=Paper=
{{Paper
|id=Vol-3006/43_short_paper
|storemode=property
|title=Verification of the chemical subsystem of the regional climate model RegCM-CHEM4
|pdfUrl=https://ceur-ws.org/Vol-3006/43_short_paper.pdf
|volume=Vol-3006
|authors=Nikolay V. Volkov,Anatoly A. Lagutin,Egor Yu. Mordvin
}}
==Verification of the chemical subsystem of the regional climate model RegCM-CHEM4==
https://ceur-ws.org/Vol-3006/43_short_paper.pdf
Verification of the chemical subsystem of the regional
climate model RegCM-CHEM4
Nikolay V. Volkov1 , Anatoly A. Lagutin1,2 and Egor Yu. Mordvin1
1
Altai State University, Barnaul, Russia
2
Federal Research Center for Information and Computational Technologies, Novosibirsk, Russia
Abstract
New simulation results, obtained from the chemical version of the regional climate model RegCM-
CHEM4, are presented for Siberian region. The verification of the chemical subsystem of the model
with non-hydrostatic dynamical core is carried out using the atmospheric chemical transfer scheme
CBMZ (Carbon Bond Mechanism-Z). To define chemical emissions the global RCP (Representative
Concentration Pathways) emission dataset prepared by the International Institute for Applied Systems
Analysis (IIASA), is used. For gas phase species, we have prepared the 6 hourly chemical boundary
conditions from our modified version of the Model for Ozone and Related chemical Tracers, version 4
(MOZART-4). Quantitative estimates of methane emission in the atmosphere of the Siberian region have
been obtained.
Keywords
Regional climate, Siberian region, atmospheric chemistry, methane, emission.
1. Introduction
Research over the past 20 years has shown that inverse atmospheric modeling combined with
ground-based observations leads to more precise (versus direct measurements of the global
terrestrial network) estimates of the spatial distribution of sources of the greenhouse gases
(GHGs) [1]. At the same time, it was found that the scale of retrieval (e.g., a country, a single
region, or an entire continent) highly depends on the quantity and distribution of measurement
stations as well as their precision.
Despite the vast expansion of the global network of ground-based measurement stations,
significant territories of South America, central Africa, zones of wetland complexes of Western
Siberia, etc., which are sources of the GHGs, were not monitored.
Significant progress in the monitoring observations of the GHGs in the atmosphere of the
Siberian region was achieved in 2004 by the commissioning of the Russian-Japanese network of
ground stations [2]. Data from JR-STATION as well as the results of aircraft observations made
by V.E. Zuev Institute of Atmospheric Optics SB RAS [3] are the only sources of information on
the content of the GHGs in the atmosphere of Western Siberia.
SDM-2021: All-Russian conference, August 24–27, 2021, Novosibirsk, Russia
" volkov@theory.asu.ru (N. V. Volkov); lagutin@theory.asu.ru (A. A. Lagutin); zion0210@gmail.com
(E. Yu. Mordvin)
0000-0002-3172-0655 (N. V. Volkov); 0000-0002-1814-8041 (A. A. Lagutin)
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073 CEUR Workshop Proceedings (CEUR-WS.org)
375
Nikolay V. Volkov et al. CEUR Workshop Proceedings 375–383
The possibility for obtaining regular information on the total methane content in the at-
mosphere over difficult terrains as well as regions with sparse coverage by ground stations,
appeared only in 2002 after the European Space Agency (ESA) had launched the ENVISAT
satellite with the SCIAMACHY radiometer on board [4].
The extension of the satellite monitoring of the GHGs was the launch of the GOSAT satellite
made by the Japanese Aerospace Agency (JAXA) in January 2009 [5], as well as the OCO-2
satellite observatory launch into a sun-synchronous orbit made by the NASA in July 2014 [6, 7].
A new era in solving the problem of quantitative assessment of emissions of methane, nitro-
gen dioxide and CO from both natural and anthropogenic sources began in 2017 after the launch
of the Sentinel-5 Precurcor (Sentinel-5P) satellite made by the ESA with the TROPOspheric Mon-
itoring Instrument (TROPOMI) aboard [8, 9]. The main mission of the Sentinel-5P/TROPOMI is
to continue monitoring observations of the GHGs content in the Earth’s atmosphere, interrupted
by the completion of the SCIAMACHY project.
