Approximately 60% of New Zealand's electricity is produced from hydro generation. The Waitaki River catchment is located in the centre of the South Island of New Zealand, and produces 35-40% of New Zealand's electricity. Low inflow years in 1992 and 2001 resulted in the threat of power blackouts, and a national demand for electricity that is currently growing at 2 to 5% a year gives strong justification for better management of the hydro resource. Improved seasonal rainfall and inflow forecasts will result in the better management of the water used in hydro generation on a seasonal basis. Seasonal rainfall forecasting has been the focus of much international research in recent years, but seasonal inflow forecasting is in its relative infancy. Researchers have stated that key directions for both fields are to decrease the spatial scale of forecast products, and to tailor forecast products to end user needs, so as to provide more relevant and targeted forecasts, which will hopefully decrease the enormous socio-economic costs of climate fluctuations. This study calibrated several season ahead lake inflow and rainfall forecast models for the Waitaki river catchment, using statistical techniques to quantify relationships between land-ocean-atmosphere state variables and seasonally lagged inflows and rainfall. Techniques included principal components analysis and multiple linear regression, with cross-validation techniques applied to estimate model error. Many of both the continuous and discrete format models calibrated in this study predict anomalously wet and dry seasons better than random chance, and better than the long term mean as a predictor. 95% confidence limits around most model predictions in this study offer significant skill when compared with the range of all probable inflows (based on the 80 year recording history in the catchment). Models predicting winter Lake Pukaki inflows are those with the strongest predictive relationships in this study. Spring and summer predictions were generally less skilful than those for winter and autumn. Inflows could be predicted with some skill in winter and summer, but not rainfall, and rainfall could be predicted with some skill in autumn and spring, but not inflows. Models predicting inflows and rainfall for different seasons in this study use very different sets of predictor variables to accomplish their seasonal predictability. This may be related to the significant seasonal snow storage in the catchment, so that other factors such as temperature and the number of north-westerly storms may have a large part to play in the magnitude of inflows. Similarly, predicting the same dependent variable but for different seasons led to different contributing variables, leading to the conclusion that different wider physical causative mechanisms are behind the predictability in different seasons, and that they too should be studied separately in any future research. SST5 (sea surface temperature to the north of New Zealand) was found to have more relevance than any other predictor in predicting Waitaki river inflows and rainfall in any season. The models calibrated with SOI and IPO included as predictor variables were almost invariably worse in their predictive skill than those without, and the list of the most important predictor variables in all models did not include equatorial sea surface temperatures, sea level pressures, or 700hpa geopotential height variables. The conclusion from these findings is that equatorial ocean-atmosphere state variables do not have significant relationships with season ahead inflows and rainfall in the South Island of New Zealand. Seasonal climate forecasting on single catchment scale, and focussed to end user needs, is possible with some skill, at least in the South Island of New Zealand.

The components of the northern hemisphere cryosphere and their relationship to Atlantic tropical cyclone activity are investigated in this study. Multiple ordinary least-squares regression with a stepwise selection procedure is used to develop a new statistical forecasting scheme for 13 seasonal tropical cyclone parameters at four lead times for the period 1980-2010. Sea ice area and sea ice extent in 10 geographic regions, snow cover extent in three geographic regions and five indices reflecting major modes of climate variability were analyzed as possible predictors. Three model groups, based on predictors, were constructed and evaluated: 1) only climate mode predictors, 2) only cryosphere predictors, and 3) both cryosphere and climate mode predictors. Models using only climate mode predictors showed poor predictability of the tropical cyclone parameters across all four lead times while the models using only cryosphere predictors and those using both sets of predictors showed improved predictability. Baffin Bay and Hudson Bay sea ice area were found to be the most significant predictors, exhibiting an inverse relationship with overall tropical cyclone activity. The developed models were also compared to current operational statistical models of tropical cyclone activity. While the operational models were generally more skillful, June hindcasts of major hurricanes outperformed the operational models by as much as 20%.

No description available.

