Evaluation of Simulated CO2 Concentrations from the CarbonTracker-Asia Model Using In-situ Observati

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1. Introduction

Atmospheric carbon dioxide (CO₂) is a key greenhouse gas that causes global warming (IPCC, 2013). The increase of the CO₂ concentration, on average 2 ppm yr^-1 globally, is significantly related with contribution from human activities. In fact, the relative contribution of CO₂ to the atmosphere varies greatly with region. East Asia is an important region that emits a large amount of CO₂ into the atmosphere (Tian et al., 2016). On the other hand, the sink of CO₂ is predominantly controlled by terrestrial vegetation uptake via photosynthesis and oceanic uptake (Keeling et al., 1989; Hansen et al., 2007; Watson et al., 2011).
Accurate estimates of CO₂ sources and sinks are of great importance for validating carbon emissions reduction efforts and reducing the uncertainties of carbon cycle-climate feedbacks. Efforts have been undertaken to reduce the uncertainty of model estimations of fluxes through the assimilation of accurate observations of CO₂ data from the global network (Tans et al., 1990; Gurney et al., 2002). However, as previous studies point out, there are uncertainties in terms of the localization of sources and sinks on regional scales (e.g. Baker et al., 2006). Owing to less spatial coverage of accurate observations of CO₂ over East Asia, the regional CO₂ fluxes estimated from atmospheric inversions are still uncertain (e.g., Swathi et al., 2013). In addition, uncertainties in the planetary boundary layer height, or in horizontal winds, affect the modeled near-surface CO₂ concentrations (Gurney et al., 2002; Lin and Gerbig, 2005; Gerbig et al., 2008; Prather et al., 2008; Ahmadov et al., 2009). Therefore, continuous assessment and evaluation of model simulations of CO₂ concentrations against accurate in-situ observations is vital.
Recently, the nested-grid CarbonTracker (CT)-Asia model was run using versions CT2013B (Cheng et al., 2013) and CT2016 (documented at http://carbontracker.noaa.gov, Peters et al., 2007) by the National Institute of Meteorological Sciences (NIMS), Republic of Korea. In this simulation, in-situ continuous hourly CO₂ concentrations (including daytime and nighttime) at Ryori, Minamitorishima, Yonagunijima, and Tae-ahn Peninsula, which are close to Anmyeondo station, and CONTRAIL (Comprehensive Observation Network for Trace gases by Airliner) data were assimilated. This result could be used as an indicator for where the model needs improvement in order to estimate accurate fluxes through accurate estimates of flux error in the assimilation process.
In the present study, we investigated the spatiotemporal distribution of simulated near-surface CO₂ concentrations along with CO₂ fluxes from fossil fuel emissions and the biosphere. Then, we statistically evaluated the model's performance in reproducing diurnal and seasonal variations of near-surface CO₂ concentrations through comparison with in-situ observations over target stations. We also examined the wind speed and directions over the target stations during daytime and nighttime for both the winter and summer seasons.

3. Data and methods

3.1. CT inverse model

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3.1. CT inverse model

CT is an inverse atmospheric model developed by the National Oceanic and Atmospheric Administration Earth System Research Laboratory Global Monitoring Division (http://www.esrl.noaa.gov/gmd/ccgg/carbontracker). Here, we adopt a nested-grid CT inverse model with a horizontal resolution of 1^°× 1^° for the simulation of atmospheric CO₂ concentrations over East Asia. Model simulations were run by NIMS based on the CT2016 version. The model uses Transport Model 5 (Krol et al., 2005), forced by meteorological fields from ERA-Interim (Dee et al., 2011). The model gives four components of CO₂ signals, which respectively derive from fossil fuel emissions, air-sea CO₂ exchange, and terrestrial fluxes from wildfire emissions and non-fire net ecosystem exchange. The model uses the a priori information for surface CO₂ flux data for each module. The biosphere CO₂ flux is supplied by a biosphere model, CASA (the Carnegie-Ames Stanford Approach). In the fire module, CO₂ released by fire is taken from the Global Fire Emission Database, version 4.1s, at a three-hourly temporal resolution. We used two different fossil fuel CO₂ emissions datasets——namely, "Miller" and ODIAC (Open Source Data Inventory for Anthropogenic CO₂)——which were used to help assess the uncertainty in the mapping process. In the ocean module, prior estimates of air-sea CO₂ flux were determined from the Ocean Inversion Fluxes scheme, and the updated version of the (Takahashi et al., 2009) pCO2 climatology. Further details are provided in the CT document (CT2016 release, https:/www.esrl.noaa.gov/gmd/ccgg/carbontracker/CT2016_doc.php). While obtaining CO₂ signals from oceanic and terrestrial biospheric surface fluxes, we used data from Tae-ahn Peninsula, Minamitorishima, Yonagunjima, and Ryori for optimizing those fluxes in the assimilation process. The model uses an ensemble Kalman filter to estimate the surface CO₂ flux with atmospheric CO₂ measurements as a constraint (Peters et al., 2007, 2010).

