Dissertations / Theses on the topic 'Seasonal climatology'

This dissertation develops multivariate statistical models for seasonal forecasting and downscaling of climate variables. In the case of seasonal climate forecasting, where record lengths are typically short and signal-to-noise ratios are low, particularly at long lead-times, forecast models must be robust against noise. To this end, two models are developed. Robust nonlinear canonical correlation analysis, which introduces robust cost functions to an existing model architecture, is outlined in Chapter 2. Nonlinear principal predictor analysis, the nonlinear extension of principal predictor analysis, a linear model of intermediate complexity between multivariate regression and canonical correlation analysis, is developed in Chapter 3. In the case of climate downscaling, the goal is to predict values of weather elements observed at local or regional scales from the synoptic-scale atmospheric circulation, usually for the purpose of generating climate scenarios from Global Climate Models. In this context, models must not only be accurate in terms of traditional model verification statistics, but they must also be able to replicate statistical properties of the historical observations. When downscaling series observed at multiple sites, correctly specifying relationships between sites is of key concern. Three models are developed for multi-site downscaling. Chapter 4 introduces nonlinear analog predictor analysis, a hybrid model that couples a neural network to an analog model. The neural network maps the original predictors to a lower-dimensional space such that predictions from the analog model are improved. Multivariate ridge regression with negative values of the ridge parameters is introduced in Chapter 5 as a means of performing expanded downscaling, which is a linear model that constrains the covariance matrix of model predictions to match that of observations. The expanded Bernoulli-gamma density network, a nonlinear probabilistic extension of expanded downscaling, is introduced in Chapter 6 for multi-site precipitation downscaling. The single-site model is extended by allowing multiple predictands and by adopting the expanded downscaling covariance constraint.

The area chosen for this study is the Central Elburz of Iran surrounding the southern end of the Caspian Sea. It includes an important rainfall dependent agricultural area and merges into the semi arid zone with a well defined boundary. From social and agricultural points of view rainfall is by far the most important climatic factor in many tropical and subtropical countries. An important aspect in the development of agriculture in the Central Elburz is the determination of the seasonal rainfall patterns. This thesis should be considered as a contribution to the study of the seasonal rainfall regime of Iran, with direct value to agriculture in the region. The purpose of this thesis is to examine the pattern of rainfall especially in relation to the growing season both in time and spatially. Also the thesis describes the variation in the availability of moisture for growth throughout this region. The thesis examines annual, monthly and seasonal rainfall over the region. Multivariate analysis has demonstrated that the study area can be divided into three rainfall regions. Before considering any analysis which could allow prediction on probability of future rainfall amounts, it is important to consider whether or not there on trends or fluctuations. The result of this analysis is that, few stations show positive trends, others negative trends. Rainfall variability has been quantified by the coefficient of variation index and analysed in a similar way to that of rainfall distribution. The results for most stations shown a relationship between variability indices and rainfall amounts. In relation to the study of variability, an assessment of rainfall probability and reliability has been considered with reference to rainfall critical for crops. Probability has a particular value in its application to agriculture. In this study 90% probability is examined and this is related to the economy of the agriculture units in the Central Elburz. Also 80 and 75 percent probability are presented. These spatial patterns of probability thus provide maps of agricultural potential. Rainfall records are analysed to provide estimates, percentage points of rainfall totals, variation in the start, and end of the rainy season and or the probability of dry spells within the rainy season. The estimation of potential evapotranspiration which are discussed and explained in this study are based on the Blaney- Criddle and Pan evaporation methods. The water balance approach provides the most rational method of analysing seasonal pattern since it considers characteristics of the dominant crops in this area, wheat and citrus, and it examines effectiveness of rainfall for these crops. Blaney- Criddle and Pan evaporation have been used to estimate potential evaporation for wheat and citrus and compared to actual evapotranspiration to give potential water deficit. The adoption of probability analysis of potential water deficit provides a valuable description of water availability for crops in this region of variable rainfall.

This thesis presents the first in-depth study on interannual tropical cyclone activity in the Northeast (NE) Pacific, using statistical methods to investigate tropical cyclone frequency and its relationship with seasonal environmental conditions from 1972 to 1997. An improved method of calculating wind shear is first presented. It is demonstrated that the NE Pacific has more than one population of tropical cyclones with regard to causal factors, and tropical cyclones in the two regions show large differences in trends with time and in their relationships with environmental variables. Large increasing trends are found in the western development region (10˚N to 20˚N , 116˚W to 180˚W), with no significant trends in the eastern development region (10˚N to 20˚N, < 116˚W). No significant relationships were found in the eastern development region between tropical cyclone frequency and any of the environmental variables tested, except outgoing long-wave radiation, implying that the main causal factor here is triggering disturbances and their variations. However, in the western development region, some highly significant relationships exist. Important local variables there include relative humidity (RH) and SST. El Niño Southern Oscillation (ENSO) is also a significant factor. The local relationships are probably largely due to the intensity-frequency effect and the spatial averaging of the variables, with threshold effects acting locally to provide conducive/non-conducive conditions in different parts of the region. Physical influences on the most important of these variables (RH) are investigated. (The reverse influence, of hurricanes on RH, is shown to be negligible. ) RH is shown to be significantly influenced, via the wind field, by ENSO and the intensity of the thermal low in North America. ENSO influences provide significant inverse relationships between tropical cyclone frequencies in the western development region and the North Atlantic.

Includes bibliographical references
A good understanding of seasonal climate and the limit to which it can be predicted is crucial in addressing various socio-economic challenges in Africa. However, how to improve the capability of the dynamical models of the climate system in reproducing the regional seasonal climate variability and in replicating the role of various atmospheric circulation anomalies on the regional variability remains a major challenge. Thus far, understanding of seasonal climate over these regions, as well as the ability of climate models to predict them, has focused on the agreement of simulations of dynamical models of the climate system, rather than considering outliers as potentially vital contributors to understanding and predictability. This thesis uses discrepancy in a large ensemble of climate simulations as a tool to investigate variability in dominant seasonal rainfall and temperature patterns (i.e. classes) over West and Southern Africa, to examine the capability of climate models in reproducing the variability, and to study the predictability of the seasonal climates over South Africa. The dominant classes of variability (of rainfall and maximum temperature fields) in both regions are examined based on the Self-Organizing Map (SOM) classifications. The sequences in which each class occurs cannot be linked simply to a single common index of global scale atmospheric circulation anomalies, implying that the chaotic regional atmospheric circulations that modulate the global scale modes of variability are indispensable. The climate model examined adequately reproduces the dominant classes of seasonal climate over West and Southern Africa.