A Tropical Temperate Trough (TTT) is a type of weather system that links the tropics and the extra-tropics across southern Africa. TTT events have been studied statistically in detail, but very little research has been done to study this phenomenon dynamically and especially on a seasonal scale. This study therefore focuses on the predictability of the characteristics of TTTs across southern Africa on a seasonal scale, by using a state-of-the-art seasonal forecasting model, namely the GloSea5 developed by the UK Met Office. Gridded hindcast data for the months of November, December, January and February from 1996/1997 to 2009/2010 are compared to observed data. The different ensemble members of the GloSea5 model (with lead-times of 1 week up to 2 months) are first compared separately to the observed data, after which the model average, with a 0-month, a 1-month and a 2-month lead-time, is calculated and also compared to the observed dataset. TTT events have distinctive characteristics during the formation and the development phases. Most prominent of these characteristics are the cloud bands associated with these weather systems, which have a north-west to south-east orientation and move from west to east across southern Africa. To identify the TTTs, daily outgoing long-wave radiation values are processed by a Meteorological Robot (MetBot), with a strict criterion to identify the cloud bands that characterise these systems. The MetBot’s algorithm produces the information needed to further investigate the different characteristics of TTTs, such as the frequency, the location and the intensity of these systems. Analysis of the MetBot output includes calculating the Root Mean Square Error, the percentage error and in some cases the percentage deviation of the number of cloud bands, as well as the anchor point, the centroid position, the area, the tilt and the minimum and maximum OLR values of the cloud bands. This investigation revealed that the characteristics of TTT events can to some extent be predicted on a seasonal scale for the summer rainfall season of southern Africa. The model used in this study fared particularly well with a 1-month lead-time forecast (compared to a 0-month and a 2-month lead-time forecast). The intensity and the location of the cloud bands associated with TTT events are forecast with a smaller percentage error than the frequency of these systems, as the frequency of TTTs tend to be significantly under-predicted by the model. For some predicted quantities, such as the area of the cloud bands, a bias-adjustment is necessary which produces significantly better results with smaller percentage errors. In the conclusions, suggestions are made on possible future studies, and how to develop this study further to create seasonal forecasts with higher skill with special regards to TTT events. / Dissertation (MSc)--University of Pretoria, 2019. / Geography, Geoinformatics and Meteorology / MSc / Unrestricted

La glace de mer Arctique connaît actuellement de profondes mutations dans sa structure et sa variabilité. Le déclin récent de la couverture estivale de glace de mer Arctique, qui a atteint un nouveau record en septembre 2012, a relancé l'intérêt stratégique de cette région longtemps oubliée. La prévision de glace de mer à l'échelle saisonnière est ainsi un problème d'océanographie opérationnelle qui pourrait intéresser nombre d'acteurs économiques (pêche, énergie, recherche, tourisme). De plus, en tant que conditions aux limites pour l'atmosphère, la glace de mer peut induire une prévisibilité de l'atmosphère à l'échelle saisonnière, au même titre que les anomalies de température de surface de l'océan sous les tropiques. Nous présentons dans cette thèse la construction d'un système de prévisions saisonnières dédié à la glace de mer Arctique avec le modèle couplé CNRM-CM5.1, développé conjointement par le CNRM-GAME et le CERFACS. Nous passons en revue la stratégie d'initialisation, la réalisation et l'évaluation des hindcasts (ou rétro-prévisions). La communauté dispose d'observations de concentration de glace de mer, mais de très peu de données d'épaisseur à l'échelle du bassin. Afin d'initialiser la glace de mer et l'océan dynamiquement et thermodynamiquement, nous avons choisi d'utiliser la composante océan-glace de mer de CNRM-CM5.1, NEMO-GELATO. L'initialisation consiste à forcer NEMO-GELATO avec les champs météorologiques issus de la réanalyse ERA-Interim, sur la période 1990-2010. Des corrections appliquées aux forçages basées sur des observations satellitaires et in-situ nous permettent d'obtenir une bonne simulation de l'océan et de la glace de mer en terme d'état moyen et de variabilité interannuelle. L'épaisseur reste néanmoins sous-estimée. Quelques propriétés de prévisibilité intrinsèque de la glace de mer Arctique sont ensuite présentées. Une étude de prévisibilité potentielle diagnostique nous a permis de distinguer deux modes de prévisibilité de la glace de mer à l'aide du volume et de la structure sous-maille d'épaisseur. Un « mode de persistance » concerne la prévisibilité de la couverture d'hiver. La surface de glace de mars est potentiellement prévisible à 3 mois à l'avance par la seule persistance, et dans une moindre mesure à l'aide des surfaces couvertes par la glace relativement fine. Un « mode de mémoire » concerne la prévisibilité de la couverture estivale. La surface de glace de septembre est potentiellement prévisible jusqu'à 6 mois à l'avance à l'aide du volume et surtout de la surface couverte par la glace relativement épaisse. Ces résultats suggèrent donc qu'une bonne initialisation du volume et de la structure d'épaisseur en fin d'hiver permettrait une bonne prévisibilité des étendues de fin d'été. Les