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3.2. In-situ measurements

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3.2. In-situ measurements

In-situ measurements of CO₂ concentrations were taken using non-dispersive infrared (NDIR) absorption sensors and cavity ring-down analyzers (CRDS). Measurements derived from both instruments were used for evaluating the model's ability to simulate diurnal and seasonal variations of near-surface CO₂ concentrations. The accuracy of CO₂ measurements from those systems is typically better than 0.1 ppm (Andrews et al., 2014). All sites used in this study only have one air intake height. More information concerning the sites can be found at http://www.esrl.noaa.gov/gmd/ccgg/insitu/ data were obtained from the World Data Centre for Greenhouse Gases (WDCGG) (https://ds.data.jma.go.jp/gmd/wdcgg/cgi-bin/wdcgg/catalogue.cgi), which operates under the framework of the WMO's Global Atmosphere Watch. Simultaneous measurements of meteorological parameters, such as atmospheric temperature, wind speed and direction, and relative humidity, were also provided by the WDCGG. In this work, surface sampling stations were chosen at inland, coastal, mountainous, and remote sites (see Fig. 1 and Tables 1 and 2 for more information).

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3.3. Comparison method

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3.3. Comparison method

To evaluate the performance of the model's ability in simulating CO₂ concentrations through comparison with in-situ observations, we first applied temporal and spatial coincidence criteria over all selected stations. Model outputs were sampled at the nearest grid point and the model vertical level that corresponded to the in-situ inlet height of the stations. In fact, there were other methods/techniques that we could have applied for the spatial coincidence criteria, such as the nearest grid point method, interpolation with concentration slope or linear interpolation, and grid-averaging, but they were found to affect the correlations between the simulated and observed results, particularly at coastal and inland stations. This finding is consistent with (Patra et al., 2008). In evaluating the model's performance at seasonal time scales, we considered daytime (1330-1630 LST), nighttime, and all-hourly averaged data. In addition, we investigated the amplitude and phase of the seasonal cycle, and quantitatively determined the Pearson's correlation coefficient (R) between simulations and observations. Linear regression analysis was applied to examine how the data were spread over the linear fitting. We also assessed the model's ability to reproduce the diurnal variations of the observations by comparing the phase and amplitude of the diurnal cycle. The bias was expressed as the difference between simulation and observation.

4. Results and discussion

We compared the downscaled CT model-simulated atmospheric CO₂ concentration based on CT2016 versions with in-situ observations over East Asia. Here, we discuss the comparison of CO₂ at diurnal and seasonal time scales.

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4.1. Spatiotemporal distribution of CO₂ concentrations

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4.1. Spatiotemporal distribution of CO₂ concentrations

Here, we highlight the spatiotemporal variations of the simulated near-surface CO₂ concentrations and fluxes during the period 2009-13. The distribution of the multi-year seasonal mean near-surface CO₂ concentrations in East Asia shows significant spatial heterogeneity. The spatial and temporal variations of near-surface CO₂ concentrations were predominantly driven by the anthropogenic emissions, and by the variations of the biospheric CO₂, resulting from the seasonal phenomena of growth and decay of land vegetation, as well as atmospheric transport. The surface-level simulated CO₂ concentration was maximum in winter and minimum in summer. Figure 2 displays the simulated near-surface atmospheric CO₂ concentrations for each season over East Asia in the period 2009-13, while Fig. 3 shows the CO₂ fluxes derived from fossil fuels and biogenic emissions in different seasons. The seasonal spatial distribution of the simulated CO₂ concentrations had a consistent pattern with that of the spatial distribution of CO₂ fluxes from fossil fuel emissions and the biosphere over East Asia (20^°-51^°N, 90^°-150^°E). The hot spots of higher fossil-fuel fluxes (see Fig. 3, top panel) were located over a region encompassing the megacities of Korea, Japan, and eastern China, which is a recognized region of high CO₂ emissions (e.g., Ballav et al., 2012; Shim et al., 2013).

