstein et al. (2013).
Relationship between -NBP change and increase of NBP seasonal
amplitude, calculated as the differences between 2081–2090 and 1961–1970 for
10 CMIP5 ESMs. The negative cross-model correlation (R=-0.73, p<0.05) suggests that a model with a larger net carbon sink increase is likely
to simulate a higher increase in NBP seasonal amplitude.
Discussions
We have primarily focused on model ensembles of aggregated quantities.
Ensemble patterns are sometimes dominated by only a few models due to large
seasonality variations among the models. However, the close examination of
each individual model show that the spatial patterns of -NBP change during
peak growing season (May–July) are all dominated by high latitude regions
(approximately north of 45∘ N). In CESM1-BGC and NorESM1-ME
models, enhanced net carbon uptake are confined to some of the high latitude
regions (Fig. S2 in the Supplement). Models differ on finer details. For example, about half
of the models predict an obvious increase of net carbon uptake for the
Tibetan Plateau. It is worth mentioning that the esmFixClim2 experiment of
MIROC-ESM shows little change in NBP for this region under elevated CO2
alone. High latitude regions also dominate the increase of net carbon
release in October–December for most models (Fig. S3). One exception is
INM-CM4, which displays very small change in the dormant season, and most of
its NBP amplitude increase comes from enhanced carbon uptake during peak
growing season. BNU-ESM and CanESM2 have some limitations in reproducing the
correct phase of global -NBP seasonal cycle. Exclusion of these two models
from ensemble mean calculation exhibits very similar spatial and zonal
patterns as shown in Fig. 4. Another caveat is the assumption of 1961–1970
as the historical condition and 2081–2090 as future condition. This choice
is valid if the selected variables have roughly monotonic trends, and 10
years is long enough to smooth out most of the interannual variability.
Figure 2 suggests that this assumption is quite reasonable for model
ensembles, and acceptable for individual models.
We presented aggregated quantities due to large model uncertainty in space.
We have largely omitted model evaluation against observations (due to
limited observation during 1961–1970). However, this step can be helpful in
model evaluation studies (Anav et al., 2013; Peng et al., 2014). One concern
is to examine whether the models can reproduce observed CO2 seasonal
amplitude increase at the two stations with longest observation
records – Mauna Loa, Hawaii and Point Barrow, Alaska. To address this issue,
we extracted simulated CO2 concentration from eight models at their
model grid that is closest to Mauna Loa in the three-dimensional space
(similar procedure for Point Barrow). The results of this comparison at one
model grid can reflect multiple sources of model uncertainties (such as
uncertainties in the atmospheric tracer transport and mixing simulations).
For example, GFDL-ESM2M is known to simulate a damped CO2 gradient
(Dunne et al., 2013) which has long been identified as a deficit in models
of the atmospheric CO2 cycle (Fung et al., 1987).
CO2 mean seasonal amplitude (ppm) during 2001–2005 and
increase in CO2 seasonal amplitude at Mauna Loa during 1959–2005
(% yr-1, linear trend) from eight CMIP5 models and observation. The big
black circle represent surface CO2 observation at Mauna Loa, Hawaii
(19.5∘ N, 155.6∘ W; 3400 m above sea level). The colored
squares represent the 700 hPa (close to the altitude of Mauna Loa station
surface) CO2 output at the original grid that covers Mauna Loa from
each of the eight models. Error bars indicate ±1 standard error in
the trend calculation. Compared to the surface observation, only MPI-ESM-LR
and GFDL-ESM2M overestimate CO2 mean seasonal amplitude at Mauna Loa,
while the other models underestimate this amplitude. Models split between
overestimating and underestimating the CO2 seasonal amplitude increase
at Mauna Loa.