The five imaging systems of the Suomi-NPP satellite [10], launched in October 2011, and
the JPSS-1 (NOAA-20) satellite [11], launched in November 2017, also provide unique data for
solving the problems of the GHGs monitoring. For example, in [12], data from a 22-channel
VIIRS/SNPP/NOAA-20 radiometer [13] were used to estimate the GHGs emissions from the
combustion of associated petroleum gas in flares of oil industry enterprises in Western Siberia.
It is clearly that to obtain quantitative estimates of the GHGs emissions, their total content in
the atmosphere, to understand the mechanisms of their sinks as well as to obtain long-term
forecasts, we need data covering a significant time period with good spatial resolution. These
problems can be dealt by means of global and regional climate models as well as chemical
transport models along with satellite observations. For example, in [14, 15, 16, 17, 18], the
success of the application of the Regional Climate Model (RegCM4) for the study of the GHG
emissions over Europe, Southeast Asia, India, and northern Africa (Egypt) was shown.
The aim of this work is to simulate the methane emission in the atmosphere of the Siberian
region using the chemical version of the regional climate model RegCM-CHEM4.
2. The regional climate chemistry model: Description and
simulation design
RegCM-CHEM4 is an online climate chemistry model based on the Abdus Salam International
Centre for Theoretical Physics (ICTP, Trieste, Italy) regional climate model (RegCM4) [19, 20].
RegCM4 is a hydro- and/or non-hydrostatic, sigma coordinate model, which has been imple-
mented for a wide range of climate researches across the globe. The model includes a wide range
of parameterization schemes for physical processes in the atmosphere and underlying surface.
In this paper we employ the mass-flux cumulus scheme of Grell [21], the non-local planetary
boundary layer parameterization of Holtslag [22], and the Rapid Radiative Transfer Model,
RRTM [23]. Surface processes are treated using the Community Land Model version 4.5 [24].
Tropospheric gas-phase chemistry is integrated into the RegCM4 using the fixed sets of
schemes which define the nature and number of chemical species and/or transported aerosols.
In this study we have chosen the atmospheric chemical transfer scheme CBMZ [25]. This
scheme supports over 30 chemical tracers and aerosols.
376
Nikolay V. Volkov et al. CEUR Workshop Proceedings 375–383
Figure 1: Flowchart for data preparation and simulation using the RegCM-CHEM4.
RegCM4 has a modular structure comprising two main subsystems: preparation of input
data and modeling (see [19, 20]). Figure 1 shows a flowchart with the main modules of RegCM-
CHEM4.
A description of the Terrain, MksurfData, SST and ICBC modules are given in [19, 20].
Configurations of these modules are discussed in series of our previous works [26, 27, 28, 29, 30].
In these works, based on the results of simulations of the contemporary and the future climate
of the Siberian region, the verification of the atmospheric and radiation schemes of RegCM4 as
well as the Community Land Model, coupled with the RegCM4, were carried out.
The chemical subsystem of the RegCM-CHEM4 is shown in Figure 1 inside the rectangular
dashed area. The EMCRE_GRID module is used to create the model grid description file to be used
to calculate weights for a remapping. The INTERP_EMISSIONS module is used to interpolation
of the global emissions data on the RegCM4 grid. The CHEM_ICBC module is designed to set
the boundary conditions of the chemical model.
To test the capability of the coupled RegCM-CHEM4 to simulate atmospheric chemistry of
the Siberian region, we perform simulation for 16 years from 1 January 1990 to 31 December
2005. The first five years of the simulation is for climate model spin up and is not included in
the analysis time period of 1995–2005. The simulations presented here use a non-hydrostatic
dynamical core model with time step of 120 s with the land model called every 600 s. The main
parameters and simulating schemes are shown in Table 1.
