Geoscientific Model Development
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A global carbon assimilation system using a modified ensemble Kalman filter Abstract. A Global Carbon Assimilation System based on the ensemble Kalman filter (GCAS-EK) is developed for assimilating atmospheric CO2 data into an ecosystem model to simultaneously estimate the surface carbon fluxes and atmospheric CO2 distribution. This assimilation approach is similar to CarbonTracker, but with several new developments, including inclusion of atmospheric CO2 concentration in state vectors, using the ensemble Kalman filter (EnKF) with 1-week assimilation windows, using analysis states to iteratively estimate ensemble forecast errors, and a maximum likelihood estimation of the inflation factors of the forecast and observation errors. The proposed assimilation approach is used to estimate the terrestrial ecosystem carbon fluxes and atmospheric CO2 distributions from 2002 to 2008. The results show that this assimilation approach can effectively reduce the biases and uncertainties of the carbon fluxes simulated by the ecosystem model.
Geoscientific Model Development - Tập 8 Số 3 - Trang 805-816
Assimilating compact phase space retrievals (CPSRs): comparison with independent observations (MOZAIC in situ and IASI retrievals) and extension to assimilation of truncated retrieval profiles Abstract. Assimilation of atmospheric composition retrievals presents computational challenges due to their high data volume and often sparse information density. Assimilation of compact phase space retrievals (CPSRs) meets those challenges and offers a promising alternative to assimilation of raw retrievals at reduced computational cost (Mizzi et al., 2016). This paper compares analysis and forecast results from assimilation of Terra/Measurement of Pollution in the Troposphere (MOPITT) carbon monoxide (CO) CPSRs with independent observations. We use MetOp-A/Infrared Atmospheric Sounding Interferometer (IASI) CO retrievals and Measurement of OZone, water vapor, carbon monoxide, and nitrogen oxides by in-service AIrbus airCraft (MOZAIC) in situ CO profiles for our independent observation comparisons. Generally, the results confirm that assimilation of MOPITT CPSRs improves the Weather Research and Forecasting model with chemistry coupled to the ensemble Kalman filter data assimilation from the Data Assimilation Research Testbed (WRF-Chem/DART) analysis fit and forecast skill at a reduced computational cost compared to assimilation of raw retrievals. Comparison with the independent observations shows that assimilation of MOPITT CO generally improved the analysis fit and forecast skill in the lower troposphere but degraded it in the upper troposphere. We attribute that degradation to assimilation of MOPITT CO retrievals with a possible bias of ∼ 14 % above 300 hPa. To discard the biased retrievals, in this paper, we also extend CPSRs to assimilation of truncated retrieval profiles (as opposed to assimilation of full retrieval profiles). Those results show that not assimilating the biased retrievals (i) resolves the upper tropospheric analysis fit degradation issue and (ii) reduces the impact of assimilating the remaining unbiased retrievals because the total information content and vertical sensitivities are changed.
Geoscientific Model Development - Tập 11 Số 9 - Trang 3727-3745
Development of the Real-time On-road Emission (ROE v1.0) model for street-scale air quality modeling based on dynamic traffic big data Abstract. Rapid urbanization in China has led to heavy traffic flows in
street networks within cities, especially in eastern China, the economically
developed region. This has increased the risk of exposure to vehicle-related
pollutants. To evaluate the impact of vehicle emissions and provide an
on-road emission inventory with higher spatiotemporal resolution for
street-network air quality models, in this study, we developed the Real-time
On-road Emission (ROE v1.0) model to calculate street-scale on-road hot
emissions by using real-time big data for traffic provided by the Gaode Map
navigation application. This Python-based model obtains street-scale traffic
data from the map application programming interface (API), which are
open-access and updated every minute for each road segment. The results of
application of the model to Guangzhou, one of the three major cities in
China, showed on-road vehicle emissions of carbon monoxide (CO), nitrogen
oxide (NOx), hydrocarbons (HCs), PM2.5, and PM10 to be 35.22×104, 12.05×104, 4.10×104, 0.49×104, and 0.55×104 Mg yr−1, respectively. The spatial distribution reveals that the emission
hotspots are located in some highway-intensive areas and suburban town
centers. Emission contribution shows that the dominant contributors are
light-duty vehicles (LDVs) and heavy-duty vehicles (HDVs) in urban areas and
LDVs and heavy-duty trucks (HDTs) in suburban areas, indicating that the
traffic control policies regarding trucks in urban areas are effective.