The climatology of the seasonal cycle of water mass variation and transformation in Bass Strait, south-eastern Australia, is studied using a high resolution three-dimensional sigma-coordinate hydrodynamic model coupled with data from observations and previous studies. Model forcing consists of the principal tidal constituents from the Australian National Tidal Centre and long-term monthly mean atmospheric forcing fields from NCEP reanalysis. The initial density field is established using temperature and salinity means and annual and semi-annual harmonics from the CARS2000 hydrographic atlas. This is also used to prescribe incoming water mass properties at model open-sea boundaries with seasonal variation. Far-field forcing is included with open-sea boundary parameterisation of residual sea-level representing both the South Australian Current and the East Australian Current. Lagrangian and Eulerian tracer methods are used to derive transport timescales, such as age, residence times and flushing times. These are used to examine and summarise model predictions and as a diagnostic tool in sensitivity studies. Currents, sea-level and water mass properties in the model compare favourably with previous studies and observations, despite limitations in the model and in the data used for comparison. The seasonal cycle, in model results, is characterised by formation of a shallow (< 20 m) saltier surface-layer in late spring to summer and subsequent downward mixing and erosion of the salinity field in autumn to winter with water mass from the west. This leaves behind water mass with positive age and salinity anomalies in areas of low flushing. In late winter-early spring most parts of this water mass leave the Strait interior. These areas are thought to be related to the source water of the Bass Strait Cascade. The residual circulation in all model experiments is shown to be related to seasonal-mean sea-level anomalies, arising from both barotropic and baroclinic adjustment, both in and surrounding the Strait.

To investigate seasonal patterns of precipitation, statistical analysis was performed on a dataset of daily rainfall observed at 63 south Louisiana stations from 1836 to 2002. Each station record was examined for data quality and continuity with special attention to time periods surrounding station relocation or equipment exchange. Mean Areal Precipitation (MAP) sheets were compiled for every month from 1836 to 2002 to document the daily rainfall across south Louisiana and neighboring portions of southern and coastal Mississippi. Using these MAP sheets, missing data was examined to see if a reasonable value could be substituted to extend the continuity of a station's rainfall record. Once these data quality and continuity checks were completed, a series of statistical tests were conducted to determine an accurate scheme to form station groups. To group stations together, each station was required to have a normal distribution of monthly average rainfall, a statistically equivalent variance, and a statistically equivalent mean when compared with other stations in the group. As a result of the Shapiro-Wilk Test, the F-Test, and the Student T-test, eight station groups were formed. To define seasonal rainfall patterns across south Louisiana, statistical tests were conducted for a 12 month period and six and three month intervals. For the six month intervals, group rainfall averages and pooled variances were calculated for each interval beginning with January-July and ending with December-May. For the three month intervals, group rainfall averages and pooled variances were calculated for January-March and concluded with December-February. To test the hypothesis of a statistically significant difference in mean rainfall between the eight groups for a 12, six, and three month period, the Student T-test was conducted. For an annual basis, there is a statistically significant difference in average rainfall at a five percent level of significance between all of the groups except the Southshore (S.S) group when compared to the SW1 group. For six and three month intervals, statistically significant differences exist between the eight groups especially during winter and segments of the Hurricane season from June to November.

The aim of the research undertaken in this dissertation was to use medium-range to seasonal precipitation forecasts for hydrologic applications for catchments in the core North American Monsoon (NAM) region. To this end, it was necessary to develop a better understanding of the physical and statistical relationships between runoff processes and the temporal statistics of rainfall. To achieve this goal, development of statistically downscaled estimates of warm season precipitation over the core region of the North American Monsoon Experiment (NAME) were developed. Currently, NAM precipitation is poorly predicted on local and regional scales by Global Circulation Models (GCMs). The downscaling technique used here, the K-Nearest Neighbor (KNN) model, combines information from retrospective GCM forecasts with simultaneous historical observations to infer statistical relationships between the low-resolution GCM fields and the locally-observed precipitation records. The stochastic nature of monsoon rainfall presents significant challenges for downscaling efforts and, therefore, necessitate a regionalization and an ensemble or probabilistic-based approach to quantitative precipitation forecasting. It was found that regionalization of the precipitation climatology prior to downscaling using KNN offered significant advantages in terms of improved skill scores.Selected output variables from retrospective ensemble runs of the National Centers for Environmental Predictions medium-range forecast (MRF) model were fed into the KNN downscaling model. The quality of the downscaled precipitation forecasts was evaluated in terms of a standard suite of ensemble verification metrics. This study represents the first time the KNN model has been successfully applied within a warm season convective climate regime and shown to produce skillful and reliable ensemble forecasts of daily precipitation out to a lead time of four to six days, depending on the forecast month.Knowledge of the behavior of the regional hydrologic systems in NAM was transferred into a modeling framework aimed at improving intra-seasonal hydrologic predictions. To this end, a robust lumped-parameter computational model of intermediate conceptual complexity was calibrated and applied to generate streamflow in three unregulated test basins in the core region of the NAM. The modeled response to different time-accumulated KNN-generated precipitation forcing was investigated. Although the model had some difficulty in accurately simulating hydrologic fluxes on the basis of Hortonian runoff principles only, the preliminary results achieved from this study are encouraging. The primary and most novel finding from this study is an improved predictability of the NAM system using state-of-the-art ensemble forecasting systems. Additionally, this research significantly enhanced the utility of the MRF ensemble forecasts and made them reliable for regional hydrologic applications. Finally, monthly streamflow simulations (from an ensemble-based approach) have been demonstrated. Estimated ensemble forecasts provide quantitative estimates of uncertainty associated with our model forecasts.

