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Martin J. Otte and John C. Wyngaard

Abstract

The structure of the interfacial layer capping the atmospheric boundary layer is not well understood. The dominant influence on turbulence within the interfacial layer is the stable stratification induced by the capping inversion. A series of 26 high-resolution large eddy simulation runs ranging from neutral, inversion-capped to free-convection cases are used to study interfacial layer turbulence. The interfacial layer is found to be similar in many aspects to a classic stable boundary layer. For example, the shapes of interfacial layer spectra and cospectra, including the locations of the spectral peaks, agree with previous observations from nocturnal PBLs. The eddy diffusivities, variances, structure-function parameters, and dissipation rates within the interfacial layer, suitably nondimensionalized using local scaling, also agree with observations from nocturnal PBLs. These results may lead to improved models of the interfacial layer and entrainment, and may also have implications for remote sensing of the interfacial layer.

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Martin J. Otte and John C. Wyngaard

Abstract

Mixed-layer models are computationally efficient, but they do not realistically represent the structure of the boundary layer under many conditions. Many of the deficiencies of the mixed-layer model can be attributed to the assumed flat profiles. A new method is proposed that, by relaxing the assumption of well-mixed profiles, makes possible an integral PBL parameterization that is computationally efficient, yet accurately describes the mean structure of the boundary layer. The vertical structure of the mean variables in the PBL is represented by a truncated series of Legendre polynomials. The first Legendre mode, the layer average, is identically a mixed-layer model. Additional modes add structure to the vertical profiles and represent corrections to the mixed-layer model. Only a few modes an necessary to produce vertical profiles comparable to the predictions of high-resolution models. Results of the model are shown for a variety of PBL stability regimes.

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Shari J. Kimmel, John C. Wyngaard, and Martin J. Otte

Abstract

Turbulent fluctuations of a conservative scalar in the atmospheric boundary layer (ABL) can be generated by a scalar flux at the surface, a scalar flux of entrainment at the ABL top, and the “chewing up” of scalar variations on the mesoscale. The first two have been previously studied, while the third is examined in this paper through large-eddy simulation (LES). The LES results show that the scalar fluctuations due to the breakdown of mesoscale variations in advected conservative scalar fields, which the authors call the “log-chipper” component of scalar fluctuations, are uniformly distributed through the depth of the convective ABL, unlike the top–down and bottom–up components.

A similarity function, similar to those for the top–down and bottom–up scalars, is derived for the log-chipper scalar variance in the convective ABL and used to compare the relative importance of these three processes for generating scalar fluctuations. Representative mesoscale gradients for water vapor mixing ratio and potential temperature are computed from airplane measurements over both land and water. In situations where the entrainment and surface fluxes are sufficiently small, or the ABL depth, turbulence intensity, or the mesoscale scalar gradient is sufficiently large, the variance of the log-chipper scalar fluctuations in mid-ABL can be of the order of the variance of top–down and bottom–up scalars.

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David Medvigy, Robert L. Walko, Martin J. Otte, and Roni Avissar

Abstract

This work continues the presentation and evaluation of the Ocean–Land–Atmosphere Model (OLAM), focusing on the model’s ability to represent radiation and precipitation. OLAM is a new, state-of-the-art earth system model, capable of user-specified grid resolution and local mesh refinement. An objective optimization of the microphysics parameterization is carried out. Data products from the Clouds and the Earth’s Radiant Energy System (CERES) and the Global Precipitation Climatology Project (GPCP) are used to construct a maximum likelihood function, and thousands of simulations using different values for key parameters are carried out. Shortwave fluxes are found to be highly sensitive to both the density of cloud droplets and the assumed shape of the cloud droplet diameter distribution function. Because there is considerable uncertainty in which values for these parameters to use in climate models, they are targeted as the tunable parameters of the objective optimization procedure, which identified high-likelihood volumes of parameter space as well as parameter uncertainties and covariances. Once optimized, the model closely matches observed large-scale radiative fluxes and precipitation. The impact of model resolution is also tested. At finer characteristic length scales (CLS), smaller-scale features such as the ITCZ are better resolved. It is also found that the Amazon was much better simulated at 100- than 200-km CLS. Furthermore, a simulation using OLAM’s variable resolution functionality to cover South America with 100-km CLS and the rest of the world with 200-km CLS generates a precipitation pattern in the Amazon similar to the global 100-km CLS run.

