Do global warming-induced circulation pattern changes affect temperature and precipitation over Europe during summer?

Future climate change projections are not limited to a simple warming, but changes in precipitation and sea level pressure (SLP) are also projected. The SLP changes and the associated atmospheric circulation changes could directly mitigate or enhance potential projected changes in temperature and precipitation associated with rising temperatures. With the aim of analysing the projected circulation changes and their possible impacts on temperature and precipitation over Europe in summer [June–July–August (JJA)], we apply an automatic circulation type classification method, based on daily SLP, on general circulation model (GCM) outputs from the Coupled Model Intercomparison Project phase 5 (CMIP5) database over the historical period (1951–2005) and for climate under two future scenarios (2006–2100). We focus on summer as it is the season when changes in temperature and precipitation have the highest impact on human health and agriculture. Over the historical observed reference period (1960–1999), our results show that most of the GCMs have significant biases over Europe when compared to reanalysis data sets, both for simulating the observed circulation types and their frequencies, as well as for reproducing the intraclass means of the studied variables. The future projections suggest a decrease of circulation types favouring a low centred over the British Isles for the benefit of more anticyclonic conditions. These circulation changes mitigate the projected precipitation increase over north-western Europe in summer, but they do not significantly affect the projected temperature increase and the precipitation decrease over the Mediterranean region and eastern Europe. However, the circulation changes and the associated precipitation changes are tarnished by a high uncertainty among the GCM projections

Applications of Self-Organizing Maps to Statistical Downscaling of Major Regional Climate Variables

This research developed a practical methodological framework, which integrated most of the important aspects related to statistical downscaling. The framework showed high skills when applied to downscale daily precipitation, minimum and maximum temperatures over southeast Australia. Within the framework, self-organizing maps (SOM) algorithm was incorporated as the core technique for interpreting the relationship between the predictor and predictand under consideration following the latest advances in synoptic climatology. The SOM classified large-scale predictors into a small number of synoptic patterns on a physically meaningful basis. By mapping the observed local climate variable (predictand) to these patterns, a downscaling model structure, SOM-SD, was constructed based on the NCAR/NCEP reanalysis data. Moreover, for a new atmospheric state, an ensemble of predictand values was generated by a stochastic re-sampling technique inside the SOM-SD. To improve seasonality of downscaled results, a simple seasonal predictand pool (SPP) scheme was introduced, which can acquire similar skills as the traditional solutions of dividing a year into four seasons. The framework identified and applied a broad suite of statistical indices, including mean, variance, cumulative distribution function (CDF), extreme events to assess the performance of the SOM-SD. In addition, some non-parametric methods were also employed to evaluate the uncertainty of the downscaling approach, which improved its robustness in practice. The quality control of the input data consists of another important component of the framework, which assessed GCM predictors from three aspects: (a) replicate reliably synoptic patterns depicted by the reanalysis data; (b) remain relatively stable in the future; and (c) produce similar downscaling skills as the reanalysis data. Finally, the framework provided an equal-distance CDF mapping method to adjust the discrepancies between the downscaled values and the corresponding observations. This method adjusted the downscaled CDF for the projection period on the difference between the CDFs of the downscaled GCM baseline and observed values. Thus the framework combines the advantages of statistical downscaling model and bias correction method. Moreover, the framework puts a strong emphasis on its flexibility, which underpins its application to other regions, as well as to support impact assessment studies

Wind Data Mining by Kohonen Neural Networks

Time series of Circulation Weather Type (CWT), including daily averaged wind direction and vorticity, are self-classified by similarity using Kohonen Neural Networks (KNN). It is shown that KNN is able to map by similarity all 7300 five-day CWT sequences during the period of 1975–94, in London, United Kingdom. It gives, as a first result, the most probable wind sequences preceding each one of the 27 CWT Lamb classes in that period. Inversely, as a second result, the observed diffuse correlation between both five-day CWT sequences and the CWT of the 6(th) day, in the long 20-year period, can be generalized to predict the last from the previous CWT sequence in a different test period, like 1995, as both time series are similar. Although the average prediction error is comparable to that obtained by forecasting standard methods, the KNN approach gives complementary results, as they depend only on an objective classification of observed CWT data, without any model assumption. The 27 CWT of the Lamb Catalogue were coded with binary three-dimensional vectors, pointing to faces, edges and vertex of a “wind-cube,” so that similar CWT vectors were close

