Regional temperature-ozone relationships across the U.S. under multiple climate and emissions scenarios.

The potential effects of 21st century climate change on ozone (O3) concentrations in the United States are investigated using global climate simulations to drive higher-resolution regional meteorological and chemical transport models. Community Earth System Model (CESM) and Coupled Model version 3 (CM3) simulations of the Representative Concentration Pathway 8.5 scenario are dynamically downscaled using the Weather Research and Forecasting model, and the resulting meteorological fields are used to drive the Community Multiscale Air Quality model. Air quality is modeled for five 11-year periods using both a 2011 air pollutant emission inventory and a future projection accounting for full implementation of promulgated regulatory controls. Across the U.S., CESM projects daily maximum temperatures during summer to increase 1-4°C by 2050 and 2-7°C by 2095, while CM3 projects warming of 2-7°C by 2050 and 4-11°C by 2095. The meteorological changes have geographically varying impacts on O3 concentrations. Using the 2011 emissions dataset, O3 increases 1-5 ppb in the central Great Plains and Midwest by 2050 and more than 10 ppb by 2095, but it remains unchanged or even decreases in the Gulf Coast, Maine, and parts of the Southwest. Using the projected emissions, modeled increases are attenuated while decreases are amplified, indicating that planned air pollution control measures ameliorate the ozone climate penalty. The relationships between changes in maximum temperature and changes in O3 concentrations are examined spatially and quantified to explore the potential for developing an efficient approach for estimating air quality impacts of other future climate scenarios.Implications: The effects of climate change on ozone air quality in the United States are investigated using two global climate model simulations of a high warming scenario for five decadal periods in the 21st century. Warming summer temperatures simulated under both models lead to higher ozone concentrations in some regions, with the magnitude of the change increasing with temperature over the century. The magnitude and spatial extent of the increases are attenuated under a future emissions projection that accounts for regulatory controls. Regional linear regression relationships are developed as a first step toward development of a reduced form model for efficient estimation of the health impacts attributable to changes in air quality resulting from a climate change scenario.

Effects of Mosaic Land Use on Dynamically Downscaled WRF Simulations of the Contiguous U.S.

The representation of land use (LU) in meteorological modeling strongly influences the simulation of fluxes of heat, moisture, and momentum; affecting the accuracy of 2-m temperature and precipitation. Here, the Weather Research and Forecasting (WRF) model is used with the Noah land surface model to compare a mosaic approach, which accounts for subgrid scale variability of LU types, to the default option which only considers the dominant category in each grid cell. Three-year historical dynamically downscaled WRF simulations are generated using a 12-km domain over the contiguous U.S. to assess the sensitivities to using mosaic LU and to changes to parameters associated with LU and soil categories. Compared to dominant LU, mosaic LU features decreased coverage of forest and agricultural types and increased low-density urban LU throughout much of the eastern and central U.S. However, highly urbanized areas show the opposite trend, as mosaic LU represents partial greenspace within areas that are exclusively urban within dominant LU. Mosaic LU results in widespread increases in sensible heat fluxes and 2-m temperatures, with reductions in latent heat flux, 2-m mixing ratio, and monthly precipitation across the central and eastern U.S. These changes exacerbate an existing warm bias found with dominant LU but reduce overestimations of precipitation. Highly urbanized areas in the eastern U.S. tend to have cooler, more realistic temperatures with mosaic LU relative to dominant LU. A pair of runs with updated surface parameters corroborates these results. Overall, differences between the simulations are largely attributable to their representations of urban LU.

Examining WRF's Sensitivity to Contemporary Land-Use Datasets across the Contiguous United States Using Dynamical Downscaling.

