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A. C. Fowler and G. Kember

Abstract

The authors scale the five-mode model introduced by Lorenz and Krishnamurthy and show how explicit solutions may be obtained in the limit of small Rossby number by using the method of multiple scales. They thus obtain a characterization of the “slow manifold” of this model.

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R. J. Zammett and A. C. Fowler

Abstract

Katabatic winds on ice sheets and glaciers are buoyancy-driven flows, much like turbidity currents in the ocean. These winds are driven by radiative cooling of the ice surface and are not resolved by the typical horizontal and vertical discretization of general circulation models; therefore, a parameterization of their magnitude is desirable. In this paper, it is shown that the simplest such parameterization, based on the classical Prandtl model of slope winds, is physically inadmissible, and an improved model, which removes this irregularity, is presented. It is also shown that the improved model allows favorable comparison with both observations and regional numerical models.

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W. J. Emery, C. Fowler, and C. A. Clayson

Abstract

Sequential infrared satellite imagery is used to objectively compute surface currents in the Gulf Stream region using the maximum correlation (MCC) method. The infrared images, filtered for cloud cover, are used to find the displacement of surface temperature patterns by locating the maximum cross correlation in windowed portions of the image pair. Statistical significance and next-neighbor filter techniques are applied to remove fictitious surface current vectors due to the presence of residual cloud or other nonadvective processes. The core of the Gulf Stream is found to require special treatment due to the high local velocities and the weak sea surface temperature gradients. For the central Gulf Stream, currents are inferred by the MCC tracking of features along the northern edge of the stream. Other special MCC techniques are applied to the strong rotational motions in the Gulf Stream rings. To test the validity of the MCC technique in this geographic region where no in situ measurements were available, a quasigeostrophic numerical model was used to simulate ocean surface currents in the Gulf Stream region. A random surface tracer was introduced into the model field, tracked with the MCC method, and the resulting velocities were validated by comparisons with the model surface currents. Excellent agreement was found for those realizations less than 12 h apart in time, suggesting the reliability of MCC surface currents computed from sequential infrared images separated by less than 12 h.

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Ute C. Herzfeld, Sheldon Drobot, Wanli Wu, Charles Fowler, and James Maslanik

Abstract

The Western Arctic Linkage Experiment (WALE) is aimed at understanding the role of high-latitude terrestrial ecosystems in the response of the Arctic system to global change through collection and comparison of climate datasets and model results. In this paper, a spatiotemporal approach is taken to compare and validate model results from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) with commonly used analysis and reanalysis datasets for monthly averages of temperature and precipitation in 1992–2000 and for a study area at 55°–65°N, 160°–110°W in northwestern Canada and Alaska.

Objectives include a quantitative assessment of similarity between datasets and climate model fields, and identification of geographic areas and seasons that are problematic in modeling, with potential causes that may aid in model improvement. These are achieved by application of algebraic similarity mapping, a simple yet effective method for synoptic analysis of many (here, 45) different spatial datasets, maps, and models. Results indicate a dependence of model–data similarity on seasonality, on climate variable, and on geographic location. In summary, 1) similarity of data and models is better for temperature than for precipitation; and 2) modeling of summer precipitation fields, and to a lesser extent, temperature fields, appears more problematic than that of winter fields. The geographic distribution of areas with best and worst agreement shifts throughout the year, with generally better agreement between maps and models in the northeastern and northern inland areas than in topographically complex and near-coastal areas. The study contributes to an understanding of the geographic complexity of the Arctic system and modeling its diverse climate.

