Abstract

Time-averaged vertically integrated 3D advections are inferred from heat and moisture budgets obtained from observations at the Surface Heat Budget of the Arctic Ocean (SHEBA) field experiment for April, May, June, and July. Advection was a source of heat and moisture in the column budgets during the time period, balanced mostly by precipitation and radiative cooling. These inferred advections are used to evaluate and correct the 3D temperature and water vapor advection profiles obtained from operational forecasts of the ECMWF model. Advections from the ECMWF model are generally too warm and moist, particularly in July. These biases lead to overpredictions of temperature and water vapor mixing ratio, often exceeding 12 K and 50%, respectively, in monthlong single-column model simulations. A correction algorithm is developed that constrains the ECMWF advections to the observed column budgets, thereby eliminating a first-order source of error in the advective forcing. The approach described here differs from other constrained analysis techniques since it does not require a spatial network of observed or analyzed fields. Simulations forced with the corrected advections show significant improvements in the modeled temperature and water vapor profiles and precipitation. It is demonstrated that using the new observationally constrained advection profiles allows for a less ambiguous evaluation of the model's physical parameterizations.

Abstract

A persistent, weakly forced, horizontally extensive mixed-phase boundary layer cloud observed on 4–5 May 1998 during the Surface Heat Budget of the Arctic Ocean (SHEBA)/First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment–Arctic Clouds Experiment (FIRE–ACE) is modeled using three different bulk microphysics parameterizations of varying complexity implemented into the polar version of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). The two simpler schemes predict mostly ice clouds and very little liquid water, while the complex scheme is able to reproduce the observed persistence and horizontal extent of the mixed-phase stratus deck. This mixed-phase cloud results in radiative warming of the surface, the development of a cloud-topped, surface-based mixed layer, and an enhanced precipitation rate. In contrast, the optically thin ice clouds predicted by the simpler schemes lead to radiative cooling of the surface, a strong diurnal cycle in the boundary layer structure, and very weak precipitation. The larger surface precipitation rate using the complex scheme is partly balanced by an increase in the turbulent flux of water vapor from the surface to the atmosphere. This enhanced vapor flux is attributed to changes in the surface and boundary layer characteristics induced by the cloud itself, although cloud–surface interactions appear to be exaggerated in the model compared with reality. The prediction of extensive mixed-phase stratus by the complex scheme is also associated with increased surface pressure and subsidence relative to the other simulations. Sensitivity tests show that the detailed treatment of ice nucleation and prediction of snow particle number concentration in the complex scheme suppresses ice particle concentration relative to the simpler schemes, reducing the vapor deposition rate (for given values of bulk ice mass and ice supersaturation) and leading to much greater amounts of liquid water and mixed-phase cloudiness. These results suggest that the treatments of ice nucleation and the snow intercept parameter in the simpler schemes, which are based upon midlatitude observations, are inadequate for simulating the weakly forced mixed-phase clouds endemic to the Arctic.

Abstract

A new two-moment bulk microphysics scheme is implemented into the polar version of the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) to simulate arctic mixed-phase boundary layer stratiform clouds observed during Surface Heat Budget of the Arctic (SHEBA) First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE) Arctic Cloud Experiment (ACE). The microphysics scheme predicts the number concentrations and mixing ratios of four hydrometeor species (cloud droplets, small ice, rain, snow) and includes detailed treatments of droplet activation and ice nucleation from a prescribed distribution of aerosol obtained from observations. The model is able to reproduce many features of the observed mixed-phase cloud, including a near-adiabatic liquid water content profile located near the top of a well-mixed boundary layer, droplet number concentrations of about 200–250 cm⁻³ that were distributed fairly uniformly through the depth of the cloud, and continuous light snow falling from the cloud base to the surface. The impacts of droplet and ice nucleation, radiative transfer, turbulence, large-scale dynamics, and vertical resolution on the simulated mixed-phase stratiform cloud are examined. The cloud layer is largely self-maintained through strong cloud-top radiative cooling that exceeds 40 K day⁻¹. It persists through extended periods of downward large-scale motion that tend to thin the layer and reduce water contents. Droplet activation rates are highest near cloud base, associated with subgrid vertical motion that is diagnosed from the predicted turbulence kinetic energy. A sensitivity test neglecting subgrid vertical velocity produces only weak activation and small droplet number concentrations (<90 cm⁻³). These results highlight the importance of parameterizing the impact of subgrid vertical velocity to generate local supersaturation for aerosol-droplet closure. The primary ice nucleation mode in the simulated mixed-phase cloud is contact freezing of droplets. Sensitivity tests indicate that the assumed number and size of contact nuclei can have a large impact on the evolution and characteristics of mixed-phase cloud, especially the partitioning of condensate between droplets and ice.