The detection and prediction of climate change in Ireland using an automated classification of atmospheric circulation patterns

The primary objective of this thesis is to investigate whether Irish climate is changing, and if so, what are the possible driving forces of this change. Analyses of surface climate records appear to support global trends. Annual temperature records indicate an increase of 0.5°C since the beginning of the 20th century, with more rapid warming in the past three decades. Irish precipitation changes are also consistent with the predictions of Global Climate Models (GCMs), with evidence of a shift towards winter increases. Other important trends include a decrease in frequency of frost days and an increase in frequency of wet and rain days in certain months of the year. An important element of the research, therefore, is to investigate what is steering this change in climate. A circulation-type catalogue for Ireland has been constructed from National Centers for Environmental Prediction (NCEP) Reanalysis data, to objectively classify atmospheric circulation patterns. It is thus possible to determine to what extent the changing frequency of circulation types is influencing the spatial and temporal variability of the local climate. As a further step, by using the HadCM3 GCM data for the 2041-2070 period, it is possible to outline what changes in frequency of circulation types may be expected to occur with respect to the emission scenarios. Based on the relationships derived in the present, between CTs and precipitation, these can be applied to future CT frequencies to derive precipitation scenarios. The seasonal precipitation changes found are most likely attributed to changes in the westerly and southwesterly flow, associated with a shift in the North Atlantic Oscillation Index

Climate Change and Atlantic salmon (Salmo salar): Changes in Flow and Freshwater Habitat in the Burrishoole Catchment

Climate change is anticipated to impact the flow regime of riverine systems with resultant consequences for the freshwater habitat of Atlantic salmon (Salmo salar) and the long-term sustainability of their population numbers. The Burrishoole catchment, a relatively small but productive salmon catchment (~90 km2) located on Ireland’s west coast, is used as a case study to investigate this. A series of high resolution climate scenarios were employed to examine potential changes in the climate and hydrology of this catchment. The climate scenarios used represent different combinations of greenhouse gas emission scenarios, driving GCMs and statistical/dynamical downscaling models; in addition, three different rainfall-runoff models (HBV, HYSIM and TOPMODEL) were employed – integrating across both structural and parameter uncertainty. By considering multiple model pathways this study attempts to sample across the uncertainties encountered at each stage in the process of translating prescribed anthropogenic forcings into local scale responses in the catchment system. The hydrological projections were examined in the context of the habitat and flow requirements of Atlantic salmon at key stages in their life-cycle (e.g. spawning, migration). Model projections suggest that the catchment is likely to become warmer, with wetter winters and drier summers occurring. The results of the hydrological modelling suggest that this will be accompanied by an increase in the seasonality of its flow regime - manifest in an increase in low (Q95) summer and high (Q05) winter flows. If realised, these changes are likely to impact salmon through a reduction in the availability of preferred habitat, a loss in connectivity across the catchment system and a disruption to the evolved synchrony between the occurrence of optimal in-stream conditions and the time at which certain life history events occur. Each of these factors is likely to impact the processes of migration, reproduction and recruitment - each of which is critical for the long-term viability of healthy, self-sustaining wild stocks in the catchment. Based on the projected flow data it is likely that the carrying capacity and productivity of the catchment may be reduced. In addition, by affecting those life stages which are already subject to significant mortality losses (e.g. fry emergence, smolt migration), changes in climate may result in population collapse - particularly if successive year-classes are affected. The results of the hydrological modelling highlight the sensitivity of smaller spatey catchments to changes in climate. Given that the Burrishoole system is typical of many catchment systems found along Ireland’s western seaboard, the results highlight a vulnerability to climate change which is present more generally across the region

Climate Change and Atlantic salmon (Salmo salar): Changes in Flow and Freshwater Habitat in the Burrishoole Catchment

