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The ambient atmospheric environment affects the growth and spread of wildland fires, whereas heat and moisture released from the fires and the reduction of the surface drag in the burned areas can significantly alter local atmospheric conditions. Observational studies on fire–atmosphere interactions have used instrumented towers to collect data during prescribed fires, but a few towers in an operational-scale burn plot (usually > 10 3 m2) have made it extremely challenging to capture the myriad of factors controlling fire–atmosphere interactions, many of which exhibit strong spatial variability. Here, we present analyses of atmospheric turbulence data collected using a 4 × 4 array of fast-response sonic anemometers during a fire experiment on a 10 m × 10 m burn plot. In addition to confirming some of the previous findings on atmospheric turbulence associated with low-intensity surface fires, our results revealed substantial heterogeneity in turbulent intensity and heat and momentum fluxes just above the combustion zone. Despite the small plot (100 m2), fire-induced atmospheric turbulence exhibited strong dependence on the downwind distance from the initial line fire and the relative position specific to the fire front as the surface fire spread through the burn plot. This result highlights the necessity for coupled atmosphere–fire behavior models to have 1–2 m grid spacing to resolve heterogeneities in fire–atmosphere interactions that operate on spatiotemporal scales relevant to atmospheric turbulence. The findings here have important implications for modeling smoke dispersion, as atmospheric dispersion characteristics in the vicinity of a wildland fire are directly affected by fire-induced turbulence. [ABSTRACT FROM AUTHOR]

The ambient atmospheric environment affects the growth and spread of wildland fires, whereas heat and moisture release from the fires and the reduction of the surface drag in the burned areas can significantly alter local atmospheric conditions. Observational studies on fire-atmosphere interactions have used instrumented towers to collect data during prescribed fires, but a few towers in an operational scale burn plot (usually > 103 m2) have made it extremely challenging to capture the myriad of factors controlling fire-atmosphere interactions, many of which exhibit strong spatial variability. Here, we present analyses of atmospheric turbulence data collected using a 4×4 array of fast-response sonic anemometers during a fire experiment on a 10 m × 10 m burn plot. In addition to confirming some of the previous findings on atmospheric turbulence associated with low-intensity surface fires, our results revealed substantial heterogeneity in turbulent intensity and heat and momentum fluxes just above the combustion zone. Despite the small plot (100 m2), fire-induced atmospheric turbulence exhibited strong dependence on the downwind distance from the initial line fire and the relative position specific to the fire front as the surface fire spread through the burn plot. This result highlights the necessity for coupled atmosphere-fire behavior models to have 1–2 m grid spacing to resolve heterogeneities in fire-atmosphere interactions that operate on spatiotemporal scales relevant to atmospheric turbulence. The findings here have important implications for modeling smoke dispersion, as atmospheric dispersion characteristics in the vicinity of a wildland fire are directly affected by fire-induced turbulence. [ABSTRACT FROM AUTHOR]

In this study, a North American Regional Reanalysis (NARR)‐based climate dataset of wind and Pasquill stability class is developed for the North American Great Lakes region. Motivated by the need to update the limited wind and Pasquill stability class climatology previously incorporated into a livestock odour nuisance‐mitigation application used in Michigan, this study is motivated more generally by the lack of a long‐term climate dataset suitable for studies of atmospheric dispersion in the Great Lakes region. As a preliminary step, NARR 10‐m wind speed, 10‐m wind direction, and 2‐m temperature are evaluated at eight weather stations in Michigan and northern Indiana, and the wind speed variable is subsequently bias‐corrected. To derive a Pasquill stability class dataset from the NARR variables, an existing empirical relationship between Pasquill stability class and Richardson number is applied to a previously developed NARR‐derived Richardson number dataset covering the period 1979–2008. Pasquill stability class frequency in NARR is subsequently evaluated using observations at two flux towers in Illinois. Although a tendency is noted for the NARR‐derived Pasquill stability class dataset to underestimate the frequency of stable and unstable conditions, and overestimate the frequency of neutral conditions, the analysis shows that the 10‐m wind speed bias‐correction procedure yields a frequency distribution that is in better agreement with the observations than the non‐bias‐corrected dataset. Finally, an analysis of the spatial and temporal variability of livestock odour dispersion potential in Michigan is provided as an example application of the NARR‐based wind and Pasquill stability class climate dataset. [ABSTRACT FROM AUTHOR]

