During the last decade, carbon cycle data assimilation systems (CCDAS) have
focused on improving the simulation of seasonal and mean global carbon
fluxes over a few years by simultaneous assimilation of multiple data
streams. However, the ability of a CCDAS to predict longer-term trends and
variability of the global carbon cycle and the constraint provided by the
observations have not yet been assessed. Here, we evaluate two near-decade-long assimilation experiments of the Max Planck Institute – Carbon Cycle
Data Assimilation System (MPI-CCDAS v1) using spaceborne estimates of the
fraction of absorbed photosynthetic active radiation (FAPAR) and atmospheric
CO2 concentrations from the global network of flask measurement sites
from either 1982 to 1990 or 1990 to 2000. We contrast these simulations with
independent observations from the period 1982–2010, as well as a third
MPI-CCDAS assimilation run using data from the full 1982–2010 period, and an
atmospheric inversion covering the same data and time. With 30 years of
data, MPI-CCDAS is capable of representing land uptake to a sufficient
degree to make it compatible with the atmospheric CO2 record. The
long-term trend and seasonal amplitude of atmospheric CO2
concentrations at station level over the period 1982 to 2010 is considerably
improved after assimilating only the first decade (1982–1990) of
observations. After 15–19 years of prognostic simulation, the simulated
CO2 mixing ratio in 2007–2010 diverges by only 2±1.3 ppm from
the observations, the atmospheric inversion, and the MPI-CCDAS assimilation
run using observations from the full period. The long-term trend,
phenological seasonality, and interannual variability (IAV) of FAPAR in the
Northern Hemisphere over the last 1 to 2 decades after the assimilation
were also improved. Despite imperfections in the representation of the IAV
in atmospheric CO2, model–data fusion for a decade of data can already
contribute to the prognostic capacity of land carbon cycle models at
relevant timescales.
Introduction
The observed contemporary increase in atmospheric CO2 is driven by
anthropogenic emissions from fossil fuels, land use change (2007–2016
average: 11.1±0.6 GtC yr-1), and the concurrent net carbon
uptake of the ocean and land from the atmosphere, which take up
approximately 22.4 % and 28 % of the anthropogenic flux, respectively.
Despite recent advances in atmospheric observations and ocean and land
modeling, there is an imbalance of 5.6 % (0.6 GtC yr-1) between
the ocean and land sinks, carbon emissions, and changes in the atmospheric
CO2 concentration (Le Quéré et al., 2018). Throughout past
decades, notable progress has been made to improve the performance of
terrestrial biosphere models, but the simulated global terrestrial carbon
fluxes and the net land carbon balance still have the highest uncertainties
from all of the components of the global carbon cycle (Friedlingstein et
al., 2014; Le Quéré et al., 2018). Quantifying the magnitude and
dynamics of the global terrestrial carbon cycle across different temporal
scales, and their contribution to the global carbon cycle, is challenging
because the substantial heterogeneity and complexity in land ecosystems and
challenges in the quantification of contemporary effects and response of
these ecosystems to increasing postindustrial CO2 concentrations
(Lienert and Joos, 2018; Stocker et al., 2014; Wang et al., 2017).
One strategy to reduce the mismatch between carbon flux predictions from
land surface models and measured atmospheric CO2 concentrations is
through data assimilation (DA) techniques, meaning to “train” the land
models by confronting them systematically with observations of
carbon-related variables (Raupach et al., 2005).
During DA, process parameters of land surface models are adjusted through
numerical minimization techniques to reduce the misfit between model results
and actual observations under consideration of the statistical properties of
both data sets. While atmospheric transport inversions are a method used to
infer the sinks and sources of CO2 between the atmosphere and land, or
ocean, from atmospheric CO2 measurements (Newsam and Enting, 1988;
Peylin et al., 2013; Rayner et al., 1999; Rödenbeck et al., 2003), the
application of carbon cycle data assimilation systems (CCDASs) provides
additional opportunities. In CCDAS, the process-based carbon cycle
mechanisms in land surface models are informed with measurements to support
a better estimate of the terrestrial carbon cycle, and improve the capacity
to project its dynamics. With this purpose, several CCDASs have been
developed in the past (e.g., Kaminski et al., 2012,
2013; Lienert and Joos, 2018; Peylin et al., 2016; Scholze et al., 2016).
The difference among some of these models is the variational or sequential
statistical approach they follow during the data assimilation process
(Montzka et al., 2012). A common characteristic in these
models is their capacity for integrating long-term and time-consistent
globally available observational records related to the carbon cycle such as
atmospheric CO2 measurements from flask and in situ networks
(Conway et al., 1994), as well as remote-sensing
products of canopy phenology properties such as MODIS NDVI (Moderate
Resolution Imaging Spectroradiometer – Normalized Difference Vegetation
Index) (Rouse et al., 1974) and FAPAR (Disney et al., 2016;
Pinty et al., 2011a).
Previous studies have analyzed the prognostic capability of the data
assimilation systems (e.g., Rayner et al., 2011, 2005;
Scholze et al., 2007; Schürmann et al., 2016), but only for a few years of
prognosis after the assimilation. Scholze et al. (2007) concluded that the
CCDAS built around BETHY (Biosphere Energy Transfer Hydrology) is capable of
providing a prognostic period of 4 years (2000–2003) of atmospheric
CO2 after data assimilation of 21 years (1979 to 1999) of CO2
concentrations. Schürmann et al. (2016) discussed the prognosis
capability of the Max Planck Institute – Carbon Cycle Data Assimilation
System (MPI-CCDAS v1) for 2 years after a short assimilation period of
5 years. Rayner et al. (2011) showed that the uncertainty related to
model parameters during the prediction of CO2 fluxes with a CCDAS is
considerably reduced when the model parameters are constrained with 2
decades of atmospheric measurements; however, these results were obtained
with a model that ignores the interacting effects of water, energy, and
phenology on the carbon cycle predictions.
The overarching aim of this work is to understand the ability of the
MPI-CCDAS v1 to make decadal projections of the land C cycle when the
assimilation is confronted with different temporal windows from two
observational constraints: FAPAR from remote-sensing data and atmospheric
CO2 concentrations from the global flask measurement network. For
this, we present 3 decades of modeled land carbon fluxes with the
MPI-CCDAS and investigate the effect of withholding information from recent
decades in the projected carbon fluxes and the ability of the model to
reproduce the observations during the period of data assimilation. We also
analyze trends and seasonal variations in the simulated signals during the
periods of the assimilation and compare to independent results to evaluate
the model performance. With these results, we gain insights into the number of
observations (in terms of decadal scale) necessary in data assimilation
systems to improve the representation of the global terrestrial carbon cycle
components. These results open the possibility of including newly measured
data in CCDAS that are only available for periods of less than a decade.
MethodsMPI-CCDAS
The MPI-CCDAS was built around the Jena Scheme Biosphere-Atmosphere Coupling
in Hamburg (JSBACH) land surface model (Dalmonech and Zaehle, 2013;
Raddatz et al., 2007; Reick et al., 2013) and follows a variational approach
that simultaneously reduces the model–data misfit for multiple independent
carbon cycle data sets (Kaminski
et al., 2013). Since its first development based on the BETHY – CCDAS, the
MPI-CCDAS has undergone several code modifications and improvements, as well
as tests of the assimilation of new observational data sets (e.g.,
Kaminski et al., 2012, 2013; Rayner et al., 2005; Scholze
et al., 2016; Schürmann et al., 2016), with the aim of further improving
the representation of land carbon fluxes. The history of the MPI-CCDAS and
other current CCDASs is extensively discussed in Scholze et al. (2017).
The code of the MPI-CCDAS version in this work is identical to the one used
in Schürmann et al. (2016). The model calculates the half-hourly storage
and surface fluxes of energy, water, and carbon in terrestrial ecosystems at
coarse spatial resolution (8∘× 10∘ grid) (Fig. 1). This horizontal resolution allows computational feasibility and a
realistic computational cost for the set of experiments presented in this
work. Furthermore, previous evidence has shown that a higher spatial
resolution in global vegetation models does not exert a considerable
influence in the simulated carbon fluxes at global or regional scales when
compared to results obtained with a coarse grid (Müller and
Lucht, 2007). The lack of influence to improve the simulated global C fluxes
due to changes in the model spatial resolution might also apply to CCDAS
(Peylin et al., 2016).