Figure 9 (and Fig. S4 for more details) presents the changes of CO2
seasonal amplitude at Mauna Loa for the models and observation. CO2
seasonal amplitude is underestimated by a factor of 2 in three-quarters of
the models. However, the amplitude increase from ensemble model estimate
(0.36±0.24 % yr-1, error range represents one standard
deviation model spread) is much closer to observation (0.34±0.07 % yr-1, error range represents one standard error of the least-squared
trend calculation). MPI-ESM-LR reproduces both the magnitude and trend of
Mauna Loa CO2 seasonal amplitude reasonably well. For Point Barrow
(Figs. 10 and S5), MPI-ESM-LR also simulates a similar amplitude
increase to observation, but the magnitude of amplitude is much larger
(almost twice). All other models underestimate the amplitude, but for the
amplitude increase, the model ensemble (0.46±0.21 % yr-1) again
is similar to observation (0.43±0.10 % yr-1). MRI-ESM1 is found
to reproduce both the magnitude and increase of Point Barrow CO2
amplitude quite well.
CO2 mean seasonal amplitude (ppm) during 2001–2005 and
increase in CO2 seasonal amplitude at Pt. Barrow during 1974–2005
(% yr-1, linear trend) from eight CMIP5 ESMs and observation. The big
black circle represent surface CO2 observation at Point Barrow, Alaska
(71.3∘ N, 156.5∘ W; 11 m above sea level). The colored
squares represent the CO2 output at lowest model level (four models at
1000 hPa, and four at 925 hPa) at the original grid that covers Point Barrow
from each of the eight models. Error bars indicate ±1 standard error
in the trend calculation. Compared to the surface observation, only
MPI-ESM-LR overestimate the CO2 mean seasonal amplitude at Point
Barrow, while the other models underestimate this amplitude. Models split
between overestimating and underestimating the CO2 seasonal amplitude
increase at Point Barrow.
Changes of tree cover fractions between future (2081–2090) and
historical (1961–1970) periods from six CMIP5 ESMs. The values represent
fractional cover changes relative to the whole grid cell, instead of
relative change of tree cover. For MPI-ESM-LR and INM-CM4, tree fraction has
increased over wide areas of the northern high latitude regions. For
MIROC-ESM, tree fraction has generally decreased over the same regions,
possibly in response to a hotter and drier climate condition.
Changes of natural grass fractions between future (2081–2090) and
historical (1961–1970) periods from six CMIP5 ESMs. The values represent
fractional cover changes relative to the whole grid cell, instead of
relative change of natural grass cover. Notable increase over the northern
high latitude regions is found for BNU-ESM.
Graven et al. (2013) found the CMIP5 models substantially underestimate the
amplitude increase of CO2 north of 45∘ N at altitude of 3 to
6 km. However, we did not find that the models underestimate Point Barrow
CO2 amplitude increase at surface level. One big difference is the
observational data used for comparison. During the 1974–2005 period,
CO2 seasonal amplitude increases by 0.43 % yr-1, or 21.5 %
over 50 years at the Point Barrow station. This is much lower than the
∼50 % amplitude increase found between the two aircraft
campaigns during 1958–1961 and 2009–2011 (Graven et al., 2013). This
difference might be attributed to mechanisms controlling the vertical
profile of CO2 concentration. It is also not clear to what extent the
large interannual variability of CO2 seasonal amplitude affects the
trend estimation of observed CO2 amplitude increase.
Under the RCP8.5 emission scenario, CMIP5 showed a 62±19 % increase
of CO2 seasonal cycle globally from 1961–1970 to 2081–2090. The
increase is 85±48 % at Mauna Loa (range indicates one standard
deviation model spread), and 110±42 % at Point Barrow. Even though
the CMIP5 models are able to reproduce the increase of CO2 seasonal
amplitude at the two locations, some of the models rely heavily on the
CO2 fertilization mechanism, which may be too strong compared to
observational evidence. Previous research suggest it should explain no more
than 25 % of the observation at a high fertilization effect permitted by
lab experiments (Kohlmaier et al., 1989). Similarly, Randerson et al. (1997)
found the linear factor of CO2 fertilization has to be 4 to 6 times
greater than the mean of the experimental values, in order to explain the
0.66 % yr-1 amplitude increase (north of 55∘ N) during
1981–1995. Recent studies have indicated that some important mechanisms,
such as changes in ecosystem structure and distribution (Graven et al.,
2013) and land use intensification (Zeng et al., 2014), are missing in the
current CMIP5 models. Yet another main source of uncertainty is future
CO2 emissions. The RCP8.5 scenario used to drive the ESMs is on the
high side of future scenarios. Also, the emission-driven runs simulate
higher CO2 than observed over the historical period, and such biases
are likely to accumulate over time as the increase of atmospheric CO2
growth rate accelerates (Hoffman et al., 2014).
