This study assesses the impact of different
state of the art global biospheric
Carbon dioxide (
An alternate approach to estimate biospheric
In addition to the variables listed above, the assumed prior fluxes and the
associated prior error covariance can also impact top-down
global/regional
Therefore, during this study we conduct a series of controlled experiments
to quantitatively assess the impact of assumed prior fluxes and prior
uncertainty on global and regional
To quantify the impact of prior model NEE predictions on posterior estimates
of biospheric
NEE is the net difference of gross primary production (GPP) and total
ecosystem respiration (
CASA is an ecosystem model predicting NPP based on light use efficiency and
The SiB-4 model was developed at Colorado State University (Sellers et al.,
1986; Denning et al., 1996), with details of the newest versions described in
Haynes et al. (2013). This model is a mechanistic, prognostic land surface
model that integrates heterogeneous land cover, environmentally responsive
prognostic phenology, dynamic carbon allocation, and cascading carbon pools
from live biomass to surface litter and soil organic matter (Haynes et al.,
2013; Baker et al., 2013; Lokupitiya et al., 2009; Schaefer et al., 2008;
Sellers et al., 1996). By combining biogeochemical, biophysical, and
phenological processes, SiB-4 predicts vegetation and soil moisture states,
land surface energy and water budgets, and the terrestrial carbon cycle.
Rather than relying on satellite input data, SiB-4 fully simulates the
terrestrial carbon cycle by using the carbon fluxes to determine the above-
and belowground biomass, which in turn feed back to impact carbon
assimilation and respiration. Similar to NASA-CASA, the SiB4 model
redistributes crop harvest
The LPJ model is a process-based dynamic global vegetation model (Sitch et
al., 2003; Polter et al., 2014). The LPJ-wsl dynamic global vegetation model
(Sitch et al., 2003) was used to simulate NEE using meteorological data from
the Climate Research Unit (Harris et al., 2013). LPJ is fully prognostic,
meaning that the establishment, growth, and mortality of vegetation are
represented by first-order physiological principles. The model includes nine
plant functional types distinguished by their phenology, photosynthetic
pathway, and physiognomy. Phenology status is determined daily and
photosynthesis is estimated using a modified Farquhar scheme (Haxeltine and
Prentice, 1996). NPP is calculated from photosynthesis after accounting for
In order to provide a “true” (hereafter quotation marks will be omitted) NEE flux for the OSSEs conducted in this study (Sect. 2.4), we use the multi-model ensemble NEE mean from the Multiscale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) (Huntzinger et al., 2013, 2018; Fisher et al., 2016a, b). The MsTMIP NEE fluxes are from a weighted ensemble mean of 15 biosphere models (Schwalm et al., 2015) for the year 2010. Here we apply the MsTMIP data for the year 2010 as the “truth” (hereafter quotation marks will be omitted) with year-specific prior model predictions for 2015. This procedure is justified in our case as within an OSSE framework there needs to be a difference between true and prior fluxes, as long as the true values are realistic in nature. The MsTMIP ensemble NEE mean represents a summary over all 15 models which smooths out errors particular to any given model. This true NEE flux is used to produce the synthetic OCO-2 observations applied in this study (described further in Sect. 2.4.3).
The true and four prior model NEE fluxes were regridded from their
native horizontal resolutions to the grid resolution of the inverse model
simulations (4.0
Prior and (posterior) global annual mean NEE fluxes and
The GEOS-Chem chemical transport model (CTM) (Bey et
al., 2001) used in this study has the capability to run forward
To simulate concentrations of atmospheric
Summary of the different OSSEs conducted during this work.
This study conducted several OSSEs to assess the impact of prior biospheric
We use identical initial atmospheric concentrations of
In this study, we used synthetic satellite data that are directly
representative of version 8 of the OCO-2 product. The OCO-2 satellite sensor
is in sun-synchronous polar orbit with a repeat cycle of 16 days and a local
overpass time in the early afternoon (Crisp et al., 2017). OCO-2 has three
different viewing modes: soundings over land from LN and LG and over oceans
from OG. The algorithm from O'Dell et al. (2012) is used to retrieve
column-average dry air mole fraction of
The transport of atmospheric
For each iteration, the inversion system uses the forward model simulated
profiles of
For a perfect optimization, the prior error covariance matrix (
As described in Sect. 2.4.3, the synthetic
During this study, the posterior NEE values from the OSSEs are compared to the true fluxes to assess accuracy and are also intercompared to assess the spread in posterior estimates due to the assumed prior NEE and prior error statistics. The primary statistical parameters used to evaluate the spread in posterior NEE fluxes are the SD (hereafter the term “spread” will be used to represent SD) and range (difference between maximum and minimum in NEE). The SD/spread and range of posterior NEE estimates, when using the different prior models, will provide an understanding of the spatiotemporal residual impact of the prior models in top-down estimates of global/regional NEE fluxes when assimilating OCO-2 data.
