ALBERT

Description: Snowfall was measured at 11 sites in the McMurdo Dry Valleys to determine its magnitude, its temporal changes, and spatial patterns. Annual values ranged from 3 to 50 mm water equivalent with the highest values nearest the coast and decreasing inland. A particularly strong spatial gradient exists in Taylor Valley, probably resulting from local uplift conditions at the coastal margin and valley topography that limits migration inland. More snow occurs in winter near the coast, whereas inland no seasonal pattern is discernable. This may be due, again, to local uplift conditions, which are common in winter. We find no influence of the distance to the sea ice edge. Katabatic winds play an important role in transporting snow to the valley bottoms and essentially double the precipitation. That much of the snow accumulation sublimates prior to making a hydrologic contribution underscores the notion that the McMurdo Dry Valleys are indeed an extreme polar desert.

Description: Foehn winds resulting from topographic modification of airflow in the lee of mountain barriers are frequently experienced in the McMurdo Dry Valleys (MDVs) of Antarctica. Strong foehn winds in the MDVs cause dramatic warming at onset and have significant effects on landscape forming processes; however, no detailed scientific investigation of foehn in the MDVs has been conducted. As a result, they are often misinterpreted as adiabatically warmed katabatic winds draining from the polar plateau. Herein observations from surface weather stations and numerical model output from the Antarctic Mesoscale Prediction System (AMPS) during foehn events in the MDVs are presented. Results show that foehn winds in the MDVs are caused by topographic modification of south-southwesterly airflow, which is channeled into the valleys from higher levels. Modeling of a winter foehn event identifies mountain wave activity similar to that associated with midlatitude foehn winds. These events are found to be caused by strong pressure gradients over the mountain ranges of the MDVs related to synoptic-scale cyclones positioned off the coast of Marie Byrd Land. Analysis of meteorological records for 2006 and 2007 finds an increase of 10% in the frequency of foehn events in 2007 compared to 2006, which corresponds to stronger pressure gradients in the Ross Sea region. It is postulated that the intra- and interannual frequency and intensity of foehn events in the MDVs may therefore vary in response to the position and frequency of cyclones in the Ross Sea region.

Description: Wind turbines remove kinetic energy from the atmospheric flow, which reduces wind speeds and limits generation rates of large wind farms. These interactions can be approximated using a vertical kinetic energy (VKE) flux method, which predicts that the maximum power generation potential is 26% of the instantaneous downward transport of kinetic energy using the preturbine climatology. We compare the energy flux method to the Weather Research and Forecasting (WRF) regional atmospheric model equipped with a wind turbine parameterization over a 105 km2 region in the central United States. The WRF simulations yield a maximum generation of 1.1 We⋅m−2, whereas the VKE method predicts the time series while underestimating the maximum generation rate by about 50%. Because VKE derives the generation limit from the preturbine climatology, potential changes in the vertical kinetic energy flux from the free atmosphere are not considered. Such changes are important at night when WRF estimates are about twice the VKE value because wind turbines interact with the decoupled nocturnal low-level jet in this region. Daytime estimates agree better to 20% because the wind turbines induce comparatively small changes to the downward kinetic energy flux. This combination of downward transport limits and wind speed reductions explains why large-scale wind power generation in windy regions is limited to about 1 We⋅m−2, with VKE capturing this combination in a comparatively simple way.

Description: Weather and climate variability strongly influence the people, infrastructure, and economy of Alaska. However, the sparse observational network in Alaska limits our understanding of meteorological variability, particularly of precipitation processes that influence the hydrologic cycle. Here, a new 14-yr (September 2002–August 2016) dataset for Alaska with 4-km grid spacing is described and evaluated. The dataset, generated with the Weather Research and Forecasting (WRF) Model, is useful for gaining insight into meteorological and hydrologic processes, and provides a baseline against which to measure future environmental change. The WRF fields are evaluated at annual, seasonal, and daily time scales against observation-based gridded and station records of 2-m air temperature, precipitation, and snowfall. Pattern correlations between annual mean WRF and observation-based gridded fields are r = 0.89 for 2-m temperature, r = 0.75 for precipitation, r = 0.82 for snow-day fraction, r = 0.55 for first snow day of the season, and r = 0.71 for last snow day of the season. A shortcoming of the WRF dataset is that spring snowmelt occurs too early over a majority of the state, due partly to positive 2-m temperature biases in winter and spring. Strengths include an improved representation of the interannual variability of 2-m temperature and precipitation and accurately simulated (relative to regional station observations) winter and summer precipitation maxima. This initial evaluation suggests that the 4-km WRF climate dataset robustly simulates meteorological processes and recent climatic variability in Alaska. The dataset may be particularly useful for applications that require high-temporal-frequency weather fields, such as driving hydrologic or glacier models. Future studies will provide further insight on its ability to represent other aspects of Alaska’s climate.

Description: The mosquito virus vector Aedes (Ae.) aegypti exploits a wide range of containers as sites for egg laying and development of the immature life stages, yet the approaches for modeling meteorologically sensitive container water dynamics have been limited. This study introduces the Water Height and Temperature in Container Habitats Energy Model (WHATCH’EM), a state-of-the-science, physically based energy balance model of water height and temperature in containers that may serve as development sites for mosquitoes. The authors employ WHATCH’EM to model container water dynamics in three cities along a climatic gradient in México ranging from sea level, where Ae. aegypti is highly abundant, to ~2100 m, where Ae. aegypti is rarely found. When compared with measurements from a 1-month field experiment in two of these cities during summer 2013, WHATCH’EM realistically simulates the daily mean and range of water temperature for a variety of containers. To examine container dynamics for an entire season, WHATCH’EM is also driven with field-derived meteorological data from May to September 2011 and evaluated for three commonly encountered container types. WHATCH’EM simulates the highly nonlinear manner in which air temperature, humidity, rainfall, clouds, and container characteristics (shape, size, and color) determine water temperature and height. Sunlight exposure, modulated by clouds and shading from nearby objects, plays a first-order role. In general, simulated water temperatures are higher for containers that are larger, darker, and receive more sunlight. WHATCH’EM simulations will be helpful in understanding the limiting meteorological and container-related factors for proliferation of Ae. aegypti and may be useful for informing weather-driven early warning systems for viruses transmitted by Ae. aegypti.

Description: 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.

Description: Foehn winds resulting from topographic modification of airflow in the lee of mountain barriers are frequently experienced in the McMurdo Dry Valleys (MDVs) of Antarctica. Strong foehn winds in the MDVs cause dramatic warming at onset and have significant effects on landscape forming processes; however, no detailed scientific investigation of foehn in the MDVs has been conducted. As a result, they are often misinterpreted as adiabatically warmed katabatic winds draining from the polar plateau. Herein observations from surface weather stations and numerical model output from the Antarctic Mesoscale Prediction System (AMPS) during foehn events in the MDVs are presented. Results show that foehn winds in the MDVs are caused by topographic modification of south-southwesterly airflow, which is channeled into the valleys from higher levels. Modeling of a winter foehn event identifies mountain wave activity similar to that associated with midlatitude foehn winds. These events are found to be caused by strong pressure gradients over the mountain ranges of the MDVs related to synoptic-scale cyclones positioned off the coast of Marie Byrd Land. Analysis of meteorological records for 2006 and 2007 finds an increase of 10% in the frequency of foehn events in 2007 compared to 2006, which corresponds to stronger pressure gradients in the Ross Sea region. It is postulated that the intra- and interannual frequency and intensity of foehn events in the MDVs may therefore vary in response to the position and frequency of cyclones in the Ross Sea region.

Description: 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.