The model domain (Figure 2) has a horizontal resolution of 40 km×40 km and 18 vertical
levels. Because RegCM4 is a limited-area model, meteorological lateral boundary conditions
are required. For present-day simulations such as the one here, initial and lateral boundary
377
Nikolay V. Volkov et al. CEUR Workshop Proceedings 375–383
Table 1
Parameters of simulations, configuration of domain and schemes of physical processes employed in this
study.
Simulation design Schemes and models
Parameter Value Scheme or model Description
Projection Lambert conformal Surface model Community Land
conic projection Model, CLM4.5 [24]
Central longitude and 𝜆0 = 80 E, Radiation scheme Rapid Radiative Trans-
latitude 𝜑0 = 60 N fer Model, RRTM [23]
Standard parallels 𝜑1 = 52.5 N, Cumulus convection Grell [21]
𝜑2 = 67.5 N scheme
Time step of simula- Δ𝑡𝑎𝑡𝑚 = 120 sec, Boundary layer Holtslag PBL [22]
tions and the time Δ𝑡𝑟𝑎𝑑 = 30 min, scheme
intervals in solar Δ𝑡𝑐ℎ𝑒𝑚 = 300 sec,
radiation model, chem- Δ𝑡𝑐𝑢𝑚 = 5 min,
istry solver, cumulus Δ𝑡𝑠𝑢𝑟𝑓 = 10 min
scheme and land
model.
Frequency of writing of Δ𝑇𝑎𝑡𝑚 = Δ𝑇𝑟𝑎𝑑 = Atmospheric chemical Carbon Bond
the output fields in the 6 hours, transfer scheme Mechanism-Z,
files Δ𝑇𝑠𝑟𝑓 = Δ𝑇𝑐ℎ𝑒𝑚 = CBMZ [25]
3 hours
Lateral buffer zone 12 cells Lateral Boundary con- Relaxation, exponen-
ditions scheme tial technique
Figure 2: Spatial resolution and projection parameters (left) as well as surface elevation of modeling
domain in meters at sea level (right) used in RegCM-CHEM4. Blue lines are the central longitude and
latitude of a Lambert conformal conic projection. Green lines are the standard parallels. Dashed lines
are the boundaries of relaxation (lateral buffer zone).
378
Nikolay V. Volkov et al. CEUR Workshop Proceedings 375–383
conditions for the meteorological fields are provided by NCEP-DOE AMIP-II Reanalysis (R-2)
every six hours [31] with weekly sea surface temperatures (NOAA Optimum Interpolation (OI)
SST V2) [32].
To define chemical emissions the global RCP (Representative Concentration Pathways) emis-
sion dataset prepared by the International Institute for Applied Systems Analysis (IIASA) was
used [33]. For gas phase species, we prepared the 6-hourly chemical boundary conditions from
the Model for Ozone and Related chemical Tracers, version 4 (MOZART-4) [34] modified by the
authors.
3. Initial and lateral boundary conditions for chemical subsystem
The IIASA dataset [33] provides access to global data on the emissions of the following gas
components: CH4 , SO2 , NO𝑥 , CO, NH3 , BC (Black Carbon), OC (Organic Carbon), all fluorinated
gases controlled under the Kyoto Protocol and ozone depleting substances controlled under
the Montreal Protocol. Model data were prepared within the framework of the Coupled Model
Intercomparison Project Phase 5 (CMIP5) for the historical period from 1850 to 2005, as well
as for the so-called “Representative Concentration Pathway” (RCPs) 2005–2100. The spatial
resolution of the data is 0.5∘ × 0.5∘ . According to the sectors of emissions, all data are divided
into 4 sectors: anthropogenic emissions, as well as emissions from biomass burning, shipping
and aviation.
The RegCM-CHEM4 preprocessor can manage IIASA emissions. To implement it, the global
RCP emission dataset have been processed to extract only species adopted in the chemical solver
CBMZ. At the next stage, the data sets are aggregated according to different sectors that are
presented in the RCP fields.
Figure 3 shows, for example, the spatial distribution of methane sources in the modeling
domain for July 2005 according to IIASA RCP emission dataset. It can be seen that the main
Figure 3: Total monthly emissions (anthropogenic and biomass burning) of CH4 (Tg/month) for July
2005 according to IIASA RCP emission dataset.