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David Medvigy, Robert L. Walko, Martin J. Otte, and Roni Avissar

Abstract

Numerical models have long predicted that the deforestation of the Amazon would lead to large regional changes in precipitation and temperature, but the extratropical effects of deforestation have been a matter of controversy. This paper investigates the simulated impacts of deforestation on the northwest United States December–February climate. Integrations are carried out using the Ocean–Land–Atmosphere Model (OLAM), here run as a variable-resolution atmospheric GCM, configured with three alternative horizontal grid meshes: 1) 25-km characteristic length scale (CLS) over the United States, 50-km CLS over the Andes and Amazon, and 200-km CLS in the far-field; 2) 50-km CLS over the United States, 50-km CLS over the Andes and Amazon, and 200-km CLS in the far-field; and 3) 200-km CLS globally. In the high-resolution simulations, deforestation causes a redistribution of precipitation within the Amazon, accompanied by vorticity and thermal anomalies. These anomalies set up Rossby waves that propagate into the extratropics and impact western North America. Ultimately, Amazon deforestation results in 10%–20% precipitation reductions for the coastal northwest United States and the Sierra Nevada. Snowpack in the Sierra Nevada experiences declines of up to 50%. However, in the coarse-resolution simulations, this mechanism is not resolved and precipitation is not reduced in the northwest United States. These results highlight the need for adequate model resolution in modeling the impacts of Amazon deforestation. It is concluded that the deforestation of the Amazon can act as a driver of regional climate change in the extratropics, including areas of the western United States that are agriculturally important.

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Tanya L. Otte, Christopher G. Nolte, Martin J. Otte, and Jared H. Bowden

Abstract

An important question in regional climate downscaling is whether to constrain (nudge) the interior of the limited-area domain toward the larger-scale driving fields. Prior research has demonstrated that interior nudging can increase the skill of regional climate predictions originating from historical data. However, there is concern that nudging may also inhibit the regional model’s ability to properly develop and simulate mesoscale features, which may reduce the value added from downscaling by altering the representation of local climate extremes. Extreme climate events can result in large economic losses and human casualties, and regional climate downscaling is one method for projecting how climate change scenarios will affect extreme events locally. In this study, the effects of interior nudging are explored on the downscaled simulation of temperature and precipitation extremes. Multidecadal, continuous Weather Research and Forecasting model simulations of the contiguous United States are performed using coarse reanalysis fields as proxies for global climate model fields. The results demonstrate that applying interior nudging improves the accuracy of simulated monthly means, variability, and extremes over the multidecadal period. The results in this case indicate that interior nudging does not inappropriately squelch the prediction of temperature and precipitation extremes and is essential for simulating extreme events that are faithful in space and time to the driving large-scale fields.

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Jared H. Bowden, Tanya L. Otte, Christopher G. Nolte, and Martin J. Otte

Abstract

This study evaluates interior nudging techniques using the Weather Research and Forecasting (WRF) model for regional climate modeling over the conterminous United States (CONUS) using a two-way nested configuration. NCEP–Department of Energy Atmospheric Model Intercomparison Project (AMIP-II) Reanalysis (R-2) data are downscaled to 36 km × 36 km by nudging only at the lateral boundaries, using gridpoint (i.e., analysis) nudging and using spectral nudging. Seven annual simulations are conducted and evaluated for 1988 by comparing 2-m temperature, precipitation, 500-hPa geopotential height, and 850-hPa meridional wind to the 32-km North American Regional Reanalysis (NARR). Using interior nudging reduces the mean biases for those fields throughout the CONUS compared to the simulation without interior nudging. The predictions of 2-m temperature and fields aloft behave similarly when either analysis or spectral nudging is used. For precipitation, however, analysis nudging generates monthly precipitation totals, and intensity and frequency of precipitation that are closer to observed fields than spectral nudging. The spectrum of 250-hPa zonal winds simulated by the WRF model is also compared to that of the R-2 and NARR. The spatial variability in the WRF model is reduced by using either form of interior nudging, and analysis nudging suppresses that variability more strongly than spectral nudging. Reducing the nudging strengths on the inner domain increases the variability but generates larger biases. The results support the use of interior nudging on both domains of a two-way nest to reduce error when the inner nest is not otherwise dominated by the lateral boundary forcing. Nevertheless, additional research is required to optimize the balance between accuracy and variability in choosing a nudging strategy.

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Renato Ramos da Silva, Gil Bohrer, David Werth, Martin J. Otte, and Roni Avissar

Abstract

Meteorological observations and model simulations are used to show that the catastrophic ice storm of 4–5 December 2002 in the southeastern United States resulted from the combination of a classic winter storm and a warm sea surface temperature (SST) anomaly in the western Atlantic Ocean. At the time of the storm, observations show that the Atlantic SST near the southeastern U.S. coast was 1.0°–1.5°C warmer than its multiyear mean. The impact of this anomalous SST on the ice accumulation of the ice storm was evaluated with the Regional Atmospheric Modeling System. The model shows that a warmer ocean leads to the conversion of more snow into freezing rain while not significantly affecting the inland surface temperature. Conversely, a cooler ocean produces mostly snowfall and less freezing rain. A similar trend is obtained by statistically comparing observations of ice storms in the last decade with weekly mean Atlantic SSTs. The SST during an ice storm is significantly and positively correlated with a deeper and warmer melting layer.

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