The models do not have the same strength of carbon-climate feedback, but
even if they do, their response to climate change may vary significantly
simply because they simulate very different climate change. To briefly
address this issue, we present soil moisture (Figs. S6 and S7) and
near-surface temperature (Figs. S8 and S9) changes for all models. All the
models show temperature increase, but in different ranges. The more
prominent difference was observed in the spatial pattern of soil moisture
changes predicted by models. The combined effect of soil moisture regimes,
temperature change and plant functional type (PFT) specifications could cause diverse behaviors of
models over same regions. Such are important caveats that highlight the
importance of sensitivity experiments and warrant more in-depth future
studies.
The combined effect of climate and CO2 changes not only alters the
balance between production and respiration for existing ecosystems, but also
leads to changes of ecosystem types. For example, Fig. 11 shows that the
tree fraction has increased over wide areas of the northern high latitude
regions for MPI-ESM-LR and INM-CM4. Figure 12 reveals notable natural grass
increase over the northern high latitude regions for BNU-ESM. Such
widespread vegetation change has not been observed during the satellite era,
and it is possibly yet another highly uncertain mechanism contributing to
amplitude increase in some CMIP5 models.
The major crops are characterized by high productivity in a short growing
season, and they tend to have larger NBP seasonal amplitude compared to the
natural vegetation they replace (usually natural grass). An increase in
cropland fraction over high latitude regions could contribute to the
seasonal ampltiude increase of NBP. As far as we know, no CMIP5 model has
accounted for agricultural intensification, and only some models have
implemented a conversion matrix (Brovkin et al., 2013). Therefore, the most
important change implemented in the CMIP5 models is fractional land cover
change based on Hurtt et al. (2011). In Fig. S10 we present the change of
crop fraction, available from five models. It is apparent that crop area has
increased mostly in the tropics, while regions north of 30∘ N have actually
seen a decrease (due to a variety of factors: cropland abandonment,
reforestation, urbanization, etc.). Therefore, crop fractional cover change
alone may decrease the NBP seasonal amplitude in CMIP5 simulations. A better
representation of land use change, especially the agricultural
intensification, is needed in CMIP5 models to represent the CO2 and NBP
seasonal cycle better. On a side note, the other major part of land cover
change – pasture (often treated as natural grass in ESMs, Brovkin et al.,
2013) fraction change is unlikely to have a significant effect on NBP
seasonal amplitude in the CMIP5 simulations.
Conclusions
Under the RCP8.5 emission scenario, all models examined in this study
project an increase in seasonal amplitude of both CO2 and NBP. The
models' results indicate an earlier onset and peak of Northern Hemisphere
biosphere growth and decay under future climate and CO2 conditions. The
amplitude increase is dominated by changes in net primary productivity, and
changes in regions north of 45∘ N. Our results suggest the models
simulating a larger mean carbon sink increase are likely to project a larger
increase in NBP seasonal amplitude. Considerable model spread is found,
likely due to different model setup and complexity, different climate
conditions simulated by the models, sensitivity to CO2 and climate and
their combined effects, and strength of feedbacks. Our findings indicate
factors including enhanced CO2 fertilization and lengthening of growing
season in high-latitude regions outcompetes possible severe drought and
forest degradation (leading to loss of biosphere productivity) in the
future.