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Sang-Hun Park, William C. Skamarock, Joseph B. Klemp, Laura D. Fowler, and Michael G. Duda

Abstract

The hydrostatic and nonhydrostatic atmospheric solvers within the Model for Prediction Across Scales (MPAS) are tested using an extension of Jablonowski and Williamson baroclinic wave test case that includes moisture. This study uses the dry test case to verify the correctness of the solver formulation and coding by comparing results from the two different MPAS solvers and from the global version of the Advanced Research Weather Research and Forecasting Model (ARW-WRF). A normal mode initialization is used in the Jablonowski and Williamson test, and the most unstable mode is found to be wavenumber 9. The three solvers produce very similar normal mode structures and nonlinear baroclinic wave evolutions. Solutions produced using MPAS variable-resolution meshes are quite similar to the results from the quasi-uniform mesh with equivalent resolution. Importantly, the small-scale flow features are better resolved in the fine-resolution region and there is no apparent wave distortion in the fine-to-coarse mesh transition region, thus demonstrating the potential value of MPAS for multiscale flow simulation.

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Steven C. Chan, Elizabeth J. Kendon, Nigel Roberts, Stephen Blenkinsop, and Hayley J. Fowler

Abstract

Midlatitude extreme precipitation events are caused by well-understood meteorological drivers, such as vertical instability and low pressure systems. In principle, dynamical weather and climate models behave in the same way, although perhaps with the sensitivities to the drivers varying between models. Unlike parameterized convection models (PCMs), convection-permitting models (CPMs) are able to realistically capture subdaily extreme precipitation. CPMs are computationally expensive; being able to diagnose the occurrence of subdaily extreme precipitation from large-scale drivers, with sufficient skill, would allow effective targeting of CPM downscaling simulations. Here the regression relationships are quantified between the occurrence of extreme hourly precipitation events and vertical stability and circulation predictors in southern United Kingdom 1.5-km CPM and 12-km PCM present- and future-climate simulations. Overall, the large-scale predictors demonstrate skill in predicting the occurrence of extreme hourly events in both the 1.5- and 12-km simulations. For the present-climate simulations, extreme occurrences in the 12-km model are less sensitive to vertical stability than in the 1.5-km model, consistent with understanding the limitations of cumulus parameterization. In the future-climate simulations, the regression relationship is more similar between the two models, which may be understood from changes to the large-scale circulation patterns and land surface climate. Overall, regression analysis offers a promising avenue for targeting CPM simulations. The authors also outline which events would be missed by adopting such a targeted approach.

Open access
Laura D. Fowler, William C. Skamarock, Georg A. Grell, Saulo R. Freitas, and Michael G. Duda

Abstract

The authors implemented the Grell–Freitas (GF) parameterization of convection in which the cloud-base mass flux varies quadratically as a function of the convective updraft fraction in the global nonhydrostatic Model for Prediction Across Scales (MPAS). They evaluated the performance of GF using quasi-uniform meshes and a variable-resolution mesh centered over South America, the resolution of which varied between hydrostatic (50 km) and nonhydrostatic (3 km) scales. Four-day forecasts using a 50-km and a 15-km quasi-uniform mesh, initialized with GFS data for 0000 UTC 10 January 2014, reveal that MPAS overestimates precipitation in the tropics relative to the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis data. Results of 4-day forecasts using the variable-resolution mesh reveal that over the refined region of the mesh, GF performs as a precipitating shallow convective scheme, whereas over the coarse region of the mesh, GF acts as a conventional deep convective scheme. As horizontal resolution increases and subgrid-scale motions become increasingly resolved, the contribution of convective and grid-scale precipitation to the total precipitation decreases and increases, respectively. Probability density distributions of precipitation highlight a smooth transition in the partitioning between convective and grid-scale precipitation, including at gray-zone scales across the transition region between the coarsest and finest regions of the global mesh. Variable-resolution meshes spanning between hydrostatic and nonhydrostatic scales are shown to be ideal tools to evaluate the horizontal scale dependence of parameterized convective and grid-scale moist processes.