Thesis (Ph. D.)--Earth and Atmospheric Sciences, Georgia Institute of Technology, 2007.
Dr. Paul Steffes, Committee Member ; Dr. Irina Sokolik, Committee Member ; Dr. Robert Black, Committee Member ; Dr. Robert G. Roper, Committee Chair ; Dr. Derek Cunnold, Committee Member.

L’étude de la variabilité du climat est désormais indispensable pour anticiper les conséquences des changements climatiques futurs. Nous disposons pour cela de quantité de données issues de modèles de circulation générale (GCMs). Néanmoins, ces modèles ne permettent qu’une résolution partielle des interactions entre le climat et les activités humaines entre autres parce que ces modèles ont des résolutions spatiales souvent trop faibles. Il existe aujourd’hui toute une variété de modèles répondant à cette problématique et dont l’objectif est de générer des variables climatiques à l’échelle locale àpartir de variables à grande échelle : ce sont les modèles de régionalisation ou encore appelés modèles de réduction d’échelle spatiale ou de downscaling en anglais.Cette thèse a pour objectif d’approfondir les connaissances à propos des modèles de downscaling statistiques (SDMs) parmi lesquels on retrouve plusieurs approches. Le travail s’articule autour de quatre objectifs : (i) comparer des modèles de réduction d’échelle statistiques (et dynamiques), (ii) étudier l’influence des biais des GCMs sur les SDMs au moyen d’une procédure de correction de biais, (iii) développer un modèle de réduction d’échelle qui prenne en compte la non-stationnarité spatiale et temporelle du climat dans un contexte de modélisation dite spatiale et enfin, (iv) établir une définitiondes saisons à partir d’une modélisation des régimes de circulation atmosphérique ou régimes de temps.L’intercomparaison de modèles de downscaling a permis de mettre au point une méthode de sélection de modèles en fonction des besoins de l’utilisateur. L’étude des biais des GCMs révèle une influence indéniable de ces derniers sur les sorties de SDMs et les apports de la correction des biais. Les différentes étapes du développement d’un modèle spatial de réduction d’échelle donnent des résultats très encourageants. La définition des saisons par des régimes de temps se révèle être un outil efficace d’analyse et de modélisation saisonnière.Tous ces travaux de “Climatologie Statistique” ouvrent des perspectives pertinentes, non seulement en termes méthodologiques ou de compréhension de climat à l’échelle locale, mais aussi d’utilisations par les acteurs de la société
The study of climate variability is vital in order to understand and anticipate the consequences of future climate changes. Large data sets generated by general circulation models (GCMs) are currently available and enable us to conduct studies in that direction. However, these models resolve only partially the interactions between climate and human activities, namely du to their coarse resolution. Nowadays there is a large variety of models coping with this issue and aiming at generating climate variables at local scale from large-scale variables : the downscaling models.The aim of this thesis is to increase the knowledge about statistical downscaling models (SDMs) wherein there is many approaches. The work conducted here pursues four main goals : (i) to discriminate statistical (and dynamical) downscaling models, (ii) to study the influences of GCMs biases on the SDMs through a bias correction scheme, (iii) to develop a statistical downscaling model accounting for climate spatial and temporal non-stationarity in a spatial modelling context and finally, (iv) to define seasons thanks to a weather typing modelling.The intercomparison of downscaling models led to set up a model selection methodology according to the end-users needs. The study of the biases of the GCMs reveals the impacts of those biases on the SDMs simulations and the positive contributions of the bias correction procedure. The different steps of the spatial SDM development bring some interesting and encouraging results. The seasons defined by the weather regimes are relevant for seasonal analyses and modelling.All those works conducted in a “Statistical Climatologie” framework lead to many relevant perspectives, not only in terms of methodology or knowlegde about local-scale climate, but also in terms of use by the society

While the spring and summer months are typically the severe weather climatological peak for the East-Central United States, severe thunderstorms and deadly regional tornado outbreaks can occur during the cool season months (e.g., October-March). In an effort to better document and improve operational forecasting of these events, this study focuses on cool season severe thunderstorms in the Ohio and Tennessee Valleys during the 1995-2002 cool seasons.Most severe thunderstorm and tornado events in the East-Central United States during the cool season are characterized by a high frequency of wind reports compared to hail and tornado reports. All severe report classes (i.e. tornadoes, hail, and wind) displayed a frequency tendency to remain high in the late evening and overnight hours. Additionally, it was found that tornado occurrence typically came in the form of a tornado outbreak. Additionally, when tornadoes did occur, they were found to be statistically more intense than tornadoes outside of the EC region during the same period. It is also concluded tornadoes favor the southern half of the region, whereas hail and wind reports tend to favor the southern two thirds of the region. In examination of cool season supercell characteristics, supercells favors a west-southwest to east-northeast mean motion around 45 mph. The relationship of only 11 percent between the distances of supercell tornado paths to tornadic supercell paths is an operationally important discovery. Knowing on average, how long a cool season supercell tornado is on the ground with respect to the parent supercell can aid operational warning decisions.
Department of Geography

Thesis (M.S. in Meteorology)--Naval Postgraduate School, March 2006.
Thesis Advisor(s): Tom Murphree, Carlyle Wash. "March 2006." Includes bibliographical references (p.115-117). Also available online.