prévisions d'été et d'hiver présentent des scores particulièrement encourageants, que ce soit en anomalies brutes ou en anomalies par rapport à la tendance linéaire. Cela suggère une prévisibilité liée à l'état initial et non aux forçages externes imposés. L'analyse des prévisions d'été montre que le volume et les structures d'épaisseur de l'état initial expliquent l'essentiel des différentes prévisions, ce qui confirme l'existence du « mode de mémoire » malgré un fort biais radiatif. L'analyse des prévisions d'hiver suggère que l'étendue initiale explique une partie des différentes prévisions, un indice du « mode de persistance » des prévisions hivernales. Une analyse régionale des prévisions d'hiver permet de préciser le rôle de l'océan dans ces prévisions, et montre dans quelle mesure nos prévisions pourraient être utilisées de manière opérationnelle, notamment en mer de Barents / Sea ice experiences some major changes in the early 21st century. The recent decline of the summer Arctic sea ice extent, reaching an all-time record low in September 2012, has woken renewed interest in this remote marine area. Sea ice seasonal forecasting is a challenge of operational oceanography that could benefit to several stakeholders : fishing, energy, research, tourism. Moreover, sea ice is a boundary condition of the atmosphere. As such, as tropical sea surface temperature, it may drive some atmosphere seasonal predictability. The goal of this PhD work was to set up a dedicated Arctic sea ice seasonal forecasting system, using CNRM-CM5.1 coupled climate model. We address the initialization strategy, the creation and the evaluation of the hindcasts (or re-forecasts). In contrast to sea ice concentration, very few thickness data are available over the whole Arctic ocean. In order to initialize sea ice and the ocean dynamically and thermodynamically, we used the ocean-sea ice component of CNRM-CM5.1, named NEMO-GELATO, in forced mode. The initialization run is a forced simulation driven by ERA-Interim forcing over the period 1990-2010. Corrections based on satellite data and in-situ measurements leads to skilful simulation of the ocean and sea ice mean state and interannual variability. Sea ice thickness seems overall underestimated, based on the most recent estimates. Some characteristics of sea ice inherent predictability are then addressed. A diagnostic potential predictability study allowed us to identify two regimes of predictability using sea ice volume and the ice thickness distribution. The first one is the 'persistence regime', for winter sea ice area. March sea ice area is potentially predictable up to 3 months in advance using simple persistence, and surface covered by thin ice to a lesser extent. The second one is the 'memory regime', for summer sea ice area. September sea ice area is potentially predictable up to 6 months in advance using volume and to a greater extent the area covered by relatively thick ice. These results suggest that a comprehensive winter volume and thickness initialization could improve the summer forecasts. Summer and winter seasonal hindcasts shows very encouraging skills, in terms of raw and detrended anomalies. These skills suggest a predicatibility from initial conditions besides predictability due to the trend. Summer forecasts analysis shows that the volume and the ice thickess distribution explains a high fraction of the variance of predicted sea ice extent, which confirms the existence of the 'memory regime'. Winter forecasts also suggest the 'persistence regime'. A regional investigation of the winter hindcast helps precising the role of the ocean in the forecasts, and shows to what extent our system predictions could be used operationally, especially in the Barents Sea

The Sahel region located in Western Africa is well known for its high rainfall variability. Severe and recurring droughts have plagued the region during the last three decades of the 20th century, while heavy precipitation events (with return periods of up to 1,200 years) were reported between 2007 and 2014. Vulnerability to extreme events is partly due to the fact that people are not prepared to cope with them. It would be of great benefit to farmers if information about the magnitudes of precipitation and streamflow in the upcoming rainy season were available a few months before; they could then switch to more adapted crops and farm management systems if required. Such information would also be useful for other sectors of the economy, such as hydropower production, domestic/industrial water consumption, fishing and navigation. A logical solution to the above problem would be seasonal rainfall and streamflow forecasting, which would allow to generate knowledge about the upcoming rainy season based on information available before it's beginning. The research in this thesis sought to improve seasonal rainfall and streamflow forecasting in the Sahel by developing statistical rainfall and streamflow seasonal forecasting models. Sea surface temperature (SST) were used as pools of predictor. The developed method allowed for a systematic search of the best period to calculate the predictor before it was used to predict average rainfall or streamflow over the upcoming rainy season. Eight statistical models consisted of various statistical methods including linear and polynomial regressions were developed in this study. Two main approaches for seasonal streamflow forecasting were developed here: 1) A two steps streamflow forecasting approach (called the indirect method) which first linked the average SST over a period prior to the date of forecast to average rainfall amount in the upcoming rainy season using the eight statistical models, then linked the rainfall