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Steven C. Chan, Elizabeth J. Kendon, Hayley J. Fowler, Stephen Blenkinsop, Nigel M. Roberts, and Christopher A. T. Ferro

Abstract

Extreme value theory is used as a diagnostic for two high-resolution (12-km parameterized convection and 1.5-km explicit convection) Met Office regional climate model (RCM) simulations. On subdaily time scales, the 12-km simulation has weaker June–August (JJA) short-return-period return levels than the 1.5-km RCM, yet the 12-km RCM has overly large high return levels. Comparisons with observations indicate that the 1.5-km RCM is more successful than the 12-km RCM in representing (multi)hourly JJA very extreme events. As accumulation periods increase toward daily time scales, the erroneous 12-km precipitation extremes become more comparable with the observations and the 1.5-km RCM. The 12-km RCM fails to capture the observed low sensitivity of the growth rate to accumulation period changes, which is successfully captured by the 1.5-km RCM. Both simulations have comparable December–February (DJF) extremes, but the DJF extremes are generally weaker than in JJA at daily or shorter time scales. Case studies indicate that “gridpoint storms” are one of the causes of unrealistic very extreme events in the 12-km RCM. Caution is needed in interpreting the realism of 12-km RCM JJA extremes, including short-return-period events, which have return values closer to observations. There is clear evidence that the 1.5-km RCM has a higher degree of realism than the 12-km RCM in the simulation of JJA extremes.

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William C. Skamarock, Joseph B. Klemp, Michael G. Duda, Laura D. Fowler, Sang-Hun Park, and Todd D. Ringler

Abstract

The formulation of a fully compressible nonhydrostatic atmospheric model called the Model for Prediction Across Scales–Atmosphere (MPAS-A) is described. The solver is discretized using centroidal Voronoi meshes and a C-grid staggering of the prognostic variables, and it incorporates a split-explicit time-integration technique used in many existing nonhydrostatic meso- and cloud-scale models. MPAS can be applied to the globe, over limited areas of the globe, and on Cartesian planes. The Voronoi meshes are unstructured grids that permit variable horizontal resolution. These meshes allow for applications beyond uniform-resolution NWP and climate prediction, in particular allowing embedded high-resolution regions to be used for regional NWP and regional climate applications. The rationales for aspects of this formulation are discussed, and results from tests for nonhydrostatic flows on Cartesian planes and for large-scale flow on the sphere are presented. The results indicate that the solver is as accurate as existing nonhydrostatic solvers for nonhydrostatic-scale flows, and has accuracy comparable to existing global models using icosahedral (hexagonal) meshes for large-scale flows in idealized tests. Preliminary full-physics forecast results indicate that the solver formulation is robust and that the variable-resolution-mesh solutions are well resolved and exhibit no obvious problems in the mesh-transition zones.

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N. Forsythe, A. J. Hardy, H. J. Fowler, S. Blenkinsop, C. G. Kilsby, D. R. Archer, and M. Z. Hashmi

Abstract

Clouds play a key role in hydroclimatological variability by modulating the surface energy balance and air temperature. This study utilizes MODIS cloud cover data, with corroboration from global meteorological reanalysis (ERA-Interim) cloud estimates, to describe a cloud climatology for the upper Indus River basin. It has specific focus on tributary catchments in the northwest of the region, which contribute a large fraction of basin annual runoff, including 65% of flow originating above Besham, Pakistan or 50 km3 yr−1 in absolute terms. In this region there is substantial cloud cover throughout the year, with spatial means of 50%–80% depending on the season. The annual cycles of catchment spatial mean daytime and nighttime cloud cover fraction are very similar. This regional diurnal homogeneity belies substantial spatial variability, particularly along seasonally varying vertical profiles (based on surface elevation).

Correlations between local near-surface air temperature observations and MODIS cloud cover fraction confirm the strong linkages between local atmospheric conditions and near-surface climate variability. These correlations are interpreted in terms of seasonal and diurnal variations in apparent cloud radiative effect and its influence on near-surface air temperature in the region. The potential role of cloud radiative effect in recognized seasonally and diurnally asymmetrical temperature trends over recent decades is also assessed by relating these locally observed trends to ERA-Interim-derived trends in cloud cover fraction. Specifically, reduction in nighttime cloud cover fraction relative to daytime conditions over recent decades appears to provide a plausible physical mechanism for the observed nighttime cooling of surface air temperature in summer months.

Open access