Land-use (LU) representation plays a critical role in simulating air-surface interactions that affect meteorological conditions and regional climate. In the Noah LSM within the WRF Model, LU categories are used to set the radiative properties of the surface and to influence exchanges of heat, moisture, and momentum between the air and land surface. Previous literature examined the sensitivity of WRF simulations to LU using short-term meteorological modeling approaches. Here, the sensitivity to LU representation is studied using continental-scale dynamical downscaling, which typically uses longer temporal and larger spatial scales. Two LU datasets, the U.S. Geological Survey (USGS) dataset and the 2006 National Land Cover Dataset (NLCD), are utilized in 3-yr dynamically downscaled WRF simulations over a historical period. Precipitation and 2-m air temperature are evaluated against observation-based datasets for simulations covering the contiguous United States. The WRF-NLCD simulation tends to produce lower precipitation than the WRF-USGS run, with slightly warmer mean monthly temperatures. However, WRF-NLCD results in more notable increases in the frequency of hot days [i.e., days with temperature >90°F (32.2°C)]. These changes are attributable to reductions in forest and agricultural area in the NLCD relative to USGS. There is also subtle but important sensitivity to the method of interpolating LU data to the WRF grid in the model preprocessing. In all cases, the sensitivity resulting from changes in the LU is smaller than model error. Although this sensitivity is small, it persists across spatial and temporal scales.

A Maieutic Exploration of Nudging Strategies for Regional Climate Applications Using the WRF Model.

The use of nudging in the Weather Research and Forecasting (WRF) Model to constrain regional climate downscaling simulations is gaining in popularity because it can reduce error and improve consistency with the driving data. While some attention has been paid to whether nudging is beneficial for downscaling, very little research has been performed to determine best practices. In fact, many published papers use the default nudging configuration (which was designed for numerical weather prediction), follow practices used by colleagues, or adapt methods developed for other regional climate models. Here, a suite of 45 three-year simulations is conducted with WRF over the continental United States to systematically and comprehensively examine a variety of nudging strategies. The simulations here use a longer test period than did previously published works to better evaluate the robustness of each strategy through all four seasons, through multiple years, and across nine regions of the United States. The analysis focuses on the evaluation of 2-m temperature and precipitation, which are two of the most commonly required downscaled output fields for air quality, health, and ecosystems applications. Several specific recommendations are provided to effectively use nudging in WRF for regional climate applications. In particular, spectral nudging is preferred over analysis nudging. Spectral nudging performs best in WRF when it is used toward wind above the planetary boundary layer (through the stratosphere) and temperature and moisture only within the free troposphere. Furthermore, the nudging toward moisture is very sensitive to the nudging coefficient, and the default nudging coefficient in WRF is too high to be used effectively for moisture.

The potential effects of climate change on air quality across the conterminous U.S. at 2030 under three Representative Concentration Pathways.

The potential impacts of climate change on regional ozone (O3) and fine particulate (PM2.5) air quality in the United States are investigated by linking global climate simulations with regional scale meteorological and chemical transport models. Regional climate at 2000 and at 2030 under three Representative Concentration Pathways (RCPs) is simulated by using the Weather Research and Forecasting (WRF) model to downscale 11-year time slices from the Community Earth System Model (CESM). The downscaled meteorology is then used with the Community Multiscale Air Quality (CMAQ) model to simulate air quality during each of these 11-year periods. The analysis isolates the future air quality differences arising from climate-driven changes in meteorological parameters and specific natural emissions sources that are strongly influenced by meteorology. Other factors that will affect future air quality, such as anthropogenic air pollutant emissions and chemical boundary conditions, are unchanged across the simulations. The regional climate fields represent historical daily maximum and daily minimum temperatures well, with mean biases less than 2 K for most regions of the U.S. and most seasons of the year and good representation of variability. Precipitation in the central and eastern U.S. is well simulated for the historical period, with seasonal and annual biases generally less than 25%, with positive biases exceeding 25% in the western U.S. throughout the year and in part of the eastern U.S. during summer. Maximum daily 8-h ozone (MDA8 O3) is projected to increase during summer and autumn in the central and eastern U.S. The increase in summer mean MDA8 O3 is largest under RCP8.5, exceeding 4 ppb in some locations, with smaller seasonal mean increases of up to 2 ppb simulated during autumn and changes during spring generally less than 1 ppb. Increases are magnified at the upper end of the O3 distribution, particularly where projected increases in temperature are greater. Annual average PM2.5 concentration changes range from -1.0 to 1.0 µg m-3. Organic PM2.5 concentrations increase during summer and autumn due to increased biogenic emissions. Aerosol nitrate decreases during winter, accompanied by lesser decreases in ammonium and sulfate, due to warmer temperatures causing increased partitioning to the gas phase. Among meteorological factors examined to account for modeled changes in pollution, temperature and isoprene emissions are found to have the largest changes and the greatest impact on O3 concentrations.