Global distribution of the temporal mean (1982–2006) of the
merged satellite FAPAR product used in the assimilation procedure. It also shows
the spatial coverage of eight regions globally distributed: boreal west
and east (BW and BE, for latitudes north of 60∘ N), temperate
northwest and northeast (TNW and TNE, between latitudes 20 and
60∘ N); tropical west and east (TW and TE, between latitudes 20∘ N and 20∘ S); temperate southwest and southeast (TSW
and TSE, for latitudes south of 20∘ S). Also shown are the six selected pixels: P1,
for the coniferous deciduous (CD) phenotype in the East Siberian Taiga; P2,
for the C4 pastures, and grasses (TrH) of central Brazil; P3, for the C3 and
C4 crops, pastures and grasses (TeCr and TeH) of the northern USA; P4 and P5,
for tropical evergreen trees (TrBe) situated in northwestern Brazil and
central Africa; and P6, for coniferous evergreen (CE) located in Canada. The location of 28 stations of the CO2 network measurements (filled
triangles, stations only included in DEC1; empty triangles, stations
included also in ALL and DEC2) for analysis of the assimilation results are also included.
The spatial distribution of the different plant functional types (PFTs) in
JSBACH is shown in Fig. S1 (Supplement). The selected parameters for the
assimilation procedure and their prior and range of values were based on
Schürmann et al. (2016), where an extensive sensitivity study led to
retaining those parameters with a substantial effect on the simulated carbon
and water fluxes, as well as on phenology. The majority of the selected
parameters for the optimization are linked to phenology, but there are also
parameters related to photosynthesis and global parameters that control the
land carbon turnover during the assimilation. The final list of parameters
together with their initial value obtained from an independent forward
simulation of JSBACH 3.0 (see Sect. 2.3.1) are shown in Table 1.
Model parameters selected for the optimization. Nos. 1 to 6:
related to phenology; no. 7 to photosynthesis and nos. 8 to 11 to land carbon
turnover. The values in the table for each PFT (where applies only) are for
the prior conditions: ppr±Cpr. a Values in
fphotos are the photosynthetic parameters Vcmax/Jmax (µmol CO2 m-2 s-1/µmol m-2 s-1). In Λmax the values marked with b are multiplied in the model by a factor
of 1±0.2 and those with c (in Λmax and
in fphotos) by a factor of 1±0.1; in fphotos values with
d are multiplied by 1±0.02, e by 1±0.03, and f by 1±0.06; these operations allowed a change in the standard values
in the model. Letters in parentheses below each PFT name are the predominant
environmental controls that influence each group: T, temperature; D,
daylight; W, water. Nos. 6 and 8 to 11 are global parameters and apply to all
PFTs.
No.ParameterDescriptionTrBeTrBDETDCECDRSTeH)TeCrTrHTrCr(W)(W)(T, D)(T, D)(T, D)(W)(T, W)(T, W)(T, W)(T, W)1ΛmaxMaximum LAI7.0b7.0b5.0b1.7b5.0b2.0b3.0c4.0c3.0c4.0c(m2 m-2)21/τlLeaf shedding timescale (d-1)––0.07±0.015×10-4±1×10-40.07±0.010.07±0.010.07±0.010.07±0.013τwWater stress tolerance time (d)300±30114±10–––50±5250±25250±254TφTemperature at leaf onset (∘C)––9.21±19.21±19.21±1–1.92±0.51.92±0.55tcDay length at leaf shedding (h)––13.37±113.37±113.37±1–––––6ξInitial leaf growth state (d-1)0.37±0.037fphotosaPhotosynthesis rate modifier39.0/74.1c31.0/58.9c66.0/125.4d62.5/118.8e39.1/74.3f61.7/117.2c78.2/148.6c100.7/191.3c8.0/140.0c39.0/700.0c8Q10Temperature sensitivity to resp.1.8±0.159fslowMultiplier for initial slow pool1.0±0.110faut_leafLeaf fraction of maintenance resp.0.4±0.111CO2offsetInitial atmospheric carbon (ppm)0±3
The MPI-CCDAS starts with an initial guess for the model control vector
(ppr) of carbon cycle properties, model states, and their
Gaussian uncertainty (“prior”) with covariance Cpr. The model control
vector p is iteratively updated to minimize a joint cost function J (Eq. 1)
describing the misfit between observational data streams (d; FAPAR and
atmospheric CO2, both with covariance Cd) and the corresponding
simulated observation operators of the MPI-CCDAS M(p), taking into account the
uncertainties in the observational data assuming a Gaussian distribution and
the information from the prior.
Jp=12Mp-dTCd-1Mp-d+p-pprTCpr-1p-ppr
During the optimization procedure, a new model trajectory is determined in
each iteration (i.e., in every cycle when the model recalculates the cost
function for the difference between the model parameters and the
observational constraint), such that energy and mass are conserved through
the entire assimilation window (Kaminski and Mathieu, 2017). The
gradient of the cost function with respect to the model control vector
(∂J∂p) is evaluated with a tangent-linear version
of JSBACH 3.0, which was generated through automatic differentiation using a
TAF (transformation of algorithms in Fortran) compiler tool (Giering
and Kaminski, 1998). With this tangent-linear version of the model code, the
derivatives for the parts of the model code where J(p) is evaluated (i.e.,
code parts that depend on the control variables) are accurately calculated
following the chain rule of calculus. Thus, the mathematical formulation of
the code involved in the cost function must be differentiable. Since this
was not the case for the phenological code of JSBACH 3.0, the phenology
scheme was updated following Knorr et al. (2010), where the
minimum and maximum calculations in the entire code were replaced by
smoothing functions to avoid abrupt transitions
(Schürmann et al., 2016).
Observational data setsFAPAR
The fraction of the radiation that is absorbed by plants during
photosynthesis (FAPAR) is a component of the land surface radiation budget
that dynamically indicates the status of the vegetation canopy over space
and time (Gobron et al., 2006). In a previous study,
MPI-CCDAS was constrained by MODIS TIP (Two-Stream Inversion Package) FAPAR
(hereafter TIP-FAPAR) generated from the inversion of a 1-D radiation
transfer model (Pinty et al., 2006, 2007) using the MODIS
broadband visible and near-infrared spectral white sky surface albedo as
input (Clerici et al., 2010; Pinty et al., 2011a, b).
For this study, the TIP-FAPAR product was available only from 2003 to 2011,
making it unsuitable for the indented longer assimilation period. While
there are long-term remotely sensed proxies of FAPAR, such as the NDVI
(Rouse et al., 1974), it has been found previously that NDVI was
less reliable than TIP-FAPAR in terms of the seasonal cycle amplitude of
vegetation seasonality (Dalmonech and Zaehle, 2013; Dalmonech et al.,
2015). Therefore, we used as FAPAR proxy the Global Inventory Monitoring and
Modeling System (GIMMS) NDVI product for the period 1982 to 2006
(Tucker et al., 2005), and merged it with the
TIP-FAPAR product to provide a longer record of vegetation greenness. The
maximum and minimum NDVI values were rescaled at the pixel level to coincide
with those from the TIP-FAPAR for the overlapping periods (i.e., 2003 to
2006) following
FAPARmod=NDVI-NDVImin,xNDVImax,x-NDVImin,x×TIPmax,x-TIPmin,x+TIPmin,x,
where x is the period from 2003 to 2006 for each data set, and NDVI is the full NDVI
product from 1982 to 2006, with minimum values given by NDVImin and
maximum values by NDVImax. TIPmin and TIPmax are the corresponding
minimum and maximum values from the TIP-FAPAR product. With this approach,
the resulting merged product maintains the maximum and minimum values from
TIP-FAPAR while preserving the temporal dynamics of NDVI. The median
uncertainty of the available TIP-FAPAR data was considered to be the
uncertainty for the entire time series. Due to a technical failure in the
MPI-CCDAS, the final FAPARmod product used in the assimilation
procedure only spans from 1982 to 2006 and the last 4 years from the
TIP-FAPAR product were not considered. For this study, this product was
aggregated to match the model grid horizontal resolution considering
background snow-free and snow-covered conditions separately
(Schürmann et al., 2016).
To discard pixels in the global FAPAR data that might lead to bias during
the assimilation procedure, we applied a mask to the global FAPAR grid
following three criteria. (1) We masked out the grid cells with
crop-dominating phenology of >20 % since no explicit crop
phenology is described in JSBACH. This step has consequences in areas where
other relevant functional types are also present in the same grid cells,
such as deciduous broadleaves that are also abundant in the USA and Europe.
As a result, the parameters related to deciduous broadleaves are constrained
from other locations; (2) we further masked out pixels that hold a low
correlation (R2<0.2) when the prior model results are compared to
the observations, as we had previously found that the MPI-CCDAS is incapable
of correcting such poor model behaviors (Schürmann et al., 2016).
Finally, (3) we masked out pixels located in areas where phenology abundance
is low, i.e., deserts, because they would influence the optimization causing
significant bias due to global compensating effects. The final FAPAR product
used during the assimilation contains 40 % of the original number of
pixels after the applied mask, resulting in more pixels distributed in the
Northern Hemisphere compared to the southern areas. These observational data
will be referred to hereafter as FAPARobs (see Fig. 1 for the global
distribution of mean FAPARobs from 1982 to 2006).
