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- Author or Editor: Roger A. Pielke Sr. x
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Abstract
A two-dimensional cloud-resolving model (CRM) was used to simulate the evolution of convection over the western Pacific between 19 and 26 December 1992, during the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment. A control simulation (CONTROL) was performed in which observed, time-evolving, spatially homogeneous SSTs were used as a lower boundary condition. It showed that the CRM was able to properly represent the evolution of the cloud systems.
Sensitivity experiments were carried out, in which the sea surface temperature was increased (SST+) or decreased (SST−) by 1°C and the same evolving large-scale forcing used in CONTROL. The similarities among all simulations suggested that the large-scale forcing is the dominant mechanism controlling the statistics of the cloud systems, including the total precipitation. However, the convective–stratiform partition of the cloud systems was altered, the convective part being favored in SST+ and the stratiform part favored in SST−. In terms of the radiative budget, the reduced low-level cloud coverage in SST+ acted to compensate the enhancement of high-cloud coverage produced by more vigorous convection (the opposite occurred in SST−). As a consequence, the surface downward radiation was approximately the same in CONTROL, SST+, and SST−.
Abstract
A two-dimensional cloud-resolving model (CRM) was used to simulate the evolution of convection over the western Pacific between 19 and 26 December 1992, during the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment. A control simulation (CONTROL) was performed in which observed, time-evolving, spatially homogeneous SSTs were used as a lower boundary condition. It showed that the CRM was able to properly represent the evolution of the cloud systems.
Sensitivity experiments were carried out, in which the sea surface temperature was increased (SST+) or decreased (SST−) by 1°C and the same evolving large-scale forcing used in CONTROL. The similarities among all simulations suggested that the large-scale forcing is the dominant mechanism controlling the statistics of the cloud systems, including the total precipitation. However, the convective–stratiform partition of the cloud systems was altered, the convective part being favored in SST+ and the stratiform part favored in SST−. In terms of the radiative budget, the reduced low-level cloud coverage in SST+ acted to compensate the enhancement of high-cloud coverage produced by more vigorous convection (the opposite occurred in SST−). As a consequence, the surface downward radiation was approximately the same in CONTROL, SST+, and SST−.
Abstract
This paper addresses the physics and numerical simulation of the adiabatic generation of infrasound by tornadoes. Classical analytical results regarding the production of infrasound by vortex Rossby waves and by corotating “suction vortices” are reviewed. Conditions are derived for which critical layers damp vortex Rossby waves that would otherwise grow and continually produce acoustic radiation. These conditions are similar to those that theoretically suppress gravity wave radiation from larger mesoscale cyclones, such as hurricanes. To gain perspective, the Regional Atmospheric Modeling System (RAMS) is used to simulate the infrasound that radiates from a single-cell thunderstorm in a shear-free environment. In this simulation, the dominant infrasound in the 0.1–10-Hz frequency band appears to radiate from the vicinity of the melting level, where diabatic processes involving hail are active. It is shown that the 3D Rossby waves of a tornado-like vortex (simulated with RAMS) can generate stronger infrasound if the maximum wind speed of the vortex exceeds a modest threshold. Technical issues regarding the numerical simulation of tornado infrasound are also addressed. Most importantly, it is shown that simulating tornado infrasound likely requires a spatial resolution that is an order of magnitude finer than the current practical limit (10-m grid spacing) for modeling thunderstorms.
Abstract
This paper addresses the physics and numerical simulation of the adiabatic generation of infrasound by tornadoes. Classical analytical results regarding the production of infrasound by vortex Rossby waves and by corotating “suction vortices” are reviewed. Conditions are derived for which critical layers damp vortex Rossby waves that would otherwise grow and continually produce acoustic radiation. These conditions are similar to those that theoretically suppress gravity wave radiation from larger mesoscale cyclones, such as hurricanes. To gain perspective, the Regional Atmospheric Modeling System (RAMS) is used to simulate the infrasound that radiates from a single-cell thunderstorm in a shear-free environment. In this simulation, the dominant infrasound in the 0.1–10-Hz frequency band appears to radiate from the vicinity of the melting level, where diabatic processes involving hail are active. It is shown that the 3D Rossby waves of a tornado-like vortex (simulated with RAMS) can generate stronger infrasound if the maximum wind speed of the vortex exceeds a modest threshold. Technical issues regarding the numerical simulation of tornado infrasound are also addressed. Most importantly, it is shown that simulating tornado infrasound likely requires a spatial resolution that is an order of magnitude finer than the current practical limit (10-m grid spacing) for modeling thunderstorms.