Figure2. Model-simulated seasonal mean near-surface CO₂ concentrations (units: ppm) during 2009-13. Plus signs denote the in-situ observation stations used in the study; DJF, MAM, JJA and SON denote the winter (December-February), spring (March-May), summer (June-August) and autumn (September-November) seasons, respectively.

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4.2. Diurnal cycle of CO₂

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4.2. Diurnal cycle of CO₂

Validating the amplitude of the diurnal variability of the model simulation through comparison with in-situ near-surface observations is vital, as the diurnal variability of near-surface CO₂ represents the sources, sinks, and related surface processes (e.g., Bakwin et al., 1998). The phenomena of photosynthesis and respiration, boundary layer dynamics, and pollution transport are key factors for the observed diurnal cycles of CO₂ concentrations near the surface (Bakwin et al., 1998). (Law et al., 2008) examined model simulations of CO₂ concentrations along with observational data and found that the diurnal amplitude errors were contributed by sampling choice in the vertical and horizontal directions, the model resolutions, and the land surface flux.
Figures 4 and 5 provide a comparison of the in-situ and model-simulated diurnal variations of atmospheric CO₂ concentrations during winter and summer over the course of the study period for the following four stations: Anmyeondo, Gosan, Ryori, and Kisai. The overall patterns of the diurnal cycles of the simulated CO₂ concentrations followed the observations during daytime, but with large discrepancy during nighttime. Pronounced diurnal cycles of CO₂ were present in summertime, with peaks at night and troughs in the afternoon. The bias, as shown in Table 2, was less than 6.3 ppm on the all-hourly mean basis, and this bias was further reduced to a maximum of 4.6 ppm (see Table 3) when considering only daytime.
Looking specifically at Anmyeondo and Gosan stations, the model result agreed well with observations in that both showed no distinct diurnal cycle. At Anmyeondo, there was an estimated small positive bias of the model simulation (0.2 ppm) against the in-situ observations during winter. In summertime, the bias was 3.6 ppm. Representation error is subject to flux gradients and the direction of land or sea breezes (Tolk et al., 2008). Although the flux gradient was high over Anmyeondo in winter (see Fig. 3), a small bias was estimated. As evident in the wind rose plot in Fig. S1 in the Electronic Supplementary Material (ESM), there was a strong influence of sea breezes bringing ocean airmasses containing depleted CO₂. We can infer that, to a large extent, such a phenomenon was captured well by the model.

Figure3. Model-simulated seasonal mean CO₂ flux (units: μmol m^-2 s^-1) (fossil fuels flux in the top panel and biospheric flux in the bottom four panels) during the period 2009-13. DJF, MAM, JJA and SON denote the winter (December-February), spring (March-May), summer (June-August) and autumn (September-November) seasons, respectively.

Figure4. Mean diurnal cycle of CO₂ in winter during 2009-13 (except for Gosan, which is in the period 2009-11). The blue curve shows the observed result and the black curve the model result. The 1-σ standard deviation is shown by the blue shading for the observations and by the black vertical lines for the model outputs. The red line represents the model-simulated CO₂ flux. Note: the scale of the y-axis is different.

On the other hand, the relative influence of local and regional emissions might have been strong in summer, since the prevailing wind directions were southwest and northwest, which brought airmasses rich with CO₂ from the land (see right-hand panels of Fig. S1 in the ESM). Furthermore, the standard deviation of the in-situ observations was higher than the model results, which reflects the influence of local emissions and sinks, typically playing a more important role in observed CO₂ concentrations at low wind speeds (Figs. S1, S2, and S3 in the ESM). Moreover, there was a strong terrestrial biospheric flux gradient around the station, where the signal was high compared to the corresponding nearest grid point. The bias was estimated to be 3.6 ppm in this particular season, as shown in Table 2.
Regarding Gosan station, located at the tip of the west coast of Jeju Island, it is too small to be captured by the resolution of the regional inverse model. This could also be a factor contributing to the differences between the simulation and observation. In summer, the in-situ observations depicted a large amplitude (peak-to-peak CO₂ concentrations) on the order of 8.3 ppm, while the model only produced an amplitude of 3.3 ppm, which was less than half that of the in-situ observations (see Fig. 5b).

Figure5. As in Fig. 4 but for the summer season.

