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

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

In response to the need for improved weather prediction capabilities in support of the U.S. Antarctic Program’s Antarctic field operations, the Antarctic Mesoscale Prediction System (AMPS) was implemented in October 2000. AMPS employs a limited-area model, the Polar fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5), optimized for use over ice sheets. Twice-daily forecasts from the 3.3-km resolution domain of AMPS are joined together to study the climate of the McMurdo region from June 2002 to May 2003. Annual and seasonal distributions of wind direction and speed, 2-m temperature, mean sea level pressure, precipitation, and cloud fraction are presented. This is the first time a model adapted for polar use and with relatively high resolution is used to study the climate of the rugged McMurdo region, allowing several important climatological features to be investigated with unprecedented detail.

Orographic effects exert an important influence on the near-surface winds. Time-mean vortices occur in the lee of Ross Island, perhaps a factor in the high incidence of mesoscale cyclogenesis noted in this area. The near-surface temperature gradient is oriented northwest to southeast with the warmest temperatures in the northwest near McMurdo and the gradient being steepest in winter. The first-ever detailed precipitation maps of the region are presented. Orographic precipitation maxima occur on the southerly slopes of Ross Island and in the mountains to the southwest. The source of the moisture is primarily from the large synoptic systems passing to the northeast and east of Ross Island. A precipitation-shadow effect appears to be an important influence on the low precipitation amounts observed in the McMurdo Dry Valleys. Total cloud fraction primarily depends on the amount of open water in the Ross Sea; the cloudiest region is to the northeast of Ross Island in the vicinity of the Ross Sea polynya.

Abstract

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.

Abstract

To support the forecasting needs of the United States Antarctic Program at McMurdo, Antarctica, a special numerical weather prediction program, the Antarctic Mesoscale Prediction System (AMPS), was established for the 2000–01 field season. AMPS employs the Polar MM5, a version of the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) that has physics modifications for polar environments. This study assesses the performance of AMPS in forecasting an event of mesoscale cyclogenesis in the western Ross Sea during 13–17 January 2001. Observations indicate the presence of a complex trough having two primary mesoscale lows that merge to the east of Ross Island shortly after 0700 UTC 15 January. In contrast, AMPS predicts one primary mesoscale low throughout the event, incorrectly placing it until the 1800 UTC 15 January forecast, when the observed system carries a prominent signature in the initialization. The model reproduces the evolution of upper-level conditions in agreement with the observations and shows skill in resolving many small-scale surface features common to the region (i.e., katabatic winds; lows and highs induced by wind/topography). The AMPS forecasts can rely heavily on the representation of surface lows and upper-level forcing in the first-guess fields derived from NCEP's Aviation Model (AVN). Furthermore, even with relatively high spatial resolution, mesoscale models face observation-related limitations on performance that can be particularly acute in Antarctica.

Abstract

The West Nile region in northwestern Uganda is a focal point for human plague, which peaks in boreal autumn and is spread by fleas that travel on rodent hosts. The U.S. Centers for Disease Control and Prevention is collaborating with the National Center for Atmospheric Research to quantitatively address the linkages between climate and human plague in this region. The aim of this paper is to advance knowledge of the climatic conditions required to maintain enzootic cycles and to trigger epizootic cycles and ultimately to target limited surveillance, prevention, and control resources. A hybrid dynamical–statistical downscaling technique was applied to simulations from the Weather Research and Forecasting Model (WRF) to generate a multiyear 2-km climate dataset for modeling plague in the West Nile region. The resulting dataset resolves the spatial variability and annual cycle of temperature, humidity, and rainfall in West Nile relative to satellite-based and in situ records. Topography exerts a first-order influence on the climatic gradients in West Nile, which lies in a transition zone between the drier East African Plateau and the wetter Congo Basin, and between the unimodal rainfall regimes of the Sahel and the bimodal rainfall regimes characteristic of equatorial East Africa. The results of a companion paper in which the WRF-based climate fields were applied to develop an improved logistic regression model of human plague occurrence in West Nile are summarized, revealing robust positive associations with rainfall at the tails of the rainy season and negative associations with rainfall during a dry spell each summer.