Abstract
From 24 to 26 November 2004, an extreme heavy rainfall event occurred in the mountainous provinces of central Vietnam, resulting in severe flooding along local rivers. The Regional Atmospheric Modeling System, version 4.4, is used to simulate this event. In the present study, the convective parameterization scheme includes the original Kain–Fritsch scheme and a modified one in which a new diagnostic equation to compute updraft velocity, closure assumption, and trigger function are developed. These modifications take the vertical gradient of the Exner function perturbation into account, with an on–off coefficient to account for the role of the advective terms. According to the event simulations, the simulated precipitation shows that the modified scheme with the new trigger function gives much better results than the original one. Moreover, the interaction between convection and the larger-scale environment is much stronger near the midtroposphere where the return flow associated with lower-level winter monsoon originates. As a result, the modified scheme produces larger and deeper stratiform clouds and leads to a significant amount of resolvable precipitation. On the contrary, the resolvable precipitation is small when the original scheme is used. The improvement in the simulated precipitation is caused by a more explicit physical mechanism of the new trigger function and suggests that the trigger function needs to be developed along with other components of the scheme, such as closure assumption and cloud model, as a whole. The formalistic inclusion of the advective terms in the new equation gives almost no additional improvement of the simulated precipitation.
Abstract
From 24 to 26 November 2004, an extreme heavy rainfall event occurred in the mountainous provinces of central Vietnam, resulting in severe flooding along local rivers. The Regional Atmospheric Modeling System, version 4.4, is used to simulate this event. In the present study, the convective parameterization scheme includes the original Kain–Fritsch scheme and a modified one in which a new diagnostic equation to compute updraft velocity, closure assumption, and trigger function are developed. These modifications take the vertical gradient of the Exner function perturbation into account, with an on–off coefficient to account for the role of the advective terms. According to the event simulations, the simulated precipitation shows that the modified scheme with the new trigger function gives much better results than the original one. Moreover, the interaction between convection and the larger-scale environment is much stronger near the midtroposphere where the return flow associated with lower-level winter monsoon originates. As a result, the modified scheme produces larger and deeper stratiform clouds and leads to a significant amount of resolvable precipitation. On the contrary, the resolvable precipitation is small when the original scheme is used. The improvement in the simulated precipitation is caused by a more explicit physical mechanism of the new trigger function and suggests that the trigger function needs to be developed along with other components of the scheme, such as closure assumption and cloud model, as a whole. The formalistic inclusion of the advective terms in the new equation gives almost no additional improvement of the simulated precipitation.
Abstract
The stochastic analysis, modeling, and simulation of climatic and hydrologic processes such as precipitation, streamflow, and sea surface temperature have usually been based on assumed stationarity or randomness of the process under consideration. However, empirical evidence of many hydroclimatic data shows temporal variability involving trends, oscillatory behavior, and sudden shifts. While many studies have been made for detecting and testing the statistical significance of these special characteristics, the probabilistic framework for modeling the temporal dynamics of such processes appears to be lacking. In this paper a family of stochastic models that can be used to capture the dynamics of abrupt shifts in hydroclimatic time series is proposed. The applicability of such “shifting mean models” are illustrated by using time series data of annual Pacific decadal oscillation (PDO) indices and annual streamflows of the Niger River.
Abstract
The stochastic analysis, modeling, and simulation of climatic and hydrologic processes such as precipitation, streamflow, and sea surface temperature have usually been based on assumed stationarity or randomness of the process under consideration. However, empirical evidence of many hydroclimatic data shows temporal variability involving trends, oscillatory behavior, and sudden shifts. While many studies have been made for detecting and testing the statistical significance of these special characteristics, the probabilistic framework for modeling the temporal dynamics of such processes appears to be lacking. In this paper a family of stochastic models that can be used to capture the dynamics of abrupt shifts in hydroclimatic time series is proposed. The applicability of such “shifting mean models” are illustrated by using time series data of annual Pacific decadal oscillation (PDO) indices and annual streamflows of the Niger River.
