Simulation of Contribution of Continental ... - Springer Link

ISSN 10248560, Atmospheric and Oceanic Optics, 2013, Vol. 26, No. 1, pp. 35–40. © Pleiades Publishing, Ltd., 2013. Original Russian Text © A.V. Ganshin, R.V. Zhuravlev, Sh.Sh. Maksyutov, A.N. Lukyanov, H. Mukai, 2013, published in Optica Atmosfery i Okeana.

ATMOSPHERIC RADIATION, OPTICAL WEATHER, AND CLIMATE

Simulation of Contribution of Continental Anthropogenic Sources to Variations in the CO2 Concentration during Winter Period on Hateruma Island A. V. Ganshina, R. V. Zhuravleva, Sh. Sh. Maksyutovb, A. N. Lukyanova, and H. Mukaib a

Central Aerological Observatory, ul. Pervomayskaya 3, Dolgoprudny, Moscow region, 141700 Russia b National Institute for Environmental Studies, 162 Onogawa, Tsukuba, Ibaraki, 3058506 Japan Received February 1, 2012

Abstract—Data of continuous CO2 observations in the zone affected by pollutants emitted on the territory of China are analyzed using the method for simulating the CO2 transport with the help of a combination of grid based and Lagrangian particle dispersion models of atmospheric pollutant transport, taking into account the data on surface fluxes of carbon dioxide of anthropogenic, biospheric, and oceanic origins. The results of the model calculations are compared with observations at the Hateruma station, Japan, and demonstrated good correspondence. We analyzed how emissions from pollution sources located on the territory of China affect the simulated time series. Calculations are performed with and without accounting for emissions on the ter ritory of China; they showed that anthropogenic emissions on the territory of China are the main contributors to CO2 variations at the observation site during winter period. The fact that regional anthropogenic emissions predominate in the observed signal indicates that the data from the Hateruma station can be effectively used for transport simulation in the region and for solution of inverse problems on interannual variations in anthropogenic emissions of CO2 and other gases. DOI: 10.1134/S1024856013010089

INTRODUCTION The analysis of data from a measurement station requires a modelbased tool, permitting exact and effi cient estimation of greenhouse gas concentrations and, in particular, CO2 concentration at the observa tion point. Usually, this task is solved using Eulerian models which make it possible to obtain concentration fields on a grid with a certain resolution. In this case, values at a subgrid observation point are obtained through the interpolation of grid data. The data thus obtained well reproduce the seasonal and interannual variations, but smooth out the diurnal and hourly vari ations in the concentrations. Comparison of CO2 transport models showed that the synopticscale vari ations are better reproduced by transport models with high spatial resolution and with higherorder approxi mations compared to middleresolution models [1]. Transition to a finer resolution reconciles model results and observational data; however, the exact structure is not reproduced [2] and, moreover, consid erable computer time is required. The Lagrangian models, which are based on the calculations of trajec tories of individual particles, can be efficiently used in problems of forward and backward transports to calcu late the responses to gas emission to the atmosphere [3, 4]. Moreover, models of this type reproduce short term variations; however, the description of seasonal variations requires long (a few months as long) trajec

tories, again leading to a large consumption of com puter time. In this regard, it seems quite logical to per form a 2step simulation of concentrations at the observation point, i.e., to combine models. This paper uses the Eulerian model NIES TM [2, 5] and Lagrangian particle dispersion model FLEXPART [6], combined to give the GELCA model, which was described in detail in paper [7]. The simulation at the Hateruma monitoring station (24.05° N, 123.80° E) was performed to further test the model and to demon strate its capabilities. This is a small island (12.5 km2 in area) and is the southernmost point of Japan 220 km away from Taiwan. Southerly winds predominate dur ing summer and northwesterly winds predominate during winter. In this regard, it is hypothesized that emissions in continental Asia have a strong effect on the concentration at this station. We will use this cou pled model to try to analyze why strong shortterm bursts exist in observational data. The observations at the station have been con ducted continuously and are accessible from 1993 [8]. The data for 2002 and 2006 were used in this paper. 1. DESCRIPTION OF COUPLED MODEL Three types of CO2 sources are used as surface fluxes of carbon dioxide: anthropogenic sources with monthly variations [9], CO2 exchange between bio 35