In this study, the Model of Urban Network of Intersecting Canyons and
Highways (MUNICH) was applied to investigate the impact of traffic volume
change on street-scale photochemistry in the urban areas by using the
on-road emission results from the ROE model. The modeling results indicate
that the daytime NOx concentrations on national holidays are 26.5 %
and 9.1 % lower than those on normal weekdays and normal weekends,
respectively. Conversely, the national holiday O3 concentrations exceed
normal weekday and normal weekend amounts by 13.9 % and 10.6 %,
respectively, owing to changes in the ratio of emission of volatile organic compounds (VOCs) and
NOx. Thus, not only the on-road emissions but also other emissions should be
controlled in order to improve the air quality in Guangzhou. More
significantly, the newly developed ROE model may provide promising and
effective methodologies for analyzing real-time street-level traffic
emissions and high-resolution air quality assessment for more typical cities
or urban districts.
Geoscientific Model Development - Tập 13 Số 1 - Trang 23-40
The Meteorology-Chemistry Interface Processor (MCIP) for the CMAQ modeling system: updates through MCIPv3.4.1 Abstract. The Community Multiscale Air Quality (CMAQ) modeling system, a state-of-the-science regional air quality modeling system developed by the US Environmental Protection Agency, is being used for a variety of environmental modeling problems including regulatory applications, air quality forecasting, evaluation of emissions control strategies, process-level research, and interactions of global climate change and regional air quality. The Meteorology-Chemistry Interface Processor (MCIP) is a vital piece of software within the CMAQ modeling system that serves to, as best as possible, maintain dynamic consistency between the meteorological model and the chemical transport model (CTM). MCIP acts as both a post-processor to the meteorological model and a pre-processor to the emissions and the CTM in the CMAQ modeling system. MCIP's functions are to ingest the meteorological model output fields in their native formats, perform horizontal and vertical coordinate transformations, diagnose additional atmospheric fields, define gridding parameters, and prepare the meteorological fields in a form required by the CMAQ modeling system. This paper provides an updated overview of MCIP, documenting the scientific changes that have been made since it was first released as part of the CMAQ modeling system in 1998.
Geoscientific Model Development - Tập 3 Số 1 - Trang 243-256
Estimating surface carbon fluxes based on a local ensemble transform Kalman filter with a short assimilation window and a long observation window: an observing system simulation experiment test in GEOS-Chem 10.1 Abstract. We developed a carbon data assimilation system to estimate surface
carbon fluxes using the local ensemble transform Kalman filter (LETKF) and
atmospheric transport model GEOS-Chem driven by the MERRA-1 reanalysis of
the meteorological field based on the Goddard Earth Observing System model,
version 5 (GEOS-5). This assimilation system is inspired by the method of
Kang et al. (2011, 2012), who estimated the surface carbon fluxes in an
observing system simulation experiment (OSSE) as evolving parameters in the
assimilation of the atmospheric CO2, using a short assimilation window
of 6 h. They included the assimilation of the standard meteorological
variables, so that the ensemble provided a measure of the uncertainty in the
CO2 transport. After introducing new techniques such as “variable
localization”, and increased observation weights near the surface, they
obtained accurate surface carbon fluxes at grid-point resolution. We
developed a new version of the local ensemble transform Kalman
filter related to the “running-in-place”
(RIP) method used to accelerate the spin-up of ensemble Kalman filter (EnKF) data assimilation
(Kalnay and Yang, 2010; Wang et al., 2013; Yang et al., 2012). Like RIP, the
new assimilation system uses the “no cost smoothing” algorithm for the
LETKF (Kalnay et al., 2007b), which allows shifting the Kalman
filter solution forward or backward within an assimilation window at no cost. In the
new scheme a long “observation window” (e.g., 7 d or longer) is used to
create a LETKF ensemble at 7 d. Then, the RIP smoother is used to obtain
an accurate final analysis at 1 d. This new approach has the advantage of
being based on a short assimilation window, which makes it more accurate,
and of having been exposed to the future 7 d observations, which improves
the analysis and accelerates the spin-up. The assimilation and observation
windows are then shifted forward by 1 d, and the process is repeated.
This reduces significantly the analysis error, suggesting that the newly
developed assimilation method can be used with other Earth system models,
especially in order to make greater use of observations in conjunction with
models.