Abstract
The impact of irrigation on the surface energy budget in the U.S. high plains is investigated. Four 15-day simulations were conducted: one using a 1997 satellite-derived estimate of farmland acreage under irrigation in Nebraska (control run), two using the Olson Global Ecosystem (OGE) vegetation dataset (OGE wet run and OGE dry run), and the fourth with the Kuchler vegetation dataset (natural vegetation run) as lower boundary conditions in the Colorado State University Regional Atmospheric Modeling System (RAMS). In the control and OGE wet simulations, the topsoil in the irrigated locations, up to a depth of 0.2 m, was saturated at 0000 UTC each day for the duration of the experiment (1–15 July 1997). In the other two runs, the soil was allowed to dry out, except when replenished naturally by rainfall. Identical observed atmospheric conditions were used along the lateral boundary in all four cases.
The area-averaged model-derived quantities for the grid centered over Nebraska indicate significant differences in the surface energy fluxes between the control (irrigated) and the “dry” simulations. For example, a 36% increase in the surface latent heat flux and a 2.6°C elevation in dewpoint temperature between the control run and the OGE dry run is shown. Surface sensible heat flux of the control run was 15% less and the near-ground temperature was 1.2°C less compared to the OGE dry run. The differences between the control run and the natural vegetation run were similar but amplified compared to the control run–OGE dry run comparisons.
Results of statistical analyses of long-term (1921–2000) surface temperature data from two sites representing locations of extensive irrigated and nonirrigated land uses appear to support model results presented herein of an irrigation-related cooling in surface temperature. Growing season monthly mean and monthly mean maximum temperature data for the irrigated site indicate a steady decreasing trend in contrast to an increasing trend at the nonirrigated site.
Abstract
The impact of irrigation on the surface energy budget in the U.S. high plains is investigated. Four 15-day simulations were conducted: one using a 1997 satellite-derived estimate of farmland acreage under irrigation in Nebraska (control run), two using the Olson Global Ecosystem (OGE) vegetation dataset (OGE wet run and OGE dry run), and the fourth with the Kuchler vegetation dataset (natural vegetation run) as lower boundary conditions in the Colorado State University Regional Atmospheric Modeling System (RAMS). In the control and OGE wet simulations, the topsoil in the irrigated locations, up to a depth of 0.2 m, was saturated at 0000 UTC each day for the duration of the experiment (1–15 July 1997). In the other two runs, the soil was allowed to dry out, except when replenished naturally by rainfall. Identical observed atmospheric conditions were used along the lateral boundary in all four cases.
The area-averaged model-derived quantities for the grid centered over Nebraska indicate significant differences in the surface energy fluxes between the control (irrigated) and the “dry” simulations. For example, a 36% increase in the surface latent heat flux and a 2.6°C elevation in dewpoint temperature between the control run and the OGE dry run is shown. Surface sensible heat flux of the control run was 15% less and the near-ground temperature was 1.2°C less compared to the OGE dry run. The differences between the control run and the natural vegetation run were similar but amplified compared to the control run–OGE dry run comparisons.
Results of statistical analyses of long-term (1921–2000) surface temperature data from two sites representing locations of extensive irrigated and nonirrigated land uses appear to support model results presented herein of an irrigation-related cooling in surface temperature. Growing season monthly mean and monthly mean maximum temperature data for the irrigated site indicate a steady decreasing trend in contrast to an increasing trend at the nonirrigated site.
Abstract
Via numerical analysis of detailed simulations of an early September 1993 case night, the authors develop a conceptual model of the interaction of katabatic flow in the nocturnal boundary layer with mountain waves (MKI). A companion paper (Part I) describes the synoptic and mesoscale observations of the case night from the Atmospheric Studies in Complex Terrain (ASCOT) experiment and idealized numerical simulations that manifest components of the conceptual model of MKI presented herein. The reader is also referred to Part I for detailed scientific background and motivation.