For St. John's, Newfoundland, at the confluence of several North American storm tracks, we identify synoptic-scale characteristics and precursors of various classes of cool-season precipitation events.
Such events, based upon a climatology for 1979-2005, are separated into three categories based on precipitation amount. We find that the storm systems responsible for extreme precipitation events originate farther south and east than corresponding features in moderate and light events. A wind climatology shows that extreme precipitation events at St. John's are characterized almost exclusively by easterly surface and 925 hPa geostrophic winds, and that both the surface and 925 hPa geostrophic winds rotate clockwise with decreasing precipitation amount.
Focusing on extreme events, we utilize two methods of manual synoptic typing to further partition events. The first method uses backward air parcel trajectories to separate events by air parcel source region. One subset of events ("west") is characterized by strong upper-level dynamics and high precipitable water values in the central United States, which helps to produce a strong cyclone upon reaching the Atlantic Ocean; this is not seen for "west" events outside the extreme category. The second method of synoptic typing utilizes time series of three ascent-forcing quasi-geostrophic (QG) variables. While most events are characterized by a strong upstream sea-level cyclone originating from the Gulf of Mexico ("cyclone"), a subset are dominated by strong low-level frontogenesis, in the absence of a substantial upstream cyclone ("frontal").
Finally, a dynamic and thermodynamic analysis, and forecast model evaluation is completed for consecutive extreme events in December 2008, which produced over 125 mm of precipitation over six days. The first event is a "cyclone", and is marked by strong QG forcing for ascent in the presence of low static stability and high values of subtropical moisture. The second event is a "frontal" event, and is associated with a persistent quasi-stationary baroclinic zone in the presence of moderately stable air and large values of low-level frontogenesis, in the absence of substantial temperature and vorticity advection. These two events highlight rather disparate means by which an extreme precipitation event can occur at St. John's.
Pour St. John's, Terre-Neuve, à la confluence de plusieurs trajectoires de tempêtes nord-américaines, nous identifions des caractéristiques à l'échelle synoptique et des précurseurs d'une variété de type d'événements de précipitation de la saison froide.
Ces événements, basés sur une climatologie allant de 1979-2005, sont séparés en trois catégories basées sur la quantité de précipitation. Nous trouvons que les systèmes de tempêtes responsables des événements de précipitation extrêmes débutent plus au sud et à l'est que les événements correspondants avec précipitation modérée ou faible. Une climatologie des vents montre que les événements de précipitation extrêmes à St. John's sont caractérisés presque exclusivement par des vents de surface et géostrophique à 925 hPa provenant de l'est, et tournant dans le sens des aiguilles d'une montre avec une diminution de la quantité de précipitation.
En se concentrant sur les événements extrêmes, nous utilisons deux méthodes de classification synoptique manuelle pour séparer davantage les événements. La première méthode utilise une trajectoire renversée des parcelles d'air pour séparer les événements en fonction de leur région d'origine. Un sous-ensemble des événements ("ouest") est caractérisé par une forte dynamique dans les niveaux supérieurs et des valeurs d'eau précipitable élevées dans le centre des États-Unis, ce qui aide à produire un puissant cyclone lorsque l'océan Atlantique est atteint; ceci n'est pas observé pour les événements "ouest" en dehors de la catégorie des extrêmes. La seconde méthode utilise des séries temporelles de trois variables quasi-géostrophiques (QG) forçant l'ascension. Bien que la plupart des événements sont caractérisés par un puissant cyclone au niveau de la mer en amont qui origine du Golfe du Mexique ("cyclone"), un sous-ensemble est dominé par une forte frontogenèse dans les niveaux inférieurs, en l'absence substantielle de cyclone en amont ("frontal").
Finalement, une analyse dynamique et thermodynamique, ainsi qu'une évaluation de modèle de prévision est complétée pour des événements extrêmes consécutifs en décembre 2008, qui produisent plus de 125 mm de précipitation sur une période de six jours. Le premier événement est du type "cyclone", et est marqué par un puissant forçage QG d'ascension en présence de faible stabilité statique et de hautes valeurs d'humidité subtropicale. Le deuxième événement est de type "frontal", et est associé à une zone barocline persistante d'air modérément stable et de hautes valeurs de frontogenèse dans les niveaux inférieurs en absence d'advection de température et de vorticité substantielle. Ces deux événements mettent en valeur des manières plutôt différentes par lesquelles un événement de précipitation extrême peut se produire à St. John's.

Thermal infrared (IR, 10.5 – 12.5 m) images from the Meteosat Visible and Infrared Imager (MVIRI) of cold cloud episodes (cloud top brightness temperature < 241 K) are used as a proxy of precipitating clouds to derive a warm season (May-August) climatology of their coherency, duration, span, and speed over Europe and the Mediterranean. The analysis focuses over the 30°-54°N, 15°W-40°E domain in May-August 1996-2005. Harmonic analysis using discrete Fourier transforms is applied together with a statistical analysis and an investigation of the diurnal cycle. This study has the objective to make available a set of results on the propagation dynamics of the cloud systems with the aim of assist numerical modellers in improving summer convection parameterization. The zonal propagation of cold cloud systems is accompanied by a weak meridional component confined to narrow latitude belts. The persistence of cold clouds over the area evidences the role of orography, the Pyrenees, the Alps, the Balkans and Anatolia. A diurnal oscillation is found with a maximum marking the initiation of convection in the lee of the mountains and shifting from about 1400 UTC at 40°E to 1800 UTC at 0°. A moderate eastward propagation of the frequency maximum from all mountain chains across the domain exists and the diurnal maxima are completely suppressed west of 5°W. The mean power spectrum of the cold cloud frequency distribution evidences a period of one day all over Europe disappearing over the ocean (west of 10°W). Other maxima are found in correspondence of 6 to 10 days in the longitudes from 15° W to 0° and indicate the activity of the westerlies with frontal passage over the continent. Longer periods activities (from 15 up to 30 days) were stronger around 10° W and from 5° W to 15° E and are likely related to the Madden Julian Oscillation influence. The maxima of the diurnal signal are in phase with the presence of elevated terrain and with land masses. A median zonal phase speed of 16.1 ms-1 is found for all events ≥ 1000 km and ≥ 20 h and a full set of results divided by years and recurrence categories is also presented.