Despite of the model consensus in global CO2 and NBP seasonal amplitude
increase, and a reasonable representation of CO2 seasonal amplitude
increase at Mauna Loa and Point Barrow compared to surface in situ
observations, the mechanisms contributing to these changes are debatable.
CO2 fertilization may be too strong, and factors like ecosystem change
and agricultural intensification are under-represented or missing in the
CMIP5 ESMs. Future model-intercomparison projects should encourage models to
participate in consistent and comprehensive sensitivity experiments.
The Supplement related to this article is available online at doi:10.5194/esd-5-423-2014-supplement.
Acknowledgements
We acknowledge the World Climate Research Programme's Working Group on
Coupled Modeling, which is responsible for CMIP, and we thank the climate
modeling groups (listed in Table 1) for producing and making
available their model output. For CMIP the US Department of Energy's
Program for Climate Model Diagnosis and Intercomparison provides
coordinating support and led development of software infrastructure in
partnership with the Global Organization for Earth System Science Portals.
The authors also thank NOAA for providing global mean CO2 estimates,
and Yutong Pan for processing part of CMIP5 model data. We are grateful to
the two anonymous reviewers for their helpful comments and suggestions. This
research was supported by NOAA (NA10OAR4310248 and NA09NES4400006) and NSF
(AGS-1129088).
Edited by: J. Canadell
ReferencesAnav, A., Friedlingstein, P., Kidston, M., Bopp, L., Ciais, P., Cox, P.,
Jones, C., Jung, M., Myneni, R., and Zhu, Z.: Evaluating the Land and Ocean
Components of the Global Carbon Cycle in the CMIP5 Earth System Models, J.
Clim., 26, 6801–6843, 10.1175/JCLI-D-12-00417.1, 2013.Andres, R. J., Gregg, J. S., Losey, L., Marland, G., and Boden, T. A.:
Monthly, global emissions of carbon dioxide from fossil fuel consumption,
Tellus B, 63, 309–327, 10.1111/j.1600-0889.2011.00530.x, 2011.Arora, V. K., Scinocca, J. F., Boer, G. J., Christian, J. R., Denman, K. L.,
Flato, G. M., Kharin, V. V., Lee, W. G., and Merryfield, W. J.: Carbon
emission limits required to satisfy future representative concentration
pathways of greenhouse gases, Geophys. Res. Lett., 38, L05805,
10.1029/2010GL046270, 2011.Arora, V. K., Boer, G. J., Friedlingstein, P., Eby, M., Jones, C. D.,
Christian, J. R., Bonan, G., Bopp, L., Brovkin, V., Cadule, P., Hajima, T.,
Ilyina, T., Lindsay, K., Tjiputra, J. F., and Wu, T.: Carbon–Concentration
and Carbon–Climate Feedbacks in CMIP5 Earth System Models, J. Clim.,
26, 5289–5314, 10.1175/JCLI-D-12-00494.1, 2013.Bacastow, R. B., Keeling, C. D., and Whorf, T. P.: Seasonal amplitude
increase in atmospheric CO2 concetration at Mauna Loa, Hawaii, 1959–1982, J.
Geophys. Res., 90, 10529–10540, 10.1029/JD090iD06p10529, 1985.Brovkin, V., Boysen, L., Arora, V. K., Boisier, J. P., Cadule, P., Chini,
L., Claussen, M., Friedlingstein, P., Gayler, V., van den Hurk, B. J. J. M.,
Hurtt, G. C., Jones, C. D., Kato, E., de Noblet-Ducoudré, N., Pacifico,
F., Pongratz, J., and Weiss, M.: Effect of Anthropogenic Land-Use and
Land-Cover Changes on Climate and Land Carbon Storage in CMIP5 Projections
for the Twenty-First Century, J. Clim., 26, 6859–6881,
10.1175/JCLI-D-12-00623.1, 2013.