The interaction of these phenomena is complicated and nonlinear since the amplitude, wavelength, and vertical structure of the mountain-wave system developed by flow over the barrier owes some portion of its morphology to the evolving atmospheric stability in which the drainage flows develop. Simultaneously, katabatic flows are impacted by the topographically induced gravity wave evolution, which may include significantly changing wavelength, amplitude, flow magnitude, and wave breaking behavior. In addition to effects caused by turbulence (including scouring), perturbations to the leeside gravity wave structure at altitudes physically distant from the surface-based katabatic flow layer can be reflected in the katabatic flow by transmission through the atmospheric column. The simulations show that the evolution of atmospheric structure aloft can create local variability in the surface pressure gradient force governing katabatic flow. Variability is found to occur on two scales, on the meso-β due to evolution of the mountain-wave system on the order of one hour, and on the microscale due to rapid wave evolution (short wavelength) and wave breaking–induced fluctuations. It is proposed that the MKI mechanism explains a portion of the variability in observational records of katabatic flow.
Abstract
Via numerical analysis of detailed simulations of an early September 1993 case night, the authors develop a conceptual model of the interaction of katabatic flow in the nocturnal boundary layer with mountain waves (MKI). A companion paper (Part I) describes the synoptic and mesoscale observations of the case night from the Atmospheric Studies in Complex Terrain (ASCOT) experiment and idealized numerical simulations that manifest components of the conceptual model of MKI presented herein. The reader is also referred to Part I for detailed scientific background and motivation.
The interaction of these phenomena is complicated and nonlinear since the amplitude, wavelength, and vertical structure of the mountain-wave system developed by flow over the barrier owes some portion of its morphology to the evolving atmospheric stability in which the drainage flows develop. Simultaneously, katabatic flows are impacted by the topographically induced gravity wave evolution, which may include significantly changing wavelength, amplitude, flow magnitude, and wave breaking behavior. In addition to effects caused by turbulence (including scouring), perturbations to the leeside gravity wave structure at altitudes physically distant from the surface-based katabatic flow layer can be reflected in the katabatic flow by transmission through the atmospheric column. The simulations show that the evolution of atmospheric structure aloft can create local variability in the surface pressure gradient force governing katabatic flow. Variability is found to occur on two scales, on the meso-β due to evolution of the mountain-wave system on the order of one hour, and on the microscale due to rapid wave evolution (short wavelength) and wave breaking–induced fluctuations. It is proposed that the MKI mechanism explains a portion of the variability in observational records of katabatic flow.
Abstract
A coupled Regional Atmospheric Modeling System (RAMS) and ecosystem (CENTURY) modeling system has been developed to study regional-scale two-way interactions between the atmosphere and biosphere. Both atmospheric forcings and ecological parameters are prognostic variables in the linked system. The atmospheric and ecosystem models exchange information on a weekly time step. CENTURY receives as input air temperature, precipitation, radiation, wind speed, and relative humidity simulated by RAMS. From CENTURY-produced outputs, leaf area index, and vegetation transimissivity are computed and returned to RAMS. In this way, vegetation responses to weekly and seasonal atmospheric changes are simulated and fed back to the atmospheric–land surface hydrology model.
The coupled model was used to simulate the two-way biosphere and atmosphere feedbacks from 1 January to 31 December 1989, focusing on the central United States. Validation was performed for the atmospheric portion of the model by comparing with U.S. summary-of-the-day meteorological station observational datasets, and for the ecological component by comparing with advanced very high-resolution radiometer remote-sensing Normalized Difference Vegetation Index datasets. The results show that seasonal vegetation phenological variation strongly influences regional climate patterns through its control over land surface water and energy exchange. The coupled model captures the key aspects of weekly, seasonal, and annual feedbacks between the atmospheric and ecological systems. In addition, it has demonstrated its usefulness as a research tool for studying complex interactions between the atmosphere, biosphere, and hydrosphere.