36

GANSHIN et al.

sphere and atmosphere with diurnal variations [10], and CO2 exchange between ocean and the atmosphere with monthly seasonal variations [11]. The model interpolates the specified fluxes with respect to time to the date required. The calculation in our approach is performed by the method described below. Initially, during the year, prior to the measurement time, the Eulerian model (NIES TM) is used to calculate the global background concentrations which exhibit the seasonal variations. Then, the Lagrangian particle dispersion model (FLEXPART) is run backward in time; this model acquires the contributions of local groundbased emissions to the observation point during this time period and determines the contributions to ultimate concentration at this point coming from background values calculated by the global model. Because of the absence of numerical diffusion, inherent in Eulerian models, FLEXPART provides lesssmoothed concen trations. The calculation of the background concentration for oneyear period is motivated by the socalled “spin up” effect, whose essence is that concentration fields at the initial time are set to zero in the model, the result being that concentration rapidly increases dur ing the first few months. During a oneyear period, the concentrations and fluxes arrive at a certain balance and, as such, make it possible to study the seasonal variations in the concentration. The calculation formulas, as well as the proper coupled model, were described in detail in work [7]; therefore, we will write down just the final formula in discrete form for the case of surface fluxes, which was implemented in the model: C(x r , t r ) =

Tmair hNLρ mCO2 IJK

+

1 N

IJ

L

∑∑ ∑ f ij l = 0

N

∑C ∑ f B ijk

ijk

N

l

Fij

n =1

ln ij

(1)

n ijk ,

n =1

where i, j, k are the indices which characterize the posi tion of a particle in a cell; l is the time index; n is the par B ticle index; Fijl are the surface fluxes in kg ⋅ m–2 ⋅ s–1; C ijk are the background concentration fields from the Eule rian model; f ijkn equals unity if the particle is within the cell i, j, k or, otherwise, it equals zero if the particle is outside the cell; T is the duration of the trajectory; L is the number of steps in time; N is the total number of particles; h is the height up to which the effect of the surface fluxes is considered significant; ρ is the average air density below the height h; and mair and mCO2 are the molar masses of air and carbon dioxide. The FLEXPART model starts at the observation point and calculates simultaneously 7day backward trajectories of a thousand air particles which are dispersed under the influence of turbulent diffusion. The grid back

ground values of the concentrations, which are inter polated to the final points of back trajectories, are transferred to the observation point and are the second term in the righthand side of formula (1). The first term in this formula is responsible for the contribution of sources of the component considered; these sources are located along the trajectories inside the layer h (500 m). Ultimately, the value of the first term is proportional to the flux in each cell along the trajectory and to the time during which the air particle is inside this cell. JCDAS data [12] were used as meteorological fields; these data were interpolated into the regular grid (1.25° × 1.25° resolution) keeping the vertical structure (40 model levels). 2. COMPARISON OF SIMULATIONS WITH COUPLED POLLUTANT TRANSPORT MODEL AGAINST OBSERVATIONAL DATA The algorithm that we described in section 1 can be equally successfully applied to analyze satellite, bal loon, aircraft, and station observations. This section presents the calculations of the CO2 concentration with the coupled model for the Hater uma station where CO2 and other greenhouse gases are regularly measured. Air samples were collected at a height of 51 m above the Earth’s surface. As paper [13] showed, NIES TM reproduces the seasonal behavior, but smooths out the diurnal and hourly variations. On the other hand, sevenday FLEXPART simulations reproduce hourly variations, but exhibit no seasonal behavior. Longer than three month trajectories are required to describe the sea sonal behavior with the help of Lagrangian models and this method; these longterm trajectories are prone to growing errors and require considerable computer time [14]. The simulations most closely correlate with observations when they employ the coupled model. One thousand particles were used in the calcula tions with our method, and this number was found to be optimal by comparing calculations using different numbers of particles. Increasing the number of parti cles by an order of magnitude (up to 10000) improves the results slightly but increases the required computer time many times. On the other hand, decreasing the number of particles to below 100 markedly worsens model data. The duration of FLEXPART back trajectories is varied as a function of the distance from the sources and sinks of the gas under consideration to the obser vation point. We examined how this correlation depends on the duration of the trajectories and found that twoday period is optimal for most stations. However, the dura tion of the trajectories depends on how close are the local sources to the observation point and, as such, may be different for different stations.

