Abstract. 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 2 O 5 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 2.5 ) is dominated by overpredictions of unspeciated PM 2.5 (PM other ) in the winter and by underpredictions of carbon in the summer. The CMAQv4.7 model results show slightly worse performance for ozone predictions.Correspondence to: K. M. Foley (foley.kristen@epa.gov) 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.

Abstract. 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 2.5 relative to observed values for a test case with significant tropospheric aerosol loading from California wildfires.

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 MeteorologyChemistry 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.

An important question in regional climate downscaling is whether to constrain (nudge) the interior of the limited-area domain toward the larger-scale driving fields. Prior research has demonstrated that interior nudging can increase the skill of regional climate predictions originating from historical data. However, there is concern that nudging may also inhibit the regional model's ability to properly develop and simulate mesoscale features, which may reduce the value added from downscaling by altering the representation of local climate extremes. Extreme climate events can result in large economic losses and human casualties, and regional climate downscaling is one method for projecting how climate change scenarios will affect extreme events locally. In this study, the effects of interior nudging are explored on the downscaled simulation of temperature and precipitation extremes. Multidecadal, continuous Weather Research and Forecasting model simulations of the contiguous United States are performed using coarse reanalysis fields as proxies for global climate model fields. The results demonstrate that applying interior nudging improves the accuracy of simulated monthly means, variability, and extremes over the multidecadal period. The results in this case indicate that interior nudging does not inappropriately squelch the prediction of temperature and precipitation extremes and is essential for simulating extreme events that are faithful in space and time to the driving largescale fields.

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This study evaluates interior nudging techniques using the Weather Research and Forecasting (WRF) model for regional climate modeling over the conterminous United States (CONUS) using a two-way nested configuration. NCEP–Department of Energy Atmospheric Model Intercomparison Project (AMIP-II) Reanalysis (R-2) data are downscaled to 36 km × 36 km by nudging only at the lateral boundaries, using gridpoint (i.e., analysis) nudging and using spectral nudging. Seven annual simulations are conducted and evaluated for 1988 by comparing 2-m temperature, precipitation, 500-hPa geopotential height, and 850-hPa meridional wind to the 32-km North American Regional Reanalysis (NARR). Using interior nudging reduces the mean biases for those fields throughout the CONUS compared to the simulation without interior nudging. The predictions of 2-m temperature and fields aloft behave similarly when either analysis or spectral nudging is used. For precipitation, however, analysis nudging generates monthly precipitation totals, and intensity and frequency of precipitation that are closer to observed fields than spectral nudging. The spectrum of 250-hPa zonal winds simulated by the WRF model is also compared to that of the R-2 and NARR. The spatial variability in the WRF model is reduced by using either form of interior nudging, and analysis nudging suppresses that variability more strongly than spectral nudging. Reducing the nudging strengths on the inner domain increases the variability but generates larger biases. The results support the use of interior nudging on both domains of a two-way nest to reduce error when the inner nest is not otherwise dominated by the lateral boundary forcing. Nevertheless, additional research is required to optimize the balance between accuracy and variability in choosing a nudging strategy.

NOAA and the U.S. Environmental Protection Agency (EPA) have developed a national air quality forecasting (AQF) system that is based on numerical models for meteorology, emissions, and chemistry. The AQF system generates gridded model forecasts of ground-level ozone (O3) that can help air quality forecasters to predict and alert the public of the onset, severity, and duration of poor air quality conditions. Although AQF efforts have existed in metropolitan centers for many years, this AQF system provides a national numerical guidance product and the first-ever air quality forecasts for many (predominantly rural) areas of the United States. The AQF system is currently based on NCEP’s Eta Model and the EPA’s Community Multiscale Air Quality (CMAQ) modeling system. The AQF system, which was implemented into operations at the National Weather Service in September of 2004, currently generates twice-daily forecasts of O3 for the northeastern United States at 12-km horizontal grid spacing. Preoperational testing to support the 2003 and 2004 O3 forecast seasons showed that the AQF system provided valuable guidance that could be used in the air quality forecast process. The AQF system will be expanded over the next several years to include a nationwide domain, a capability for forecasting fine particle pollution, and a longer forecast period. State and local agencies will now issue air quality forecasts that are based, in part, on guidance from the AQF system. This note describes the process and software components used to link the Eta Model and CMAQ for the national AQF system, discusses several technical and logistical issues that were considered, and provides examples of O3 forecasts from the AQF system.

.[1] Convective systems and associated cloudiness directly influence regional and local atmospheric radiation budgets, as well as dynamics and thermodynamics, through feedbacks. However, most subgrid-scale convective parameterizations in regional weather and climate models do not consider cumulus cloud feedbacks to radiation, resulting in biases in several meteorological parameters. We have incorporated this key feedback process into a convective parameterization and a radiation scheme in the Weather Research and Forecasting model, and evaluated the impacts of including this process in short-term weather and multiyear climate simulations. Introducing subgrid-scale convective cloudradiation feedbacks leads to a more realistic simulation of attenuation of downward surface shortwave radiation. Reduced surface shortwave radiation moderates the surface forcing for convection and results in a notable reduction in precipitation biases. Our research reveals a need for more in-depth consideration of the effects of subgrid-scale clouds in regional meteorology/climate and air quality models on radiation, photolysis, cloud mixing, and aerosol indirect effects. Citation: Alapaty, K., J. A. Herwehe, T. L. Otte, C. G. Nolte, O. R. Bullock, M. S. Mallard, J. S. Kain, and J. Dudhia (2012), Introducing subgrid-scale cloud feedbacks to radiation for regional meteorological and climate modeling, Geophys. Res. Lett., 39, L24809,