Mesoscale models are a class of atmospheric numerical model designed to simulate atmospheric phenomena with horizontal scales of about 2–200 km, although they are also applied to microscale phenomena with horizontal scales of less than about 2 km. Mesoscale models are capable of simulating wildland fire impacts on atmospheric flows if combustion byproducts (e.g., heat, smoke) are properly represented in the model. One of the primary challenges encountered in applying a mesoscale model to studies of fire-perturbed flows is the representation of the fire sensible heat source in the model. Two primary methods have been implemented previously: turbulent sensible heat flux, either in the form of an exponentially-decaying vertical heat flux profile or surface heat flux; and soil temperature perturbation. In this study, the ARPS-CANOPY model, a version of the Advanced Regional Prediction System (ARPS) model with a canopy submodel, is utilized to simulate the turbulent atmosphere during a low-intensity operational prescribed fire in the New Jersey Pine Barrens. The study takes place in two phases: model assessment and model sensitivity. In the model assessment phase, analysis is limited to a single control simulation in which the fire sensible heat source is represented as an exponentially decaying vertical profile of turbulent sensible heat flux. In the model sensitivity phase, a series of simulations are conducted to explore the sensitivity of model–observation agreement to (i) the method used to represent the fire sensible heat source in the model and (ii) parameters controlling the magnitude and vertical distribution of the sensible heat source. In both phases, momentum and scalar fields are compared between the model simulations and data obtained from six flux towers located within and adjacent to the burn unit. The multi-dimensional model assessment confirms that the model reproduces the background and fire-perturbed atmosphere as depicted by the tower observations, although the model underestimates the turbulent kinetic energy at the top of the canopy at several towers. The model sensitivity tests reveal that the best agreement with observations occurs when the fire sensible heat source is represented as a turbulent sensible heat flux profile, with surface heat flux magnitude corresponding to the peak 1 min mean observed heat flux averaged across the flux towers, and an e -folding extinction depth corresponding to the average canopy height in the burn unit. The study findings provide useful guidance for improving the representation of the sensible heat released from low-intensity prescribed fires in mesoscale models. [ABSTRACT FROM AUTHOR]

Mesoscale models are a class of atmospheric numerical model designed to simulate atmospheric phenomena with horizontal scales of about 2-200 km, although they are also applied to microscale phenomena, with horizontal scales less than about 2 km. Mesoscale models are capable of simulating wildland fire impacts on atmospheric flows if combustion by-products (e.g., heat, smoke) are properly represented in the model. One of the primary challenges encountered in applying a mesoscale model to studies of fire-perturbed flows is the representation of the fire sensible heat source in the model. Two primary methods have been implemented previously: turbulent sensible heat flux, either in the form of an exponentially-decaying vertical heat flux profile or surface heat flux; and soil temperature perturbation. In this study, the ARPS-CANOPY model, a version of the Advanced Regional Prediction System (ARPS) model with a canopy submodel, is utilized to simulate the turbulent atmosphere during a low-intensity operational prescribed fire in the New Jersey Pine Barrens. The study takes place in two phases: model assessment and model sensitivity. In the model assessment phase, analysis is limited to a single control simulation in which the fire sensible heat source is represented as an exponentially-decaying vertical profile of turbulent sensible heat flux. In the model sensitivity phase, a series of simulations are conducted to explore the sensitivity of model-observation agreement to (i) the method used to represent the fire sensible heat source in the model and (ii) parameters controlling the magnitude and vertical distribution of the sensible heat source. In both phases, momentum and scalar fields are compared between the model simulations and data obtained from six flux towers located within and adjacent to the burn unit. The multi-dimensional model assessment confirms that the model reproduces the background and fire-perturbed atmosphere as depicted by the tower observations, although the model underestimates the turbulent kinetic energy at the top of the canopy at several towers. The model sensitivity tests reveal that the best agreement with observations occurs when the fire sensible heat source is represented as a turbulent sensible heat flux profile, with surface heat flux magnitude corresponding to the peak 1-min mean observed heat flux averaged across the flux towers, and an e-folding extinction depth corresponding to the average canopy height in the burn unit. The study findings provide useful guidance for improving the representation of the sensible heat released from low-intensity prescribed fires in mesoscale models. [ABSTRACT FROM AUTHOR]