Atmospheric rivers (ARs) are important contributors to cool season precipitation in the Southwestern US, and in some cases can lead to extreme hydrometeorological events in the region. We performed a climatological analysis and identified two predominant types of ARs that affect the central mountainous region in Arizona: Type 1 ARs originate in the tropics near Hawaii (central Pacific) and enhance their moisture in the midlatitudes, with maximum moisture transport over the ocean at low-levels of the troposphere. On the other hand, moisture in Type 2 ARs has a more direct tropical origin and meridional orientation with maximum moisture transfer at mid-levels. We then analyze future projections of Southwest ARs in a suite of global and regional climate models used in the North American Regional Climate Change Assessment Program (NARCCAP), to evaluate projected future changes in the frequency and intensity of ARs under warmer global climate conditions. We find a consistent and clear intensification of the water vapor transport associated with the ARs that impinge upon Arizona and adjacent regions, however, the response of AR-related precipitation intensity to increased moisture flux and column-integrated water vapor is weak and no robust variations are projected either by the global or the regional NARCCAP models. To evaluate the effect of horizontal resolution and improve our physical understanding of these results, we numerically simulated a historical AR event using the Weather Research and Forecasting (WRF) model at a 3-km resolution. We then performed a pseudo-global warming experiment by modifying the lateral and lower boundary conditions to reflect possible changes in future ARs (as projected by the ensemble of global model simulations used for NARCCAP). Interestingly we find that despite higher specific humidity, some regions still receive less rainfall in the warming climate experiments - partially due to changes in thermodynamics, but primarily due to AR dynamics. Therefore, we conclude from this analysis that overall future increase in atmospheric temperature and water content as projected by global climate models will not necessarily translate into generalized heavier AR-related precipitation in the Southwestern US.

Thesis (Ph.D.)--Kent State University, 2007.
Title from PDF t.p. (viewed Mar. 19, 2009). Advisor: Scott C Sheridan. Keywords: climatology, irrigation, precipitation, Great Plains, atmospheric flow types, air mass types. Includes bibliographical references (p. 253-257).

The Caribbean is a complex region that heavily relies on its seasonal rainfall cycle for its economic and societal needs. This makes the Caribbean especially susceptible to hydro-meteorological disasters (e.g., droughts and floods), and other weather/climate risks. Therefore, effectively predicting the Caribbean rainfall cycle is valuable for the region. The efficacy of predicting the Caribbean rainfall cycle is largely dependent on effectively characterizing the climate dynamics of the region. However, the dynamical processes and climate drivers that shape the seasonal cycle are not fully understood, as previous observational studies show inconsistent findings as to what mechanisms influence the mean state and variability of the cycle. These inconsistencies can be attributed to the limitations previous studies have when investigating the Caribbean rainfall cycle, such as using monthly or longer resolutions in the data or analysis that often mask the seasonal transitions and regional differences of rainfall, and investigating the Caribbean under a basin-wide lens rather than a sub-regional lens. This inhibits the ability to accurately calculate and predict subseasonal-to-seasonal (S2S) rainfall characteristics in the region. To address these limitations and inconsistencies, the research in this thesis examines the seasonal climatology, variability, and characteristics of the Caribbean rainfall cycle under a sub-regional and temporally fine lens in order to investigate the prediction of the cycle. Regional variations and dynamical processes of the Caribbean annual rainfall cycle are assessed using (1) a principal component analysis across Caribbean stations using daily observed precipitation data; and, (2) a moisture budget analysis. The results show that the seasonal cycle of rainfall in the Caribbean hinges on three main facilitators of moisture convergence: the Atlantic Intertropical Convergence Zone (ITCZ), the Eastern Pacific ITCZ, and the North Atlantic Subtropical High (NASH). A warm body of sea-surface temperatures (SSTs) in the Caribbean basin known as the Atlantic Warm Pool (AWP) and a low-level jet centered at 925hPa over the Caribbean Sea known as the Caribbean Low-Level Jet (CLLJ) modify the extent of moisture provided by these main facilitators. The interactions of these dynamical processes are responsible for shaping the seasonal components of the annual rainfall cycle: The Winter Dry Season (WDS; mid-November to April); the Early-Rainy Season (ERS; mid-April to mid-June); an intermittent relatively dry period known as the mid-summer drought, (MSD; mid-June to late August), and the Late-Rainy Season (LRS; late August to late November). Five geographical sub-regions are identified in the Caribbean Islands, each with its unique set of dynamical processes, and consequently, its unique pattern of rainfall distribution throughout the rainy season: Northwestern Caribbean, the Western Caribbean, the Central Caribbean, the Central and Southern Lesser Antilles, and Trinidad and Tobago and Guianas. Convergence by sub-monthly transients contributes little to Caribbean rainfall. The wettest and driest Caribbean ERS and LRS years’ are then explored by conducting the following: (1) a spatial composite of rainfall using the daily rainfall data; and, (2) spatial composites of SSTs, sea-level pressure (SLP), and mean flow moisture convergence and transports using monthly data. The ERS and LRS are impacted in distinctly different ways by two different, and largely independent, large-scale phenomena, external to the region: a SLP dipole mode of variability in the North Atlantic known as the North Atlantic Oscillation (NAO), and the El Nino Southern Oscillation (ENSO). Dry ERS years are associated with a persistent dipole of cold and warm SSTs over the Caribbean Sea and Gulf of Mexico, respectively, that are caused by a preceding positive NAO state. This setting involves a wind-evaporation-SST (WES) feedback expressed in enhanced trade winds and consequently, moisture transport divergence over all of the Caribbean, except in portions of the Northwestern Caribbean in May. A contribution from the preceding winter cold ENSO event is also discernible during dry ERS years. Dry LRS years are due to the summertime onset of an El Niño event, developing an inter-basin SLP pattern that moves moisture out of the Caribbean, except in portions of the Northwestern Caribbean in November. Both large-scale climate drivers would have the opposite effect during their opposite phases leading to wet years in both seasons. Existing methodologies that calculate S2S rainfall characteristics were not found to be suitable for a region like the Caribbean, given its complex rainfall pattern; therefore, a novel and comprehensive method is devised and utilized to calculate onset, demise, and MSD characteristics in the Caribbean. When applying the method to calculate S2S characteristics in the Caribbean, meteorological onsets and demises, which are calculated via each year’s ERS and LRS mean thresholds, effectively characterize the seasonal evolution of mean onsets and demises in the Caribbean. The year-to-year variability of MSD characteristics, and onsets and demises that are calculated by climatological ERS and LRS mean thresholds resemble the variability of seasonal rainfall totals in the Caribbean and are statistically significantly correlated with the identified dynamical processes that impact each seasonal component of the rainfall cycle. Finally, the seasonal prediction of the Caribbean rainfall cycle is assessed using the identified variables that could provide predictive skill of S2S rainfall characteristics in the region. Canonical correlation analysis is used to predict seasonal rainfall characteristics of station-averaged sub-regional frequency and intensity of the ERS and LRS wet days, and magnitude of the MSD. Predictor fields are based on observations from the ERA-Interim reanalysis and GCM output from the North America Multi-Model Ensemble (NMME). Spearman Correlation and Relative Operating Characteristics are applied to assess the forecast skill. The use of SLP, 850-hPa zonal winds (u850), vertically integrated zonal (UQ), and meridional (VQ) moisture fluxes show comparable, if not better, forecast skill than SSTs, which is the most common predictor field for regional statistical prediction. Generally, the highest ERS predictive skill is found for the frequency of wet days, and the highest LRS predictive skill is found for the intensity of wet days. Rainfall characteristics in the Central and Eastern Caribbean have statistically significant predictive skill. Forecast skill of rainfall characteristics in the Northwestern and Western Caribbean are lower and less consistent. The sub-regional differences and consistently significant skill across lead times up to at least two months can be attributed to persistent SST/SLP anomalies during the ERS that resemble the North Atlantic Oscillation pattern, and the summer-time onset of the El Niño-Southern Oscillation during the LRS. The spatial pattern of anomalies during the MSD bears resemblance to both the ERS and LRS spatial patterns. The findings from this thesis provide a more comprehensive and complete understanding of the climate dynamics, variability, and annual mean state of the Caribbean rainfall cycle. These results have important implications for prediction, decision-making, modeling capabilities, understanding the genesis of hydro-meteorological disasters, investigating rainfall under other modes of variability, and Caribbean impact studies regarding weather risks and future climate.