Looking at Ryori and Kisai, the model exhibited a pronounced diurnal cycle in both winter and summer. The model exhibited good agreement in the afternoon at both stations, but a large discrepancy occurred at nighttime. This indicates that the model failed to simulate nocturnal CO₂ accumulations in both seasons. We can see almost comparable magnitudes of total simulated CO₂ flux (Figs. 5c and d) between 0000 and 0500 LST during summer at these stations. However, Kisai had a relatively elevated CO₂ concentration compared to Ryori. This might have been caused by underestimation of the nighttime boundary layer height and/or advection of CO₂ containing airmasses within this layer (Figs.S4 and S5 in the ESM). Some studies have examined the impact of planetary boundary layer height uncertainties on the modeled near-surface CO₂ concentrations during daytime, and estimated a bias of ～3 ppm (e.g., Kretschmer et al., 2012, Kretschmer et al., 2014), while uncertainties in the horizontal winds can induce a total CO₂ transport uncertainty of ～6 ppm (Lin and Gerbig, 2005). At night, the representation error due to unresolved topography will be amplified within the nocturnal boundary layer (Tolk et al., 2008).

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4.3. Seasonal variations of CO₂

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4.3. Seasonal variations of CO₂

Here, the ability of the model to reproduce the seasonal variations of CO₂ is evaluated through comparison with the in-situ observations at selected stations. The seasonal cycle amplitude, phase and bias were investigated. Examining the time offset of the seasonal cycle has important implications for flux estimates (Keppel-Aleks et al., 2012).
Figure 6 compares the seasonal cycle of the simulated CO₂ with the in-situ observations at selected stations. Daytime, nighttime, and all-hourly averaged data were considered separately. The in-situ data from Ryori, Yonagunijima, and Minamitorishima were assimilated in the CT2016 version, while the rest of the sites were independent of the CT2016 data assimilation system. Overall, the results for the seasonal cycle of the simulated CO₂ concentrations broadly agreed with the observations, with concentrations peaking in April and a minimum occurring in August/September. This result was found for all stations, when considering the daytime data. However, large discrepancies were identified when applying the nighttime data, in particular at Ryori, Kisai, and Shangdianzi. At Kisai and Shangdianzi, we quantified how well the model reproduced the seasonal variations when all daytime and nighttime data were incorporated; the correlation coefficients were 0.55 and 0.26 (Table 4 and Fig. 7), respectively. However, the result improved when considering only the daytime data, with correlation coefficients of about 0.84 and 0.71, and the biases were estimated to be -1.6±3.2 and 5.3±5.7 (1-σ) ppm (see Table 5 and Fig. 6). This suggests that the inclusion of nighttime and early morning data affected the seasonal comparison.

Figure6. Monthly time series of mean CO₂ concentrations for daytime, nighttime, and both day- and nighttime, during 2009-13 (except for Gosan, which is during 2009-11, and Shangdianzi, which is during 2010-13), as observed over selected sites in East Asia. Note that the in-situ monthly mean time series are not depicted specifically for daytime and nighttime at Lulin, Mt. Waliguan, and Shangdianzi.

Figure7. Model-simulated versus in-situ observed monthly mean (includes all hourly values) CO₂ concentrations during 2009-13 (except for Gosan, which is during 2009-11, and Shangdianzi, which is during 2010-13), as observed at nine selected sites in East Asia. The blue dashed line is the 1:1 fitting line. See Table 4 for the statistical results.