Atmospheric CO2 concentrations and observation operator
Measurements of atmospheric CO2 mixing ratios were taken from the flask
data continuous record of 28 sites in the NOAA CMDL station network
(Conway et al., 1994; Rödenbeck et al., 2003). The selection criteria
included the length of the record (on average 19 years) (Fig. A1 in Appendix A) and
focused on remote and ocean stations with low impact of local carbon sources
and sinks of carbon (Schürmann et al., 2016) (see the location of
CO2 stations in Fig. 1). In the MPI-CCDAS, the atmospheric transport of
CO2 is calculated by integrating the simulated half-hourly net CO2
fluxes to monthly values followed by the transport calculation with the
Jacobian representation of the atmospheric transport model TM3 that is
driven with meteorology fields from NCEP (National Centers for Environmental
Prediction) reanalysis (Heimann and Körner, 2003;
Rödenbeck et al., 2003). During the generation of the monthly transport
matrices, the precise sampling time of flask measurements as well as the
3-hourly atmospheric transport was considered to minimize the representation
error due to short-term fluctuations in atmospheric transport and to
minimize the impact of synoptic atmospheric transport variability on the
simulated seasonal and long-term dynamics of atmospheric CO2 at the
monitoring stations. Through this approach, the nonlinear effect of weather
anomalies on the surface fluxes were also taken into account. TM3 runs at
horizontal “fine grid” (fg) resolution of 4∘× 5∘. Due to computational demands, it is not possible at this
stage to use the MPI-CCDAS at the same fine grid resolution as in the TM3.
The treatment of uncertainties is performed in the same way as in the TM3
atmospheric inversion (Rödenbeck et al., 2003) but imposing a floor
value of 1 ppm on the uncertainties (Rayner et al., 2005) to
allow a range for the comparison to the observational operator.
We also compare the fluxes from the assimilation to fluxes obtained from an
atmospheric transport inversion (referred to as INV). Similar to the
MPI-CCDAS, the atmospheric transport inversion is constrained by atmospheric
CO2 data linked to surface fluxes through a tracer transport model, but
the land surface CO2 fluxes are adjusted directly rather than through
changes in the parameters of a land surface process model. The inversion
setup used in this study is similar to Jena CarboScope v4.1
(Rödenbeck, 2005; Rödenbeck et al., 2003), involving the same TM3
model as in the MPI-CCDAS. To make the inversion results as comparable as
possible to those from the MPI-CCDAS, we used in the inversion the same
prior fluxes from fossil fuel emissions and ocean (Sect. 2.2.3), as well
as the same CO2 stations. This comparison also helps to gauge the
impact of non-land surface fluxes on the ability to reproduce the
observations.
Background carbon fluxes
To account for the total carbon balance during the comparison between the
land fluxes from MPI-CCDAS and atmospheric concentrations, it is necessary
to include background carbon fluxes (i.e., from fossil fuel emissions, use
and change of land cover, and the ocean).
Land use and land cover change (LULCC). The LULCC fluxes were obtained from a transient simulation carried out with the
JSBACH 3.0 forced with prescribed annual maps of modified cover fractions
(Hurtt et al., 2006). These fluxes do not consider
disturbances such as fluxes from fires.
Fossil fuel (FF) emissions. The FF emissions used for this work are the result of a merged product from
various data sets to complete a long record of emissions, i.e., 1980 to
2012. This product was prepared for the GEOCARBON project
(http://www.geocarbon.net, Version 3, last access: June 2014) by Philippe Peylin after merging and harmonizing various data
sets: (1) for the period 1980 to 1989, the CDIAC (Carbon Dioxide Information
Analysis Center; http://cdiac.ess-dive.lbl.gov/, last access: May 2013) product prepared for the
CMIP5 exercise (Andres et al., 2013, 2011,
1996); (2) for the period 1990 to 2009, the IER-EDGAR (Institute of Energy
and Rational use of Energy, Stuttgart, Germany – Emission Database for
Global Atmospheric Research; http://www.carbones.eu/wcmqs/project/, last access: May 2013) product where
the FF emissions are constructed using the EDGAR v4.2 data set
(http://edgar.jrc.ec.europa.eu/overview.php?v=42, last access: May 2013) and completed with
profiles for different countries, emission sectors, and time zones available
for different temporal resolutions; and (3) for the period 2010 to 2012, the
CarbonTracker product derived at the NOAA Climate Monitoring and Diagnostics
Laboratory (CMDL; https://www.esrl.noaa.gov/gmd/ccgg/carbontracker/, last access: May 2013).
Ocean fluxes. Two products were merged to account for the oceanic CO2 fluxes: (1) results from the Jena CarboScope v3.4 for the period between 1990 and 2007 (Rödenbeck et al., 2013)
(http://www.bgc-jena.mpg.de/CarboScope/?ID=s, last access: June 2015) and (2) annual ocean fluxes
from the Global Carbon Budget 2014 (Le Quéré et al., 2015)
(http://cdiac.ess-dive.lbl.gov/GCP/carbonbudget/2014/, last access: June 2015). The ocean fluxes for
monthly resolution follow Takahashi et al. (2002), and
the spatial distributions follow Mikaloff Fletcher
et al. (2006).
Experimental setupSpin-up and preparation of initial files
The MPI-CCDAS was forced with meteorology from CRU-NCEP (the Climate
Research Unit from the University of East Anglia, analysis of the NCEP
reanalysis atmospheric forcing) version 6.1, available at daily resolution
from 1901 to 2014 and a spatial resolution of 0.5∘
(Viovy and Ciais, 2015). The atmospheric
forcing fields (i.e., wind speed, air temperature, precipitation, downward
short- and long-wave radiation, and specific humidity) were remapped to the
coarse (8∘× 10∘) model grid. Prescribed
annual means (one annual global mean value) of atmospheric CO2 were
also included as part of the forcing fields for the model
(https://www.esrl.noaa.gov/gmd/ccgg/trends/global.html, last access: July 2015).
Before the assimilation experiments, the JSBACH 3.0 model was spun up to
equilibrium of the vegetation and soil carbon pools with 1901 atmospheric
CO2, land cover, and 1901–1910 climate. The spin-up procedure was performed
for a model period of 1000 years with repeated cycles of atmospheric forcing
data. After this period, a transient model simulation was also performed with
JSBACH 3.0 for the period 1901 to 2012. This transient simulation included a
change in atmospheric CO2, climate, and land cover. The purposes of this
simulation were (i) to obtain the initial conditions for the CCDAS
experiments and (ii) to derive spatially resolved land use emissions from a
JSBACH 3.0 simulation as additional forcing (see Sect. 2.2.2). Due to
technical limitations, the cover fraction of each PFT is kept constant in
MPI-CCDAS during data assimilation, and thus remained fixed through the
simulation period to account for the imprint of the space-time dynamics of
land use change emissions on atmospheric CO2 concentrations. After the
spin-up procedure, an initial global scaling factor was set for the slowly
varying carbon pool (fslow, also selected as optimization parameter) to
account for non-steady-state conditions at the beginning of the assimilation
(Carvalhais et al., 2008; Schürmann et al., 2016).
Assimilation experiments
During the assimilation procedure, the model was forced with the same daily
reanalysis atmospheric data used during the model spin up. In this study we
present the results of three long-term experiments using the MPI-CCDAS,
which differ in the timeframe of the observational records used during the
assimilation: (1) ALL covers data in 1980–2010 and includes the complete
available timeframe of the two observational data sets, i.e., for FAPAR
from 1982 to 2006 and for the atmospheric CO2 concentrations from 1982
to 2010; (2) DEC1 covers observations from the two data sets available from
1982 to 1990; and (3) DEC2 covers measurements available from the two data
sets from 1990 to 2000 (Fig. A2). Because of the different lengths of the
CO2 records for some stations, this ultimately leads to a different
number of observations used for each experiment (Fig. A1).
The simulation period in the three assimilation experiments is from 1970 to
2010. The first 10 years (1970 to 1979) of the results are discarded
because during this period the phenology, vegetation productivity, and the
fast land C pools adjust to the new model control vector p. Through this
adjustment any imprint of the initial conditions on the calculation of the
cost function is avoided. The soil C pool at the beginning of the experiment
was included in the model control vector and only results from 1980 are
reported below. The results of the assimilation for the periods of time that
fall within the observational temporal window are considered for model
diagnostic, whereas the periods that fall outside the assimilation window on
each experiment are periods of model prognosis, i.e., the prognosis period
in DEC1 is from 1991 to 2010, and in DEC2 for 2001 to 2010.