VALDRIFT (valley drift) is a valley atmospheric transport, diffusion, and deposition model. The model is phenomenological - that is, the dominant meteorological processes governing the behavior of the valley atmosphere are formulated explicitly in the model, although in a highly parameterized fashion. The key meteorological processes treated are 1) nonsteady and nonhomogeneous along-valley winds and turbulent diffusivities, 2) convective boundary layer growth, 3) inversion descent, and 4) nocturnal temperature inversion breakup. The model is applicable under relatively cloud-free, undisturbed synoptic conditions in which the winds in the valley are predominantly along the valley's axis. The model is configured to operate through one diurnal cycle for a single narrow valley. The inputs required are the valley topographic characteristics, pollutant release rate as a function of time and space, wind speed and direction as functions of time measured at one height, lateral and vertical turbulent eddy diffusivities as functions of stability, and the valley temperature inversion characteristics at sunrise. The outputs are three-dimensional concentration fields and ground-level deposition fields as functions of time. The scientific foundations of VALDRIFT are given in this paper along with a brief discussion of the model inputs and outputs. Air concentrations estimated by VALDRIFT compare favorably with results from a tracer experiment conducted in a deep mountain valley.

The increase in wildfire risk in the United States in recent decades has been linked to rapid growth of the wildland‐urban interface and to changing climate. While there have been numerous studies on wildfires and climate change, few have separately assessed the impact of climate response to land‐use‐land‐cover change (LULCC) on wildfires. In this study, we analyse two 10‐year regional climate simulations driven by the current (2011) and future (2100) land‐use‐land‐cover patterns to assess modifications by the projected LULCC to the frequency and severity of fire‐prone atmospheric conditions described by two fire weather indices, the Canadian Forest Fire Weather Index and the Hot‐Dry‐Windy Index. The simulation corresponding to future land‐use‐land‐cover pattern yields higher surface temperature and vapour pressure deficit and lower precipitation compared to the simulation with the current pattern in areas where urbanized landscapes replace forests and grasslands, such as along the Piedmont and outside the Chicagoland region, while in areas where croplands replace forests, such as the southeast Coastal Plains, the results are reversed. These changes to local and regional atmospheric conditions lead to longer fire seasons and more extreme fire‐weather conditions in much of the eastern United States, specifically in the Southeast and Ohio River Valley where significant urban expansion is projected by the end of the century. Whereas in Southern California where some highly flammable shrublands will be replaced by urban or crop lands, fire‐prone atmospheric conditions are likely to be less frequent and less extreme in the future. However, much of California moves towards a year‐round fire season under the projected LULCC. The results suggest that by altering atmospheric conditions, LULCC may play an important role in determining fire regime, but the effects are highly heterogeneous and regionalized. [ABSTRACT FROM AUTHOR]

