The Weather Research and Forecasting (WRF) model is used to downscale a coarse reanalysis (National Centers for Environmental Prediction–Department of Energy Atmospheric Model Intercomparison Project reanalysis, hereafter R2) as a proxy for a global climate model (GCM) to examine the consequences of using different methods for setting lake temperatures and ice on predicted 2 m temperature and precipitation in the Great Lakes region. A control simulation is performed where lake surface temperatures and ice coverage are interpolated from the GCM proxy. Because the R2 represents the five Great Lakes with only three grid points, ice formation is poorly represented, with large, deep lakes freezing abruptly. Unrealistic temperature gradients appear in areas where the coarse-scale fields have no inland water points nearby and lake temperatures on the finer grid are set using oceanic points from the GCM proxy. Using WRF coupled with the Freshwater Lake (FLake) model reduces errors in lake temperatures and significantly improves the timing and extent of ice coverage. Overall, WRF-FLake increases the accuracy of 2 m temperature compared to the control simulation where lake variables are interpolated from R2. However, the decreased error in FLake-simulated lake temperatures exacerbates an existing wet bias in monthly precipitation relative to the control run because the erroneously cool lake temperatures interpolated from R2 in the control run tend to suppress overactive precipitation.

Previous research has demonstrated the ability to use the Weather Research and Forecasting model (WRF) and contemporary dynamical downscaling methods to refine global climate modeling results to a horizontal grid spacing of 36 km. Environmental managers and urban planners have expressed the need for even finer resolution in projections of surface-level weather to take into account local geophysical and urbanization patterns. In this study, WRF as previously applied at 36-km grid spacing is used with 12-km grid spacing with one-way nesting to simulate the year 2006 over the central and eastern United States. The results at both resolutions are compared with hourly observations of surface air temperature, humidity, and wind speed. The 12- and 36-km simulations are also compared with precipitation data from three separate observation and analysis systems. The results show some additional accuracy with the refinement to 12-km horizontal grid spacing, but only when some form of interior nudging is applied. A positive bias in precipitation found previously in the 36-km results becomes worse in the 12-km simulation, especially without the application of interior nudging. Model sensitivity testing shows that 12-km grid spacing can further improve accuracy for certain meteorological variables when alternate physics options are employed. However, the strong positive bias found for both surface-level water vapor and precipitation suggests that WRF as configured here may have an unbalanced hydrologic cycle that is returning moisture from land and/or water bodies to the atmosphere too quickly.

The potential impacts of climate change on regional ozone (O₃) and fine particulate (PM_2.5) air quality in the United States (US) 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 of less than 2 K for most regions of the US and most seasons of the year and good representation of variability. Precipitation in the central and eastern US is well simulated for the historical period, with seasonal and annual biases generally less than 25 %, with positive biases exceeding 25 % in the western US throughout the year and in part of the eastern US during summer. Maximum daily 8 h ozone (MDA8 O₃) is projected to increase during summer and autumn in the central and eastern US. 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 PM_2.5 concentration changes range from −1.0 to 1.0 µg m⁻³. Organic PM_2.5 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.

The Weather Research and Forecasting (WRF) model is commonly used to make high-resolution future projections of regional climate by downscaling global climate model (GCM) outputs. Because the GCM fields are typically at a much coarser spatial resolution than the target regional downscaled fields, lakes are often poorly resolved in the driving global fields, if they are resolved at all. In such an application, using WRF's default interpolation methods can result in unrealistic lake temperatures and ice cover at inland water points. Prior studies have shown that lake temperatures and ice cover impact the simulation of other surface variables, such as air temperatures and precipitation, two fields that are often used in regional climate applications to understand the impacts of climate change on human health and the environment. Here, alternative methods for setting lake surface variables in WRF for downscaling simulations are presented and contrasted.

The Weather Research and Forecasting (WRF) model is used in a downscaling experiment to simulate a portion of the Atlantic hurricane season both in present-day conditions and with modifications to include future thermodynamic changes. Temperature and moisture changes are derived from an ensemble of climate simulations from the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) A1B scenario and added to analyzed initial and lateral boundary conditions, leaving horizontal temperature gradients and vertical wind shear unaltered. This method of downscaling excludes future changes in shear and incipient disturbances, thereby isolating the thermodynamic component of climate change and its effect on tropical cyclone (TC) activity. The North Atlantic basin is simulated with 18- and 6-km grid spacing, and a four-member physics ensemble is composed by varying microphysical and boundary layer parameterization schemes. This ensemble is used in monthly simulations during an active (2005) and inactive (2009) season, and the simulations are able to capture the change in activity between the different years. TC frequency is better reproduced with use of 6-km grid spacing and explicitly simulated convection, relative to simulations with 18-km grid spacing. A detailed comparison of present-day and future ensemble results is provided in a companion study.