Comparative evaluation of the impact of WRF/NMM and WRF/ARW meteorology on CMAQ simulations for PM_2.5 and its related precursors during the 2006 TexAQS/GoMACCS study

This study presents a comparative evaluation of the impact of WRF-NMM and WRF-ARW meteorology on CMAQ simulations of PM_2.5, its composition and related precursors over the eastern United States with the intensive observations obtained by aircraft (NOAA WP-3), ship and surface monitoring networks (AIRNow, IMPROVE, CASTNet and STN) during the 2006 TexAQS/GoMACCS study. The results at the AIRNow surface sites show that both ARW-CMAQ and NMM-CMAQ reproduced day-to-day variations of observed PM_2.5 and captured the majority of observed PM_2.5 within a factor of 2 with a NMB value of −0.4% for ARW-CMAQ and −18% for NMM-CMAQ. Both models performed much better at the urban sites than at the rural sites, with greater underpredictions at the rural sites. Both models consistently underestimated the observed PM_2.5 at the rural IMPROVE sites by −1% for the ARW-CMAQ and −19% for the NMM-CMAQ. The greater underestimations of SO₄²⁻, OC and EC by the NMM-CMAQ contributed to increased underestimation of PM_2.5 at the IMPROVE sites. The NMB values for PM_2.5 at the STN urban sites are 15% and −16% for the ARW-CMAQ and NMM-CMAQ, respectively. The underestimation of PM_2.5 at the STN sites by the NMM-CMAQ mainly results from the underestimations of the SO₄²⁻, NH₄⁺ and TCM components, whereas the overestimation of PM_2.5 at the STN sites by the ARW-CMAQ results from the overestimations of SO₄²⁻, NO₃⁻, and NH₄⁺. The Comparison with WP-3 aircraft measurements reveals that both ARW-CMAQ and NMM-CMAQ have very similar model performance for vertical profiles for PM_2.5 chemical components (SO₄²⁻, NH₄⁺) and related gaseous species (HNO₃, SO₂, NH₃, isoprene, toluene, terpenes) as both models used the same chemical mechanisms and emissions. The results of ship along the coast of southeastern Texas over the Gulf of Mexico show that both models captured the temporal variations and broad synoptic change seen in the observed HCHO and acetaldehyde with the means NMB <30% most of the time but they consistently underestimated terpenes, isoprene, toluene and SO₂

WRF-CMAQ two-way coupled system with aerosol feedback: software development and preliminary results

Air quality models such as the EPA Community Multiscale Air Quality (CMAQ) require meteorological data as part of the input to drive the chemistry and transport simulation. The Meteorology-Chemistry Interface Processor (MCIP) is used to convert meteorological data into CMAQ-ready input. Key shortcoming of such one-way coupling include: excessive temporal interpolation of coarsely saved meteorological input and lack of feedback of atmospheric pollutant loading on simulated dynamics. We have developed a two-way coupled system to address these issues. A single source code principle was used to construct this two-way coupling system so that CMAQ can be consistently executed as a stand-alone model or part of the coupled system without any code changes; this approach eliminates maintenance of separate code versions for the coupled and uncoupled systems. The design also provides the flexibility to permit users: (1) to adjust the call frequency of WRF and CMAQ to balance the accuracy of the simulation versus computational intensity of the system, and (2) to execute the two-way coupling system with feedbacks to study the effect of gases and aerosols on short wave radiation and subsequent simulated dynamics. Details on the development and implementation of this two-way coupled system are provided. When the coupled system is executed without radiative feedback, computational time is virtually identical when using the Community Atmospheric Model (CAM) radiation option and a slightly increased (~8.5 %) when using the Rapid Radiative Transfer Model for GCMs (RRTMG) radiation option in the coupled system compared to the offline WRF-CMAQ system. Once the feedback mechanism is turned on, the execution time increases only slightly with CAM but increases about 60 % with RRTMG due to the use of a more detailed Mie calculation in this implementation of feedback mechanism. This two-way model with radiative feedback shows noticeably reduced bias in simulated surface shortwave radiation and 2 m temperatures as well improved correlation of simulated ambient ozone and PM&lt;sub&gt;2.5&lt;/sub&gt; relative to observed values for a test case with significant tropospheric aerosol loading from California wildfires

