Warren E. Heilman

Evaluation of Real-Time High-Resolution MM5 Predictions over the Great Lakes Region

Real-time high-resolution mesoscale predictions using the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) over the Great Lakes region are evaluated for the 2002/03 winter and 2003 summer seasons using surface and upper-air observations, with a focus on near-surface and boundary layer properties that are important for applications such as air quality and fire weather predictions. The summer season predictions produce a cold bias in maximum daily temperature and a warm bias in minimum temperature that together lead to a good prediction of daily mean temperature but a smallerthan-observed diurnal temperature cycle. In winter, the predicted near-surface temperatures are lower both day and night, yielding good agreement with the observed amplitude of the diurnal temperature cycle but relatively large cold bias in daily mean temperature. The predicted temperatures in the boundary layer are also systematically lower than the observed temperatures in the two seasons. The cold bias is consistent with the wetter-than-observed lower atmosphere in the model prediction, which in turn can be attributed to an inadequate specification of soil moisture. In both seasons, the model produced substantially more precipitation in all categories, especially in the heavy precipitation category, and the overprediction is primarily associated with more widespread area coverage in the model prediction. The chances of producing a false precipitation forecast are substantially higher than missing an observed precipitation event. Small systematic errors are found in the predictions of low-level winds, but above the boundary layer, the predicted winds are predominantly from the west, while the observed winds are from the west-northwest. The model is able to capture the general development and evolution of the lake–land breezes in areas surrounding Lake Michigan during summer, although errors exist in the strengths of the breezes and the timing of their transition. Predicted early morning inversions are slightly stronger than observed in winter and weaker than observed in summer. The weak summer morning inversion results in a rapid inversion breakup followed by an earlier growth of a mixed layer after sunrise. Despite the head start, the predicted mixed-layer heights in late afternoon are lower than those observed, suggesting that either the predicted surface sensible heat flux may be too low or the boundary layer flux divergence may be too high. Decreasing horizontal grid spacing from 12 to 4 km results in little improvement in the predictions of near-surface and boundary layer properties except for precipitation, for which the model bias is significantly reduced by the increase in horizontal resolution. The cold and wet biases and errors in inversion strengths and mixed-layer development call for extra caution when using products from mesoscale forecasts in applications such as air pollution and fire weather prediction.

The Potential Impact of Regional Climate Change on Fire Weather in the United States

Climate change is expected to alter the frequency and severity of atmospheric conditions conducive for wildfires. In this study, we assess potential changes in fire weather conditions for the contiguous United States using the Haines Index (HI), a fire weather index that has been employed operationally to detect atmospheric conditions favorable for large and erratic fire behavior. The index summarizes lower atmosphere stability and dryness into an integer value with higher values indicting more fire-prone conditions. We use simulations produced by the North American Regional Climate Change Assessment Program (NARCCAP) from multiple regional climate models (RCMs) driven by multiple general circulation models (GCMs) to examine changes by midcentury in the seasonal percentage of days and the consecutive number of days with high (values ≥ 5) HI across the United States. Despite differences among the six RCM–GCM combinations in the magnitude and location of the projected changes, the results consistently suggest an increase in the number of days with high HI values over most of the United States during the summer season, with the dryness factor of the HI contributing more than the stability parameter to the projected changes. In addition, the consecutive number of days with high HI is projected to increase in summer. Together, these results suggest that future summers might be more conducive to large and dangerous fires. The projections for other seasons are inconsistent among the model combinations.

Will Future Climate Favor More Erratic Wildfires in the Western United States

AbstractWildfires that occurred over the western United States during August 2012 were fewer in number but larger in size when compared with all other Augusts in the twenty-first century. This unique characteristic, along with the tremendous property damage and potential loss of life that occur with large wildfires with erratic behavior, raised the question of whether future climate will favor rapid wildfire growth so that similar wildfire activity may become more frequent as climate changes. This study addresses this question by examining differences in the climatological distribution of the Haines index (HI) between the current and projected future climate over the western United States. The HI, ranging from 2 to 6, was designed to characterize dry, unstable air in the lower atmosphere that may contribute to erratic or extreme fire behavior. A shift in HI distribution from low values (2 and 3) to higher values (5 and 6) would indicate an increased risk for rapid wildfire growth and spread. Distributions...

