CFD simulation study of the flow field in a tornado-like vortex

Because of the lack of field measurements near the ground in tornadoes, numerical simulation may provide the best estimate of the near-ground wind profiles, assuming such simulations are realistic and agree well with the available observations at higher levels. An accurate understanding of the loads requires knowledge about near-ground tornado winds. The numerical simulations based on the ISU laboratory tornado model agree well with the Spencer, South Dakota tornado of 30 May 1998. The wind measurements taken by portable Doppler radars are restricted to levels higher than at least 20-50 m above the ground. It is necessary to understand tornado-induced wind loads on typical structures to help improve structural designs to resist tornado winds. Computational Fluid dynamic (CFD) simulations are used as a tool to validate a laboratory model\u27s ability to simulate a real tornado vortex by studying the near ground flow field. The sensitivity of solutions to parameters such as the inflow depth, inflow radius, outflow radius, mesh size, boundary condition, surface roughness and Swirl ratio was explored by designing a numerical model based on the ISU laboratory tornado simulator. The study suggests that it is important to correctly choose the inflow radius to be far enough away from the tornado to minimize the influence of the boundary conditions on the vortex, but close enough to the tornado to reduce the influence of difficult-to-simulate surface roughness. The simulated core radius is greatly affected by both outflow radius and Swirl ratio. The fine mesh size provides a higher resolution than Doppler radar data, and stronger tangential velocities are thus simulated at the core radius. The effects of surface roughness are to reduce the tangential velocity and slightly enlarge the core radius. Three ways of increasing roughness in the numerical simulation are tested. It was found that the most efficient way to represent roughness was to increase the roughness elements\u27 height. These conclusions could assist in the design of future numerical or laboratory experiments exploring the near ground flow more closely

Nonstationary Synchronization of Equatorial QBO with SAO in Observations and a Model

It has often been suggested that the period of the quasi-biennial oscillation (QBO) has a tendency to synchronize with the semiannual oscillation (SAO). Apparently the synchronization is better the higher up the observation extends. Using 45 yr of the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) data of the equatorial stratosphere up to the stratopause, the authors confirm that this synchronization is not just a tendency but a robust phenomenon in the upper stratosphere. A QBO period starts when a westerly SAO (w-SAO) descends from the stratopause to 7 hPa and initiates the westerly phase of the QBO (w-QBO) below. It ends when another w-SAO, a few SAO periods later, descends again to 7 hPa to initiate the next w-QBO. The fact that it is the westerly but not the easterly SAO (e-SAO) that initiates the QBO is also explained by the general easterly bias of the angular momentum in the equatorial stratosphere so that the e-SAO does not create a zero-wind line, unlike the w-SAO. The currently observed average QBO period of 28 months, which is not an integer multiple of SAO periods, is a result of intermittent jumps of the QBO period from four SAO to five SAO periods. The same behavior is also found in the Two and a Half Dimensional Interactive Isentropic Research (THINAIR) model. It is found that the nonstationary behavior in both the observation and model is caused not by the 11-yr solar-cycle forcing but by the incompatibility of the QBO’s natural period (determined by its wave forcing) and the “quantized” period determined by the SAO. The wave forcing parameter for the QBO period in the current climate probably lies between four SAO and five SAO periods. If the wave forcing for the QBO is tuned so that its natural period is compatible with the SAO period above (e.g., at 24 or 30 months), nonstationary behavior disappears