Buermann, W., Lintner, B. R., Koven, C. D., Angert, A., Pinzon, J. E.,
Tucker, C. J., and Fung, I. Y.: The changing carbon cycle at Mauna Loa
Observatory, Proc. Natl. Acad. Sci. USA, 104, 4249–4254, 2007.Cleveland, W., Freeny, A. E., and Graedel, T. E.: The Seasonal Component of
Atmospheric CO2: Information From New Approaches to the Decomposition of
Seasonal Time Series, J. Geophys. Res., 88, 10934–10946, 1983.
Conway, T. J., Tans, P. P., Waterman, L. S., Thoning, K. W., Kitzis, D. R.,
Masarie, K. A., and Zhang, N.: Evidence for interannual variability of the
carbon cycle from the National Oceanic and Atmospheric Administration /
Climate Monitoring and Diagnostics Laboratory Global Air Sampling Network,
J. Geophys. Res., 99, 22831–22855, 1994.Dufresne, J.-L., Foujols, M.-A., Denvil, S., Caubel, A., Marti, O., Aumont,
O., Balkanski, Y., Bekki, S., Bellenger, H., Benshila, R., Bony, S., Bopp,
L., Braconnot, P., Brockmann, P., Cadule, P., Cheruy, F., Codron, F., Cozic,
A., Cugnet, D., Noblet, N., Duvel, J.-P., Ethé, C., Fairhead, L.,
Fichefet, T., Flavoni, S., Friedlingstein, P., Grandpeix, J.-Y., Guez, L.,
Guilyardi, E., Hauglustaine, D., Hourdin, F., Idelkadi, A., Ghattas, J.,
Joussaume, S., Kageyama, M., Krinner, G., Labetoulle, S., Lahellec, A.,
Lefebvre, M.-P., Lefevre, F., Levy, C., Li, Z. X., Lloyd, J., Lott, F.,
Madec, G., Mancip, M., Marchand, M., Masson, S., Meurdesoif, Y., Mignot, J.,
Musat, I., Parouty, S., Polcher, J., Rio, C., Schulz, M., Swingedouw, D.,
Szopa, S., Talandier, C., Terray, P., and Viovy, N.: Climate change
projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5,
Clim. Dyn., 40, 2123–2165, 10.1007/s00382-012-1636-1, 2013.Dunne, J. P., John, J. G., Shevliakova, E., Stouffer, R. J., Krasting, J.
P., Malyshev, S. L., Milly, P. C. D., Sentman, L. T., Adcroft, A. J., Cooke,
W., Dunne, K. A., Griffies, S. M., Hallberg, R. W., Harrison, M. J., Levy,
H., Wittenberg, A. T., Phillips, P. J., and Zadeh, N.: GFDL's ESM2 Global
Coupled Climate–Carbon Earth System Models. Part II: Carbon System
Formulation and Baseline Simulation Characteristics, J. Clim., 26,
2247–2267, 10.1175/JCLI-D-12-00150.1, 2013.Friedlingstein, P., Meinshausen, M., Arora, V. K., Jones, C. D., Anav, A.,
Liddicoat, S. K., and Knutti, R.: Uncertainties in CMIP5 climate projections
due to carbon cycle feedbacks, J. Clim., 27, 511–526,
10.1175/JCLI-D-12-00579.1, 2013.Fung, I. Y., Tucker, C. J., and Prentice, K. C.: Application of Advanced Very
High Resolution Radiometer vegetation index to study atmosphere-biosphere
exchange of CO2, J. Geophys. Res., 92, 2999–3015,
10.1029/JD092iD03p02999, 1987.Graven, H. D., Keeling, R. F., Piper, S. C., Patra, P. K., Stephens, B. B.,
Wofsy, S. C., Welp, L. R., Sweeney, C., Tans, P. P., Kelley, J. J., Daube,
B. C., Kort, E. a, Santoni, G. W., and Bent, J. D.: Enhanced Seasonal
Exchange of CO2 by Northern Ecosystems Since 1960, Science, 341,
1085–1089, 10.1126/science.1239207, 2013.Gurney, K. R. and Eckels, W. J.: Regional trends in terrestrial carbon
exchange and their seasonal signatures, Tellus B, 63, 328–339,
