ALBERT

Description: 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. MCIP acts as both a post-processor to the meteorological model and a pre-processor to 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.

Description: To develop fine particular matter (PM2.5) air quality forecasts, a National Air Quality Forecast Capability (NAQFC) system, which linked NOAA's North American Mesoscale (NAM) meteorological model with EPA's Community Multiscale Air Quality (CMAQ) model, was deployed in the developmental mode over the continental United States during 2007. This study investigates the operational use of a bias-adjustment technique called the Kalman Filter Predictor approach for improving the accuracy of the PM2.5 forecasts at monitoring locations. The Kalman Filter Predictor bias-adjustment technique is a recursive algorithm designed to optimally estimate bias-adjustment terms using the information extracted from previous measurements and forecasts. The bias-adjustment technique is found to improve PM2.5 forecasts (i.e. reduced errors and increased correlation coefficients) for the entire year at almost all locations. The NAQFC tends to overestimate PM2.5 during the cool season and underestimate during the warm season in the eastern part of the continental US domain, but the opposite is true for the pacific coast. In the Rocky Mountain region, the NAQFC system overestimates PM2.5 for the whole year. The bias-adjustment forecasts can quickly (after 2–3 days' lag) adjust to reflect the transition from one regime to the other. The modest computational requirements and systematical improvements in forecast results across all seasons suggest that this technique can be easily adapted to perform bias-adjustment for real-time PM2.5 air quality forecasts.

Description: Cosmogenic exposure dating provides a method for estimating the ages of glacial moraines deposited in the last ~105 years. Cosmic rays break atoms in surface rocks at predictable rates. Thus, the ages of moraines are directly related to the concentrations of cosmic ray-produced nuclides in rocks on the moraine surfaces, under ideal circumstances. However, many geomorphic processes may interfere with cosmogenic exposure dating. Because of these processes, boulders sometimes arrive at the moraines with preexisting concentrations of cosmogenic nuclides, or else the boulders are partly shielded from cosmic rays following deposition. Many methods for estimating moraine ages from cosmogenic exposure dates exist in the literature, but we cannot assess the appropriateness of these methods without knowing the parent distribution from which the dates were drawn on each moraine. Here, we make two contributions. First, we describe numerical models of two geomorphic processes, moraine degradation and inheritance, and their effects on cosmogenic exposure dating. Second, we assess the robustness of various simple methods for estimating the ages of moraines from collections of cosmogenic exposure dates. Our models estimate the probability distributions of cosmogenic exposure dates that we would obtain from moraine boulders with specified geomorphic histories, using Monte Carlo methods. We expand on pioneering modeling efforts to address this problem by placing these models into a common framework. We also evaluate the sensitivity of the models to changes in their input parameters. The sensitivity tests show that moraine degradation consistently produces left-skewed distributions of exposure dates; that is, the distributions have long tails toward the young end of the distribution. In contrast, inheritance produces right-skewed distributions that have long tails toward the old side of the distribution. Given representative distributions from these two models, we can determine which methods of estimating moraine ages are most successful in recovering the correct age for test cases where this value is known. The mean is a poor estimator of moraine age for data sets drawn from skewed parent distributions, and excluding outliers before calculating the mean does not improve this mismatch. The extreme estimators (youngest date and oldest date) perform well under specific circumstances, but fail in other cases. We suggest a simple estimator that uses the skewnesses of individual data sets to determine whether the youngest date, mean, or oldest date will provide the best estimate of moraine age. Although this method is perhaps the most globally robust of the estimators we tested, it sometimes fails spectacularly. The failure of simple methods to provide accurate estimates of moraine age points toward a need for more sophisticated statistical treatments. We present improved methods for estimating moraine ages in a companion paper.