Geoscientific Model Development - Tập 12 Số 7 - Trang 2899-2914
Assimilating compact phase space retrievals of atmospheric composition with WRF-Chem/DART: a regional chemical transport/ensemble Kalman filter data assimilation system Abstract. This paper introduces the Weather Research and Forecasting Model with chemistry/Data Assimilation Research Testbed (WRF-Chem/DART) chemical transport forecasting/data assimilation system together with the assimilation of compact phase space retrievals of satellite-derived atmospheric composition products. WRF-Chem is a state-of-the-art chemical transport model. DART is a flexible software environment for researching ensemble data assimilation with different assimilation and forecast model options. DART's primary assimilation tool is the ensemble adjustment Kalman filter. WRF-Chem/DART is applied to the assimilation of Terra/Measurement of Pollution in the Troposphere (MOPITT) carbon monoxide (CO) trace gas retrieval profiles. Those CO observations are first assimilated as quasi-optimal retrievals (QORs). Our results show that assimilation of the CO retrievals (i) reduced WRF-Chem's CO bias in retrieval and state space, and (ii) improved the CO forecast skill by reducing the Root Mean Square Error (RMSE) and increasing the Coefficient of Determination (R2). Those CO forecast improvements were significant at the 95 % level. Trace gas retrieval data sets contain (i) large amounts of data with limited information content per observation, (ii) error covariance cross-correlations, and (iii) contributions from the retrieval prior profile that should be removed before assimilation. Those characteristics present challenges to the assimilation of retrievals. This paper addresses those challenges by introducing the assimilation of compact phase space retrievals (CPSRs). CPSRs are obtained by preprocessing retrieval data sets with an algorithm that (i) compresses the retrieval data, (ii) diagonalizes the error covariance, and (iii) removes the retrieval prior profile contribution. Most modern ensemble assimilation algorithms can efficiently assimilate CPSRs. Our results show that assimilation of MOPITT CO CPSRs reduced the number of observations (and assimilation computation costs) by ∼ 35 %, while providing CO forecast improvements comparable to or better than with the assimilation of MOPITT CO QORs.
Geoscientific Model Development - Tập 9 Số 3 - Trang 965-978
Global sensitivity and uncertainty analysis of an atmospheric chemistry transport model: the FRAME model (version 9.15.0) as a case study Abstract. Atmospheric chemistry transport models (ACTMs) are widely used to underpin policy decisions associated with the impact of potential changes in emissions on future pollutant concentrations and deposition. It is therefore essential to have a quantitative understanding of the uncertainty in model output arising from uncertainties in the input pollutant emissions. ACTMs incorporate complex and non-linear descriptions of chemical and physical processes which means that interactions and non-linearities in input–output relationships may not be revealed through the local one-at-a-time sensitivity analysis typically used. The aim of this work is to demonstrate a global sensitivity and uncertainty analysis approach for an ACTM, using as an example the FRAME model, which is extensively employed in the UK to generate source–receptor matrices for the UK Integrated Assessment Model and to estimate critical load exceedances. An optimised Latin hypercube sampling design was used to construct model runs within ±40 % variation range for the UK emissions of SO2, NOx, and NH3, from which regression coefficients for each input–output combination and each model grid ( > 10 000 across the UK) were calculated. Surface concentrations of SO2, NOx, and NH3 (and of deposition of S and N) were found to be predominantly sensitive to the emissions of the respective pollutant, while sensitivities of secondary species such as HNO3 and particulate SO42−, NO3−, and NH4+ to pollutant emissions were more complex and geographically variable. The uncertainties in model output variables were propagated from the uncertainty ranges reported by the UK National Atmospheric Emissions Inventory for the emissions of SO2, NOx, and NH3 (±4, ±10, and ±20 % respectively). The uncertainties in the surface concentrations of NH3 and NOx and the depositions of NHx and NOy were dominated by the uncertainties in emissions of NH3, and NOx respectively, whilst concentrations of SO2 and deposition of SOy were affected by the uncertainties in both SO2 and NH3 emissions. Likewise, the relative uncertainties in the modelled surface concentrations of each of the secondary pollutant variables (NH4+, NO3−, SO42−, and HNO3) were due to uncertainties in at least two input variables. In all cases the spatial distribution of relative uncertainty was found to be geographically heterogeneous. The global methods used here can be applied to conduct sensitivity and uncertainty analyses of other ACTMs.