10.1111/j.1600-0889.2011.00534.x, 2011.Hall, C. A. S., Ekdahl, C. A., and Wartenberg, D. E.: A fifteen-year record
of biotic metabolism in the Northern Hemisphere, Nature, 255, 136–138,
10.1038/255136a0, 1975.Hoffman, F. M., Randerson, J. T., Arora, V. K., Bao, Q., Cadule, P., Ji, D.,
Jones, C. D., Kawamiya, M., Khatiwala, S., Lindsay, K., Obata, A.,
Shevliakova, E., Six, K. D., Tjiputra, J. F., Volodin, E. M., and Wu, T.:
Causes and implications of persistent atmospheric carbon dioxide biases in
Earth System Models, J. Geophys. Res.-Biogeosci., 119, 141–162,
10.1002/2013JG002381, 2014.Hurtt, G. C., Chini, L. P., Frolking, S., Betts, R. a, Feddema, J., Fischer,
G., Fisk, J. P., Hibbard, K., Houghton, R. a, Janetos, a, Jones, C. D.,
Kindermann, G., Kinoshita, T., Klein Goldewijk, K., Riahi, K., Shevliakova,
E., Smith, S., Stehfest, E., Thomson, a, Thornton, P., Van Vuuren, D. P., and
Wang, Y. P.: Harmonization of land-use scenarios for the period 1500-2100:
600 years of global gridded annual land-use transitions, wood harvest, and
resulting secondary lands, Clim. Change, 109, 117–161,
10.1007/s10584-011-0153-2, 2011.
Ilyina, T., Six, K. D., Segschneider, J., Maier-reimer, E., Li, H., and
Núñez-Riboni, I.: Global ocean biogeochemistry model HAMOCC?: Model
architecture and performance as component of the MPI-Earth system model in
different CMIP5 experimental realizations, J. Adv. Model. Earth Syst., 5,
287–315, 2013.
International Panel on Climate Change (IPCC): Climate Change 2013: the Physical
Science Basis. Working Group 1 Contribution to the Fifth Assessment Report
of the International Panel on Climate Change International Panel on Climate
Change, Cambridge University Press, Cambridge, New York., 2013.Ji, D., Wang, L., Feng, J., Wu, Q., Cheng, H., Zhang, Q., Yang, J., Dong, W.,
Dai, Y., Gong, D., Zhang, R.-H., Wang, X., Liu, J., Moore, J. C., Chen, D.,
and Zhou, M.: Description and basic evaluation of Beijing Normal University
Earth System Model (BNU-ESM) version 1, Geosci. Model Dev., 7, 2039-2064,
10.5194/gmd-7-2039-2014, 2014.Kaminski, T., Giering, R., and Heimann, M.: Sensitivity of the seasonal cycle
of CO2 at remote monitoring stations with respect to seasonal surface
exchange fluxes determined with the adjoint of an atmospheric transport
model, Phys. Chem. Earth, 21, 457–462, 1996.Keeling, C. D., Whorf, T. P., Wahlen, M., and van der Plichtt, J.:
Interannual extremes in the rate of rise of atmospheric carbon dioxide since
1980, Nature, 375, 666–670, 10.1038/375666a0, 1995.Keeling, C. D., Chin, J. F. S., and Whorf, T. P.: Increased activity of
northern vegetation inferred from atmospheric CO2 measurements, Nature, 382,
146–149, 10.1038/382146a0, 1996.Kohlmaier, G. H., Siré, E. O., Janecek, A., Keeling, C. D., Piper, S. C.,
and Revelle, R.: Modelling the seasonal contribution of a CO2 fertilization
effect of the terrestrial vegetation to the amplitude increase in
atmospheric CO2 at Mauna Loa Observatory, Tellus B, 41, 487–510, 10.1111/j.1600-0889.1989.tb00137.x, 1989.Long, M. C., Lindsay, K., Peacock, S., Moore, J. K., and Doney, S. C.:
Twentieth-Century Oceanic Carbon Uptake and Storage in CESM1(BGC), J.