In order to evaluate the spatiotemporal variability of prior and posterior regional NEE fluxes, we aggregate individual model grid boxes to the TransCom-3 land regions (TransCom-3 regions illustrated in Fig. 1). To further interpret the OSSE results, we produce additional classifications of three broad hemisphere-scale TransCom-3 land regions: northern land (NL), tropical land (TL), and southern land (SL). TL includes tropical South America, North Africa, and tropical Asia; SL includes South American temperate, South Africa, and Australia; and NL includes the other five land regions. The evaluation of the SD and range of prior model and posterior/optimized NEE fluxes were calculated for the 11 individual TransCom-3 regions, for the 3 hemisphere-scale TransCom-3 regions, and globally. Throughout the paper, seasonally averaged prior and posterior NEE fluxes will be discussed and these seasons are presented with respect to the Northern Hemisphere.
The TransCom-3 land region boundaries used to aggregate
In order to test our OSSE framework, we first run four “pseudo” (hereafter quotation marks will be omitted) experiments by conducting inverse modeling studies using pseudo surface
observations. These test OSSE simulations were conducted for 5-month
assimilation windows for two separate seasons, November 2014 to March 2015
(analysis for winter, DJF) and May to September 2015 (analysis for
summer, JJA), using all four prior model NEE values separately. Simulated
hourly concentrations of
Figure 2 shows the seasonally averaged multi-model-mean and SD of the NEE
fluxes from the four prior biosphere models used in the OSSE simulations
(individual prior model and true seasonally averaged NEE fluxes are
displayed in Fig. S4). This figure shows the main features of NEE that are
expected, such as the Northern Hemispheric fall/winter maximum in
Data corresponding to Fig. 6. Seasonally averaged NEE (PgC yr
Prior multi-model (NASA-CASA, CASA-GFED, SiB-4, and LPJ biosphere
models) seasonally averaged NEE (gC m
Table 3 displays the statistics of the prior NEE multi-model-mean and SD and
range for the 11 individual TransCom-3 land regions. The SD values for prior
NEE fluxes range from
Figure 4 shows the number of observations sampled in the OCO-2 LN and LG modes during the different seasons of 2015 summed in each model grid box. Large spatiotemporal variability can be seen in the OCO-2 observation density, with the largest values over regions with minimal cloud coverage (e.g., desert regions of North/South Africa, Middle East, Australia, and so on.). The opposite is true for many tropical regions (e.g., Amazon, central Africa, tropical Asia, and so on.) where cloud occurrence is prominent and the number of OCO-2 observations is lowest. From Fig. 4 it can also be seen that the OCO-2 observation density has noticeable seasonality. For example, during the winter months low numbers of OCO-2 observations are made in the northern boreal regions and the largest amounts are observed during the summer. Furthermore, larger numbers of OCO-2 observations are made in the SL during the summer (JJA) compared with other seasons.
Seasonally averaged NEE range (gC m
Total number of OCO-2 LN and LG
The seasonally averaged multi-model-mean GEOS-Chem simulated
From Table 1 it can be seen that annual global mean posterior NEE flux, when
using the different prior models and assimilating synthetic LN
Figure 3 shows the spatial distribution of the range of prior and posterior
NEEs. As expected, the range in optimized posterior NEE flux estimates
starting from the four separate prior models was substantially reduced
compared with the spread in prior NEE fluxes. However, the posterior NEE
fluxes for individual surface grid boxes of the model still depict some
residual range among the posteriors, with the largest residuals being found
across South America and South Africa in all seasons and in temperate
regions of the Northern Hemisphere in the spring months. As shown in Fig. 3,
the geographical pattern of the range of prior and posterior NEEs does not
indicate any noticeable correlations. From comparing Figs. 3 and 5, it is
apparent that the spread in posterior
Seasonally averaged
Figure 6 shows the seasonally averaged true, prior, and posterior NEE
flux values for the 11 individual TransCom-3 land regions (with detailed
statistics in Table 3 and monthly mean time series in Fig. S6). The first
thing noticed from this figure is that all posterior NEE values, using
variable priors, tend to reproduce the truth in most TransCom-3 land
regions. From Fig. 6 it can also be seen that the assimilation of synthetic
OCO-2 LN
Seasonally averaged NEE averaged over the 11 TransCom-3 land regions from MsTMIP (truth) vs.