Results
We first evaluate the long-term trends, seasonal and spatial variability of FAPAR, and carbon fluxes from the different assimilation experiments
(Sect. 3.1 to 3.3), and based on these analyze the prognostic ability of
the MPI-CCDAS (Sect. 3.4). To facilitate the analysis in some of our
results, the global land is divided into eight regions: boreal west and east
(BW and BE, for latitudes north of 60∘ N), temperate northwest and
northeast (TNW and TNE, between latitudes 20 and 60∘ N); tropical west and east (TW and TE, between latitudes 20∘ N and
20∘ S); temperate southwest and southeast (TSW and TSE, for
latitudes south of 20∘ S) (Fig. 1).
Phenology
In all assimilation experiments, the RMSE and the bias between the modeled
and observed FAPAR for 1982 to 2006 is reduced compared to the prior (Table 2). One important cause for this improvement is the change in the spatial
distribution of the yearly maximum leaf area index (LAI) due to the
optimization of the PFT-specific maximum LAI (Λmax) parameter
(Fig. S2) (see also Sect. A1 and Fig. A3 in the Appendix for more specific
results of parameter changes due to the assimilation). The improvement
occurs in all regions (Fig. 2), despite notable differences between the
different assimilation experiments. In the decadal experiments DEC1 and
DEC2, the largest error reduction compared to the prior is 19 % for
boreal regions, while in the temperate areas this reduction is about 16 %. In the ALL experiment, larger reductions of 21 % on average are
obtained in the tropical regions TE and TW.
Statistical analysis of FAPAR for 1982–2006 in all of the
experiments, and also for the periods of the window of assimilation only for
DEC1 and DEC2. R2 is obtained from the linear correlation between
FAPARobs and FAPARmod calculated for the entire period and by
seasons. NRMSE is the normalized root-mean-squared error, defined as RMSE divided by the mean (FAPARobs).
BiasNRMSER2All yearDJFMAMJJASONPrior0.370.950.160.140.310.210.33ALL0.100.760.200.140.340.200.37DEC10.080.640.340.150.390.180.41DEC20.090.650.340.140.390.180.41Only for the period of the assimilation window DEC1 (1980–1990)0.090.660.340.180.420.210.48DEC2 (1990–2000)0.050.480.340.180.410.210.47
RMSE for FAPAR from the model results and observations for the
period 1982–2006 and for different regions.
One important factor in the error reduction is a substantial increase in the
linear global correlation (R2) in FAPAR during spring and autumn in
experiments DEC1 (0.42 and 0.48, respectively) and DEC2 (0.48 and 0.47,
respectively) with respect to the prior (0.31 and 0.33, respectively), with
changes mostly taking place in the Northern Hemisphere (Fig. S3). An
analysis for representative pixels (Fig. 1) shows that the assimilation
procedure results in a better representation of the timing and amplitude of
the mean seasonal cycle, particularly in the temperate and boreal zones of
the Northern Hemisphere (Fig. S4). As a result, the average global R2
between modeled and observed FAPAR increased with respect to the prior
experiment from 0.17 in the prior to 0.20 for ALL and 0.34 for both DEC1 and
DEC2 (Table 2, Fig. S3). Further details on the pixel-level analysis are
presented in Sect. A2 of the Appendix.
The observed FAPAR signal exhibits positive long trends, indicating a
greening trend of vegetation for most of the regions, with the exception of
the TSW region, where the long-term trend indicates a decrease in FAPAR
(i.e., browning). In most of the regions, the assimilation
results agree on a positive long-term trend as in the observations; the
magnitude of this trend is in disagreement with the observations (Fig. 3).
Particularly in the BE region, the prior experiment overestimates the
FAPARobs trend by almost double. After the assimilation, the simulated
FAPAR trend is reduced, leading instead to a slight underestimation of the
growth rate in all of the posterior experiments. In the TWS region, the
assimilation improved the long-term trend from a positive to a negative
growth rate in the three posterior experiments. The most substantial
disagreement between FAPARobs and FAPARmod occurs in the TW
region, where the observations show a positive trend in FAPAR during the
period of analysis, whereas this is not captured in the prior and all the
posterior experiments. Despite these trend adjustments, the model–data error
(based on the 4-year mean differences to the observations at regional
scale) remains more or less constant across the 30-year period (Fig. 4).
Mean monthly growth rate of FAPAR for 1982–2006 on each analyzed
geographical region for the satellite observations and results of prior and
the posterior experiments.
The observed FAPAR signal also contains a small amount of interannual
variability (Fig. S5). Compared to observations, the simulated IAV of FAPAR
(obtained from the monthly signal for each experiment) is improved only in
the predominantly temperature-controlled boreal regions, whereas in
temperate and tropical areas with a larger contribution of
moisture-controlled phenology, the assimilation does not improve the
variability (Fig. S5).
Atmospheric CO2
To diagnose the performance of the MPI-CCDAS with respect to the atmospheric
mole fractions of CO2, we compare the observed and simulated values, in
terms of the mean seasonal cycle, IAV, and monthly growth rate, at three
stations: (1) Alert (ALT) in the Northern Hemisphere, (2) Mauna Loa (MLO) in
the tropics, and (3) South Pole (SPO) in the Southern Hemisphere. Results of
this comparison are shown in Fig. 5. For MLO and ALT, the timing of the
seasonal cycle is already well reproduced in the prior simulation, and the
assimilation corrects errors in the amplitude of the seasonal cycle and the
long-term trend. At SPO, there are also large relative differences between
the model results and the observations, however, of a much smaller magnitude
than for the two other stations. After the assimilation, the seasonal phase
of CO2 is shifted by approximately a month to better match the pattern
in the measurements in the three experiments, and the amplitude of the
seasonal cycle is in better agreement with the observations than compared to
the prior.
Time series of the 4-year mean of the FAPAR anomaly for the
satellite data for each model experiment in six selected model pixels. The
error bar indicates the ±1 standard deviation of the 4-year
differences. The first marker (as asterisk) in the time series is the single
value for 1982.
Figure 6 demonstrates that these examples are broadly representative of the
global changes due to the assimilation. Figure 6a shows a reduction in the
CO2 amplitude for stations of the Northern Hemisphere (>40∘ N) after the assimilation, which is in better agreement with the
observations than the prior simulation. The most substantial amplitude
reduction occurs at the northernmost station (ALT), where the seasonal
amplitude decreases from 23.5 ppm in the prior experiment to 16.5 ppm in the
ALL experiment, bringing it closer to the observed amplitude of 14.4 ppm.
The latitudinal distribution of the linear correlation coefficient between
the observed and simulated mean seasonal cycles is depicted in Fig. 6b, and
demonstrates a very good agreement (R2>0.9) in the
Northern Hemisphere in all of the experiments (including the prior
simulation). In the tropics (specifically between 20 and 40∘ N), the misfit of the phasing of the seasonal cycle is improved
after the assimilation, as evidenced by an increased linear correlation.
However, this is achieved at the expense of a considerable reduction in the
amplitude of the seasonal cycle compared to the observations. The results
from the atmospheric inversions (INV) show a closer statistical agreement
with the observations, as shown in Figs. 5 and 6.
Statistical analysis of atmospheric CO2 at three flask
measurement sites: Alert (ALT; top panels), Mauna Loa (MLO, center panels),
and South Pole (SPO, bottom panels), from the measurements, prior, posterior
experiments (ALL, DEC1, and DEC2), and inversion (INV1). For each station the
panels show the time series of the mean monthly values, the mean seasonal
cycle, the interannual variability, and the monthly growth rate for the
entire period of the simulation (1980–2010).
(a) Latitudinal distribution of the mean CO2 seasonal
amplitude for the 28 flask measurement stations from the observations, prior,
and posterior experiments. (b) Latitudinal distribution of R2 obtained
from the correlation between the observations and each simulation result of
the mean atm. CO2 seasonal cycle and (c) average atmospheric CO2
monthly growth rate across stations for the observations and model results.
The star on each bar is the mean of the atm. CO2 monthly growth rate,
the horizontal middle black line on each box is the median, the red whiskers
depict the error as 1 standard deviation, and the grey dots on each box
are the actual monthly growth rate values for all the stations in each data
set.
During the nearly 30 years of atmospheric CO2 data available, the
time series of the CO2 mole fractions in the prior model results
strongly underestimate the long-term trend, and start to deviate in the
first 5 years of the time series. In all the assimilation experiments,
the long-term atmospheric CO2 trend is in much better agreement with the
30-year trend of the measurements in the entire period of the
assimilation (leftmost panels of Figs. 5 and 6c). The mean growth rate
calculated from the results of the ALL experiment is in good agreement with
the results in the observations (0.15 ppm month-1 in both cases)
compared to the prior model (0.087 ppm month-1). Despite the moderate
improvement, the MPI-CCDAS is incapable of improving the IAV of the
atmospheric CO2 concentration substantially, with the most notable
deviations from the observed signals remaining unchanged after the
assimilation procedure (Fig. 5).