Clim., 26, 6775–6800, 10.1175/JCLI-D-12-00184.1, 2013.McGuire, A. D., Sitch, S., Clein, J. S., Dargaville, R., Esser, G., Foley,
J., Heimann, M., Joos, F., Kaplan, J., Kicklighter, D. W., Meier, R. a,
Melillo, J. M., Moore III, B., Prentice, I. C., Ramankutty, N., Reichenau,
T., Schloss, A., Tian, H., Williams, L. J., and Wittenberg, U.: Carbon
balance of the terrestrial biosphere in the twentieth century: Analyses of
CO2, climate and land use effects with four process-based ecosystem models,
Global Biogeochem. Cy., 15, 183–206, 2001.Moss, R. H., Edmonds, J. a, Hibbard, K. a, Manning, M. R., Rose, S. K., van
Vuuren, D. P., Carter, T. R., Emori, S., Kainuma, M., Kram, T., Meehl, G. a,
Mitchell, J. F. B., Nakicenovic, N., Riahi, K., Smith, S. J., Stouffer, R.
J., Thomson, A. M., Weyant, J. P., and Wilbanks, T. J.: The next generation
of scenarios for climate change research and assessment, Nature, 463,
747–756, 10.1038/nature08823, 2010.Pearman, G. I. and Hyson, P.: The annual variation of atmospheric CO2
concentration Observed in the Northern Hemisphere, J. Geophys. Res., 86,
9839–9843, 1981.Peng, S., Ciais, P., Chevalier, F., Peylin, P., Cadule, P., Sitch, S.,
Piao, S., Ahlström, A., Huntingford, C., Levy, P., Li, X., Liu, Y.,
Lomas, M., Poulter, B., Viovy, N., Wang, T., Wang, X., Zaehle, S., Zeng, N.,
Zhao, F. and Zhao, H.: Benchmarking the
seasonal cycle of CO2 fluxes simulated by terrestrial ecosystem models,
Global Biogeochem. Cy., in review, 2014.Peylin, P., Law, R. M., Gurney, K. R., Chevallier, F., Jacobson, A. R., Maki, T.,
Niwa, Y., Patra, P. K., Peters, W., Rayner, P. J., Rödenbeck, C., van der
Laan-Luijkx, I. T., and Zhang, X.: Global atmospheric carbon budget: results
from an ensemble of atmospheric CO2 inversions, Biogeosciences, 10, 6699–6720,
10.5194/bg-10-6699-2013, 2013.Piao, S., Sitch, S., Ciais, P., Friedlingstein, P., Peylin, P., Wang, X.,
Ahlström, A., Anav, A., Canadell, J. G., Cong, N., Huntingford, C.,
Jung, M., Levis, S., Levy, P. E., Li, J., Lin, X., Lomas, M. R., Lu, M.,
Luo, Y., Ma, Y., Myneni, R. B., Poulter, B., Sun, Z., Wang, T., Viovy, N.,
Zaehle, S., and Zeng, N.: Evaluation of terrestrial carbon cycle models for
their response to climate variability and to CO2 trends, Glob. Chang. Biol.,
19, 2117–2132, 10.1111/gcb.12187, 2013.Randerson, J. T., Thompson, M. V, Conway, T. J., Fung, I. Y., Field, C. B.,
Randerson, T., Thompson, V., Conway, J., and Field, B.: The contribution of
terrestrial sources and sinks to trends in the seasonal cycle of atmospheric
carbon dioxide, Global Biogeochem. Cy., 11, 535–560,
10.1029/97gb02268, 1997.Randerson, J. T., Field, C. B., Fung, I. Y., and Tans, P. P.: Increases in
early season ecosystem uptake explain recent changes in the seasonal cycle
of atmospheric CO2 at high northern latitudes, Geophys. Res. Lett., 26,
2765–2768, 10.1029/1999GL900500, 1999.