Description: Chemical processing of sea-salt particles in coastal environments significantly impacts concentrations of particle components and gas-phase species and has implications for human exposure to particulate matter and nitrogen deposition to sensitive ecosystems. Emission of sea-salt particles from the coastal surf zone is known to be elevated compared to that from the open ocean. Despite the importance of sea-salt emissions and chemical processing, the US EPA's Community Multiscale Air Quality (CMAQ) model has traditionally treated coarse sea-salt particles as chemically inert and has not accounted for enhanced surf-zone emissions. In this article, updates to CMAQ are described that enhance sea-salt emissions from the coastal surf zone and allow dynamic transfer of HNO3, H2SO4, HCl, and NH3 between coarse particles and the gas phase. Predictions of updated CMAQ models and the previous release version, CMAQv4.6, are evaluated using observations from three coastal sites during the Bay Regional Atmospheric Chemistry Experiment (BRACE) in Tampa, FL in May 2002. Model updates improve predictions of NO3−, SO42−, NH4+, Na+, and Cl− concentrations at these sites with only a 8% increase in run time. In particular, the chemically interactive coarse particle mode dramatically improves predictions of nitrate concentration and size distributions as well as the fraction of total nitrate in the particle phase. Also, the surf-zone emission parameterization improves predictions of total sodium and chloride concentration. Results of a separate study indicate that the model updates reduce the mean absolute error of nitrate predictions at coastal CASTNET and SEARCH sites in the eastern US. Although the new model features improve performance relative to CMAQv4.6, some persistent differences exist between observations and predictions. Modeled sodium concentration is biased low and causes under-prediction of coarse particle nitrate. Also, CMAQ over-predicts geometric mean diameter and standard deviation of particle modes at the BRACE sites. These over-predictions may cause too rapid particle dry deposition and partially explain the low bias in sodium predictions. Despite these shortcomings, the updates to CMAQ enable more realistic simulations of chemical processes in environments where marine air mixes with urban pollution. The model updates described in this article are included in the public release of CMAQv4.7 (http://www.cmaq-model.org).

Description: A new modelling tool for the investigation of large-scale behaviour of cirrus clouds has been developed. This combines two existing models, the TOMCAT/SLIMCAT chemistry transport model (nupdate library version 0.80, script mpc346_l) and cirrus parameterisation of Ren and MacKenzie (LACM implementation not versioned). The development process employed a subset of best-practice software engineering and quality assurance processes, selected to be viable for small-scale projects whilst maintaining the same traceability objectives. The application of the software engineering and quality control processes during the development has been shown to be not a great overhead, and their use has been of benefit to the developers as well as the end users of the results. We provide a step-by-step guide to the implementation of traceability tailored to the production of geo-scientific research software, as distinct from commercial and operational software. Our recommendations include: maintaining a living "requirements list"; explicit consideration of unit, integration and acceptance testing; and automated revision/configuration control, including control of analysis tool scripts and programs. Initial testing of the resulting model against satellite and in-situ measurements has been promising. The model produces representative results for both spatial distribution of the frequency of occurrence of cirrus ice, and the drying of air as it moves across the tropical tropopause. The model is now ready for more rigorous quantitative testing, but will require the addition of a vertical wind velocity downscaling scheme to better represent extra-tropical continental cirrus.

Description: 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 N2O5 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 (PM2.5) is dominated by overpredictions of unspeciated PM2.5 (PMother) in the winter and by underpredictions of carbon in the summer. The CMAQ v4.7 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.

Description: In 2008 the temporal focus of the Palaeoclimate Modelling Intercomparison Project was expanded to include a model intercomparison for the mid-Pliocene warm period (ca. 2.97 to 3.29 Ma BP). This project is referred to as PlioMIP (Pliocene Model Intercomparison Project). Two experiments have been agreed upon and comprise phase 1 of the PlioMIP. The first (Experiment 1) will be performed with atmosphere-only GCMs. The second (Experiment 2) will utilise fully coupled ocean-atmosphere GCMs. This paper describes the experimental design and boundary conditions that will be utilised for Experiment 1 of the PlioMIP project.