Despite the overall agreement between the simulation and observations at Anmyeondo, it is evident that the model slightly overestimated the CO₂ concentrations during summertime. As noted in section 4.2, the diurnal variations of the simulated CO₂ concentrations at Anmyeondo during summer were estimated to be higher than the in-situ measurements. A mean bias of 1.2 ppm was found, with a corresponding standard deviation of 4.0 (1-σ) ppm. The mismatch in the CO₂ seasonal amplitude indicates that the simulated CO₂ surface fluxes cannot capture the peak of the terrestrial carbon exchange (Yang et al., 2007).
Over the mountain stations, the seasonal cycles (Figs. 6g and h) indicated that the model overestimated the observations, with a pronounced phase difference. When applying the comparison method, the first vertical level in the model did not match with the in-situ inlet height, because the model resolution could not resolve the topography of such complex terrain accurately (see Fig. 1). Consequently, we selected the model vertical level that approximately represented the in-situ inlet height above sea level (i.e. the fifth vertical level for Mt. Waliguan and the eighth for Lulin). The slight mismatch in the model sampling vertical level with the in-situ inlet height may have had an impact on the phase difference and bias in the seasonal cycle, probably because of the timing difference in the transport process. For example, at Mt. Waliguan, the peak often occurred earlier than observed, whereas the minimum occurred later. (Fang et al., 2014) noticed the occurrence of seasonal CO₂ maximum periods fluctuate considerably, ranging from December at Longfengshan and Lin'an, to March for Shangdianzi and May for Mt. Waliguan. This difference is believed to be driven not only by regionally different terrestrial ecosystems and human activities, but also by local meteorological conditions (Zhang et al., 2008). At Mt. Waliguan and Lulin, we found a good level of agreement in capturing the variability of the observations, with correlation coefficients of 0.89 and 0.87, respectively. Both stations exhibited positive biases, with estimated values of 3.7 (Mt. Waliguan) and 3.9 ppm (Lulin). At remote stations like Minamitorishima and Yonagunijima, a very good level agreement was obtained, with a correlation coefficient of 0.99 and an RMSE less than 0.79.
We also determined the correlation coefficients for Anmyeondo, Ryori, Kisai, Lulin, and Mt. Waliguan, for each season (Fig. 8). Kisai revealed a poor correlation (R=0.29) in summer, whereas a good correlation was found in winter (0.84). Ryori showed similar results, with a correlation of 0.52 in summer and 0.96 in winter. This suggests that the model's performance in capturing the monthly variations for each season varies, with better representation of winter than other seasons. The model's ability in capturing the effect of terrestrial vegetation, particularly in the peak growing season, needs further improvement.

Figure8. Correlation coefficient (model versus in-situ observations) for the CO₂ concentration in each season at Anmyeondo, Ryori, Kisai, Lulin, and Mt. Waliguan during 2009-13.

5. Conclusions

The performance of a nested-grid CT model in simulating atmospheric CO₂ concentrations was assessed through comparison with in-situ observations over nine selected stations in East Asia during the period 2009-13. The evaluation was conducted in terms of diurnal and seasonal variations, in which the amplitude, phase differences and bias were examined. The diurnal cycles of terrestrial biospheric fluxes and the planetary boundary layer were the most likely deriving factors for the variations of surface level CO₂ concentrations.
Large discrepancies existed with regard to the diurnal cycle at night when comparing the observations and model results in winter and summer, as inferred from data at four stations (Anmyeondo, Gosan, Ryori, and Kisai). In general, the model's ability to reproduce the CO₂ diurnal cycle remains challenging. On the other hand, the model exhibited a very good level of agreement with observations in daytime at those target stations. Overall, biases were less than 6.3 ppm on an all-hourly mean basis, and this was further reduced to a maximum of 4.6 ppm when considering only daytime. For example, at Anmyeondo, a small bias was obtained in winter, on the order of 0.2 ppm, whereas in summertime the bias was higher. The observed large discrepancy at nighttime might be reduced by increasing the horizontal resolution and vertical levels.
In terms of seasonal variation, the level of agreement was judged by considering daytime, nighttime, and all-hourly averaged data. The overall performance of the model in reproducing the observed seasonal variations of CO₂ all-hourly averaged basis was good at most sites, with the exception of Ryori, Kisai, and Shangdianzi stations. The level of agreement was further improved at almost all stations when considering the comparison during daytime, with a correlation coefficient ranging from 0.70 to 0.99; however, the model performed poorly in capturing the peak drawdown of CO₂ during the summer season at Shangdianzi. Choosing the model sampling level that corresponded to the in-situ inlet height may have led to a small overestimation or underestimation of CO₂; however, this particular effect was observed mainly over mountainous areas.
The findings of this study highlight the strengths and weaknesses of the model in reproducing diurnal and seasonal variations of near-surface CO₂ concentrations. We recommend that continued efforts are made to evaluate the model's performance, particularly in capturing nighttime observations. Also, a series of model experiments are required in the future in order to explicitly quantify the biases in the simulated near-surface CO₂ concentrations on the diurnal time scale in relation to meteorological parameters (e.g., boundary layer height, wind speed and direction, relative humidity).
Electronic supplementary material. Supplementary material is available in the online version of this article at https://doi.org/10.1007/s00376-019-8150-x.

本站小编 Free考研考试/2022-01-02

Corresponding author: Samuel Takele KENEA,samueltake@yahoo.ca;

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3.1. CT inverse model

3.2. In-situ measurements

3.3. Comparison method

4.1. Spatiotemporal distribution of CO₂ concentrations

4.2. Diurnal cycle of CO₂

4.3. Seasonal variations of CO₂

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