• 대기
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• 제22권4호
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• pp.415-427
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• 2012

$CO_2$ is the most important trace gas related to climate change. Therefore, understanding surface carbon sources and sinks is important when seeking to estimate the impact of $CO_2$ on the environment and climate. CarbonTracker, developed by NOAA, is an inverse modeling system that estimates surface carbon fluxes using an ensemble Kalman filter with atmospheric $CO_2$ measurements as a constraint. In this study, to investigate the capability of CarbonTracker as an analysis tool for estimating surface carbon fluxes in Asia, an experiment with a nesting domain centered in Asia is performed. In general, the results show that setting a nesting domain centered in Asia region enables detailed estimations of surface carbon fluxes in Asia. From a rank histogram, the prior ensemble spread verified at observational sites located in Asia is well represented with a relatively flat rank histogram. The posterior flux in the Eurasian Boreal and Eurasian Temperate regions is well analyzed with proper seasonal cycles and amplitudes. On the other hand, in tropical regions of Asia, the posterior flux does not differ greatly from the prior flux due to fewer $CO_2$ observations. The root mean square error of the model $CO_2$ calculated by the posterior flux is less than the model $CO_2$ calculated by the prior flux, implying that CarbonTracker based on the ensemble Kalman filter works appropriately for the Asia region.

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• 제27권3호
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• pp.301-316
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• 2017

This study analyzes the spatio-temporal variability of terrestrial carbon flux and the response of land carbon sink with climate factors to improve of understanding of the variability of land-atmosphere carbon exchanges accurately. The coupled carbon-climate models of CMIP5 (the fifth phase of the Coupled Model Intercomparison Project) and CT (CarbonTracker) are used. The CMIP5 multi-model ensemble mean overestimated the NEP (Net Ecosystem Production) compares to CT and GCP (Global Carbon Project) estimates over the period 2001~2012. Variation of NEP in the CMIP5 ensemble mean is similar to CT, but a couple of models which have fire module without nitrogen cycle module strongly simulate carbon sink in the Africa, Southeast Asia, South America, and some areas of the United States. Result in comparison with climate factor, the NEP is highly affected by temperature and solar radiation in both of CT and CMIP5. Partial correlation between temperature and NEP indicates that the temperature is affecting NEP positively at higher than mid-latitudes in the Northern Hemisphere, but opposite correlation represents at other latitudes in CT and most CMIP5 models. The CMIP5 models except for few models show positive correlation with precipitation at $30^{\circ}N{\sim}90^{\circ}N$, but higher percentage of negative correlation represented at $60^{\circ}S{\sim}30^{\circ}N$ compare to CT. For each season, the correlation between temperature (solar radiation) and NEP in the CMIP5 ensemble mean is similar to that of CT, but overestimated.

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• 제28권2호
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• pp.113-121
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• 2018

In order to monitor greenhouse gases including $CO_2$, various types of surface-, aircraft-, and satellite-based measurement projects have been conducted. These data help understand the variations of greenhouse gases and are used in atmospheric inverse modeling systems to simulate surface fluxes for greenhouse gases. CarbonTracker is a system for estimating surface $CO_2$ flux, using an atmospheric inverse modeling method, based on only surface observation data. Because of the insufficient surface observation data available for accurate estimation of the surface $CO_2$ flux, additional observations would be required. In this study, a system that assimilates aircraft $CO_2$ measurement data in CarbonTracker (CT2013B) is developed, and the estimated results from this data assimilation system are evaluated. The aircraft $CO_2$ measurement data used are obtained from the Comprehensive Observation Network for Trace gases by the Airliner (CONTRAIL) project. The developed system includes the preprocessor of the raw observation data, the observation operator, and the ensemble Kalman filter (EnKF) data assimilation process. After preprocessing the raw data, the modeled value corresponding spatially and temporally to each observation is calculated using the observation operator. These modeled values and observations are then averaged in space and time, and used in the EnKF data assimilation process. The modeled values are much closer to the observations and show smaller biases and root-mean-square errors, after the assimilation of the aircraft $CO_2$ measurement data. This system could also be used to assimilate other aircraft $CO_2$ measurement data in CarbonTracker.

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• 제26권3호
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• pp.387-400
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• 2016

In this study, the atmospheric $CO_2$ concentrations estimated by CT2013B, a recent version of CarbonTracker, are compared with $CO_2$ measurements from the Comprehensive Observation Network for Trace gases by Airliner (CONTRAIL) project during 2010-2011. CarbonTracker is an inversion system that estimates surface $CO_2$ fluxes using atmospheric $CO_2$ concentrations. Overall, the model results represented the atmospheric $CO_2$ concentrations well with a slight overestimation compared to observations. In the case of horizontal distribution, variations in the model and observation difference were large in northern Eurasia because most of the model and data mismatch were located in the stratosphere where the model could not represent $CO_2$ variations well enough due to low model resolution at high altitude and existing phase shift from the troposphere. In addition, the model and observation difference became larger in boreal summer. Despite relatively large differences at high latitudes and in boreal summer, overall, the modeled $CO_2$ concentrations fitted well to observations. Vertical profiles of modeled and observed $CO_2$ concentrations showed that the model overestimates the observations at all altitudes, showing nearly constant differences, which implies that the surface $CO_2$ concentration is transported well vertically in the transport model. At Narita, overall differences were small, although the correlation between modeled and observed $CO_2$ concentrations decreased at higher altitude, showing relatively large differences above 225 hPa. The vertical profiles at Moscow and Delhi located on land and at Hawaii on the ocean showed that the model is less accurate on land than on the ocean due to various effects (e.g., biospheric effect) on land compared to the homogeneous ocean surface.

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• 제27권2호
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• pp.163-175
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• 2017

The global terrestrial ecosystems have shown a large spatial variability in recent decades and represented a carbon sink pattern at mid-to-high latitude in Northern Hemisphere. However, there are many uncertainties in magnitude and spatial distribution of terrestrial carbon fluxes due to the effect of climate factors. So, it needs to accurately understand the spatio-temporal variations on carbon exchange flux with climate. This study focused on the effects of climate factors, .i.e. temperature, precipitation, and solar radiation, to terrestrial biosphere carbon flux. We used the terrestrial carbon flux that is simulated by a CarbonTracker, which performs data assimilation of global atmospheric $CO_2$ mole fraction measurements. We demonstrated significant interactions between Net Ecosystem Production (NEP) and climate factors by using the partial correlation analysis. NEP showed positive correlation with temperature at mid-to-high latitude in Northern Hemisphere but showed negative correlation pattern at $0-30^{\circ}N$. Also, NEP represented mostly negative correlation with precipitation at $60^{\circ}S-30^{\circ}N$. Solar radiation affected NEP positively at all latitudes and percentage of positive correlation at tropical regions was relatively lower than other latitudes. Spring and summer warming had potentially positive effect on NEP in Northern Hemisphere. On the other hand as increasing the temperature in autumn, NEP was largely reduced in most northern terrestrial ecosystems. The NEP variability that depends on climate factors also differently represented with the type of vegetation. Especially in crop regions, land carbon sinks had positive correlation with temperature but showed negative correlation with precipitation.