This study investigates the interannual variability of wind energy resources across China and how it changes with season by applying empirical orthogonal function (EOF) analyses to gridded wind data from the Climate Forecast System Reanalysis (CFSR) from January 1979 through December 2011. The first EOF mode (EOF1) represents between 22% variance for winter and 29% for summer. Spatially, the variation is largely consistent across China for summer and autumn and almost opposite between north and south for spring and winter, and the strongest variation in all seasons is found over Inner Mongolia and Tibet. The second EOF mode (EOF2) represents between 13% variance for autumn and 16% for spring, and is largely dominated by a sharp contrast between Inner Mongolia and Tibet for all seasons. The EOF1 appears to be linked statistically to the pacific decadal oscillation for summer and autumn and to the Pacific North American pattern for spring and winter, while the EOF2 seems to be connected to the Arctic Oscillation for spring and winter and to an interdecadal variability for summer and autumn. The anomalous wind fields associated with these large‐scale circulation patterns modify the climatological wind fields in different ways that lead to an increase or a decrease of the 80‐m winds in different regions of China. Understanding the low‐frequency variability of wind speeds, or how and why wind speed and wind power vary from 1 year to another, can be beneficial for seasonal outlook or long‐range forecasting of wind power generation. Using gridded wind data from a third‐generation global reanalysis, we statically identify and characterize the main modes of the interannual variability of wind speeds at the modern wind turbine level across entire China and the dominant climate forcing for these modes.Figure caption: The correlations between the 80‐m wind speed summer season anomalies across China and the Pacific Decadal Oscillation indices for 1979–2011. [ABSTRACT FROM AUTHOR]

Abstract: An improved understanding of atmospheric perturbations within and above a forest during a wildland fire has relevance to many aspects of wildland fires including fire spread, smoke transport and dispersion, and tree mortality. In this study, the ARPS‐CANOPY model, a version of the Advanced Regional Prediction System (ARPS) model with a canopy parameterization, is utilized in a series of idealized numerical experiments to investigate the influence of vertical canopy structure on the atmospheric response to a stationary sensible heat flux at the ground (“fire heat flux”), broadly consistent in magnitude with the sensible heat flux from a low‐intensity surface fire. Five vertical canopy structures are combined with five fire heat flux magnitudes to yield a matrix of 25 simulations. Analyses of the fire‐heat‐flux‐perturbed u component of the wind, vertical velocity, kinetic energy, and temperature show that the spatial pattern and magnitude of the perturbations are sensitive to vertical canopy structure. Both vertical velocity and kinetic energy exhibit an increasing trend with increasing fire heat flux that is stronger for cases with some amount of overstory vegetation than cases with exclusively understory vegetation. A weaker trend in cases with exclusively understory vegetation indicates a damping of the atmospheric response to the sensible heat from a surface fire when vegetation is most concentrated near the surface. More generally, the results presented in this study suggest that canopy morphology should be considered when applying the results of a fire‐atmosphere interaction study conducted in one type of forest to other forests with different canopy structures. [ABSTRACT FROM AUTHOR]

We used 35-year (1979-2013) hourly rainfall data from the North American Land Data Assimilation System (NLDAS-2) to examine the relationships of the symmetric and asymmetric components of two types of El Niño-Southern Oscillation (ENSO) (El Niño and El Niño Modoki) episodes with occurrences of extreme precipitation events across the United States. During the cold season, the symmetric impacts of El Niño and El Niño Modoki are more significant than during the warm season. The asymmetric components associated with El Niño during the two seasons are comparable, while the asymmetric components associated with El Niño Modoki during the warm season are more significant than those during the cold season. The connections between the symmetric and asymmetric components of the two types of ENSO and extreme precipitation occurrences are related to atmospheric moisture convergence patterns that tend to occur in response to the anomalous large-scale atmospheric circulations during the ENSO episodes. [ABSTRACT FROM AUTHOR]