ATMOSPHERIC AND OCEANIC OPTICS

Vol. 26

No. 1

2013

SIMULATION OF CONTRIBUTION OF CONTINENTAL ANTHROPOGENIC SOURCES CO2, ppm 15

386

13

384

11

382

9

380

7

378

5

376

3

374

1

372

–1 09.01.2002

08.02.2002

10.03.2002

37

370 09.04.2002

Fig. 1. Concentrations calculated in the model with (solid line) and without (dashed line) accounting for CO2 emissions from the territory of China (left axis), as well as data of observations (dasheddotted line) at the Hateruma station in 2002 (right axis).

In work [15], the trajectory analysis of tower mea surements is used to show that observations in the boundary layer sustain a unique relation with emissions from the sources for no longer than 1.5 days and that all these sources are within 1000 × 1000 km of the observa tion station. For the continental tower observations dur ing the summer period, the variations in the CO2 concen tration are 50% due to the effect of the underlying surface within a 20–60km radius of the tower [16]. In the case of aircraft observations, the effect of the concentration measured at the tropospheric altitudes extends to larger areas, and more distant sources con tribute to the concentrations observed. Thus, the duration of the back trajectories is determined by the observation site and altitude, as well as by the season for the components with the seasonal variations of groundlevel fluxes. The Hateruma station is far away from large emission sources; therefore, the concentra tion at this station was simulated using sevenday tra jectories. Because of its location, this station exerts a signifi cant influence on the measured concentration, most appreciable during the winter period when air masses are transported from the continental part of Asia toward Japanese territory under the influence of the East Asian monsoon. We compared calculations per formed with and without consideration of the emis sions from the territory of China to estimate the effect of the anthropogenic emissions on the concentration at the Hateruma station. For this, as for China, we ATMOSPHERIC AND OCEANIC OPTICS

Vol. 26

chose a region covering territory with resolution 1° × 1°; for this region, the emissions of anthropogenic, bio spheric, and oceanic origins were set to zero in the case when the territory of China was excluded from consid eration. Figures 1 and 2 show calculations for 2002 and 2006; these calculations were performed with and without accounting for emissions of all types of pollut ants from the territory of China for the winter period. To single out the surface flux contribution from this chosen region, the figure shows only calculations per formed taking into account the Lagrangian part of the concentration (righthand side of formula (1)). As can be seen from Figs. 1 and 2, the coupled model well reproduces most of the sharp peaks which are present in the observations (solid line). On the other hand, if we remove the emissions which are due to the effect of China on this chosen station (dashed line), some sharp peaks disappear, and the obtained concentration becomes lower. As to the correlations between model data and observations, they decrease from 0.63 to 0.59 for 2002 and from 0.44 to 0.31 for 2006 when emissions from China are excluded from consideration (here we present the correlation coeffi cients for the winter time period, which correspond only to the first term in formula (1), i.e., without accounting for the seasonal behavior). From these findings we can conclude that shortterm increases in the carbon dioxide concentration at the Hateruma sta tion are caused by greenhouse gases transported from the territory of China. If we plot the concentrations

No. 1

2013

38

GANSHIN et al. CO2, ppm 15

395

13

393

11

391

9

389

7

387

5

385

3

383

1

381

–1 09.01.2006

08.02.2006

10.03.2006

379 09.04.2006

Fig. 2. Concentrations calculated in the model with (solid line) and without (dashed line) accounting for CO2 emissions from the territory of China (left axis), as well as data of observations (dasheddotted line) at the Hateruma station in 2006 (right axis).

