Tanya L. Spero

Increasing the credibility of regional climate simulations by introducing subgrid‐scale cloud‐radiation interactions

The radiation schemes in the Weather Research and Forecasting (WRF) model have previously not accounted for the presence of subgrid-scale cumulus clouds, thereby resulting in unattenuated shortwave radiation, which can lead to overly energetic convection and overpredicted surface precipitation. This deficiency can become problematic when applying WRF as a regional climate model (RCM). Therefore, modifications were made to the WRF model to allow the Kain–Fritsch (KF) convective parameterization to provide subgrid-scale cloud fraction and condensate feedback to the rapid radiative transfer model–global (RRTMG) shortwave and longwave radiation schemes. The effects of these changes are analyzed via 3 year simulations using the standard and modified versions of WRF, comparing the modeled results with the North American Regional Reanalysis (NARR) and Climate Forecast System Reanalysis data, as well as with available data from the Surface Radiation Network and Clouds and Earths Radiant Energy System. During the summer period, including subgrid cloudiness estimated by KF in the RRTMG reduces the surface shortwave radiation, leading to less buoyant energy, which is reflected in a smaller diabatic convective available potential energy, thereby alleviating the overly energetic convection. Overall, these changes have reduced the overprediction of monthly, regionally averaged precipitation during summer for this RCM application, e.g., by as much as 49 mm for the southeastern U.S., to within 0.7% of the NARR value of 221 mm. These code modifications have been incorporated as an option available in the latest version of WRF (v3.6).

Using a coupled lake model with WRF for dynamical downscaling

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.

Improving the representation of clouds, radiation, and precipitation using spectral nudging in the Weather Research and Forecasting model

Spectral nudging—a scale-selective interior constraint technique—is commonly used in regional climate models to maintain consistency with large-scale forcing while permitting mesoscale features to develop in the downscaled simulations. Several studies have demonstrated that spectral nudging improves the representation of regional climate in reanalysis-forced simulations compared with not using nudging in the interior of the domain. However, in the Weather Research and Forecasting (WRF) model, spectral nudging tends to produce degraded precipitation simulations when compared to analysis nudging—an interior constraint technique that is scale indiscriminate but also operates on moisture fields which until now could not be altered directly by spectral nudging. Since analysis nudging is less desirable for regional climate modeling because it dampens fine-scale variability, changes are proposed to the spectral nudging methodology to capitalize on differences between the nudging techniques and aim to improve the representation of clouds, radiation, and precipitation without compromising other fields. These changes include adding spectral nudging toward moisture, limiting nudging to below the tropopause, and increasing the nudging time scale for potential temperature, all of which collectively improve the representation of mean and extreme precipitation, 2 m temperature, clouds, and radiation, as demonstrated using a model-simulated 20 year historical period. Such improvements to WRF may increase the fidelity of regional climate data used to assess the potential impacts of climate change on human health and the environment and aid in climate change mitigation and adaptation studies.

Climate change impacts on projections of excess mortality at 2030 using spatially varying ozone-temperature risk surfaces.

We project the change in ozone-related mortality burden attributable to changes in climate between a historical (1995–2005) and near-future (2025–2035) time period while incorporating a non-linear and synergistic effect of ozone and temperature on mortality. We simulate air quality from climate projections varying only biogenic emissions and holding anthropogenic emissions constant, thus attributing changes in ozone only to changes in climate and independent of changes in air pollutant emissions. We estimate non-linear, spatially varying, ozone–temperature risk surfaces for 94 US urban areas using observed data. Using the risk surfaces and climate projections we estimate daily mortality attributable to ozone exceeding 40 p.p.b. (moderate level) and 75 p.p.b. (US ozone NAAQS) for each time period. The average increases in city-specific median April–October ozone and temperature between time periods are 1.02 p.p.b. and 1.94 °F; however, the results varied by region. Increases in ozone because of climate change result in an increase in ozone mortality burden. Mortality attributed to ozone exceeding 40 p.p.b. increases by 7.7% (1.6–14.2%). Mortality attributed to ozone exceeding 75 p.p.b. increases by 14.2% (1.6 28.9%). The absolute increase in excess ozone mortality is larger for changes in moderate ozone levels, reflecting the larger number of days with moderate ozone levels.

Characterizing the impact of projected changes in climate and air quality on human exposures to ozone

The impact of climate change on human and environmental health is of critical concern. Population exposures to air pollutants both indoors and outdoors are influenced by a wide range of air quality, meteorological, behavioral, and housing-related factors, many of which are also impacted by climate change. An integrated methodology for modeling changes in human exposures to tropospheric ozone (O3) owing to potential future changes in climate and demographics was implemented by linking existing modeling tools for climate, weather, air quality, population distribution, and human exposure. Human exposure results from the Air Pollutants Exposure Model (APEX) for 12 US cities show differences in daily maximum 8-h (DM8H) exposure patterns and levels by sex, age, and city for all scenarios. When climate is held constant and population demographics are varied, minimal difference in O3 exposures is predicted even with the most extreme demographic change scenario. In contrast, when population is held constant, we see evidence of substantial changes in O3 exposure for the most extreme change in climate. Similarly, we see increases in the percentage of the population in each city with at least one O3 exposure exceedance above 60 p.p.b and 70 p.p.b thresholds for future changes in climate. For these climate and population scenarios, the impact of projected changes in climate and air quality on human exposure to O3 are much larger than the impacts of changing demographics. These results indicate the potential for future changes in O3 exposure as a result of changes in climate that could impact human health.

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

AbstractThe use of nudging in WRF 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 NWP), 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 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, ...