Abstract

An enhanced National Center for Atmospheric Research (NCAR) integrated sounding system (ISS) was deployed as part of the Vertical Transport and Mixing (VTMX) field experiment, which took place in October of 2000. The enhanced ISS was set up at the southern terminus of the Great Salt Lake Valley just north of a gap in the Traverse Range (TR), which separates the Great Salt Lake and Utah Lake basins. This location was chosen to sample the dynamic and thermodynamic properties of the flow as it passes over the TR separating the two basins. The enhanced ISS allowed for near-continuous sampling of the nocturnal boundary layer (NBL) and low-level winds associated with drainage flow through the gap in the TR. Diurnally varying winds were observed at the NCAR site on days characterized by weak synoptic forcing and limited cloud cover. A down-valley jet (DVJ) was observed on about 50% of the nights during VTMX, with the maximum winds usually occurring within 150 m of the surface. The DVJ was associated with abrupt warming at low levels as a result of downward mixing and vertical transport of warm air from the inversion layer above. Several processes were observed to contribute to vertical transport and mixing at the NCAR site. Pulses in the strength of the DVJ contributed to vertical transport by creating localized areas of low-level convergence. Gravity waves and Kelvin–Helmholtz waves, which facilitated vertical mixing near the surface and atop the DVJ, were observed with a sodar and an aerosol backscatter lidar that were deployed as part of the enhanced ISS. The nonlocal nature of the processes responsible for generating turbulence in strongly stratified surface layers in complex terrain confounds surface flux parameterizations typically used in mesoscale models that rely on Monin–Obukhov similarity theory. This finding has major implications for modeling NBL structure and drainage flows in regions of complex terrain.

Abstract

This study documents the global distribution and characteristics of diurnally varying low-level jets (LLJs), including their horizontal, vertical, and temporal structure, with a special emphasis on highlighting the underlying commonalities and unique qualities of the various nocturnal jets. Two tools are developed to accomplish this goal. The first is a 21-yr global reanalysis performed with the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) using a horizontal grid spacing of 40 km. A unique characteristic of the reanalysis is the availability of hourly three-dimensional output, which permits the full diurnal cycle to be analyzed. Furthermore, the horizontal grid spacing of 40 km better resolves many physiographic features that host LLJs than other widely used global reanalyses. This makes possible a detailed examination of the systematic onset and cessation of the jets, including time–height representations of the diurnal cycle. The second tool is an index of nocturnal LLJ (NLLJ) activity based upon the vertical structure of the wind’s temporal variation, where the temporal variation is defined in local time. The first available objectively constructed global maps of recurring NLLJs are created from this index, where the various NLLJs can be simultaneously viewed at or near their peak time. These maps not only highlight all of the locations where NLLJs are known to recur, but they also reveal a number of new jets.

The authors examine the basic mechanisms that give rise to the NLLJs identified in four disparate locations, each having a profound influence on the regional climate. The first, the extensively studied Great Plains NLLJ, is used to confirm the veracity of the global analysis and the index of NLLJ activity. It also provides context for three of the many newly identified NLLJs: 1) Tarim Pendi in northwest China; 2) Ethiopia in eastern Africa; and 3) Namibia–Angola in southwest Africa. Jets in these four regions illustrate the variety of physiographic and thermal forcing mechanisms that can produce NLLJs.

Abstract

The authors’ previous idealized, two-dimensional cloud resolving model (CRM) simulations of Arctic stratus revealed a surprising sensitivity to the concentrations of ice crystals. In this paper, simulations of an actual case study observed during the Beaufort and Arctic Seas Experiment are performed and the results are compared to the observed data.

It is again found in the CRM simulations that the simulated stratus cloud is very sensitive to the concentration of ice crystals. Using midlatitude estimates of the availability of ice forming nuclei (IFN) in the model, the authors find that the concentrations of ice crystals are large enough to result in the almost complete dissipation of otherwise solid, optically thick stratus layers. A tenuous stratus can be maintained in the simulation when the continuous input of moisture through the imposed large-scale advection is strong enough to balance the ice production. However, in association with the large-scale moisture and warm advection, only by reducing the concentration of IFN to 0.3 of the midlatitude estimate values can a persistent, optically thick stratus layer be maintained. The results obtained from the reduced IFN simulation compare reasonably well with observations.

The longwave radiative fluxes at the surface are significantly different between the solid stratus and liquid-water-depleted higher ice crystal concentration experiments.

This work suggests that transition-season Arctic stratus can be very vulnerable to anthropogenic sources of IFN, which can alter cloud structure sufficiently to affect the rates of melting and freezing of the Arctic Ocean.

The authors find that the Hallett–Mossop riming splintering mechanism is not activated in the simulations because the cloud droplets are very small and cloud temperatures are outside the range supporting efficient rime splintering. Thus, the conclusions drawn from the results presented in this paper may be applicable to only a limited class of Arctic stratus.