379
Nikolay V. Volkov et al. CEUR Workshop Proceedings 375–383
Figure 4: Total annual emissions of CH4 (Tg/yr) in 1995–2005 over Western Siberia’s wetland complexes
according to RegCM-CHEM4 (purple dots) and the rate of change of methane emission (green line).
sources of methane are located in wetland complexes of Western Siberia as well as large industrial
centers of the region.
The original element of this study is the use of the data from the global transport chemical
model MOZART-4 modified by authors to set the initial and boundary chemical conditions
with an interval of 6 hours (see Egor Yu. Mordvin, Anatoly A. Lagutin, Nikolay V. Volkov
“Total methane content in atmosphere of Western Siberia in 2000–2020 according to the data of
chemical transport model MOZART-4” in this issue of CEUR Workshop proceedings for details).
4. Results
The chemical version of the regional climate model RegCM-CHEM4 was used to simulate
methane emissions over the Siberian region in 1995–2005. It should be noted that the subject
region contains one of the largest wetland complexes are natural sources of methane. Results of
simulations of CH4 emissions in 1995–2005 are shown in Figure 4.
The analysis of the simulation results was carried out only for the zone containing wetland
complexes (55–65 N, 65–85 E). It was found that for Western Siberia’s wetland complexes the
model estimates for methane emission in 1995–2005 changes from ∼3.55 to ∼3.69 Tg/yr. The
average value of emission is 3.62 Tg/yr, the rate of change of methane emission during this
period is ∼0.01 Tg/yr.
The quantitative estimates of the methane emission obtained in this paper are in good
agreement with the result 3.91 ± 1.29 Tg/yr [35], although it slightly exceeds the average
estimate of 3.0 ± 1.4 Tg/yr obtained in [36] for 2003–2009.
380
Nikolay V. Volkov et al. CEUR Workshop Proceedings 375–383
Acknowledgments
The study was carried out within the framework of the Program for the support of scientific
and pedagogical workers of the Altai State University, the project “Assessment of greenhouse
gas emissions by oil industry enterprises in Western Siberia according to satellite observations
and modeling”.
References
[1] S. Kirschke, P. Bousquet, P. Ciais et al., Three decades of global methane sources and sinks,
Nature Geoscience 6 (2013) 813–823. doi:10.1038/ngeo1955.
[2] M. Sasakawa, K. Shimoyama, T. Machida et al., Continuous measurements of methane from
a tower network over Siberia, Tellus 62B (2010) 403–416. doi:10.1111/j.1600-0889.
2010.00494.x.
[3] M. Y. Arshinov, B. D. Belan, D. K. Davydov, Vertical Distribution of Greenhouse Gases over
Western Siberia from Long-term Measurement Data, Atmospheric and Oceanic Optics 22
(2009) 316–324. doi:10.1134/S1024856009030087.
[4] H. Bovensmann, J. Burrows, M. Buchwitz et al., SCIAMACHY: Mission Objectives and
Measurement Modes, Journal of the Atmospheric Sciences 56 (1999) 127–150. doi:10.
1175/1520-0469(1999)056<0127:SMOAMM>2.0.CO;2.
[5] L. Feng, P. Palmer, H. Bosch et al., Consistent regional fluxes of CH4 and CO2 inferred from
GOSAT proxy XCH4: XCO2 retrievals, 2010-2014, Atmospheric Chemistry and Physics 17
(2017) 4781–4797. doi:10.5194/acp-17-4781-2017.
[6] C. Frankenberg, R. Pollock, R. A. M. Lee et al., The Orbiting Carbon Observatory (OCO-2):
spectrometer performance evaluation using pre-launch direct sun measurements, Atmo-
spheric Measurement Techniques 8 (2015) 301–313. doi:10.5194/amt-8-301-2015.
[7] M. Reuter, M. Buchwitz, O. Schneising et al., Towards monitoring localized CO2 emissions
from space: co-located regional CO2 and NO2 enhancements observed by the OCO-2 and
S5P satellites, Atmospheric Chemistry and Physics 19 (2019) 9371–9383. doi:10.5194/
acp-19-9371-2019.
