Serena H. Chung

BioEarth: Envisioning and developing a new regional earth system model to inform natural and agricultural resource management

As managers of agricultural and natural resources are confronted with uncertainties in global change impacts, the complexities associated with the interconnected cycling of nitrogen, carbon, and water present daunting management challenges. Existing models provide detailed information on specific sub-systems (e.g., land, air, water, and economics). An increasing awareness of the unintended consequences of management decisions resulting from interconnectedness of these sub-systems, however, necessitates coupled regional earth system models (EaSMs). Decision makers’ needs and priorities can be integrated into the model design and development processes to enhance decision-making relevance and “usability” of EaSMs. BioEarth is a research initiative currently under development with a focus on the U.S. Pacific Northwest region that explores the coupling of multiple stand-alone EaSMs to generate usable information for resource decision-making. Direct engagement between model developers and non-academic stakeholders involved in resource and environmental management decisions throughout the model development process is a critical component of this effort. BioEarth utilizes a bottom-up approach for its land surface model that preserves fine spatial-scale sensitivities and lateral hydrologic connectivity, which makes it unique among many regional EaSMs. This paper describes the BioEarth initiative and highlights opportunities and challenges associated with coupling multiple stand-alone models to generate usable information for agricultural and natural resource decision-making.

Evaluating the effects of climate change on summertime ozone using a relative response factor approach for policymakers

The impact of climate change on surface-level ozone is examined through a multiscale modeling effort that linked global and regional climate models to drive air quality model simulations. Results are quantified in terms of the relative response factor (RRFE), which estimates the relative change in peak ozone concentration for a given change in pollutant emissions (the subscript E is added to RRF to remind the reader that the RRF is due to emission changes only). A matrix of model simulations was conducted to examine the individual and combined effects of future anthropogenic emissions, biogenic emissions, and climate on the RRFE. For each member in the matrix of simulations the warmest and coolest summers were modeled for the present-day (1995–2004) and future (2045–2054) decades. A climate adjustment factor (CAFC or CAFCB when biogenic emissions are allowed to change with the future climate) was defined as the ratio of the average daily maximum 8-hr ozone simulated under a future climate to that simulated under the present-day climate, and a climate-adjusted RRFEC was calculated (RRFEC = RRFE × CAFC). In general, RRFEC > RRFE, which suggests additional emission controls will be required to achieve the same reduction in ozone that would have been achieved in the absence of climate change. Changes in biogenic emissions generally have a smaller impact on the RRFE than does future climate change itself. The direction of the biogenic effect appears closely linked to organic-nitrate chemistry and whether ozone formation is limited by volatile organic compounds (VOC) or oxides of nitrogen (NOX = NO + NO2). Regions that are generally NOX limited show a decrease in ozone and RRFEC, while VOC-limited regions show an increase in ozone and RRFEC. Comparing results to a previous study using different climate assumptions and models showed large variability in the CAFCB. Implications: We present a methodology for adjusting the RRF to account for the influence of climate change on ozone. The findings of this work suggest that in some geographic regions, climate change has the potential to negate decreases in surface ozone concentrations that would otherwise be achieved through ozone mitigation strategies. In regions of high biogenic VOC emissions relative to anthropogenic NOX emissions, the impact of climate change is somewhat reduced, while the opposite is true in regions of high anthropogenic NOX emissions relative to biogenic VOC emissions. Further, different future climate realizations are shown to impact ozone in different ways.

Application of the Wind Erosion Prediction System in the AIRPACT Regional Air Quality Modeling Framework

Abstract. Wind erosion of soil is a major concern of the agricultural community, as it removes the most fertile part of the soil and thus degrades soil productivity. Furthermore, dust emissions due to wind erosion degrade air quality, reduce visibility, and cause perturbations to regional radiation budgets. PM 10 emitted from the soil surface can travel hundreds of kilometers downwind before being deposited back to the surface. Thus, it is necessary to address agricultural air pollutant sources within a regional air quality modeling system in order to forecast regional dust storms and to understand the impact of agricultural activities and land-management practices on air quality in a changing climate. The Wind Erosion Prediction System (WEPS) is a new tool in regional air quality modeling for simulating erosion from agricultural fields. WEPS represents a significant improvement, in comparison to existing empirical windblown dust modeling algorithms used for air quality simulations, by using a more process-based modeling approach. This is in contrast with the empirical approaches used in previous models, which could only be used reliably when soil, surface, and ambient conditions are similar to those from which the parameterizations were derived. WEPS was originally intended for soil conservation applications and designed to simulate conditions of a single field over multiple years. In this work, we used the EROSION submodel from WEPS as a PM 10 emission module for regional modeling by extending it to cover a large region divided into Euclidean grid cells. The new PM 10 emission module was then employed within a regional weather and chemical transport modeling framework commonly used for comprehensive simulations of a wide range of pollutants to evaluate overall air quality conditions. This framework employs the Weather Research and Forecasting (WRF) weather model along with the Community Multi-scale Air Quality (CMAQ) model to treat ozone, particulate matter, and other air pollutants. To demonstrate the capabilities of the WRF/EROSION/CMAQ dust modeling framework, we present here results from simulations of dust storms that occurred in central and eastern Washington during 4 October 2009 and 26 August 2010. Comparison of model results with observations indicates that the modeling framework performs well in predicting the onset and timing of the dust storms and the spatial extent of their dust plumes. The regional dust modeling framework is able to predict elevated PM 10 concentrations hundreds of kilometers downwind of erosion source regions associated with the windblown dust, although the magnitude of the PM 10 concentrations are extremely sensitive to the assumption of surface soil moisture and model wind speeds. Future work will include incorporating the full WEPS model into the regional modeling framework and targeting field measurements to evaluate the modeling framework more extensively.