CO2, ppm 15

386

13

384

11

382

9

380

7

378

5

376

3

374

1

372

–1 09.01.2002

08.02.2002

10.03.2002

370 09.04.2002

Fig. 3. Comparison of modelpredicted anthropogenic emission contribution (solid line) from the territory of China to the observed CO2 concentration (left axis) (dasheddotted line) at the Hateruma station for 2002 (right axis).

produced by anthropogenic emissions in China, it becomes immediately apparent that these sources pre dominate; these dependencies are shown in Figs. 3

and 4, which compare the data of observations and how anthropogenic emissions from the territory of China contribute to these observations.

ATMOSPHERIC AND OCEANIC OPTICS

Vol. 26

No. 1

2013

SIMULATION OF CONTRIBUTION OF CONTINENTAL ANTHROPOGENIC SOURCES CO2, ppm 15

395

13

393

11

391

9

389

7

387

5

385

3

383

1

381

–1 09.01.2006

10.03.2006

08.02.2006

39

379 09.04.2006

Fig. 4. Comparison of modelpredicted anthropogenic emission contribution (solid line) from the territory of China to the observed CO2 concentration (left axis) (dasheddotted line) at the Hateruma station for 2006 (right axis).

CONCLUSION This paper demonstrates how the CO2 concentra tion can be simulated at the observation point with help of the coupled Eulerian–Lagrangian model and surface fluxes of anthropogenic, biospheric, and oce anic origins. The algorithm described above can effi ciently reconstruct tower, aircraft, and satellite obser vations for subsequent detection of global and regional sources and sinks of the measured component. The paper showed that anthropogenic emissions in China, especially during the winter season, are the main con tributor to variations in the concentration at the Hat eruma station. The fact that the contribution of regional anthropogenic emissions predominates in the received signal indicates that data monitored at the Hateruma station can be efficiently used to simulate regional transport and to solve the inverse problems on interannual variations in the anthropogenic emissions of CO2 and other gases. The model is used for recon structing the sources and sinks of greenhouse gases, and for updating the available emission data with high resolution. REFERENCES

3.

4.

5.

6.

1. P. K. Patra, R. M. Law, W. Peters, C. Rodenbeck, M. Takigawa, C. Aulagnier, I. Baker, D. J. Bergmann, P. Bousquet, J. Brandt, L. Bruhwiler, P. J. Cameron Smith, J. H. Christensen, F. Delage, A. S. Denning, S. Fan, C. Geels, S. Houweling, R. Imasu, U. Karstens, ATMOSPHERIC AND OCEANIC OPTICS

2.

Vol. 26

No. 1

7.

S. R. Kawa, J. Kleist, M. C. Krol, S.J. Lin, R. Lokupi tiya, T. Maki, S. Maksyutov, Y. Niwa, R. Onishi, N. Parazoo, G. Pieterse, L. River, M. Satoh, S. Serrar, S. Taguchi, R. Vautard, A. T. Vermeulen, and Z. Zhu, “TransCom Model Simulations of Hourly Atmospheric CO2: Analysis of SynopticScale Variations for the Period 2002–2003,” Global Biogeochem. Cycl. 22, GB4013 (2008). S. Maksyutov, P. K. Patra, R. Onishi, T. Saeki, and T. Nakazawa, “NIES/FRCGC Global Atmospheric Tracer Transport Model: Description, Validation, and Surface Sources and Sinks Inversion,” J. Earth Simula tor 9, 3–18 (2008). V. V. Penenko, “Revealing of Regions of Increased Environmental Vulnerability: Concept and Approaches to Its Realization,” Atmos. Ocean. Opt. 14 (6–7), 545–548 (2001). V. V. Penenko, “Variational Methods for Data Assimi lation and Inverse Problems for the Study of the Atmo sphere, Ocean, and Environment,”Sib. Zh. Vychisl. Matem. 12 (4), 421–434 (2009). D. Belikov, S. Maksyutov, T. Miyasaka, T. Saeki, R. Zhuravlev, and B. Kiryushov, “MassConserving Tracer Transport Modelling on a Reduced Latitude–Lon gitude Grid with NIESTM,” Geosci. Model Dev. 4, 207–222 (2011). A. Stohl, C. Forster, A. Frank, P. Seibert, and G. Wotawa, “Technical Note: The Lagrangian Particle Dispersion Model FLEXPART Version 6.2,” Atmos. Chem. Phys. 5, 2461–2474 (2005). A. Ganshin, T. Oda, M. Saito, S. Maksyutov, V. Valsala, R. J. Andres, R. E. Fisher, D. Lowry, A. Lukyanov, 2013