Global and regional carbon pools and fluxes
We next compare the simulated land carbon cycle in the prior and posterior
experiments to independent data. In the posterior experiments, the
vegetation C pool decreased between 14 % and 20 % of the value in the prior
but remained within the range of the literature estimate (442±146 PgC). The global soil C stock showed significant changes after the
assimilation. In all the posterior experiments, the soil C pool decreased by
45 %, 43 %, and 53 % with respect to the value in the prior. Still, the total
C in the soil (1362 PgC) in the ALL experiment after the assimilation is in
closer agreement with the estimate from the Harmonized World Soil Database
(http://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML, last access: January 2015) of 1343 PgC (Table 3). As for the total global
vegetation C stock, the prior and assimilation are in closer agreement with
the lower end of the estimate by Carvalhais et al. (2014) (296 PgC).
Global average of the terrestrial carbon cycle components and
carbon stocks in results from the assimilation experiments and prior
(1982–2010), and other independent estimates (see table footnote for
description).
PriorALLDEC1DEC2INVLiteratureGPP (PgC yr-1)118.896.983.197.2–118.9aNPP (PgC yr-1)54.534.237.330.3––NEE (PgC yr-1)-2.64-1.13-1.32-1.18-1.20c-2.52±0.98bNBE (NEE + LULCC) (PgC yr-1)-2.06-0.54-0.74-0.60–-1.27±0.97bER (PgC yr-1)115.795.281.095.3––Ra (PgC yr-1)64.262.745.866.9––Rh (PgC yr-1)51.532.435.228.4––Root exudates (PgC yr-1)3.32.02.21.7––Soil C (PgC)2481136414231167–1343dVegetation C (PgC)394310335311–442±146eLitter C (PgC)228166171158––
a Model tree ensemble data-driven product; Jung et al., 2011; average
for 1982–2010.
b Global Carbon Project (2017), Le Quéré et al. (2018); average
for 1982–2010. The NBE values include the LULCC reported for each individual
model.
c Inversion result is the average for 1982–2009.
dhttp://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML (last access: June 2015).
e Carvalhais et al. (2014).
The simulated latitudinal GPP values agree well with the data-driven model
tree ensemble (MTE) estimate from Jung
et al. (2011) for the period from 1982 to 2010 north of 30∘ N.
However, the assimilation results are low biased in the tropics, which
propagated into lower estimates of global GPP in all the posterior results
(Fig. 7d and Table 3). After the assimilation, the global GPP and NPP are
reduced in the three posterior experiments compared to the prior (118.8 and 54.5 PgC yr-1, respectively). In contrast to
the posterior global mean of GPP, the value in the prior simulation compares
favorably well to the global mean from the MTE product (118.9 PgC yr-1) for the same period of analysis. The global mean GPP is
reduced by up to 26 PgC yr-1 on average in the three posterior
experiments compared to the prior experiment. Simulation DEC1 experienced
the largest reduction in the global photosynthetic C uptake (83.1 PgC yr-1) relative to the prior value (Table 3 and spatial
distribution of the GPP difference to the prior in Fig. S6d, f, and h).
Time series of the anomaly to the temporal mean of the time
series (a, b), and latitudinal gradient (c, d) of the total net
ecosystem exchange (NEE including the influence of LULCC) (a, c) and gross
primary production (b, d) for the results of each model simulation. NEE
from the model is compared to the GCP 2017 and INV data set (a, c). GPP
is compared to the MTE data-data driven estimate of Jung et al. (2011) (b, d).
At the large scale, the variation in the NBE (net biome exchange of CO2
with the atmosphere, calculated as the net ecosystem exchange (NEE) minus
the flux related to land use change) from all of the simulations through the
time series is similar to that from the Global Carbon Project 2017 (GCP17;
Le Quéré et al., 2018) and INV, with the significant anomalies
collocated in time (Figs. 7a, A4). Contrary to the prior simulation, the
total annual NBE from the three posterior experiments falls within the
uncertainty (shaded green area in Fig. A4d calculated as ±1 standard
deviation) of the mean NBE from the terrestrial ecosystem models in the
GCP17. However, the 1982–2010 mean net biome exchange in all of the
assimilation experiments through the time series is on average 1.4 PgC yr-1 lower than the flux in the prior simulation (-2.06 PgC yr-1) and 0.8 PgC yr-1 less than the GCP17 value
(-1.27±0.97 PgC yr-1) (Table 3, Figs. A4d and S7 for
summary of C balance).
In all MPI-CCDAS simulations, the NEE is reduced relative to the prior in
most of the Southern Hemisphere, while it is increased in the Northern
Hemisphere (Fig. S6c, e, and g). Temperate northern areas contribute the
most to the global net CO2 uptake. In the boreal east and west regions
(BE and BW), the net land C emissions increased in all of the posterior
experiments compared to the prior (Fig. S6c, e, and g) with the most
significant increase in BE for DEC2 (-0.29 PgC yr-1) relative to
the corresponding value in the prior (-0.09 PgC yr-1). The
decrease in GPP in the tropics is depicted in the latitudinal gradient of
NBE shown in Fig. 7c and in the spatial distribution of the NEE difference
between the prior and the posterior experiments (Fig. S6c, e, and g). As in
the tropics, the NEE in the southern temperate region is consistently
reduced after the assimilation experiments, also switching the NEE of the
TSE region from a C sink of -0.18 PgC yr-1 in the prior to a
mean C source to the atmosphere of 0.016 PgC yr-1 in the DEC2
experiment.
The magnitude of the global NBE and GPP is smaller in the posterior
experiments than in the prior. However, the trend in the anomaly of these
fluxes (calculated relative to the temporal mean of each time series) is
comparable in all the experiments (Fig. 7a and b), suggesting that the
response to the environmental conditions is similar through the simulation
period after the assimilation as well. This robust response shows in GPP
a similar and gradual increasing C uptake (positive trend) during the period
of analysis, only with a slightly reduced slope in the prior experiment
(Fig. 7b).
Prognostic capability of MPI-CCDAS
Finally, we evaluate the goodness of the model–data fit of the decadal
assimilation runs with respect to their long-term carbon cycle simulation
relative to (i) that of the prior and (ii) that of the assimilation run using
data from the 30-year experiment as a reference case for “best possible”
model–data match given the structural limitations of the MPI-CCDAS to match
the observations (as evaluated in Sect. 3.1 and 3.2). We calculate the
4-year mean differences between the atmospheric CO2 mole fraction
measurements and the CO2 model results and also the INV results, for
all of the stations (Fig. 8). In the ALL assimilation experiment, the
atmospheric CO2 concentration consistently matches the observations
across the entire assimilation period (that also corresponds to the window
of assimilation) with a -0.03±1 ppm average bias to the
observations (Fig. 8). This is comparable to the trend (Fig. 6c), and
4-year mean differences inferred by the inversions, where the 4-year
mean results in the ALL fall within the standard deviation of the 4-year
mean of the INV (Fig. 8). This is in striking contrast to the prior
experiment, where the 4-year mean of the CO2 mole fraction at the
end of this simulation is 18.8 ppm lower than observed. For the DEC1
experiment, the 4-year mean difference among the measurements and the
model results is between -0.3 and 0.3 ppm in the 1980s. This level of
model–data agreement remains for the 1990s, where the experiment did not see
any observations. After the year 2000, the fit increasingly degrades, with
an underestimation of the CO2 mole fraction by 1.6 ppm for the last
4-year average. However, this is still a 90 % reduction in misfit
compared to the prior experiment.
Time series of the 4-year mean of the atm. CO2 anomaly
to the observations for each model experiment and inversion results, for all
the stations. The y axis is limited to the results in the posterior
experiments. The error bar is 1 standard deviation to the 4-year mean
of the differences to the observations. The first marker to the left in the
time series (as asterisk) is the single value for 1982 not included in the
subsequent 4-year means.
RMSE for different periods between CO2 atm. concentrations
from measurements and model results for the different assimilation
experiments and inversion results for each of the flask measurement
stations.
We next quantify the RMSE between the CO2 measurements and model
results for each station for four different periods: 1982–1990, 1990–2000,
2000–2010, and 1982–2010 (Figs. 9 and A2). The large bias of the prior is
reflected in the RMSE where the results of this experiment have the most
substantial error for all of the stations and periods (between 2.8 and 18.7 ppm) (Fig. 9). For the posterior experiments with a decadal window of
assimilation (DEC1 and DEC2), the performance of the assimilation of
CO2 mole fraction also improves substantially across all time periods.
