Adrienne Wootten

An evaluation of transported pollution and respiratory–related hospital admissions in the state of New York

Human exposure to air pollution transported from the Midwest is evaluated in eight New York State (NYS) regions over ten summers (1997 – 2006) for association with respiratory–related hospital admissions. Days when pollution is transported into the Northeastern United States (U.S.) were identified by using back–trajectories from the eight regions. These back–trajectories help identify predominant meteorological patterns associated with “polluted” air parcels (originating in the Midwest where power plant emissions are known to be relatively high) and “clean” air parcels (originating from the North where pollution is known to be relatively low). Ambient ozone concentrations measurements were used to validate the classification of “polluted” and “clean” air parcels. These classifications were then used to define the days of high– versus low–exposure for populations residing within each region. The results of this analysis indicate that the risk of being hospitalized for respiratory–related illness in NYS is greater on those days when air is transported from the Midwest as compared to days when air is transported from the North. Using a refined method to examine air parcels moving through a boundary drawn around high–emitting power plants in the Midwestern U.S. resulted in stronger associations across more regions (significant odds ratios ranging from 1.06 to 1.16 for the entire study time period for six of the eight NYS regions). An assessment of temperature and its impact on the odds ratio calculation in the New York City metropolitan region indicates that temperature alone does not explain the increased association between air pollution and respiratory–related hospital admissions.

The Sensitivity of WRF Downscaled Precipitation in Puerto Rico to Cumulus Parameterization and Interior Grid Nudging

AbstractThe sensitivity of the precipitation over Puerto Rico that is simulated by the Weather Research and Forecasting (WRF) Model is evaluated using multiple combinations of cumulus parameterization (CP) schemes and interior grid nudging. The NCEP–DOE AMIP-II reanalysis (R-2) is downscaled to 2-km horizontal grid spacing both with convective-permitting simulations (CP active only in the middle and outer domains) and with CP schemes active in all domains. The results generally show lower simulated precipitation amounts than are observed, regardless of WRF configuration, but activating the CP schemes in the inner domain improves the annual cycle, intensity, and placement of rainfall relative to the convective-permitting simulations. Furthermore, the use of interior-grid-nudging techniques in the outer domains improves the placement and intensity of rainfall in the inner domain. Incorporating a CP scheme at convective-permitting scales ( 4 km) im...

Comparison of NCEP Multisensor Precipitation Estimates with Independent Gauge Data over the Eastern United States

AbstractGauge-calibrated radar estimates of daily precipitation are compared with daily observed values of precipitation from National Weather Service (NWS) Cooperative Observer Network (COOP) stations to evaluate the multisensor precipitation estimate (MPE) product that is gridded by the National Centers for Environmental Prediction (NCEP) for the eastern United States (defined as locations east of the Mississippi River). This study focuses on a broad evaluation of MPE across the study domain by season and intensity. In addition, the aspect of precipitation type is considered through case studies of winter and summer precipitation events across the domain. Results of this study indicate a north–south gradient in the error of MPE and a seasonal pattern with the highest error in summer and autumn and the lowest error in winter. Two case studies of precipitation are also considered in this study. These case studies include instances of intense precipitation and frozen precipitation. These results suggest th...

Downscaling future climate change projections over Puerto Rico using a non-hydrostatic atmospheric model

We present results from 20-year “high-resolution” regional climate model simulations of precipitation change for the sub-tropical island of Puerto Rico. The Japanese Meteorological Agency Non-Hydrostatic Model (NHM) operating at a 2-km grid resolution is nested inside the Regional Spectral Model (RSM) at 10-km grid resolution, which in turn is forced at the lateral boundaries by the Community Climate System Model (CCSM4). At this resolution, the climate change experiment allows for deep convection in model integrations, which is an important consideration for sub-tropical regions in general, and on islands with steep precipitation gradients in particular that strongly influence local ecological processes and the provision of ecosystem services. Projected precipitation change for this region of the Caribbean is simulated for the mid-twenty-first century (2041–2060) under the RCP8.5 climate-forcing scenario relative to the late twentieth century (1986–2005). The results show that by the mid-twenty-first century, there is an overall rainfall reduction over the island for all seasons compared to the recent climate but with diminished mid-summer drought (MSD) in the northwestern parts of the island. Importantly, extreme rainfall events on sub-daily and daily time scales also become slightly less frequent in the projected mid-twenty-first-century climate over most regions of the island.

Characterizing Sources of Uncertainty from Global Climate Models and Downscaling Techniques

AbstractIn recent years, climate model experiments have been increasingly oriented toward providing information that can support local and regional adaptation to the expected impacts of anthropogenic climate change. This shift has magnified the importance of downscaling as a means to translate coarse-scale global climate model (GCM) output to a finer scale that more closely matches the scale of interest. Applying this technique, however, introduces a new source of uncertainty into any resulting climate model ensemble. Here a method is presented, on the basis of a previously established variance decomposition method, to partition and quantify the uncertainty in climate model ensembles that is attributable to downscaling. The method is applied to the southeastern United States using five downscaled datasets that represent both statistical and dynamical downscaling techniques. The combined ensemble is highly fragmented, in that only a small portion of the complete set of downscaled GCMs and emission scenario...