40

8.

9.

10.

11.

GANSHIN et al. H. Matsueda, E. G. Nisbet, M. Rigby, Y. Sawa, R. Toumi, K. Tsuboi, A. Varlagin, and R. Zhuravlev, “A Global Coupled Eulerian–Lagrangian Model and 1 × 1 km CO2 Surface Flux Dataset for HighResolution Atmospheric CO2 Transport Simulations,” Geosci. Model Dev. 5, 231–243 (2012). H. Mukai, M. Katsumoto, R. Ide, T. Machida, Y. Fuji numa, Y. Nojiri, M. Inagaki, N. Oda, and T. Watai, “Characterization of Atmospheric CO2 Observed at TwoBackground Air Monitoring Stations (Hateruma and Ochiishi) in Japan,” in Abstract of the Sixth Interna tional Carbon Dioxide Conference (Sendai, Japan, 2001). T. Oda and S. Maksyutov, “A Very HighResolution (1 km × 1 km) Global Fossil Fuel CO2 Emission Inven tory Derived Using a Point Source Database and Satel lite Observations of Nighttime Lights,” Atmos. Chem. Phys. 11, 543–556 (2011). M. Saito, A. Ito, and S. Maksyutov, “Evaluation of Biases in JRA25/JCDAS Precipitation and Their Impact on the Global Terrestrial Carbon Balance,” J. Clim. 21, 4109–4125 (2011). K. V. Valsala and S. Maksyutov, “Simulation and Assimilation of Global Ocean pCO2 and AirSea CO2 Fluxes Using Ship Observations of Surface Ocean

12.

13.

14.

15.

16.

pCO2 in a Simplified Biogeochemical Offline Model,” Tellus, B 62, 821–840 (2010). K. Onogi, J. Tsutsui, H. Koide, M. Sakamoto, S. Koba yashi, H. Hatsushika, T. Matsumoto, N. Yamazaki, H. Kamahori, K. Takahashi, S. Kadokura, K. Wada, K. Kato, R. Oyama, T. Ose, N. Mannoji, and R. Taira, “The JRA25 Reanalysis,” J. Meteorol. Soc. Jap. 85, 369–432 (2007). Y. Koyama, S. Maksyutov, H. Mukai, K. Thoning, and P. Tans, “Simulation of Atmospheric Carbon Dioxide Variability with a Global Coupled Eulerian–Lagrangian Transport Model,” Geosci. Model Dev. 4, 317–324 (2011). A. Stohl, “Computation, Accuracy and Applications of Trajectories—Review and Bibliography,” Atmos. Envi ron. 32 (6), 947–966 (1998). M. Gloor, P. Bakwin, D. Hurst, L. Lock, R. Draxler, and P. Tans, “What is the Concentration Footprint of a Tall Tower,” J. Geophys. Res., D 106 (16), 17831– 17840 (2001). C. Gerbig, A. J. Dolman, and M. Heimann, “On Observational and Modelling Strategies Targeted at Regional Carbon Exchange over Continents,” Biogeo sciences 6, 1949–1959 (2009).

ATMOSPHERIC AND OCEANIC OPTICS

Vol. 26

No. 1

2013