Thesis (M.S.)--Florida State University, 2005.
Advisor: Dr. Carol Anne Clayson, Florida State University, College of Arts and Sciences, Dept. of Meteorology. Title and description from dissertation home page (viewed June 16, 2005). Document formatted into pages; contains xii, 82 pages. Includes bibliographical references.

A thesis submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Doctor of Philosophy. July 2014.
South Africa’s climate is highly variable, implying that the national agricultural sector should make provision to have early warning services in place in order to reduce the risks of disasters. More than 70% of natural disasters worldwide are caused by weather and climate or weather and climate related hazards. Reliable Seasonal Climate Forecasting (SCF) for South Africa would have the potential to be of great benefit to users in addressing disaster risk reduction. A disaster is a serious disruption of the functioning of a community or a society, causing widespread human, material, economic or environmental losses, which exceed the ability of the affected community or society to cope when using their own resources. The negative impacts on agricultural production in South Africa due to natural disasters including disasters due to increasing climate variability and climate change are critical to the sector. The hypothesis assumed in the study is the improved early warning service and better SCF dissemination lead to more effective and better decision making for subsequent disaster risk reduction in the agricultural sector. The most important aspect of knowledge management in early warning operations is that of distributing the most useful service to the target group that needs it at the right time. This will not only ensure maximum performance of the entity responsible for issuing the early warnings, but will also ensure the maximum benefit to the target group. South Africa is becoming increasingly vulnerable to natural disasters that are afflicted by localised incidents of seasonal droughts, floods and flash floods that have devastating impacts on agriculture and food security. Such disasters might affect agricultural production decisions, as well as agricultural productivity. Planting dates and plant selection are decisions that depend on reliable and accurate meteorological and climatological knowledge and services for agriculture. Early warning services that could be used to facilitate informed decision making includes advisories on iv future soil moisture conditions in order to determine estimated planting times, on future grazing capacity, on future water availability and on forecasts of the following season’s weather and climate, whenever that is possible. The involvement of government structures, obviously, is also critical in immediate responses and long term interventions. The importance of creating awareness, of offering training workshops on climate knowledge and SCF, and of creating effective early warning services dissemination channels is realized by government. This is essential in order to put effective early warning services in place as a disaster-risk coping tool. Early warning services, however, can only be successful if the end-users are aware of what early warning systems, structures and technologies are in place, and if they are willing that those issuing the early warning services become involved in the decision-making process. Integrated disaster-risk reduction initiatives in government programmes, effective dissemination structures, natural resource-management projects and communityparticipation programmes are only a few examples of actions that will contribute to the development of effective early warning services, and the subsequent response to and adoption of the advices/services strategies by the people most affected. The effective distribution of the most useful early warning services to the end-user, who needs it at the right time through the best governing structures, may significantly improve decision making in the agricultural, food security and other water-sensitive sectors. Developed disaster-risk policies for extension and farmers as well as other disaster prone sectors should encourage self-reliance and the sustainable use of natural resources, and will reduce the need for government intervention. The SCF producers (e.g. the South African Weather Service (SAWS)) have issued new knowledge to intermediaries for some years now, and it is important to determine whether this knowledge has been used in services, and if so whether these services were applied effectively in coping with disaster-risks and in disaster v reduction initiatives and programmes. This study for that reason also intends to do an evaluation of the knowledge communication processes between forecasters, and intermediaries at national and provincial government levels. It therefore, aims to assess and evaluate the current knowledge communication structures within the national agricultural sector, seeking to improve disaster-risk reduction through effective early warning services. A boundary organisation is an organization which crosses the boundary between science, politics and end-users as they draw on the interests and knowledge of agencies on both sides to facilitate evidence base and socially beneficial policies and programmes. Reducing uncertainty in SCF is potentially of enormous economic value especially to the rural communities. The potential for climate science to deliver reduction in total SCF uncertainty is associated entirely with the contributions from internal variability and model uncertainty. The understanding of the limitations of the SCFs as a result of uncertainties is very important for decision making and to end-users during planning. Disappointing, however, is that several studies have shown a fairly narrow group of potential users actually receive SCFs, with an even a smaller number that makes use of these forecasts In meeting the objectives of the study the methodology to be followed is based on knowledge communication. For that reason two types of questionnaires were drafted. Open and closed questionnaires comprehensively review the knowledge, understanding, interpretation of SCFs and in early warning services distribution channels. These questionnaires were administered among the SCF producers and intermediaries and results analysed. Lastly the availability of useful SCFs knowledge has important implications for agricultural production and food security. Reliable and accurate climate service, as one of the elements of early warning services, will be discussed since they may be used to improve agricultural practices such as crop diversification, time of planting vi and changes in cultivation practices. It was clear from the conclusions of the study that critical elements of early warning services need to receive focused attention such as the SCF knowledge feedback programme should be improved by both seasonal climate producers and intermediaries, together with established structures through which reliable, accurate and timely early warning services can be disseminated. Also the relevant dissemination channels of SCFs are critical to the success of effective implementation of early warning services including the educating and training of farming communities. The boundary organisation and early warning structures are important in effective implementation of risk reduction measures within the agricultural sector and thus need to be prioritised. Enhancing the understandability and interpretability of SCF knowledge by intermediaries will assist in improving action needed to respond to SCFs. Multiple media used by both SCF producers and intermediaries in disseminating of SCFs should be accessible by all users and end-users. The Government should ensure that farming communities are educated, trained and well equipped to respond to risks from natural hazards.