Abstract

Extreme rainfall events have important societal impacts: for example, by causing flooding, replenishing reservoirs, and affecting agricultural yields. Previous literature has documented linkages between rainfall extremes and nocturnal low-level jets (NLLJs) over the Great Plains of North America and the La Plata River basin of South America. In this study, the authors utilize a 21-yr, hourly global 40-km reanalysis based on the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) to examine whether NLLJ–rainfall linkages are common elsewhere on the earth. The reanalysis is uniquely suited for the task because of its comparatively high spatial and temporal resolution and because a companion paper demonstrated that it realistically simulates the vertical, horizontal, and diurnal structure of the winds in well-known NLLJ regions. The companion paper employed the reanalysis to identify and describe numerous NLLJs across the planet, including several previously unknown NLLJs.

The authors demonstrate here that the reanalysis reasonably simulates the diurnal cycle, extremes, and spatial structure of rainfall globally compared to satellite-based precipitation datasets and therefore that it is suitable for examining NLLJ–rainfall linkages. A statistical approach is then introduced to categorize nocturnal precipitation extremes as a function of the NLLJ magnitude, wind direction, and wind frequency for January and July. Statistically significant relationships between NLLJs and nocturnal precipitation extremes exist in at least 10 widely disparate regions around the world, some of which are well known and others that have been undocumented until now. The regions include the U.S. Great Plains, Tibet, northwest China, India, Southeast Asia, southeast China, Argentina, Namibia, Botswana, and Ethiopia. Recent studies have recorded widespread changes in the amplitudes of near-surface diurnal heating cycles that in turn play key roles in driving NLLJs. It will thus be important for future work to address how rainfall extremes may be impacted if trends in diurnal cycles cause the position, magnitude, and frequency of NLLJs to change.

Abstract

Dynamical downscaling is a computationally expensive method whereby finescale details of the atmosphere may be portrayed by running a limited area numerical weather prediction model (often called a regional climate model) nested within a coarse-resolution global reanalysis or global climate model output. The goal of this study is to assess using sampling techniques to dynamically downscale a small subset of days to approximate the statistical properties of the entire period of interest. Two sampling techniques are explored: one where days are randomly selected and another where representative days are chosen (or targeted) based on a set of selection criteria. The relative merit of using random sampling versus targeted random sampling is demonstrated using daily mean 2-m air temperature (T2M). The first two moments of dynamically downscaled T2M can be approximated within 0.3 K using just 5% of the population of available days during a 20-yr period. Targeted random sampling can reduce the mean absolute error of these estimates by as much as 30% locally. Estimation of the more extreme values of T2M is more uncertain and requires a larger sample size. The potential reduction in computational cost afforded by these sampling techniques could greatly benefit applications requiring high-resolution dynamically downscaled depictions of regional climate, including situations in which an ensemble of regional climate simulations is required to properly characterize uncertainty in the model physics assumptions, scenarios, and so on.

Abstract

This paper describes a new computationally efficient and statistically robust sampling method for generating dynamically downscaled climatologies. It is based on a Monte Carlo method coupled with stratified sampling. A small yet representative set of “case days” is selected with guidance from a large-scale reanalysis. When downscaled, the sample closely approximates the long-term meteorological record at a location, in terms of the probability density function. The method is demonstrated for the creation of wind maps to help determine the suitability of potential sites for wind energy farms. Turbine hub-height measurements at five U.S. and European tall tower sites are used as a proxy for regional climate model (RCM) downscaled winds to validate the technique. The tower-measured winds provide an independent test of the technique, since RCM-based downscaled winds exhibit an inherent dependence upon the large-scale reanalysis fields from which the case days are sampled; these same reanalysis fields would provide the boundary conditions to the RCM. The new sampling method is compared with the current approach widely used within the wind energy industry for creating wind resource maps, which is to randomly select 365 case days for downscaling, with each day in the calendar year being represented. The new method provides a more accurate and repeatable estimate of the long-term record of winds at each tower location. Additionally, the new method can closely approximate the accuracy of the current (365 day) industry approach using only a 180-day sample, which may render climate downscaling more tractable for those with limited computing resources.

Abstract

A Doppler lidar deployed to the center of the Great Salt Lake (GSL) basin during the Vertical Transport and Mixing (VTMX) field campaign in October 2000 found a diurnal cycle of the along-basin winds with northerly up-basin flow during the day and a southerly down-basin low-level jet at night. The emphasis of VTMX was on stable atmospheric processes in the cold-air pool that formed in the basin at night. During the night the jet was fully formed as it entered the GSL basin from the south. Thus, it was a feature of the complex string of basins draining toward the Great Salt Lake, which included at least the Utah Lake basin to the south. The timing of the evening reversal to down-basin flow was sensitive to the larger-scale north–south pressure gradient imposed on the basin complex. On nights when the pressure gradient was not too strong, local drainage flow (slope flows and canyon outflow) was well developed along the Wasatch Range to the east and coexisted with the basin jet. The coexistence of these two types of flow generated localized regions of convergence and divergence, in which regions of vertical motion and transport were focused. Mesoscale numerical simulations captured these features and indicated that updrafts on the order of 5 cm s⁻¹ could persist in these localized convergence zones, contributing to vertical displacement of air masses within the basin cold pool.