Modulation of the Period of the Quasi-Biennial Oscillation by the Solar Cycle

The authors examine the mechanism of solar cycle modulation of the Quasi-Biennial Oscillation (QBO) period using the Two-and-a-Half-Dimensional Interactive Isentropic Research (THINAIR) model. Previous model results (using 2D and 3D models of varying complexity) have not convincingly established the proposed link of longer QBO periods during solar minima. Observational evidence for such a modulation is also controversial because it is only found during the period from the 1960s to the early 1990s, which is contaminated by volcanic aerosols. In the model, 200- and 400-yr runs without volcano influence can be obtained, long enough to establish some statistical robustness. Both in model and observed data, there is a strong synchronization of the QBO period with integer multiples of the semiannual oscillation (SAO) in the upper stratosphere. Under the current level of wave forcing, the period of the QBO jumps from one multiple of SAO to another and back so that it averages to 28 months, never settling down to a constant period. The “decadal” variability in the QBO period takes the form of “quantum” jumps; these, however, do not appear to follow the level of the solar flux in either the observation or the model using realistic quasi-periodic solar cycle (SC) forcing. To understand the solar modulation of the QBO period, the authors perform model runs with a range of perpetual solar forcing, either lower or higher than the current level. At the current level of solar forcing, the model QBO period consists of a distribution of four and five SAO periods, similar to the observed distribution. This distribution changes as solar forcing changes. For lower (higher) solar forcing, the distribution shifts to more (less) four SAO periods than five SAO periods. The record-averaged QBO period increases with the solar forcing. However, because this effect is rather weak and is detectable only with exaggerated forcing, the authors suggest that the previous result of the anticorrelation of the QBO period with the SC seen in short observational records reflects only a chance behavior of the QBO period, which naturally jumps in a nonstationary manner even if the solar forcing is held constant, and the correlation can change as the record gets longer

Attribution of chemistry-climate model initiative (CCMI) ozone radiative flux bias from satellites

The top-of-atmosphere (TOA) outgoing longwave flux over the 9.6 µm ozone band is a fundamental quantity for understanding chemistry–climate coupling. However, observed TOA fluxes are hard to estimate as they exhibit considerable variability in space and time that depend on the distributions of clouds, ozone (O3), water vapor (H2O), air temperature (Ta), and surface temperature (Ts). Benchmarking present-day fluxes and quantifying the relative influence of their drivers is the first step for estimating climate feedbacks from ozone radiative forcing and predicting radiative forcing evolution. To that end, we constructed observational instantaneous radiative kernels (IRKs) under clear-sky conditions, representing the sensitivities of the TOA flux in the 9.6 µm ozone band to the vertical distribution of geophysical variables, including O3, H2O, Ta, and Ts based upon the Aura Tropospheric Emission Spectrometer (TES) measurements. Applying these kernels to present-day simulations from the Chemistry-Climate Model Initiative (CCMI) project as compared to a 2006 reanalysis assimilating satellite observations, we show that the models have large differences in TOA flux, attributable to different geophysical variables. In particular, model simulations continue to diverge from observations in the tropics, as reported in previous studies of the Atmospheric Chemistry Climate Model Intercomparison Project (ACCMIP) simulations. The principal culprits are tropical middle and upper tropospheric ozone followed by tropical lower tropospheric H2O. Five models out of the eight studied here have TOA flux biases exceeding 100 mW m−2 attributable to tropospheric ozone bias. Another set of five models have flux biases over 50 mW m−2 due to H2O. On the other hand, Ta radiative bias is negligible in all models (no more than 30 mW m−2). We found that the atmospheric component (AM3) of the Geophysical Fluid Dynamics Laboratory (GFDL) general circulation model and Canadian Middle Atmosphere Model (CMAM) have the lowest TOA flux biases globally but are a result of cancellation of opposite biases due to different processes. Overall, the multi-model ensemble mean bias is −133±98  mW m−2, indicating that they are too atmospherically opaque due to trapping too much radiation in the atmosphere by overestimated tropical tropospheric O3 and H2O. Having too much O3 and H2O in the troposphere would have different impacts on the sensitivity of TOA flux to O3 and these competing effects add more uncertainties on the ozone radiative forcing. We find that the inter-model TOA outgoing longwave radiation (OLR) difference is well anti-correlated with their ozone band flux bias. This suggests that there is significant radiative compensation in the calculation of model outgoing longwave radiation

Pressure Line Broadening and Feasibility of CO_2 Profile Retrieval using Near Infrared Observations of an Absorption Line