Description: The Model for Ozone and Related chemical Tracers, version 4 (MOZART-4) is an offline global chemical transport model particularly suited for studies of the troposphere. The updates of the model from its previous version MOZART-2 are described, including an expansion of the chemical mechanism to include more detailed hydrocarbon chemistry and bulk aerosols. Online calculations of a number of processes, such as dry deposition, emissions of isoprene and monoterpenes and photolysis frequencies, are now included. Results from an eight-year simulation (2000–2007) are presented and evaluated. The MOZART-4 source code and standard input files are available for download from the NCAR Community Data Portal (http://cdp.ucar.edu).

Description: The efficient primitive-equation coupled atmosphere-ocean model SPEEDO is presented. The model includes an interactive sea-ice and land component. SPEEDO is a global earth system model of intermediate complexity. It has a horizontal resolution of T30 (triangular truncation at wave number 30) and 8 vertical layers in the atmosphere, and a horizontal resolution of 2 degrees and 20 levels in the ocean. The parameterizations in SPEEDO are developed in such a way that it is a fast model suitable for large ensembles or long runs on a workstation. The model has no flux correction. We compare the mean state and inter-annual variability of the model with observational fields of the atmosphere and ocean. In particular the atmospheric circulation, the mid-latitude patterns of variability and teleconnections from the tropics are well simulated. To show the model's capabilities, we performed a long control run and an ensemble experiment with enhanced greenhouse gasses. The long control run shows that the model is stable. CO2 doubling and future climate change scenario experiments show a climate sensitivity of 1.84 K W−1 m−2, which is within the range of state-of-the-art climate models. The spatial response patterns are comparable to state-of-the-art, higher resolution models. However, for very high greenhouse concentrations the parameterizations are not valid. We conclude that the model is suitable for past, current and future climate simulations and for exploring wide parameter ranges and mechanisms of variability. However, as with any model, users should be careful when using the model beyond the range of physical realism of the parameterizations and model setup.

Description: This paper presents a comparison of the operational performances of two Community Multiscale Air Quality (CMAQ) model v4.7 simulations that utilize input data from the 5th-generation Mesoscale Model (MM5) and the Weather Research and Forecasting (WRF) meteorological models. Two sets of CMAQ model simulations were performed for January and August 2006. One set utilized MM5 meteorology (MM5-CMAQ) and the other utilized WRF meteorology (WRF-CMAQ), while all other model inputs and options were kept the same. For January, predicted ozone (O3) concentrations were higher in the Southeast and lower Mid-west regions in the WRF-CMAQ simulation, resulting in slightly higher bias and error as compared to the MM5-CMAQ simulations. The higher predicted O3 concentrations are attributed to less dry deposition of O3 in the WRF-CMAQ simulation due to differences in the calculation of the vegetation fraction between the MM5 and WRF models. The WRF-CMAQ results showed better performance for particulate sulfate (SO42−), similar performance for nitrate (NO3−) and total nitrate (TNO3), and slightly worse performance for total carbon (TC) and total fine particulate (PM2.5) mass than the corresponding MM5-CMAQ results. For August, predictions of O3 were notably higher in the WRF-CMAQ simulation, particularly in the southern United States, resulting in increased model bias. Concentrations of predicted particulate SO42− were lower in the region surrounding the Ohio Valley and higher along the Gulf of Mexico in the WRF-CMAQ simulation, contributing to poorer model performance. The primary cause of the differences in predicted concentrations between the MM5-CMAQ and WRF-CMAQ simulations is due to differences in the calculation of the friction velocity (u*) in MM5 and WRF models, which has a large effect on the dry deposition of NO, NO2 and HNO3. Differences in the calculation of the vegetation fraction and the predicted cloud cover, along with several other minor differences in the simulations also affect the predicted concentrations from CMAQ. The performance for SO42−, NO3− and NH4+ wet deposition was similar for both simulations for January and August.