the prior biosphere models (NASA-CASA, CASA-GFED, SiB-4, and LPJ) (left
column), posterior estimates (middle column) from the OSSE simulations, and
the corresponding range of prior and posterior NEE estimates (right column).
The synthetic observations in these OSSE simulations correspond to the OCO-2
LN
Results of this study have demonstrated the sensitivity of posterior NEE
estimates to prior NEE flux assumptions. In this section, the sensitivity of
posterior NEE estimates to the assumed prior uncertainty is tested, when
assimilating synthetic OCO-2 LN
Seasonally averaged NEE averaged over the 11 TransCom-3 land regions from MsTMIP (truth) vs.
CASA-GFED prior biosphere model (left column), posterior estimates with the
three different prior uncertainties (middle column), and the corresponding
range of posterior NEE (right column). The synthetic observations in OSSE
simulations correspond to the OCO-2 LN
Seasonally averaged NEE averaged over the 11 TransCom-3 land regions from MsTMIP (truth) vs. the prior biosphere models (NASA-CASA, CASA-GFED, SiB-4, and LPJ) (left column), posterior estimates (middle column) from the OSSE simulations, and the corresponding range of prior and posterior NEE (right column). The synthetic observations in these OSSE simulations correspond to the OCO-2 OG observing mode. Detailed statistics of the truth, multi-model means of prior and posterior NEE estimates, standard deviations, and ranges displayed in this figure are listed in Table S2.
This portion of the study investigates the impact of assimilating OCO-2 OG
To the best of our understanding, this is the first study directly
quantifying the impact of different prior global land biosphere models on
the estimate of terrestrial
We found that the assimilation of synthetic OCO-2
There have been previous studies that have investigated similar scientific
objectives, such as the impact of prior uncertainties on inverse model
estimates of NEE (Gurney et al., 2003; Chevalier et al., 2005; Baker et al.,
2006a, 2010). The sensitivity of
As explained earlier in this study, estimates of surface
The results of this study suggest the need to be aware of the residual
impact from prior assumptions for
The forward and inverse model simulations for this work were performed using the GEOS-Chem model which is publicly available for free download at
The supplement related to this article is available online at:
SP and MJ designed the methods and experiments presented in the study and analyzed the results. CP, VG, DB, KH, and BP were instrumental in providing biosphere model and OCO-2 data and guidance when applying these products. DH, JL, and DB provided components implemented in the modeling framework applied during this study. Finally, SP prepared the paper with contributions from all coauthors.
The authors declare that they have no conflict of interest.
Resources supporting this
work were provided by the NASA High-End Computing Program through the NASA
Advanced Supercomputing Division at the NASA Ames Research Center. We thank the
OCO-2 Science Team for providing the version 8 OCO-2 product. We also thank
the OCO-2 Flux Inversion Team, the GEOS-Chem model developers, the CASA-GFED team,
and the NASA Carbon Monitoring System program for the free availability of their
products. CarbonTracker CT2016 prior and posterior ocean fluxes were
provided by National Oceanographic and Atmospheric Administration's Earth
System Research Laboratory, Boulder, Colorado, USA, from
Sajeev Philip's research was supported by an appointment to the NASA Postdoctoral Program at the NASA Ames Research Center, administered by the Universities Space Research Association under contract with NASA. Sajeev Philip acknowledges partial support from the NASA Academic Mission Services by the Universities Space Research Association at the NASA Ames Research Center. Daven K. Henze recognizes support from National Oceanic and Atmospheric Administration (grant no. NA14OAR4310136).
This paper was edited by Joshua Fu and reviewed by four anonymous referees.