ABSTRACT Valleys can trap cold air to form Valley Cold Pools ( VCPs). Characterized by stable stratification and weak winds, VCPs, especially those that last over multiple days, can produce adverse effects such as poor visibility and severe air pollution. Using the gridded data set of the North American Regional Reanalysis ( NARR) for the period of 1979 through 2012, this study examines the climatology of VCPs in the western United States with a focus on spatial and temporal variability. The results reveal a widespread occurrence of short-lived (1-2 days) VCPs over both the mountainous areas of the West and the Northern Plains. Longer VCP episodes, however, tend to be limited to large basins/valleys in the Northwest and the Intermountain West. The leading mode of variability in the annual number of cold-season multi-day VCP event anomalies appears to be linked to a sea-surface temperature anomaly pattern typically found during the warm phase of Pacific Decadal Oscillation. Corresponding to the positive phase of the leading mode of variability is an anomalous 500- hPa ridge over the western United States induced by a Rossby wave train, which, by blocking cold-air intrusions and producing mid-level subsidence warming, leads to more persistent VCP episodes in the western United States. In addition to large-scale wintertime circulation anomalies, local surface temperature anomalies also contribute to the variability. [ABSTRACT FROM AUTHOR]

ABSTRACT The inter-annual variability of southerly low-level jets ( SLLJs) over North America during the warm (April-September) and cool (October-March) seasons is investigated. SLLJ occurrences over a 31-year period (1979-2009) were identified from the North American Regional Reanalysis ( NARR) vertical wind profiles. The first empirical orthogonal function ( EOF) modes of the SLLJ frequency during the warm and cool seasons account for about 30 and 20% of the total variance, respectively. Both modes can be interpreted as a strengthening or weakening of the core area of SLLJ anomalies. The principal component ( PC) time series display significant positive trends, suggesting an increase in SLLJ activity during both seasons on inter-decadal time scales and are significantly correlated to the summertime Pacific Decadal Oscillation ( PDO) and Atlantic Multidecadal Oscillation ( AMO) for the warm season and the wintertime PDO, AMO and El Niño Modoki for the cool season. The second modes account for about 20 and 15% of the total variance for the warm and cool seasons, respectively, and are interpreted as primarily a subseasonal latitudinal shift in SLLJ activity between the central Great Plains and the western Gulf of Mexico and southern Texas during the warm season and a longitudinal shift between the western Gulf of Mexico and the Caribbean during the cool season. The second mode appears to be significantly correlated to El Niño Modoki for the warm season and to Niño 3.4 for the cool season. [ABSTRACT FROM AUTHOR]

ABSTRACT The mean climatology, seasonal and interannual variability and trend of wind speeds at the hub height (80 m) of modern wind turbines over China and its surrounding regions are revisited using 33-year (1979-2011) wind data from the Climate Forecast System Reanalysis ( CFSR) that has many improvements including higher spatial resolution over previous global reanalysis products. Mean 80-m wind speeds are consistently higher over China Seas and the ocean areas than over land, and inside China high winds are found in areas of Inner Mongolia and the Tibetan Plateau. There is a considerable seasonal variability that reflects primarily the influence of East and Southeast Asia Monsoon with generally higher speeds in winter followed by summer, and weaker winds in autumn, followed by spring. There is also a strong interannual variability, and regions of larger amplitude of variability coincide with regions of higher mean winds. A decreasing trend, dominated by a sharp decline beginning in 2005, is seen across China and the surrounding seas in summer and autumn, and the summer trends over land and over ocean appear to be related respectively to the Pacific Decadal Oscillation ( PDO) and the East Asian summer monsoon. Trends are not consistent across the region in spring and winter, however, with positive trends over some areas in northeastern and northwestern China, Mongolia and tropical oceans whereas negative trends in other regions. Nearly all areas of China experience mean annual 80-m wind speed less than 6.9 m s−1 (wind power classes of 1-2) except for some areas of Inner Mongolia where mean annual 80-m wind speeds exceed 6.9 m s−1 (Classes 3 or higher, suitable for wind energy development). China Seas and ocean areas generally fall in Class 3 or above, with the Taiwan and Luzon Straights reaching the highest Class 7. [ABSTRACT FROM AUTHOR]