Within the period of the assimilation, the difference to the measurements
and RMSE is most strongly reduced, and the error increases somewhat outside
of the window of assimilation. The model results show that when only the
first decade of data is assimilated (DEC1), a more significant deviation to
the long-term trend of atmospheric CO2 occurs between 2000 and 2010
compared to DEC2 and ALL (Fig. 9c). Similarly, a larger bias is also
observed in the results from DEC2 where the lowest 4-year mean
difference between the observations and the assimilation results takes place
in the period of the window of assimilation for this experiment (1990–2000)
(Figs. 8 and 9b for RMSE). During this period, the model overestimates
the CO2 atmospheric concentration only by 0.15 ppm on average, whereas
for the periods outside the window of assimilation, the CO2
concentration is underestimated by 0.64 ppm in the period 1982–1990, and by
1.04 ppm in the period 2000–2010. Thus, in experiment DEC2 the
prognostic skill of CCDAS is also reduced outside the window of assimilation, and
the long-term trend is less well reproduced than in the ALL experiment.
The analysis of the 4-year mean differences for the period 1982–2006
between FAPARobs and the results of the prior and assimilation
experiments at the regional scale (areas in Fig. 1) reveals, contrary to the
CO2 observations, a near-constant 4-year mean FAPAR difference
within the time series and each of the experiments (Fig. 4). In general
terms, the decadal experiments are better able to reproduce the mean FAPAR
across all regions. The largest difference between posterior results to the
observations is in the tropical regions, where the FAPAR 4-year mean
difference showed that the observations remained consistently larger than
the ALL results by on average 0.042 in TE and 0.095 in TW (Fig. 4).
Importantly, however, the trend correction for the boreal and temperate
areas (Fig. 3) is similar across the different assimilation experiments,
suggesting that important biases of the JSBACH 3.0 model, including the
tendency to simulate too strong boreal greening, can be readily reduced with
only 10 years of data, as further improvement with the 30-year record
is small.
Discussion
The parameter optimization with a simultaneous assimilation of long-term
spaceborne FAPAR and atmospheric CO2 measurements in the MPI-CCDAS,
resulted in a considerable reduction in the cost function and norm of the
gradient, which can be seen as an overall improvement in the modeled global
carbon fluxes with a decrease in the root-mean-squared error of the
MPI-CCDAS compared to the CO2 and FAPAR and observations (Figs. 9 and
2). The trajectory of model parameters involved in the optimization
differed for each experiment and each phenotype. While some parameters were
consistently retrieved after the assimilation, such as the maximum leaf area
of grasses and shrubs and the correction parameter for the initial soil pool
size, some final parameter estimates varied considerably between the three
experiments, e.g., the tropical maximum leaf area index and some of the
parameters controlling the seasonality of the phenology (Fig. A3). These
variations lead to regional differences in the simulated compartment flux
GPP and ecosystem respiration, which are not well constrained from the
observations. Interestingly, these differences result in very similar
absolute values in global carbon fluxes and their trends. This demonstrates
a certain degree of equifinality in the results and cautions a too stringent
interpretation of the MPI-CCDAS outcome in terms of improving understanding
about biosphere processes and their long-term trends.
FAPAR
MPI-CCDAS is capable of extracting information about the seasonal cycle and
the long-term trends from the FAPAR observations. Using decade-long FAPAR
data during the assimilation (DEC1 and DEC2) already leads to notable
improvement of the simulated seasonal phenology of the land surface within
and outside the window of assimilation, i.e., maintaining these changes
during the prognosis periods. This improvement is predominantly the result
of the ability in the model to simulate the timing of green-up and
brown-down in spring and summer through the optimization of parameters that
regulate the onset and end of the growing season (i.e., parameters for
temperature and day-length thresholds). The greening effect is especially
taking place in the Northern Hemisphere, dominated by the phenotypes
deciduous and evergreen needleleaf and extratropical deciduous trees.
The long-term greening trend in the vegetation of boreal regions previously
observed in spaceborne data (Forkel et al., 2016; Lucht et al., 2002)
was captured in the results of MPI-CCDAS before the assimilation, but it was
mostly overestimated in northern regions and underestimated in the Southern
Hemisphere. After the assimilation experiments, the greening trend was
improved primarily in the boreal areas and is in closer agreement to the
reported satellite FAPAR data. The modest improvements achieved in the
simulated greening trend of temperate areas in the western hemisphere are
associated with a decreased performance in the eastern hemisphere,
indicating that the model structure of MPI-CCDAS is incapable of reconciling
regional differences. This difference could be an indicator of the need to
parameterize both hemispheres differently in terms of their phenological
response to the underlying driving factors (such as temperature, moisture
availability, and day length); also, this could be due to the lack of process
to account for the land use or vegetation dynamics in the MPI-CCDAS.
Despite these broadscale improvements, the MPI-CCDAS does not reproduce the
magnitude of the greening trend and its interannual variability in all the
posterior experiments at pixel and regional levels. This is likely a result
of the MPI-CCDAS structure, which relies on few globally relevant PFT-level
parameters. Although some of the phenological parameters in CCDAS adapt to
local mean growing season temperature, other thresholds are only globally
applicable, causing a trend to temperature grasslands that cover a wide
climatological range. For example, some of the global parameters such as
faut_leaf and fslow imply that improvements of
modeled fluxes in the boreal regions directly affect fluxes in the tropics,
inducing parameter changes to compensate for the altered C fluxes. Defining
instead such global parameters per PFT would alleviate this issue but will
compromise the computational cost and might not necessarily reduce the
overall uncertainty. Another technical challenge is the use of a spatially
mixed signal at the coarse spatial model resolution (due to the high
computational requirements necessary to increase model resolution) to infer
PFT-specific parameters. A likely better strategy for constraining
PFT-specific parameters would be to resample the highly resolved satellite
product to PFT-specific FAPAR classes per pixel before the aggregation into
a global grid. This change would allow us to find more spatially refined
classes and provide PFT-specific FAPAR maps to the CCDAS to reduce issues in
the identification of phenological parameters for different climatic
regions.
Except for the tropical latitudes, the difference between the regional IAV
of the observations and model output is small compared to seasonal
variations. The modeled signal remains within a range of 0.05
(dimensionless) FAPARobs. The signal and the model–data difference is
also smaller than the global mean retrieval error of the FAPAR product,
which is ±0.2088 (Schürmann et al., 2016). This error was used to
quantify the observational FAPAR uncertainty in the assimilation, thereby
reducing the ability of the MPI-CCDAS to detect and correct such smaller
variation. Overall, the lacking match of the IAV may therefore be of little
overall concern. Nevertheless, the lower than observed IAV in the tropical
bands may be indicative of too weak drought response in the maximum leaf
area index of the model. Although the assimilation procedure allows changes
in the phenology response to water stress (given by parameter τw), the assimilation procedure decreased the drought sensitivity of
tropical phenology given the entire spatially explicit FAPAR time series,
and therefore did not allow capture of the regional drought events that could
be in principle linked to changes in LAI.
The technical error during the assimilation procedure to not include the
period from 2007 to 2010 in the FAPARmod product does not however influence
the decadal results observed here because the main information gain
of the CCDAS in terms of phenology stems from the seasonal cycle, with
little effect on the overall trends between the three assimilation
experiments with different time periods.
Bearing in mind the different spatial resolution of methods (i.e., model
grids and remote-sensing pixels), a robust comparison between the mean and
maximum LAI values before and after the assimilation per region are
presented in Table A1 of the Appendix. The results fall within LAI values
from MODIS and lidar reported in the literature. Ground-based observations
in the tropical Amazon–savanna transition forest between 2005 and 2008 show
an annual mean LAI value for the total canopy of 7.4±0.6 m2 m-2, and for the seasonally flooded forest the value of 3.4±0.1 m2 m-2. For the remote-sensing data from MODIS, the reported
values are 6.2±0.2 and 5.8±0.3 m2 m-2, respectively (Biudes et al., 2014). In the
eastern Amazon forest, the remote-sensing-based LAI has been reported as 6.2 m2 m-2 from lidar, and 4.8 m2 m-2 with a low end of 2.0 m2 m-2 from MODIS (Qu et al., 2011).
Atmospheric CO2
The considerable improvement of the seasonal amplitude and the long-term
trend of atmospheric CO2 at Northern Hemisphere stations is independent
of the different periods of data used for the assimilation. However, the
MPI-CCDAS consistently fails to resolve some of the features of the
year-to-year variability detected in the measured atmospheric CO2 stations, which translates into an acceptable, but far from perfect fit to
the inferred annual carbon budget of the GCP17 (Le Quéré et al.,
2018). We compared the performance to the results from an atmospheric
CO2 inversion (INV) with the same input fields and atmospheric
transport model as MPI-CCDAS to illustrate that these deviations do not
reflect uncertainties in the representation of the atmospheric transport. It
needs to be mentioned that both the choice of the atmospheric transport
model (and associated imperfections at resolving the vertical and lateral
atmospheric transport of CO2) and the method to aggregate atmospheric
observations to obtain an estimate of the annual growth rate in the global
carbon budget introduce some error in any forecast of the interannual
variability. As a consequence, only the occurrence of more significant
model–data mismatches can be interpreted as an actual result of the
MPI-CCDAS' inability to correctly resolve the carbon flux variation.