This study investigated the seasonal, monthly and weekly variation of fatal suicidal behaviour in Pietermaritzburg, as well as the relationship between fatal suicidal behaviour and climate. Secondary analysis was performed on suicide data collected for two unpublished honours theses. Results indicate that there is significant monthly variation in the distribution of fatal suicidal behaviour in Pietermaritzburg, with the expected spring-summer peak and winter trough . This pattern was more pronounced for adults than for the youth or the elderly. There was no significant variation in the weekly distribution of suicide, and suicides did not reach a peak on Monday as has been evidenced in previous research. Instead, the distribution showed a peak on Sunday with a steady decrease towards the end of the week. An increase in humidity, ambient temperature and minimum temperature was associated with an increase in suicide rates . Overall this relationship was stronger for violent, than non-violent suicide. However, a significant negative relationship was found between hours of sunshine and suicide, but this was only significant for non-violent suicide. This study was largely descriptive, and further research is indicated in order to develop a theoretical framework for understanding the temporal distribution of suicide .
Thesis (M.Soc.Sc.)-University of Natal, Pietermaritzburg, 2000.

Doctor of Philosophy
Department of Geography
John A. Harrington Jr
The climate of the Middle East is warming and extreme hot temperature events are becoming more common, as observed by the significant upward trends in mean and extreme temperatures during the last few decades. Climate modeling studies suggest that the frequency, intensity, and duration of extreme temperature events are expected to increase as the global and local climate continues to warm. Existing literature about heat waves (HWs) in Saudi Arabia provides information about HW duration using a single index, without considering the observed effects of climate change and the subtropical arid climate. With that in mind, this dissertation provides a series of three stand-alone papers evaluating temporal, geographic, and atmospheric aspects of the character of warm season (May-September) HWs in Saudi Arabia for 1985 to 2014. Chapter 2 examines the temporal behavior(s) of the frequency, duration, and intensity of HWs under the observed recent climate change. Several issues are addressed including the identification of some improved methodological practices for HW indices. A time-sensitive approach to define and detect HWs is proposed and assessed. HW events and their duration are considered as count data; thus, different Poisson models were used for trend detection. Chapter 3 addresses the spatio-temporal patterns of the frequency and intensity of hot days and nights, and HWs. The chapter reemphasizes the importance of considering the on-goings effects of climate warming and applies a novel time-series clustering approach to recognize hot temperature event behavior through time and space. Chapter 4 explores the atmospheric circulation conditions that are associated with warm season HW event occurrence and how different HWs aspects are related to different circulation types. Further, possible teleconnections between HWs and sea surface temperature (SST) anomalies of nearby large bodies are examined. Results from Chapters 2 and 3 detected systematic upward trends in maximum and minimum temperatures at most of the 25 stations, suggesting an on-going change in the climatology of the upper-tail of the frequency distribution. The analysis demonstrated the value of using a time-sensitive approach in studying extreme thermal events. Different patterns were observed over time and space not only across stations but also among extreme temperature events (i.e., hot days and nights, and HWs). The overall results suggest that not only local and regional factors, such as elevation, latitude, land cover, atmospheric humidity, and distance from a large body of water, but also large-scale factors such as atmospheric circulation patterns are responsible for the observed temporal and spatial patterns. Chapter 4 confirmed that as the Indian Summer Monsoon Trough and the Arabian heat low were key atmospheric features related to HW days. SST anomalies seemed to be a more important factor for HWs intensity. Extreme thermal events in Saudi Arabia tended to occur during regional warming due to atmospheric circulation conditions and SSTs teleconnections. This study documents the value of a time-sensitive approach and should initiate further research as some of temporal and spatial variabilities were not fully explained

碩士
國立臺灣大學
大氣科學研究所
98
This study focused on the characteristics of rainfall distribution during Mei-yu season in 1991-2006. Operational heavy rainfall checklist data from Central Weather Bureau were also used to study the relationship between heavy rainfall and synoptic environment conditions within 6 selected river basins. The result showed that the heavier rainfall occurred frequently over windward side of southwesterlies. Though the Mei-yu front provided lifting mechanism, the forcing from the Taiwan topography also played an important role. Synoptic environment conditions highly related to the heavy rainfall events filtered by statistic methods included: approaching of Mei-yu fronts, moist environment, low-level jets and 700/500 hPa short wave troughs. The result also indicated that 12-hourly heavy rainfall over northern Taiwan relied on the divergence provided by upper-level jet streaks to help convection developing while heavy rainfall over sourthern Taiwan was highly related to 500-1000 hPa diffluent thickness pattern. Based on the above analyses, this study established a conceptual climatology model and tried to estimate the model usability to the quantitative precipitation forecast (QPF). The validation was done by 17 independent cases during 2007-2008. Result showed that the conceptual climatology model performed better on predicting heavier rainfall than climatology model especially for cenrtral and southern river basins. Analysis of prefigurance and postagreement indicated that the conceptual climatology model was over prediction. However, when the results of numerical weather prediction models are still unstable or disagree with each other, the model can still provide an additional infomation.