Geoscientific Model Development - Tập 11 Số 4 - Trang 1653-1664
The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions Abstract. The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1) is a modeling framework for estimating fluxes of biogenic compounds between terrestrial ecosystems and the atmosphere using simple mechanistic algorithms to account for the major known processes controlling biogenic emissions. It is available as an offline code and has also been coupled into land surface and atmospheric chemistry models. MEGAN2.1 is an update from the previous versions including MEGAN2.0, which was described for isoprene emissions by Guenther et al. (2006) and MEGAN2.02, which was described for monoterpene and sesquiterpene emissions by Sakulyanontvittaya et al. (2008). Isoprene comprises about half of the total global biogenic volatile organic compound (BVOC) emission of 1 Pg (1000 Tg or 1015 g) estimated using MEGAN2.1. Methanol, ethanol, acetaldehyde, acetone, α-pinene, β-pinene, t-β-ocimene, limonene, ethene, and propene together contribute another 30% of the MEGAN2.1 estimated emission. An additional 20 compounds (mostly terpenoids) are associated with the MEGAN2.1 estimates of another 17% of the total emission with the remaining 3% distributed among >100 compounds. Emissions of 41 monoterpenes and 32 sesquiterpenes together comprise about 15% and 3%, respectively, of the estimated total global BVOC emission. Tropical trees cover about 18% of the global land surface and are estimated to be responsible for ~80% of terpenoid emissions and ~50% of other VOC emissions. Other trees cover about the same area but are estimated to contribute only about 10% of total emissions. The magnitude of the emissions estimated with MEGAN2.1 are within the range of estimates reported using other approaches and much of the differences between reported values can be attributed to land cover and meteorological driving variables. The offline version of MEGAN2.1 source code and driving variables is available from
Geoscientific Model Development - Tập 5 Số 6 - Trang 1471-1492
Accounting for forest management in the estimation of forest carbon balance using the dynamic vegetation model LPJ-GUESS (v4.0, r9710): implementation and evaluation of simulations for Europe Abstract. Global forests are the main component of the land carbon sink, which acts as a partial buffer to CO2 emissions into the atmosphere. Dynamic vegetation models offer an approach to projecting the development of forest carbon sink capacity in a future climate. Forest management capabilities are important to include in dynamic vegetation models to account for the effects of age and species structure and wood harvest on carbon stocks and carbon storage potential. This article describes the implementation of a forest management module containing even-age and clear-cut and uneven-age and continuous-cover management alternatives in the dynamic vegetation model LPJ-GUESS. Different age and species structure initialisation strategies and harvest alternatives are introduced. The model is applied at stand and European scales. Different management alternatives are applied in simulations of European beech (Fagus sylvaticus) and Norway spruce (Picea abies) even-aged monoculture stands in central Europe and evaluated against above-ground standing stem volume and harvested volume data from long-term experimental plots. At the European scale, an automated thinning and clear-cut strategy is applied. Modelled carbon stocks and fluxes are evaluated against reported data at the continent and country levels. Including wood harvest in regrowth forests increases the simulated total European carbon sink by 32 % in 1991–2015 and improves the fit to the reported European carbon sink, growing stock, and net annual increment (NAI). Growing stock (156 m3 ha−1) and NAI (5.4 m3 ha1 yr1) densities in 2010 are close to reported values, while the carbon sink density in 2000–2007 (0.085 kg C m−2 yr1) equates to 63 % of reported values, most likely reflecting uncertainties in carbon fluxes from soil given the unaccounted for forest land-use history in the simulations. The fit of modelled and reported values for individual European countries varies, but NAI is generally closer to reported values when including wood harvest in simulations.
Geoscientific Model Development - Tập 14 Số 10 - Trang 6071-6112
CAM-chem: description and evaluation of interactive atmospheric chemistry in the Community Earth System Model Abstract. We discuss and evaluate the representation of atmospheric chemistry in the global Community Atmosphere Model (CAM) version 4, the atmospheric component of the Community Earth System Model (CESM). We present a variety of configurations for the representation of tropospheric and stratospheric chemistry, wet removal, and online and offline meteorology. Results from simulations illustrating these configurations are compared with surface, aircraft and satellite observations. Major biases include a negative bias in the high-latitude CO distribution, a positive bias in upper-tropospheric/lower-stratospheric ozone, and a positive bias in summertime surface ozone (over the United States and Europe). The tropospheric net chemical ozone production varies significantly between configurations, partly related to variations in stratosphere-troposphere exchange. Aerosol optical depth tends to be underestimated over most regions, while comparison with aerosol surface measurements over the United States indicate reasonable results for sulfate , especially in the online simulation. Other aerosol species exhibit significant biases. Overall, the model-data comparison indicates that the offline simulation driven by GEOS5 meteorological analyses provides the best simulation, possibly due in part to the increased vertical resolution (52 levels instead of 26 for online dynamics). The CAM-chem code as described in this paper, along with all the necessary datasets needed to perform the simulations described here, are available for download at www.cesm.ucar.edu.
Geoscientific Model Development - Tập 5 Số 2 - Trang 369-411
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