amount to streamflow using a rainfall-runoff model (Soil and Water Assessment Tool (SWAT)). In this approach, the forecasted rainfall was disaggregated to daily time step using a simple approach (the fragment method) before being fed into SWAT. 2) A one step streamflow forecasting approach (called as the direct method) which linked the average SST over a period prior to the date of forecast to the average streamflow in the upcoming rainy season using the eight statistical models. To decrease the uncertainty due to model selection, Bayesian Model Averaging (BMA) was also applied. This method is able to explore the possibility of combining all available potential predictors (instead of selecting one based on an arbitrary criterion). The BMA is also capability to produce the probability density of the forecast which allows end-users to visualize the density of expected value and assess the level of uncertainty of the generated forecast. Finally, the economic value of forecast system was estimated using a simple economic approach (the cost/loss ratio method). Each developed method was evaluated using three well known model efficiency criteria: the Nash-Sutcliffe coefficient (Ef), the coefficient of determination (R2) and the Hit score (H). The proposed models showed equivalent or better rainfall forecasting skills than most research conducted in the Sahel region. The linear model driven by the Pacific SST produced the best rainfall forecasts (Ef = 0.82, R2 = 0.83, and H = 82%) at a lead time of up to 12 months. The rainfall forecasting model based on polynomial regression and forced by the Atlantic ocean SST can be used using a lead time of up to 5 months and had a slightly lower performance (Ef = 0.80, R2 = 0.81, and H = 82%). Despite the fact that the natural relationship between rainfall and SST is nonlinear, this study found that good results can be achieved using linear models. For streamflow forecasting, the direct method using polynomial regression performed slightly better than the indirect method (Ef = 0.74, R2 = 0.76, and H = 84% for the direct method; Ef = 0.70, R2 = 0.69, and H = 77% for the indirect method). The direct method was driven by the Pacific SST and had five months lead time. The indirect method was driven by the Atlantic SST and had six months lead time. No significant difference was found in terms of performance between BMA and the linear regression models based on a single predictor for streamflow forecasting. However, BMA was able to provide a probabilistic forecast that accounts for model selection uncertainty, while the linear regression model had a longer lead time. The economic value of forecasts developed using the direct and indirect methods were estimated using the cost/loss ratio method. It was found that the direct method had a better value than the indirect method. The value of the forecast declined with higher return periods for all methods. Results also showed that for the particular watershed under investigation, the direct method provided a better information for flood protection. This research has demonstrated the possibility of decent seasonal streamflow forecasting in the Sirba watershed, using the tropical Pacific and Atlantic SSTs as predictors.The findings of this study can be used to improve the performance of seasonal streamflow forecasting in the Sahel. A package implementing the statistical models developed in this study was developed so that end users can apply them for seasonal rainfall or streamflow forecasting in any region they are interested in, and using any predictor they may want to try.

As the decline in Arctic sea ice extent makes this region more accessible, the need is increasing for effective seasonal sea ice forecasting to facilitate operational planning. Recently, coupled global climate models (CGCMs) have been used to address the need for effective sea ice forecasting on seasonal time scales. This thesis assesses the operational utility of the Canadian Seasonal to Interannual Prediction System (CanSIPS) for seasonal sea ice forecasting. This assessment consists of two separate studies. The first uses hindcasting to analyze the skill of two versions of CanSIPS, as well as an intermediate version, on the pan-Arctic as well as regional scales. This approach allows for an overall assessment of the system's skill in addition to providing insight with regards to the features in each version which improved that skill. This study finds that the use of a new initialization procedure for sea ice concentration and thickness improved forecast skill on the pan-Arctic scale as well as in the Central Arctic, Barents Sea, Laptev Sea, and Sea of Okhotsk. This study also shows that the substitution of one of the constituent models in the system improved forecast skill on the pan-Arctic scale as well as in the GIN, Barents, Kara, East Siberian, Chukchi, Bering, and Beaufort Seas. Overall, the new version of CanSIPS was found to be generally more skillful than previous versions. The second study conducts a potential predictability experiment on CanCM4, the constituent CGCM common to all versions of CanSIPS considered in this study. This study follows the methodology introduced by \cite{Bushuk2018} which allows for a more complete assessment of the dependency of potential predictability on initialization month than previous studies and for comparisons to be made between potential predictability and operational skill. This analysis is again done on both the pan-Arctic and regional scale. The findings of this experiment show that CanCM4 has relatively low potential predictability relative to other models and explains results previously presented in a multi-model study by \cite{Day2016}. Further, the characteristics