Spatial Application of WEPS for Estimating Wind Erosion in the Pacific Northwest

Abstract. The Wind Erosion Prediction System (WEPS) is used to simulate soil erosion by wind on cropland and was originally designed to run simulations on a field scale. This study extended WEPS to run on multiple fields (grid cells) independently to cover a large region and conducted an initial investigation to assess how well WEPS performed in that environment by comparing simulations for two historical dust events with field observations and satellite images in the Columbia Plateau region of Washington. We modified the WEPS source code to allow it not only to run on multiple grid cells but also to save the state of the simulation so that it can be re-initiated from that state in future runs, allowing the model to be started and then stepped through time incrementally under various future climate or forecast weather scenarios. We initially ran WEPS on the entire state of Washington, with the entire Pacific Northwest region as our ultimate target area, to provide PM 10 and eventually PM 2.5 emissions from wind erosion events as input to the chemical transport model CMAQ, which is used by the AIRPACT regional air quality modeling system for the Pacific Northwest. Three principal inputs to WEPS are meteorological data, soil data, and crop management practices. These data, at a 1 km A— 1 km grid cell resolution, are the basic input data for running the spatially distributed model. The climatic data from a three-year period were stochastically generated based on statistical representations of past meteorological measurements from stations in the region and were used for initializing WEPS, and then a three-day set of meteorological data corresponding with historical dust storm events were selected for simulation by WEPS of wind erosion of cropland in the state of Washington. The crop management data were selected based on the land use and USDA Natural Resources Conservation Service (NRCS) crop management zones, and the soil data were derived from the NRCS SSURGO database. We aggregated the outputs from 1 km A— 1 km grid cells into 12 km A— 12 km grid cells for easier visualization and then mapped the total surface soil erosion, suspension, and PM 10 emissions for each 12 km A— 12 km grid cell. This study shows that WEPS can be successfully extended to run from one field grid cell to multiple field grid cells, and the model can identify regions with high potential for soil erosion by wind. It also demonstrates that WEPS can be used for real-time monitoring of soil erosion and air quality in a large region if actual and forecast weather inputs are available.

Relationships between the El Niño–Southern Oscillation, precipitation, and nitrogen wet deposition rates in the contiguous United States

Human activities have significantly increased reactive nitrogen (N) in the environment, leading to adverse effects on various ecosystems. We used 1979-2012 seasonal inorganic N wet deposition data from the National Atmospheric Deposition Program to evaluate the relationship between the El Nino Southern Oscillation (ENSO) and N wet deposition in the contiguous U.S. The correlations between precipitation and inorganic N wet deposition were the strongest and most spatially extensive during winter; up to 62% and 53% of the 2- to 6-year variations of precipitation and N wet deposition rates, respectively, in the Rocky Mountains, along the coast of the Gulf of Mexico, and near the Great Lakes can be explained by variation in the NINO3.4 climate index, which was used as a measure of ENSO activity. During El Nino winters, precipitation and N wet deposition rates were higher than normal in the southern U.S., while La Nina events brought higher precipitation and N wet deposition to the Rocky Mountains and Great Lakes regions. Wintertime N wet deposition correlations held through springtime in the Great Lakes and the Northeast; however, correlations between NINO3.4 and precipitation were not significant at most sites, suggesting factors besides precipitation amount contributed to the 2- to 6-year variation of N wet deposition in these regions. As the frequency, strength, and types of ENSO change in the future, inter-annual variability of N wet deposition will be affected, indirectly affecting spatial distribution of dry N deposition and potentially changing the overall spatial patterns of N deposition.