Abstract

This second paper examines the Southern Hemisphere annular mode (SAM) variability from reconstructions, observed indices, and simulations from 17 Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) models from 1865 to 2005. Comparisons reveal the models do not fully simulate the duration of strong natural variability within the reconstructions during the 1930s and 1960s.

Seasonal indices are examined to understand the relative roles of forced and natural fluctuations. The models capture the recent (1957–2005) positive SAM trends in austral summer, which reconstructions indicate is the strongest trend during the last 150 yr; ozone depletion is the dominant mechanism driving these trends. In autumn, negative trends after 1930 in the reconstructions are stronger than the recent positive trend. Furthermore, model trends in autumn during 1957–2005 are the most different from observations. Both of these conditions suggest the recent autumn trend is most likely natural climate variability, with external forcing playing a secondary role. Many models also produce significant spring trends during this period not seen in observations. Although insignificant, these differences arise because of vastly different spatial structures in the Southern Hemisphere pressure trends. As the trend differences between models and observations in austral spring have been increasing over the last 30 yr, care must be exercised when examining the future SAM projections and their impacts in this season.

Abstract

In late April 2001, an unprecedented late-season flight to Amundsen–Scott South Pole Station was made in the evacuation of Dr. Ronald Shemenski, a medical doctor seriously ill with pancreatitis. This case study analyzes the performance of four of the numerical weather prediction models that aided meteorologists in forecasting weather throughout the operation: 1) the Antarctic Mesoscale Prediction System (AMPS) Polar MM5 (fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model), 2) the National Centers for Environmental Prediction Aviation Model (AVN), 3) the European Centre for Medium-Range Weather Forecasts (ECMWF) global forecast model, and 4) the NCAR Global MM5. To identify specific strengths and weaknesses, key variables for each model are statistically analyzed for all forecasts initialized between 21 and 25 April for several points over West Antarctica at the surface and at 500- and 700-hPa levels. The ECMWF model performs with the highest overall skill, generally having the lowest bias and rms errors and highest correlations for the examined fields. The AMPS Polar MM5 exhibits the next best skill, followed by AVN and Global MM5. For the surface variables, all of the models show high skill in predicting surface pressure but demonstrate modest skill in predicting temperature, wind speed, and wind direction. In the free atmosphere, the models show high skill in forecasting geopotential height, considerable skill in predicting temperature and wind direction, and good skill in predicting wind speed. In general, the models produce very useful forecasts in the free atmosphere, but substantial efforts are still needed to improve the surface prediction. The spatial resolution of each model exerts an important influence on forecast accuracy, especially in the complex topography of the Antarctic coastal regions. The initial and boundary conditions for the AMPS Polar MM5 exert a significant influence on forecasts.

Abstract

Meningitis remains a major health burden throughout Sahelian Africa, especially in heavily populated northwest Nigeria with an annual incidence rate ranging from 18 to 200 per 100 000 people for 2000–11. Several studies have established that cases exhibit sensitivity to intra- and interannual climate variability, peaking during the hot and dry boreal spring months, raising concern that future climate change may increase the incidence of meningitis in the region. The impact of future climate change on meningitis risk in northwest Nigeria is assessed by forcing an empirical model of meningitis with monthly simulations of seven meteorological variables from an ensemble of 13 statistically downscaled global climate model projections from phase 5 of the Coupled Model Intercomparison Experiment (CMIP5) for representative concentration pathway (RCP) 2.6, 6.0, and 8.5 scenarios, with the numbers representing the globally averaged top-of-the-atmosphere radiative imbalance (in W m⁻²) in 2100. The results suggest future temperature increases due to climate change have the potential to significantly increase meningitis cases in both the early (2020–35) and late (2060–75) twenty-first century, and for the seasonal onset of meningitis to begin about a month earlier on average by late century, in October rather than November. Annual incidence may increase by 47% ± 8%, 64% ± 9%, and 99% ± 12% for the RCP 2.6, 6.0, and 8.5 scenarios, respectively, in 2060–75 with respect to 1990–2005. It is noteworthy that these results represent the climatological potential for increased cases due to climate change, as it is assumed that current prevention and treatment strategies will remain similar in the future.

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.