The Southwest United States (SWUS) is facing an ongoing drought which has led to water short- ages, in addition to forest mortality due to wildfire and bark beetle outbreaks associated with increased temperatures. This region has a population of 9.6 million people and is one of the fastest growing parts of the United States, and pressure on its resources can be expected to increase in the future. The SWUS is also projected to become more arid in the coming century under greenhouse gas induced climate change, which will impact its environmental, economic and social vitality. This thesis explores the climate dynamics which control water availability, streamflow, and vegetation green-up in the SWUS, in order to constrain our understanding of the mechanisms controlling the ecohydrology of the region, and to inform projections for the 21st century. Chapters 1 and 2 investigate the climate drivers responsible for producing the observed vari- ability in streamflow for the Gila River, a tributary of the Colorado, and the upper Rio Grande. The Gila is the southernmost snowfed river in the SWUS, and has a spring streamflow peak that responds to melting of the snowpack at its headwaters in New Mexico. The Gila is also sufficiently south so that it has a secondary streamflow peak in the summer which is fed by rains from the North American Monsoon (NAM). On interannual timescales, the Gila’s spring peak is primarily influenced by natural variability associated with Pacific sea surface temperature (SST), while the summer peak apparently does not respond to interannual variability. The upper Rio Grande is fur- ther north and east in the SWUS, and only has one streamflow peak occurring in spring-summer which is influenced by both tropical Pacific SST and Atlantic SST. Spring streamflow has also declined in each river post-1998, and this is due to a shift in the tropical Pacific leading to negative precipitation anomalies and drying in the SWUS. Chapter 2 assess a region of the SWUS that receives both winter storm track precipitation and NAM, and therefore has two periods of vegetation green-up annually with an intervening spring dry season. The first peak in vegetation occurs during the spring, and is influenced by the magnitude of winter precipitation and snowmelt, which gradually adds water to the soils. The second peak in vegetation follows the spring dry season when soil moisture recovers with the arrival of the NAM. A climatic shift in the tropical Pacific occurred in 1997/98 and produced a shift to an earlier and more severe spring dry season, and reduced vegetation green-up. An earlier extended dry period in the mid-century (1948 to 1966) also was influenced by a cool phase of the tropical Pacific, which led to a reduction in precipitation of a similar magnitude as the recent drought. However, the recent drought is more severe - and temperatures also have been greater during the recent period. Using a decomposition of the impact of precipitation and potential evapotranspiration (PET) on soil moisture, we found that PET contributed 39% to the negative soil drying anomalies in the recent post-1998 drought, compared to 8% during the earlier extended dry period. This indicates an increased role of temperature during the recent drying. In Chapter 4 we evaluated 18 CMIP5 models based on comparisons with observations of pre- cipitation, net ecosystem exchange, leaf area index and soil moisture from land surface model output. Following our evaluation, we selected three models which best simulated the bimodal region: CanEMS2, GFDL-ESM2G and GFDL-ESM2M. These models indicate that overall this region will be drier in the 21st century; runoff is projected to decrease, particularly in the spring, soil moisture is reduced, and snow fall declines. The variability in projected precipitation, how- ever, is large, and we find that for the most part does not exceed what can be expected from model natural climate variability. The multi-model ensemble from the rest of the CMIP5 models indicate an overall decline in annual precipitation by the end of the 21st century, particularly during the spring. The three models also project an increase in net primary productivity in both the spring and summer growing seasons due to the effects of CO2 fertilization. Enhanced vegetation growth is likely to further exacerbate drying of the soils as vegetation draws down moisture, and enhances water losses via evapotranspiration. The fertilization process is, however, still uncertain and fur- ther studies are needed on the representation of CO2 enhanced vegetation growth in the SWUS to constrain this result. The findings of this thesis have contributed enhanced our knowledge of how climate dynamics, natural variability, and recent warming have influenced the ecohydrology of the SWUS, and also inform future climate projections. Constraining our understanding of this region is of importance given the growing populations, mounting pressures on natural resources, and anthropogenically induced climate change which is expected to affect this region in the 21st century.

MESHWR
Department of Hydrology and Water Resources
This study dealt with the influence of climate variability on flood and drought cycles and implications on rainy season characteristics in Luvuvhu River Catchment (LRC) in Limpopo of South Africa. Extreme weather events resulting in hazards such as floods and droughts are becoming more frequent due to climate change. Extreme events affect rainy season characteristics and hence have an influence on water availability and agricultural production. Annual temperature was obtained from Water Research Commission for stations 0723485W, 0766628W and 0766898W from 1950-2013 were used to show/or confirm if there is climate variability in LRC. Daily rainfall data was obtained from SAWS for stations 0766596 9, 0766563 1, 0723485 6 and 0766715 5 were used to detect climate variability and determine the onset, duration and cessation of the rainy season. Streamflow data obtained from the Department of Water and Sanitation for stations A9H004, A9H012, and A9H001 for at least a period of 30 years for each station were used for climate variability detection and determination of flood and drought cycles. Influence of climate variability on floods and droughts and rainy season characteristic were determined in the area of study. Trends were evaluated for temperature, rainfall and streamflow data in the area of study using Mann Kendall (MK) and linear regression (LR) methods. MK and LR detected positive trends for temperature (maximum and minimum) and streamflow stations. MK and LR results of rainfall stations showed increasing trends for stations 0766596 9, and 0766563 1 whereas stations 0723485 6 and 0766715 5 showed decreasing trends. Standardized precipitation index (SPI) was used to determine floods and droughts cycles. SPI results have been classified either as moderately, severely and extremely dry or, moderately, very and extremely wet. This SPI analysis provides more details of dominance of distinctive dry or wet conditions for a rainy season at a particular station. Mean onset of rainfall varied from day 255 to 297, with 0766715 5 showing the earliest onset compared to the rest of the stations. Cessation of rainfall for most of the hydrological years was higher than the mean days of 88, 83 and 86 days in 0766596 9, 0766563 1 and 0723485 6 stations. Mean duration of rainfall varied from 102 to 128, with station 0766715 5 showing shortest duration of rainfall. The results of the study showed that the mean onset, duration and cessation were comparable for all stations except 0766715 5 which had lower values. The study also found that climate variability greatly affects onset, duration and cessation of rainfall during dry years. This led to late onset, early cessation and relatively short duration of the rainfall season. Communities within the catchment must be educated to practice activities such as conservation of indigenous plants, reduce carbon dioxide emissions.