Analytic expressions are derived for the transmittance and reflectance of sunlight and their Jacobians for an absorption line with Lorentz line broadening. Rodgers information analysis is applied to calculate the information content, the degrees of freedom and the averaging kernel for a simple atmospheric model to investigate the feasibility of retrieving the profile of CO_2 using near-infrared (NIR) measurements over a single absorption line. The results have implications for the design of future space instruments with high spectral resolution and high signal to noise ratios to obtain global scale information on the CO_2 vertical distribution which is important for inferring the sources, sinks, and transport of CO_2

A link between tropical intraseasonal variability and Arctic stratospheric ozone

Previous studies using satellite measurements showed evidence that subtropical upper troposphere/lower stratosphere ozone (O_3) can be modulated by tropical intraseasonal variability, the most dominant form of which is the Madden Julian Oscillation (MJO) with a period of 30–60 days. Here we further study the MJO modulation in the upper troposphere/lower stratosphere O_3 over the northern extratropics and the Arctic. Significant MJO-related O_3 signals (13–20 Dobson units) are found over the northern extratropics (north of 30°N). The O_3 anomalies change their magnitude and patterns depending on the phase of the MJO. Over the Arctic, the MJO-related O_3 anomalies are dominated by a wave number 2 structure and are anticorrelated with the geopotential height (GPH) anomalies at 250 hPa. The latter is similar to the findings in the previous studies over subtropics and indicates that the Arctic upper troposphere/lower stratosphere O_3 anomalies are associated with dynamical motions near the tropopause. The teleconnection from the tropics to the Arctic is likely through propagation of planetary waves generated by the equatorial heating that affects the tropopause height and O_3 at high latitudes

Inverse modelling of carbonyl sulfide: implementation, evaluation and implications for the global budget

Carbonyl sulfide (COS) has the potential to be used as a climate diagnostic due to its close coupling to the biospheric uptake of CO2 and its role in the formation of stratospheric aerosol. The current understanding of the COS budget, however, lacks COS sources, which have previously been allocated to the tropical ocean. This paper presents a first attempt at global inverse modelling of COS within the 4-dimensional variational data-assimilation system of the TM5 chemistry transport model (TM5-4DVAR) and a comparison of the results with various COS observations. We focus on the global COS budget, including COS production from its precursors carbon disulfide (CS2) and dimethyl sulfide (DMS). To this end, we implemented COS uptake by soil and vegetation from an updated biosphere model (Simple Biosphere Model-SiB4). In the calculation of these fluxes, a fixed atmospheric mole fraction of 500 pmol mol-1 was assumed. We also used new inventories for anthropogenic and biomass burning emissions. The model framework is capable of closing the COS budget by optimizing for missing emissions using NOAA observations in the period 2000-2012. The addition of 432 Gg a-1 (as S equivalents) of COS is required to obtain a good fit with NOAA observations. This missing source shows few year-to-year variations but considerable seasonal variations. We found that the missing sources are likely located in the tropical regions, and an overestimated biospheric sink in the tropics cannot be ruled out due to missing observations in the tropical continental boundary layer. Moreover, high latitudes in the Northern Hemisphere require extra COS uptake or reduced emissions. HIPPO (HIAPER Pole-to-Pole Observations) aircraft observations, NOAA airborne profiles from an ongoing monitoring programme and several satellite data sources are used to evaluate the optimized model results. This evaluation indicates that COS mole fractions in the free troposphere remain underestimated after optimization. Assimilation of HIPPO observations slightly improves this model bias, which implies that additional observations are urgently required to constrain sources and sinks of COS. We finally find that the biosphere flux dependency on the surface COS mole fraction (which was not accounted for in this study) may substantially lower the fluxes of the SiB4 biosphere model over strong-uptake regions. Using COS mole fractions from our inversion, the prior biosphere flux reduces from 1053 to 851 Gg a-1, which is closer to 738 Gg a-1 as was found by Berry et al. (2013). In planned further studies we will implement this biosphere dependency and additionally assimilate satellite data with the aim of better separating the role of the oceans and the biosphere in the global COS budget..</p