Assimilation of Satellite Data in Regional Air Quality Models

In terms of important uncertainty in regional-scale air-pollution models, probably no other aspect ranks any higher than the current ability to specify clouds and soil moisture on the regional scale. Because clouds in models are highly parameterized, the ability of models to predict the correct spatial and radiative characteristics is highly suspect and subject to large error. The poor representation of cloud fields from point measurements at National Weather Services stations and the almost total absence of surface moisture availability observations has made assimilation of these variables difficult to impossible. Yet, the correct inclusion of clouds and surface moisture are of first-order importance in regional-scale photochemistry

Incremental testing of the Community Multiscale Air Quality (CMAQ) modeling system version 4.7

This paper describes the scientific and structural updates to the latest release of the Community Multiscale Air Quality (CMAQ) modeling system version 4.7 (v4.7) and points the reader to additional resources for further details. The model updates were evaluated relative to observations and results from previous model versions in a series of simulations conducted to incrementally assess the effect of each change. The focus of this paper is on five major scientific upgrades: (a) updates to the heterogeneous N&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;5&lt;/sub&gt; parameterization, (b) improvement in the treatment of secondary organic aerosol (SOA), (c) inclusion of dynamic mass transfer for coarse-mode aerosol, (d) revisions to the cloud model, and (e) new options for the calculation of photolysis rates. Incremental test simulations over the eastern United States during January and August 2006 are evaluated to assess the model response to each scientific improvement, providing explanations of differences in results between v4.7 and previously released CMAQ model versions. Particulate sulfate predictions are improved across all monitoring networks during both seasons due to cloud module updates. Numerous updates to the SOA module improve the simulation of seasonal variability and decrease the bias in organic carbon predictions at urban sites in the winter. Bias in the total mass of fine particulate matter (PM&lt;sub&gt;2.5&lt;/sub&gt;) is dominated by overpredictions of unspeciated PM&lt;sub&gt;2.5&lt;/sub&gt; (PM&lt;sub&gt;other&lt;/sub&gt;) in the winter and by underpredictions of carbon in the summer. The CMAQv4.7 model results show slightly worse performance for ozone predictions. However, changes to the meteorological inputs are found to have a much greater impact on ozone predictions compared to changes to the CMAQ modules described here. Model updates had little effect on existing biases in wet deposition predictions

Estimating NH3 emissions from agricultural fertilizer application in China using the bi-directional CMAQ model coupled to an agro-ecosystem model

Atmospheric ammonia (NH3) plays an important role in atmospheric aerosol chemistry. China is one of the largest NH3 emitting countries with the majority of NH3 emissions coming from agricultural practices, such as fertilizer application and livestock production. The current NH3 emission estimates in China are mainly based on pre-defined emission factors that lack temporal or spatial details, which are needed to accurately predict NH3 emissions. This study provides the first online estimate of NH3 emissions from agricultural fertilizer application in China, using an agricultural fertilizer modeling system which couples a regional air quality model (the Community Multi-scale Air Quality model, or CMAQ) and an agro-ecosystem model (the Environmental Policy Integrated Climate model, or EPIC). This method improves the spatial and temporal resolution of NH3 emissions from this sector. We combined the cropland area data of 14 crops from 2710 counties with the Moderate Resolution Imaging Spectroradiometer (MODIS) land use data to determine the crop distribution. The fertilizer application rates and methods for different crops were collected at provincial or agricultural region levels. The EPIC outputs of daily fertilizer application and soil characteristics were input into the CMAQ model and the hourly NH3 emissions were calculated online with CMAQ running. The estimated agricultural fertilizer NH3 emissions in this study were approximately 3 Tg in 2011. The regions with the highest modeled emission rates are located in the North China Plain. Seasonally, peak ammonia emissions occur from April to July. Compared with previous researches, this study considers an increased number of influencing factors, such as meteorological fields, soil and fertilizer application, and provides improved NH3 emissions with higher spatial and temporal resolution

WRF-CMAQ two-way coupled system with aerosol feedback: software development and preliminary results