WRF Model Sensitivity to Land Surface Model and Cumulus Parameterization under Short-Term Climate Extremes over the Southern Great Plains of the United States

Extreme weather and climate events, especially short-term excessive drought and wet periods over agricultural areas, have received increased attention. The Southern Great Plains (SGP) is one of the largest agriculturalregionsinNorthAmericaandfeaturestheunderlyingOgallala-HighPlainsAquifersystemworth great economic value in large part due to production gains from groundwater. Climate research over the SGP isneededtobetterunderstandcomplexcoupledclimate‐hydrology‐socioeconomicinteractionscriticaltothe sustainability of this region, especially under extreme climate scenarios. Here the authors studied growingseason extreme conditionsusing the WeatherResearchand Forecasting(WRF) Model.The six most extreme recent years, both wet and dry, were simulated to investigate the impacts of land surface model and cumulus parameterization on the simulated hydroclimate. The results show that under short-term climate extremes, the land surface model plays a more important role modulating the land‐atmosphere water budget, and thus theentireregionalclimate,thanthecumulus parameterizationunder current model configurations.Betweenthe two land surface models tested, the more sophisticated land surface model produced significantly larger wet bias inlargepartduetooverestimationofmoisturefluxconvergence, which is attributedmainlytoanoverestimation ofthesurfaceevapotranspirationduringthesimulatedperiod.Thedeficienciesofthecumulusparameterizations resultedinthemodel’sinabilitytodepictthediurnalrainfallvariability.Bothlandsurfaceprocessesandcumulus parameterizations remain the most challenging parts of regional climate modeling under extreme climates over the SGP, with the former strongly affecting the precipitation amount and the latter strongly affecting the precipitation pattern.

Temporal and spatial variability of wind resources in the United States as derived from the climate forecast system reanalysis

AbstractThis study examines the spatial and temporal variability of wind speed at 80 m above ground (the average hub height of most modern wind turbines) in the contiguous United States using Climate Forecast System Reanalysis (CFSR) data from 1979 to 2011. The mean 80-m wind exhibits strong seasonality and large spatial variability, with higher (lower) wind speeds in the winter (summer), and higher (lower) speeds over much of the Midwest and U.S. Northeast (U.S. West and Southeast). Trends are also variable spatially, with more upward trends in areas of the Great Plains and Intermountain West of the United States and more downward trends elsewhere. The leading EOF mode, which accounts for 20% (summer) to 33% (winter) of the total variance and represents in-phase variations across the United States, responds mainly to the North Atlantic Oscillation (NAO) in summer and El Nino–Southern Oscillation (ENSO) in the other seasons. The dominant variation pattern can be explained by a southerly/southwesterly (wes...

Hydroclimate and variability in the Great Lakes region as derived from the North American Regional Reanalysis

[1]xa0We investigated the seasonal and interannual variability of the moisture budget in the Great Lakes region of the United States using the North American Regional Reanalysis (NARR) data set from 1979 through 2007. The much higher spatial and temporal resolution and improved precipitation and land surface data assimilation of the NARR data set compared with its global counterparts enable more accurate depictions of the moisture budget and hydrological cycle in the Great Lakes region. The analyses reveal that in the past three decades except for two drought years, the evaporation over the region is insufficient to account for the total precipitation. Transport mechanisms supply additional moisture, with a net gain in moisture associated with meridional transport by southerly winds overcoming a net loss in moisture due to zonal transport by westerly winds. The interannual variability of the moisture deficit (the difference between evaporation and precipitation) is associated mainly with the interannual variability in the moisture flux convergence. These results highlight the critical importance of remote moisture sources and large-scale moisture transport to the hydrological cycle in the Great Lakes region. The trend analyses show an upward trend for evaporation that is consistent with warming over the region in all seasons during the NARR data period. Precipitation exhibits an increasing trend in spring and winter, with the largest increase in winter, but no clear trends in summer and autumn.