Abstract
A coupled Regional Atmospheric Modeling System (RAMS) and ecosystem (CENTURY) modeling system has been developed to study regional-scale two-way interactions between the atmosphere and biosphere. Both atmospheric forcings and ecological parameters are prognostic variables in the linked system. The atmospheric and ecosystem models exchange information on a weekly time step. CENTURY receives as input air temperature, precipitation, radiation, wind speed, and relative humidity simulated by RAMS. From CENTURY-produced outputs, leaf area index, and vegetation transimissivity are computed and returned to RAMS. In this way, vegetation responses to weekly and seasonal atmospheric changes are simulated and fed back to the atmospheric–land surface hydrology model.
The coupled model was used to simulate the two-way biosphere and atmosphere feedbacks from 1 January to 31 December 1989, focusing on the central United States. Validation was performed for the atmospheric portion of the model by comparing with U.S. summary-of-the-day meteorological station observational datasets, and for the ecological component by comparing with advanced very high-resolution radiometer remote-sensing Normalized Difference Vegetation Index datasets. The results show that seasonal vegetation phenological variation strongly influences regional climate patterns through its control over land surface water and energy exchange. The coupled model captures the key aspects of weekly, seasonal, and annual feedbacks between the atmospheric and ecological systems. In addition, it has demonstrated its usefulness as a research tool for studying complex interactions between the atmosphere, biosphere, and hydrosphere.
Abstract
Meteorological forcing data are necessary to drive many of the spatial models used to simulate atmospheric, biological, and hydrological processes. Unfortunately, many domains lack sufficient meteorological data and available point observations are not always suitable or reliable for landscape or regional applications. NOAA’s Local Analysis and Prediction System (LAPS) is a meteorological assimilation tool that employs available observations (meteorological networks, radar, satellite, soundings, and aircraft) to generate a spatially distributed, three-dimensional representation of atmospheric features and processes. As with any diagnostic representation, it is important to ascertain how LAPS outputs deviate from a variety of independent observations. A number of surface observations exist that are not used in the LAPS system, and they were employed to assess LAPS surface state variable and precipitation analysis performance during two consecutive years (1 September 2001–31 August 2003). LAPS assimilations accurately depicted temperature and relative humidity values. The ability of LAPS to represent wind speed was satisfactory overall, but accuracy declined with increasing elevation. Last, precipitation estimates performed by LAPS were irregular and reflected inherent difficulties in measuring and estimating precipitation.
Abstract
Meteorological forcing data are necessary to drive many of the spatial models used to simulate atmospheric, biological, and hydrological processes. Unfortunately, many domains lack sufficient meteorological data and available point observations are not always suitable or reliable for landscape or regional applications. NOAA’s Local Analysis and Prediction System (LAPS) is a meteorological assimilation tool that employs available observations (meteorological networks, radar, satellite, soundings, and aircraft) to generate a spatially distributed, three-dimensional representation of atmospheric features and processes. As with any diagnostic representation, it is important to ascertain how LAPS outputs deviate from a variety of independent observations. A number of surface observations exist that are not used in the LAPS system, and they were employed to assess LAPS surface state variable and precipitation analysis performance during two consecutive years (1 September 2001–31 August 2003). LAPS assimilations accurately depicted temperature and relative humidity values. The ability of LAPS to represent wind speed was satisfactory overall, but accuracy declined with increasing elevation. Last, precipitation estimates performed by LAPS were irregular and reflected inherent difficulties in measuring and estimating precipitation.
Abstract
Over 50 years since Lorenz’s 1963 study and a follow-up presentation in 1972, the statement “weather is chaotic” has been well accepted. Such a view turns our attention from regularity associated with Laplace’s view of determinism to irregularity associated with chaos. In contrast to single-type chaotic solutions, recent studies using a generalized Lorenz model (GLM) have focused on the coexistence of chaotic and regular solutions that appear within the same model using the same modeling configurations but different initial conditions. The results, with attractor coexistence, suggest that the entirety of weather possesses a dual nature of chaos and order with distinct predictability. In this study, based on the GLM, we illustrate the following two mechanisms that may enable or modulate two kinds of attractor coexistence and, thus, contribute to distinct predictability: 1) the aggregated negative feedback of small-scale convective processes that can produce stable nontrivial equilibrium points and, thus, enable the appearance of stable steady-state solutions and their coexistence with chaotic or nonlinear oscillatory solutions, referred to as the first and second kinds of attractor coexistence; and 2) the modulation of large-scale time-varying forcing (heating) that can determine (or modulate) the alternative appearance of two kinds of attractor coexistence. Based on our results, we then discuss new opportunities and challenges in predictability research with the aim of improving predictions at extended-range time scales, as well as subseasonal to seasonal time scales.