Notably, the model lacks the representation of some key processes that
contribute to climate-induced interannual variability of the carbon cycle,
such as the possibility to dynamically account for fire disturbance
(Lasslop et al., 2014), tropical peatland fires related to El Niño–Southern Oscillation (ENSO)
(van der Werf et al., 2008), or the increase
in terrestrial carbon uptake after large-scale volcanic eruptions such as
for Mt. Pinatubo in 1991 (Lucht et al., 2002;
Mercado et al., 2009). Omitting fluxes in the current model configuration
due to fire events may impair the ability of the model to infer the
atmospheric growth rate of CO2 associated with El Niño events
(Frölicher et al., 2011, 2013). One way to
overcome the IAV mismatch would be to include fire fluxes in the model by
prescribing them from the Global Fire Emissions Database
(GFED; van der Werf et al., 2010); however the
latest version of this data set (version 4.0) is only available for years
from 1997, which is a limiting factor for the timeframe of the simulations in
this work. However, the contribution of these interannual variations to the
overall CO2 cost function is low in comparison to the signal contained
in the seasonal cycle and deviations in the long-term trend, such that the
MPI-CCDAS may simply not be sensitive enough to these aggregate system
properties like the response of the tropical carbon cycle to El Niño
events given the uncertainty in the atmospheric transport and the
observational representation error.
Carbon cycle simulation
Independent of the amount of data used in the assimilation window, our
results show that the GPP and NEE were consistently reduced globally
compared to the prior run, i.e., less carbon uptake by plants leading to the
model results being in closer agreement with other independent estimates such
as the GCP17. The MPI-CCDAS suggests a somewhat lower average annual
atmospheric CO2 growth rate (calculated by the sum of the net C fluxes
from the ocean, land, and fossil fuel emissions) than the one estimated in
the GCP17 (Le Quéré et al., 2018), even if the MPI-CCDAS estimate
falls within the uncertainty of the GCP17 (Figs. 7 and S7). Most of the
difference stems from small differences in the assumed fossil and ocean
carbon fluxes. In the case of the carbon fluxes from fossil fuels, the data
prescribed in MPI-CCDAS do not contain fluxes due to cement and
flaring; thus the magnitude of the annual carbon sources through the time
series is consistently lower but still within the ±5 % uncertainty
of the GCP17 data (Le Quéré et al., 2018) (Fig. A4). As for the
ocean carbon sink, the annual mean values prescribed in MPI-CCDAS are also
of lower magnitude than the mean value in the GCP17 but falling in the lower
limit of the uncertainty value (Figs. A4c and S7). The flux due to LULCC
prescribed in MPI-CCDAS is also of smaller magnitude than that from the
GCP17 because the simulation made by JSBACH 3.0 does not consider
disturbances like fires and gross transitions, which might have also
contributed to the lower land C sink obtained in the assimilation
experiments compared to the total land C sink in GCP17 (Fig. A4d).
The MPI-CCDAS GPP matches well the observation-based product MTE-GPP
(Jung et al., 2007) in regions with a distinct light-
and temperature-driven seasonal cycle (i.e., north of approx. 30∘ N), translating to a reduction in modeled GPP by 0.7 PgC yr-1 in
boreal regions. However, similar to the results in Schürmann et al. (2016) with only 5 years of assimilation, the tropical productivity is
strongly reduced by the assimilation to estimates that are substantially
lower than independent estimates such as MTE. This feature is likely the
result of a global compensating effect on heterotrophic respiration, and
this effect is observed in the drop of the photosynthetic capacity
(fphotos) in the tropical evergreen and deciduous PFTs, as well as in
the reduction of the maximum tropical LAI in the three assimilation
experiments compared to the prior. In addition, another critical factor
influencing the global reduction of GPP and the tropical uptake of C appears
to be related to the difference in data availability of CO2 stations
between the defined assimilation windows. Specifically, this is evident in
the results of the data-poorer experiment DEC1, where the topical GPP is
substantially lower than in the independent estimates and in the
assimilation experiments that use more stations (DEC2 and ALL). As a result,
the mean tropical land C source to the atmosphere in the prior experiment
(mean NBE value of 0.12 PgC yr-1, and minimum value of -0.07 PgC yr-1, reflecting C uptake in the 4∘ S latitudinal band)
was increased to 0.37±0.17 PgC yr-1 on average for all the
posterior results.
The NPP:GPP ratio in ALL and DEC2 decreased to 0.35 and 0.31, respectively,
when compared to the prior value (0.45). This reduction might be mainly
because the NPP is not well constrained from the atmospheric record. In
JSBACH 3.0, autotrophic respiration (Ra) is directly coupled to GPP; hence
the fraction of GPP partitioned to Ra leads to an increase in the seasonal
cycle of the ecosystem respiration. An increase in Ra with respect to the
prior (which is only visible in the global average value in DEC2; Table 3)
results in a reduced net land carbon uptake, masking the smaller changes in
the vegetation turnover.
The reduction in the soil C pool after the assimilation can be explained due
to an unavoidable effect in the model. The MPI-CCDAS was initially spun up
until the soil C pools reached equilibrium considering preindustrial
forcing; however, this new initial state does not consider climate
variability. To compensate for this and to reduce the respiration when the
MPI-CCDAS is confronted with contemporary changes in the climate, the model
creates an artificial C sink that leads to a reduction in the soil C stocks.
It is important to note that the JSBACH 3.0 version used in this MPI-CCDAS
does not include permafrost processes; therefore, the global soil C stock
might still be underestimated.
Value of long-term data sets to constrain CCDAS
Notwithstanding the MPI-CCDAS conceptual issues, the setup of this study
enables us to test by how much the quality of the data–model agreement is
reduced after exposing the MPI-CCDAS to shorter observational time series.
In terms of FAPAR, there is no apparent degradation of fit with time,
despite that in general terms, the trend in the data is best matched with
the ALL experiment. This is foremost a consequence of comparatively small
trends in the observed FAPAR, implying that extracting the mean seasonal
patterns and amplitude for a few years is essential for simulating current
and near-term FAPAR. Issues with model structure and with the assimilation
setup prevent a better model–data fit irrespective of the length of the
record. This would suggest that a focus of assimilation on high-quality and
highly spatially resolved FAPAR should be a priority over the use of
long-term data sets. The results are different for the case of projecting
atmospheric CO2, where a long record of atmospheric CO2
measurements favorably contributes to a better representation of the
long-term values after the assimilation, whereas a shorter window leads to
deviations to the observations in the periods outside the assimilation
years. The model–data agreement is of approximately ±0.5 ppm during
the assimilation period and starts to deviate for the DEC1 experiment later
than 10 years after the end of the assimilation window, whereas in the DEC2
experiment, the degradation of the model–data match already starts after
approximately 5 years. Still, the average deviation from the observations by
using shorter assimilation periods is not far from the upper limit
of the uncertainty when using the longest record. Nonetheless, with the
caveat that MPI-CCDAS does not fully explain the interannual variability of
the land net carbon flux, this suggests a reasonable short-term (for a small
number of years) forecasting skill of atmospheric CO2.
Conclusion
The MPI-CCDAS is capable of simultaneously integrating two independent
observational data sets over three consecutive decades at the global scale
to estimate global carbon fluxes. The results demonstrate that assimilating
only 1 decade of observations, for two observational data sets (FAPAR and
atmospheric CO2 concentrations), leads to broadly comparable results
and trends in the global carbon cycle components than using the entire time
series of available observations (30 years). Currently, the system can
confidently predict the carbon fluxes on short timescales (up to 5 years
after the end of the window of assimilation), e.g., for atmospheric CO2
concentrations at the site level, and the mean prediction remains within the
uncertainty of the observations. However, long-term forecasts with CCDAS are
less certain, as the observational record does not sufficiently constrain
the interannual variability of the simulated land carbon fluxes and
longer-term changes in the decadal net carbon uptake. Nevertheless, the
comparatively small error of 2±1.3 ppm after 15–19 years of
prognostic simulation shows the potential for midterm carbon cycle
predictions constrained using the CCDAS approach.
The MPI-CCDAS is a computationally expensive system, and the demonstration
that large-scale carbon fluxes can be improved by only using a limited
period of observations increases the feasibility of using data assimilation
systems to constrain the land carbon budget in land surface models. However,
we also show that there are considerable variations in the estimated
parameters and the regional distribution of the land C uptake, suggesting
that further improvements in the land surface model, especially in the
current structure and design, must be first solved to improve the model and
computational efficiencies of the system. This is recommended
before an attempt to include another observational stream or other
modifications aiming to test an enhancement on the prognostic skill in the
full MPI-CCDAS.