[8] J. P. Veefkind, I. Aben, K. McMullan et al., TROPOMI on the ESA Sentinel-5 Precursor:
A GMES mission for global observations of the atmospheric composition for climate, air
quality and ozone layer applications, Remote Sensing of Environment 120 (2012) 70–83.
doi:10.1016/j.rse.2011.09.027.
[9] A. Butz, A. Galli, O. Hasekamp et al., TROPOMI aboard Sentinel-5 Precursor: Prospective
performance of CH4 retrievals for aerosol and cirrus loaded atmospheres, Remote Sensing
of Environment 120 (2012) 267–276. doi:10.1016/j.rse.2011.05.030.
[10] D. Hillger, T. Kopp, T. Lee et al., First-Light Imagery from Suomi NPP VIIRS, Bulletin of the
American Meteorological Society 94 (2013) 1019–1029. doi:10.1175/BAMS-D-12-00097.
1.
[11] S. Uprety, C. Cao, S. Blonski, X. Shao, Evaluating NOAA-20 and S-NPP VIIRS radiometric
consistency, Proceedings of SPIE 10781 (2018). doi:10.1117/12.2324464.
[12] A. A. Lagutin, E. Y. Mordvin, N. V. Volkov, N. V. Tuchina, Estimation of Natural Gas Flaring
381
Nikolay V. Volkov et al. CEUR Workshop Proceedings 375–383
Volume at the Western Siberia Flares using Satellite Night-Time Data in the Visible and
Near-Infrared Range, CEUR Workshop Proceedings 2534 (2019) 22–26.
[13] C. Cao, F. J. De Luccia, X. Xiong et al., Early On-Orbit Performance of the Visible Infrared
Imaging Radiometer Suite Onboard the Suomi National Polar-Orbiting Partnership (S-
NPP) Satellite, IEEE Transactions on Geoscience and Remote Sensing 52 (2014) 1142–1156.
doi:10.1109/TGRS.2013.2247768.
[14] A. Shalaby, A. S. Zakey, A. B. Tawfik et al., Implementation and evaluation of online
gas-phase chemistry within a regional climate model (RegCM-CHEM4), Geoscientific
Model Development 5 (2012) 741–760. doi:10.5194/gmd-5-741-2012.
[15] B. Abish, K. Arun, Resolving the weakening of orographic rainfall over India using a
regional climate model RegCM 4.5, Atmospheric Research 227 (2019) 125–139. doi:10.
1016/j.atmosres.2019.05.003.
[16] X. Pu, T. J. Wang, X. Huang et al., Enhanced surface ozone during the heat wave of 2013
in Yangtze River Delta region, China, Science of the Total Environment 603–604 (2017)
807–816. doi:10.1016/j.scitotenv.2017.03.056.
[17] A. Saber, H. Abdel Basset, M. Morsy et al., Characteristics of the simulated pollutants and
atmospheric conditions over Egypt, NRIAG Journal of Astronomy and Geophysics 9 (2020)
402–419. doi:10.1080/20909977.2020.1755479.
[18] A. N. Mostafa, A. S. Zakey, S. C. Alfaro et al., Validation of RegCM-CHEM4 model
by comparison with surface measurements in the Greater Cairo (Egypt) megacity,
Environmental Science and Pollution Research 26 (2019) 23524–23541. doi:10.1007/
s11356-019-05370-0.
[19] F. Giorgi, E. Coppola, F. Solmon et al., RegCM4: Model description and preliminary tests
over multiple CORDEX domains, Climate Research 52 (2012) 7–29. doi:10.3354/cr01018.
[20] J. S. Pal, F. Giorgi, B. Xunqiang et al., Regional climate modeling for the developing world:
the ICTP RegCM3 and RegCNET, Bulletin American Meteorological Society 88 (2007)
1395–1409. doi:10.1175/BAMS-88-9-1395.