Reiners, W.: Terrestrial detritus and the carbon cycle, in: US AEC Conf.
720510, 317–327., 1973.Taylor, K. E., Stouffer, R. J., and Meehl, G. a.: An Overview of CMIP5 and
the Experiment Design, B. Am. Meteorol. Soc., 93, 485–498,
10.1175/BAMS-D-11-00094.1, 2012.Thoning, K. W., Tans, P. P., and Komhyr, W. D.: Atmospheric carbon dioxide at
Mauna Loa Observatory: 2, Analysis of the NOAA GMCC data, 1974–1985, J.
Geophys. Res., 94, 8549, 10.1029/JD094iD06p08549, 1989.Tjiputra, J. F., Roelandt, C., Bentsen, M., Lawrence, D. M., Lorentzen, T.,
Schwinger, J., Seland, Ø., and Heinze, C.: Evaluation of the carbon cycle
components in the Norwegian Earth System Model (NorESM), Geosci. Model Dev., 6,
301–325, 10.5194/gmd-6-301-2013, 2013.Volodin, E. M., Dianskii, N. A., and Gusev, A. V.: Simulating present-day
climate with the INMCM4.0 coupled model of the atmospheric and oceanic
general circulations, Izv. Atmos. Ocean. Phys., 46, 414–431,
10.1134/S000143381004002X, 2010.Wang, X., Piao, S., Ciais, P., Friedlingstein, P., Myneni, R. B., Cox, P.,
Heimann, M., Miller, J., Peng, S., Wang, T., Yang, H., and Chen, A.: A
two-fold increase of carbon cycle sensitivity to tropical temperature
variations., Nature, 506, 212–215, 10.1038/nature12915, 2014.Watanabe, S., Hajima, T., Sudo, K., Nagashima, T., Takemura, T., Okajima, H.,
Nozawa, T., Kawase, H., Abe, M., Yokohata, T., Ise, T., Sato, H., Kato, E.,
Takata, K., Emori, S., and Kawamiya, M.: MIROC-ESM 2010: model description
and basic results of CMIP5-20c3m experiments, Geosci. Model Dev., 4,
845–872, 10.5194/gmd-4-845-2011, 2011.
Whittaker, R. H. and Likens, G. E.: Primary production: The biosphere and
man, Hum. Ecol., 1, 357–369, 10.1007/BF01536732, 1973.
Williams, D. N., Taylor, K. E., Cinquini, L., Evans, B., Kawamiya, M.,
Lawrence, B. N., and Middleton, D. E.: The Earth System Grid Federation?:
Software Framework Supporting CMIP5 Data Analysis and Dissemination, CLIVAR
Exch., 16, 40–42, 2011.
Yukimoto, S., Yoshimura, H., Hosaka, M., Sakami, T., Tsujino, H., Hirabara,
M., Tanaka, T. Y., Deushi, M., Obata, A., Nakano, H., Adachi, Y., Shindo,
E., Yabu, S., Ose, T., and Kitoh, A.: Meteorological Research Institute-Earth
System Model Version 1 (MRI-ESM1), Tech. Reports, 64, 88, 2011.Zeng, N., Zhao, F., Collatz, G. J., Kalnay, E., Salawitch, R. J., West, T.
O., and Guanter, L.: Agricultural Green Revolution as a driver of increasing
atmospheric CO2 seasonal amplitude, Nature, 515, 394–397, 10.1038/nature13893, 2014.Zimov, S. A., Davidov, S. P., Zimova, G. M., Davidova, A. I., Chapin, F. S.,
Chapin, M. C., and Reynolds, J. F.: Contribution of Disturbance to Increasing
Seasonal Amplitude of Atmospheric CO2, Science, 284, 1973–1976,
10.1126/science.284.5422.1973, 1999.