Code availability
The code of the JSBACH model is available upon request to Sönke Zaehle
(szaehle@bgc-jena.mpg.de). The TM3 model code is available upon request to
Christian Rödenbeck (christian.roedenbeck@bgc-jena.mpg.de). The TAF-generated
derivative code is not available and it is subject to license restrictions.
Assimilation performance
Figure A3 depicts the final posterior value (Xf) for each optimization
parameter I and in each assimilation experiment. The last parameter value is
normalized to its corresponding prior value (Xp, shown in Table 1), i.e.,
(Xf/Xp)-1; this is done to make a comparison between parameters on
their response and the assimilation because each parameter holds a different
range of values. The normalized result is also shown for each phenotype for
the phenology and photosynthesis-related parameters, and also for the
initial leaf growth rate (ξ), CO2 initial offset, and land carbon
turnover parameters that are applied globally.
More significant changes in some phenology parameter values are observed;
e.g., the maximum LAI (Λmax) decreased in almost all PFTs and
in all experiments, except for the phenotypes CE (coniferous evergreen) in
the ALL experiment and ETD (temperate broadleaf evergreen and deciduous; mostly
dominating in Europe and the eastern USA and Asia). In CD (coniferous–deciduous
trees; located in northeast Asia, specifically in the east Siberian taiga)
the Λmax value increased notably in the DEC1 and DEC2
experiments (Fig. A3e).
In the tropical forest areas, the reduction of the Λmax was
from 3.17 in the prior experiment to 2.27 (33 %) in ALL for the TW area,
and from 3.27 in the prior to 2.43 (26 %) in ALL for the TE area. For the other assimilation experiments the average maximum LAI
moderately decreased in TW from 3.17 in the prior to 2.89 (8.8 %) in DEC1
and from 3.17 in the prior to 3.00 (5.3 %) in DEC2.
In other extratropical areas results from experiments DEC1 and DEC2
experienced an average increase in Λmax by 5.6 % in BE
(from 2.29 in the prior to 2.42), 24 % in BW (from 1.62 in the prior to
2.01), and 3.8 % in TNW (from 3.11 in the prior to 3.23).
Data availability and latitudinal location of the 28 stations
where the long-term flask measurements of atmospheric CO2 mole
fractions were taken for assimilation in CCDAS. The ALL experiment used all the
stations of the time series (blue and red bars) (1980–2010), DEC1 used data
only from stations with blue bars (1980–1990), and DEC2 also used the data
at the stations with red bars (1990–2000) (except stations SBL and CRZ
marked with a patterned bar).
Experimental setup for posterior experiments ALL, DEC1, and
DEC2 with different temporal windows for the assimilation of FAPAR and molar
fractions of atmospheric CO2.
Final value for each parameter p at the end of the assimilation
experiments, normalized to the prior value (ppr), i.e.,
(p/ppr)-1. This is shown for each model plant functional type (a–h)
and globally for the land C turnover parameters (i, j).
As a result, the temperature- and daylight-related parameters were modulated
such that the largest decrease with respect to the prior value in the
temperature at leaf onset (Tφ) was also observed for these two
PFTs, especially for CD in the DEC1 and DEC2 experiments. Also, the day
length at leaf shedding (tc) and the timescale of leaf senescence (leaf
shedding timescale, 1/τ1) primarily increased for CD. As for the
PFTs influenced by temperature and water (TeH, TeCr, TrH, and TrCr), the
most significant change with respect to the prior value took place in the
posterior value for the C3 crops (TeCr; distributed in Europe, the USA, and East
Asia) whose value decreased considerably for the water stress tolerance
(τw) in experiments DEC1 and DEC2, whereas the value of the
timescale of leaf senescence (leaf shedding timescale, 1/τ1) also
increased considerably for the same experiments. These changes seemed to be
a response of the large decrease in the foliar area Λmax for
this PFT, which took place in all three experiments. The value of the
photosynthesis rate modifier (fphotos) influences the productivity at
leaf level. Thus, a lower value of fphotos will lead to lower GPP (less
carbon uptake and a potential increase in NEE). Our results show that after
the assimilation experiments the value of fphotos decreased with respect
to the prior experiment, mainly for the C3 grasses and pasture (TeH;
distributed mostly in the Northern Hemisphere) as well as for the tropical
evergreen and deciduous trees (TrBE and TrBD), and this is more noticeable
in the DEC1 experiment.
Regional mean and maximum leaf area index in prior and posterior
experiments.
As for the global parameters, significant deviations from the prior value
are observed in the parameter that controls the initial size of the slow
soil C pool (fslow) and also in the parameter that defines the initial
atmospheric CO2 mole fraction (CO2offset), which is globally set to
be constant. The posterior value of both of these parameters decreased in
the three posterior experiments. Variations in fslow induce changes in
the global heterotrophic respiration, controlling in this way the
disequilibrium between GPP and the ecosystem respiration. Because JSBACH
tends to overestimate the soil C pool, optimizing fslow is a means to
improve this estimation; however, the spatial distribution of the carbon
pools remains unchanged, and the prior value controls the prior value,
meaning that the GPP and ER relation remains similar in the posterior
experiments to that in the prior experiment. Since the magnitude of the
initial slow carbon pool was set, this might influence the other modeled
carbon pools compared to the soil carbon pool, leading to biased soil and vegetation
carbon stocks; therefore, the assessment on the predicted pools should be
made with care. We compare the resulting global total soil and vegetation
carbon pools robustly to independent estimates from the literature or other
products, and results are shown in the main text of the Discussion section.
Pixel-level phenology analysis
The FAPAR analysis at the pixel level shows that in pixels P1 (located in
eastern Siberia), P2 (located in eastern Brazil), and P6 (located in
Canada), the magnitude of the mean seasonal cycle is better represented when
compared to the observations (Fig. S4). Also, the timing of the mean
seasonal cycle is corrected, e.g., in pixels with large seasonal amplitude
such as in P1 and in P6. While in the prior experiment (and ALL experiment)
the onset and peak of the growing season in P1 and P6 are delayed by up to
2 months, in the results from experiments DEC1 and DEC2 this delay is
reduced to only 1 month. This correction might be partially due to changes
in some optimized parameters: increase in the day length at leaf shedding
(tc) and reduction in the temperature at leaf onset Tφ detected
for both the CE and CD, as well as for ETD, TeCr, and TeH phenotypes (Fig. A3c, d, e, and g); this is because these parameters control the onset
and end of the vegetation activity. Despite changes in Tφ and
tc after the assimilation in TrH, this temporal shift is less evident in
P2. In this pixel, the amplitude of the seasonal cycle is small, and only
changes in the magnitude of the amplitude are visible after the assimilation
(Fig. S4). In the results of DEC1 and DEC2 for pixel P3 (located in the USA and
dominated by TeCr), the water stress tolerance time (τw) and
Tφ were primarily reduced, whereas the leaf shedding timescale
(1/τl; earlier shedding) increased.
Time series of the annual mean of the major components of the C
cycle used as background fluxes in CCDAS compared to those from the GCP
2017. The atm. CO2 growth from the model output is the result of the
sum of fossil fuel, ocean, and land C fluxes. The blue shadow in the ocean C
sink of the GCP 2017 data is the standard deviation of the mean sink from
the models that contributed to the GCP. The land C flux is the total NEE
with contribution of the flux due to LULCC. The green shadow area is the
standard deviation of the mean land C flux from the terrestrial models that
contributed to the GCP.
The supplement related to this article is available online at: https://doi.org/10.5194/bg-16-3009-2019-supplement.
Author contributions
This paper was prepared with contributions of all authors: MH and SZ
contributed in conceiving and developing the MPI-CCDAS; GS and CK developed
the model code; GS, SZ, and KCM designed the study; CR provided the
inversion results; KCM ran the model experiments and designed the figures;
KCM and SZ analyzed the results. KCM wrote the paper with comments
from all authors.
Competing interests
Sönke Zaehle is an associate editor for Biogeosciences.
Special issue statement
This article is part of the special issue “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) (AMT/ACP/BG/CP/ESD inter-journal SI)”. It is a result of the 10th International Carbon Dioxide Conference, Interlaken, Switzerland, 21–25 August 2017.
Acknowledgements
The authors thank Philippe Peylin for providing the fossil fuel
emission data and Tea Thum for the constructive comments during the
preparation of the paper.
Financial support
This research has been supported by the European Space Agency (STSE Carbonflux, grant no. 4000107086/12/NL/Fv0), the European Commission (GEOCARBON, grant no. 283080), and the Max-Planck-Gesellschaft (ENIGMA project).The article processing charges for this open-access publication were covered by the Max Planck Society.
Review statement
This paper was edited by Fortunat Joos and reviewed by Mathew Williams and D. Schimel.
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