[21] G. Grell, Prognostic evaluation of assumptions used by cumulus parameteriza-
tions, Monthly Weather Review 121 (1993) 764–787. doi:10.1175/1520-0493(1993)
121<0764:PEOAUB>2.0.CO;2.
[22] A. A. M. Holtslag, E. I. F. de Bruijn, H.-L. Pan, A high resolution air mass transformation
model for short-range weather forecasting, Monthly Weather Review 118 (1990) 1561–1575.
doi:10.1175/1520-0493(1990)118<1561:AHRAMT>2.0.CO;2.
[23] E. J. Mlawer, S. J. Taubman, P. D. Brown et al., Radiative transfer for inhomogeneous atmo-
spheres: RRTM, a validated correlated-k model for the longwave, Journal of Geophysical
Research 102 (1997) 16663–16682. doi:10.1029/97JD00237.
[24] D. M. Lawrence, K. W. Oleson, M. G. Flanner et al., Parameterization improvements and
functional and structural advances in version 4 of the Community Land Model, Journal of
Advances in Modeling Earth Systems 3 (2011) M03001. doi:10.1029/2011MS00045.
[25] R. Zaveri, L. K. Peters, A new lumped structure photochemical mechanism for large-scale
applications, Journal of Geophysical Research 104 (1999) 30387–30415. doi:10.1029/
1999JD900876.
[26] A. A. Lagutin, N. V. Volkov, E. Y. Mordvin, A. N. Reznikov, Modelling Western Siberia
Climate: Results of the RegCM4 Model, Bulletin of Altai State University (2012) 181–189
382
Nikolay V. Volkov et al. CEUR Workshop Proceedings 375–383
(in Russian).
[27] A. A. Lagutin, N. V. Volkov, E. Y. Mordvin, Modelling Siberian Region Climate: Results
of the RegCM/CLM Model for 1970-2029, Vestnik Altaiskoy Nauki (2013) 191–197 (in
Russian).
[28] A. A. Lagutin, N. V. Volkov, K. Makushev, E. Y. Mordvin, Outgoing Longwave Radiation
from the Data of Regional Climate Model and AIRS/AMSU Satellite Suite, Bulletin of Altai
State University (2014) 155–161 (in Russian).
[29] K. M. Makushev, A. A. Lagutin, N. V. Volkov, E. Y. Mordvin, Validation of the
RegCM4/CLM4.5 regional climate modeling system over the Western Siberia, Proceedings
of SPIE 10035 (2016). doi:10.1117/12.2249163.
[30] A. A. Lagutin, N. V. Volkov, E. Y. Mordvin, The Influence of a Global Climate Changes on
Western Siberia Climate in the First Half of XXI Century, Computational Technologies 23
(2018) 83–94 (in Russian). doi:10.25743/ICT.2018.23.16505.
[31] M. Kanamitsu, W. Ebisuzaki, J. Woollen, NCEP-DOE AMIP-II REANALYSIS (R-2),
Bulletin of the American Meteorological Society 83 (2002) 1631–1643. doi:10.1175/
BAMS-83-11-1631.
[32] NOAA-ESRL Physical Sciences Laboratory, Boulder Colorado, Last access: July 25, 2021.
URL: https://psl.noaa.gov/.
[33] Technical specification. RCP Database Version 2.0.5, Last access: July 25, 2021. URL:
https://tntcat.iiasa.ac.at/RcpDb.
[34] L. K. Emmons, S. Walters, P. G. Hess et al., Description and evaluation of the Model
for Ozone and Related chemical Tracers, version 4 (MOZART-4), Geoscientific Model
Development 3 (2002) 43–67. doi:10.5194/gmd-3-43-2010.
[35] M. Glagolev, I. Kleptsova, I. Fillipov et al., Regional methane emission from West
Siberia mire landscapes, Environmental Research Letters 6 (2011) 045214. doi:10.1088/
1748-9326/6/4/045214.
[36] H. S. Kim, S. Maksyutov, M. V. Glagolev et al., Evaluation of methane emissions from West
Siberian wetlands based on inverse modeling, Environmental Research Letters 6 (2011)
035201. doi:10.1088/1748-9326/6/3/035201.
383