Abstract
Over 50 years since Lorenz’s 1963 study and a follow-up presentation in 1972, the statement “weather is chaotic” has been well accepted. Such a view turns our attention from regularity associated with Laplace’s view of determinism to irregularity associated with chaos. In contrast to single-type chaotic solutions, recent studies using a generalized Lorenz model (GLM) have focused on the coexistence of chaotic and regular solutions that appear within the same model using the same modeling configurations but different initial conditions. The results, with attractor coexistence, suggest that the entirety of weather possesses a dual nature of chaos and order with distinct predictability. In this study, based on the GLM, we illustrate the following two mechanisms that may enable or modulate two kinds of attractor coexistence and, thus, contribute to distinct predictability: 1) the aggregated negative feedback of small-scale convective processes that can produce stable nontrivial equilibrium points and, thus, enable the appearance of stable steady-state solutions and their coexistence with chaotic or nonlinear oscillatory solutions, referred to as the first and second kinds of attractor coexistence; and 2) the modulation of large-scale time-varying forcing (heating) that can determine (or modulate) the alternative appearance of two kinds of attractor coexistence. Based on our results, we then discuss new opportunities and challenges in predictability research with the aim of improving predictions at extended-range time scales, as well as subseasonal to seasonal time scales.
Abstract
Summer simulations over the contiguous United States and Mexico with the Regional Atmospheric Modeling System (RAMS) dynamically downscaling the NCEP–NCAR Reanalysis I for the period 1950–2002 (described in Part I of the study) are evaluated with respect to the three dominant modes of global SST. Two of these modes are associated with the statistically significant, naturally occurring interannual and interdecadal variability in the Pacific. The remaining mode corresponds to the recent warming of tropical sea surface temperatures. Time-evolving teleconnections associated with Pacific SSTs delay or accelerate the evolution of the North American monsoon. At the period of maximum teleconnectivity in late June and early July, there is an opposite relationship between precipitation in the core monsoon region and the central United States. Use of a regional climate model (RCM) is essential to capture this variability because of its representation of the diurnal cycle of convective rainfall. The RCM also captures the observed long-term changes in Mexican summer rainfall and suggests that these changes are due in part to the recent increase in eastern Pacific SST off the Mexican coast. To establish the physical linkage to remote SST forcing, additional RAMS seasonal weather prediction mode simulations were performed and these results are briefly discussed. In order for RCMs to be successful in a seasonal weather prediction mode for the summer season, it is required that the GCM provide a reasonable representation of the teleconnections and have a climatology that is comparable to a global atmospheric reanalysis.
Abstract
Summer simulations over the contiguous United States and Mexico with the Regional Atmospheric Modeling System (RAMS) dynamically downscaling the NCEP–NCAR Reanalysis I for the period 1950–2002 (described in Part I of the study) are evaluated with respect to the three dominant modes of global SST. Two of these modes are associated with the statistically significant, naturally occurring interannual and interdecadal variability in the Pacific. The remaining mode corresponds to the recent warming of tropical sea surface temperatures. Time-evolving teleconnections associated with Pacific SSTs delay or accelerate the evolution of the North American monsoon. At the period of maximum teleconnectivity in late June and early July, there is an opposite relationship between precipitation in the core monsoon region and the central United States. Use of a regional climate model (RCM) is essential to capture this variability because of its representation of the diurnal cycle of convective rainfall. The RCM also captures the observed long-term changes in Mexican summer rainfall and suggests that these changes are due in part to the recent increase in eastern Pacific SST off the Mexican coast. To establish the physical linkage to remote SST forcing, additional RAMS seasonal weather prediction mode simulations were performed and these results are briefly discussed. In order for RCMs to be successful in a seasonal weather prediction mode for the summer season, it is required that the GCM provide a reasonable representation of the teleconnections and have a climatology that is comparable to a global atmospheric reanalysis.