Multiscale Simulation of a Prescribed Fire Event in the New Jersey Pine Barrens Using ARPS-CANOPY

Smoke prediction products are one of the tools used by land management personnel for decision making regarding prescribed fires. This study documents the application to a prescribed fire of a smoke prediction system that employs ARPS-CANOPY, a modified version of the Advanced Regional Prediction System (ARPS) model containing a canopy submodel, as the meteorological driver. In this paper, the performance of ARPS-CANOPY in simulating meteorological fields in the vicinity of a low-intensity fire is assessed using flux-tower data collected prior to and during a low-intensity prescribed fire in the New Jersey Pine Barrens in March 2011. A three-dimensional high-resolution plant area density dataset is utilized to define the characteristics of the canopy, and the fire is represented in ARPS-CANOPY as a heat flux to the atmosphere. The standard ARPS model is compared with reanalysis and upper-air data to establish that the model can simulate the observed synoptic-mesoscale and planetary boundary layer features that are salient to this study. ARPS-CANOPY profiles of mean turbulent kinetic energy, wind speed/direction, and temperature exhibit patterns that appear in the flux-tower observations during both the preburn phase of the experiment and the period of time the flux tower experienced perturbed atmospheric conditions due to the impinging fire. Last, the character and source of turbulence in and around the fire line are examined. These results are encouraging for smoke prediction efforts since transport of smoke from low-intensity fires is highly sensitive to the near-surface meteorological conditions and, in particular, turbulent flows.

Mean and turbulent flow downstream of a low-intensity fire: Influence of canopy and background atmospheric conditions

This study examines the sensitivity of mean and turbulent flow in the planetary boundary layer and roughness sublayer to a low-intensity fire and evaluates whether the sensitivity is dependent on canopy and background atmospheric properties. The ARPS-CANOPY model, a modified version of the Advanced Regional Prediction System (ARPS) model with a canopy parameterization, is utilized for this purpose. A series of numerical experiments are conducted to evaluate whether the ability of the fire to alter downstream wind, temperature, turbulent kinetic energy (TKE), and vertical heat flux differs between forested and open areas, sparse and dense forests, weak and strong background flow, and neutral and convective background stability. Analysis of all experiments shows that, in general, mean and turbulent flow both prior to and during a low-intensity fire is damped in the presence of a canopy. Greater sensitivity to the fire is found in cases with strong ambient wind speed than in cases with quiescent or weak wind speed. Furthermore, sensitivity of downstreamatmospheric conditions to the fire is shown to be strongest with a neutrally stratified background. An analysis of the TKE budget reveals that both buoyancy and wind shear contribute to TKE production duringtheperiodoftimeinwhichthefireconditionsareappliedtothemodel.Onthebasisoftheresultsofthe ARPS simulations, caution is advised when applying ARPS-simulation results to predictions of smoke transport and dispersion: smoke-model users should consider whether canopy impacts on the atmosphere are accounted for and whether neutral stratification is assumed.

Climatic Variability of Near-Surface Turbulent Kinetic Energy over the United States: Implications for Fire-Weather Predictions

AbstractRecent research suggests that high levels of ambient near-surface atmospheric turbulence are often associated with rapid and sometimes erratic wildland fire spread that may eventually lead to large burn areas. Previous research has also examined the feasibility of using near-surface atmospheric turbulent kinetic energy (TKEs) alone or in combination with the Haines index (HI) as an additional indicator of anomalous atmospheric conditions conducive to erratic or extreme fire behavior. However, the application of TKEs-based indices for operational fire-weather predictions in the United States on a regional or national basis first requires a climatic assessment of the spatial and temporal patterns of the indices that can then be used for testing their operational effectiveness. This study provides an initial examination of some of the spatial and temporal variability patterns across the United States of TKEs and the product of HI and TKEs (HITKEs) using data from the North American Regional Reanalysi...

Climate and Atmospheric Deposition Patterns and Trends

One of the most important factors impacting terrestrial and aquatic ecosystems is the atmospheric environment. Climatic and weather events play a significant role in governing the natural processes that occur in these ecosystems. The current characteristics of the vast number of ecosystems that cover the northeast and north central United States are, in part, the result of climate, weather, disturbance, and atmospheric pollution patterns that exist in the northeast and north central United States. For example, basic ecosystem processes (e.g., heat and moisture exchanges with the atmosphere, photosynthesis, and respiration) along with species diversity and ecosystem health throughout the region all depend, to some degree, on these patterns. Furthermore, future characteristics of ecosystems in the region will depend on future climate, weather, disturbance, and pollution patterns that may develop in response to natural or human-caused changes in our atmospheric environment.