of CanCM4's potential predictability share similarities with other models including greater predictability at longer lead times for winter target months than summer target months, greater predictability in the Atlantic sector than the Pacific sector, and the presence of the spring predictability barrier on the pan-Arctic scale as well as in several regions. The comparison of operational skill to potential predictability provides a general overview of the ``skill gap" which may be closed with improvements in initialization procedures and model physics. This comparison does, however, come with some caveats due to differences in the statistical characteristics of the perfect model and the climate system it represents. Together, the operational skill assessment of different versions of CanSIPS and the potential predictability experiment conducted on one of its constituent models, CanCM4, demonstrate that while room for improvement exists, the recent development of this forecast system has clearly increased its operational utility as a seasonal sea ice forecasting tool. / Graduate

The eastward winds that form each winter in the Arctic stratosphere are intermittently disrupted by planetary-scale waves propagating up from the surface in events known as stratospheric sudden warmings. It is shown here that following roughly half of these sudden warmings, the winds take as long as three months to recover, during which time the polar stratosphere evolves in a robust and predictable fashion. These extended recoveries, termed here Polar-night Jet Oscillation (PJO) events, are relevant to understanding the response of the extratropical troposphere to forcings such as solar variability and climate change. They also represent a possible source of improvement in our ability to predict weather regimes at seasonal timescales. Four projects are reported on here. In the first, the approximation of stratospheric radiative cooling by a linear relaxation is tested and found to hold well enough to diagnose effective damping rates. In the polar night, the rates found are weaker than those typically assumed by simplified modelling studies of the extratropical stratosphere and troposphere. In the second, PJO events are identified and characterized in observations, reanalyses, and a comprehensive chemistry-climate model. Their observed behaviour is reproduced well in the model. Their duration correlates with the depth in the stratosphere to which the disruption descends, and is associated with the strong suppression of further planetary wave propagation into the vortex. In the third, the response of the zonal mean winds and temperatures to the eddy-driven torques that occur during PJO events is studied. The collapse of planetary waves following the initial warming permits radiative processes to dominate. The weak radiative damping rates diagnosed in the first project are required to capture the redistribution of angular momentum responsible for the circulation anomalies. In the final project, these damping rates are imposed in a simplified model of the coupled stratosphere and troposphere. The weaker damping is found to change the warmings generated by the model to be more PJO-like in character. Planetary waves in this case collapse following the warmings, confirming the dual role of the suppression of wave driving and extended radiative timescales in determining the behaviour of PJO events.

The eastward winds that form each winter in the Arctic stratosphere are intermittently disrupted by planetary-scale waves propagating up from the surface in events known as stratospheric sudden warmings. It is shown here that following roughly half of these sudden warmings, the winds take as long as three months to recover, during which time the polar stratosphere evolves in a robust and predictable fashion. These extended recoveries, termed here Polar-night Jet Oscillation (PJO) events, are relevant to understanding the response of the extratropical troposphere to forcings such as solar variability and climate change. They also represent a possible source of improvement in our ability to predict weather regimes at seasonal timescales. Four projects are reported on here. In the first, the approximation of stratospheric radiative cooling by a linear relaxation is tested and found to hold well enough to diagnose effective damping rates. In the polar night, the rates found are weaker than those typically assumed by simplified modelling studies of the extratropical stratosphere and troposphere. In the second, PJO events are identified and characterized in observations, reanalyses, and a comprehensive chemistry-climate model. Their observed behaviour is reproduced well in the model. Their duration correlates with the depth in the stratosphere to which the disruption descends, and is associated with the strong suppression of further planetary wave propagation into the vortex. In the third, the response of the zonal mean winds and temperatures to the eddy-driven torques that occur during PJO events is studied. The collapse of planetary waves following the initial warming permits radiative processes to dominate. The weak radiative damping rates diagnosed in the first project are required to capture the redistribution of angular momentum responsible for the circulation anomalies. In the final project, these damping rates are imposed in a simplified model of the coupled stratosphere and troposphere. The weaker damping is found to change the warmings generated by the model to be more PJO-like in character. Planetary waves in this case collapse following the warmings, confirming the dual role of the suppression of wave driving and extended radiative timescales in determining the behaviour of PJO events.