Air quality models such as the EPA Community Multiscale Air Quality (CMAQ) require meteorological data as part of the input to drive the chemistry and transport simulation. The Meteorology-Chemistry Interface Processor (MCIP) is used to convert meteorological data into CMAQ-ready input. Key shortcoming of such one-way coupling include: excessive temporal interpolation of coarsely saved meteorological input and lack of feedback of atmospheric pollutant loading on simulated dynamics. We have developed a two-way coupled system to address these issues. A single source code principle was used to construct this two-way coupling system so that CMAQ can be consistently executed as a stand-alone model or part of the coupled system without any code changes; this approach eliminates maintenance of separate code versions for the coupled and uncoupled systems. The design also provides the flexibility to permit users: (1) to adjust the call frequency of WRF and CMAQ to balance the accuracy of the simulation versus computational intensity of the system, and (2) to execute the two-way coupling system with feedbacks to study the effect of gases and aerosols on short wave radiation and subsequent simulated dynamics. Details on the development and implementation of this two-way coupled system are provided. When the coupled system is executed without radiative feedback, computational time is virtually identical when using the Community Atmospheric Model (CAM) radiation option and a slightly increased (~8.5%) when using the Rapid Radiative Transfer Model for GCMs (RRTMG) radiation option in the coupled system compared to the offline WRF-CMAQ system. Once the feedback mechanism is turned on, the execution time increases only slightly with CAM but increases about 60% with RRTMG due to the use of a more detailed Mie calculation in this implementation of feedback mechanism. This two-way model with radiative feedback shows noticeably reduced bias in simulated surface shortwave radiation and 2-m temperatures as well improved correlation of simulated ambient ozone and PM2.5 relative to observed values for a test case with significant tropospheric aerosol loading from California wildfires

Lateral terrestrial water flow contribution to summer precipitation at continental scale – A comparison between Europe and West Africa with WRF‐Hydro‐tag ensembles

It is well accepted that summer precipitation can be altered by soil moisture condition. Coupled land surface – atmospheric models have been routinely used to quantify soil moisture – precipitation feedback processes. However, most of the land surface models (LSMs) assume a vertical soil water transport and neglect lateral terrestrial water flow at the surface and in the subsurface, which potentially reduces the realism of the simulated soil moisture – precipitation feedback. In this study, the contribution of lateral terrestrial water flow to summer precipitation is assessed in two different climatic regions, Europe and West Africa, for the period June–September 2008. A version of the coupled atmospheric-hydrological model WRF-Hydro with an option to tag and trace land surface evaporation in the modelled atmosphere, named WRF-Hydro-tag, is employed. An ensemble of 30 simulations with terrestrial routing and 30 simulations without terrestrial routing is generated with random realizations of turbulent energy with the stochastic kinetic energy backscatter scheme, for both Europe and West Africa. The ensemble size allows to extract random noise from continental-scale averaged modelled precipitation. It is found that lateral terrestrial water flow increases the relative contribution of land surface evaporation to precipitation by 3.6% in Europe and 5.6% in West Africa, which enhances a positive soil moisture – precipitation feedback and generates more uncertainty in modelled precipitation, as diagnosed by a slight increase in normalized ensemble spread. This study demonstrates the small but non-negligible contribution of lateral terrestrial water flow to precipitation at continental scale

Evaluation of the diurnal cycle in the atmospheric boundary layer over land as represented by a variety of single-column models: the second GABLS experiment

Postprint (published version

Progestin Receptor-Mediated Reduction of Anxiety-Like Behavior in Male Rats

BACKGROUND: It is well known progesterone can have anxiolytic-like effects in animals in a number of different behavioral testing paradigms. Although progesterone is known to influence physiology and behavior by binding to classical intracellular progestin receptors, progesterone's anxiety reducing effects have solely been attributed to its rapid non-genomic effects at the GABA A receptor. This modulation occurs following the bioconversion of progesterone to allopregnanolone. Seemingly paradoxical results from some studies suggested that the function of progesterone to reduce anxiety-like behavior may not be entirely clear; therefore, we hypothesized that progesterone might also act upon progestin receptors to regulate anxiety. METHODOLOGY/PRINCIPAL FINDINGS: To test this, we examined the anxiolytic-like effects of progesterone in male rats using the elevated plus maze, a validated test of anxiety, and the light/dark chamber in the presence or absence of a progestin receptor antagonist, RU 486. Here we present evidence suggesting that the anxiolytic-like effects of progesterone in male rats can be mediated, in part, by progestin receptors, as these effects are blocked by prior treatment with a progestin receptor antagonist. CONCLUSION/SIGNIFICANCE: This indicates that progesterone can act upon progestin receptors to regulate anxiety-like behavior in the male rat brain