Climate change is anticipated to impact the flow regime of riverine systems with resultant consequences for the freshwater habitat of Atlantic salmon (Salmo salar) and the long-term sustainability of their population numbers. The Burrishoole catchment, a relatively small but productive salmon catchment (~90 km2) located on Ireland’s west coast, is used as a case study to investigate this. A series of high resolution climate scenarios were employed to examine potential changes in the climate and hydrology of this catchment. The climate scenarios used represent different combinations of greenhouse gas emission scenarios, driving GCMs and statistical/dynamical downscaling models; in addition, three different rainfall-runoff models (HBV, HYSIM and TOPMODEL) were employed – integrating across both structural and parameter uncertainty. By considering multiple model pathways this study attempts to sample across the uncertainties encountered at each stage in the process of translating prescribed anthropogenic forcings into local scale responses in the catchment system. The hydrological projections were examined in the context of the habitat and flow requirements of Atlantic salmon at key stages in their life-cycle (e.g. spawning, migration). Model projections suggest that the catchment is likely to become warmer, with wetter winters and drier summers occurring. The results of the hydrological modelling suggest that this will be accompanied by an increase in the seasonality of its flow regime - manifest in an increase in low (Q95) summer and high (Q05) winter flows. If realised, these changes are likely to impact salmon through a reduction in the availability of preferred habitat, a loss in connectivity across the catchment system and a disruption to the evolved synchrony between the occurrence of optimal in-stream conditions and the time at which certain life history events occur. Each of these factors is likely to impact the processes of migration, reproduction and recruitment - each of which is critical for the long-term viability of healthy, self-sustaining wild stocks in the catchment. Based on the projected flow data it is likely that the carrying capacity and productivity of the catchment may be reduced. In addition, by affecting those life stages which are already subject to significant mortality losses (e.g. fry emergence, smolt migration), changes in climate may result in population collapse - particularly if successive year-classes are affected. The results of the hydrological modelling highlight the sensitivity of smaller spatey catchments to changes in climate. Given that the Burrishoole system is typical of many catchment systems found along Ireland’s western seaboard, the results highlight a vulnerability to climate change which is present more generally across the region

Assessment of climate change statistical downscaling methods: Application and comparison of two statistical methods to a single site in Lisbon

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia do AmbienteClimate change impacts are very dependent on regional geographical features, local climate variability, and socio-economic conditions. Impact assessment studies on climate change should therefore be performed at the local or at most at the regional level for the evaluation of possible consequences. However, climate scenarios are produced by Global Circulation Models for the entire Globe with spatial resolutions of several hundred kilometres. For this reason, downscaling methods are needed to bridge the gap between the large scale climate scenarios and the fine scale where local impacts happen. An overview on downscaling techniques is presented, referring the main limitation and advantages on dynamical, statistical and statistical-dynamic approaches. For teams with limited computing power and non-climate experts, statistical downscaling is currently the most feasible approach at obtaining climate data for future impact studies. To assess the capability of statistical downscaling methods to represent local climate variability it is shown an inter-comparison and uncertainties analysis study between a stochastic weather generator, using LARS-WG tool, and a hybrid of stochastic weather generator and transfer function methods, using the SDSM tool. Models errors and uncertainties were estimated using non-parametric statistical methods at the 95% confidence interval for precipitation, maximum temperature and minimum temperature for the mean and variance for a single site in Lisbon. The comparison between the observed dataset and the simulations showed that both models performance are acceptable. However, the SDSM tool was able to better represent the minimum and maximum temperature while LARS-WG simulations on precipitation are better. The analysis of both models uncertainties for the mean are very close to the observed data in all months, but the uncertainties for the variances showed that the LARWG simulation performance is slightly better for precipitation and that both model simulations for minimum and maximum temperature are very close from the observed. It is also presented the simulations for the A2a SRES scenario for the 2041-2070 periods showing that both methods can produce similar general tendencies, but an uncertainties analysis on the scenarios is also advised

Identification and simulation of extreme precipitation using a computationally inexpensive methodology

Includes abstract.Includes bibliographical references (leaves 164-187).An examination of characteristics extreme precipitation in the greater Cape Town region is undertaken. Thereafter, an investigation into the characteristics of these changes is made using two approaches. The first is an empirical methodology to explore the historical attributes of extreme events and the second a numerical method. These are used to demonstrate an approach to produce high resolution forecasts of extreme precipitation if computational resources are scarce. Initially, changes in the characteristics of extreme precipitation in the greater Cape Town region is documented. Then self organizing maps are used to identify archetypal synoptic circulations that are associated with extreme precipitation over the region. Thereafter, days whose synoptic state matched those of the synoptic archetypes are simulated at a resolution of one kilometer to capture regional topographic modification of extreme precipitation. Following this, the simulated precipitation is validated against observed data and the model performance is assessed. These approaches were tested over Cape Town, South Africa which has complex topography where extreme rainfall is not well predicted. As this methodology is computationally relatively inexpensive, it has applicability to regions of the world where these resources are limited, more especially Africa where the state of climate science is poor. An analysis of historical station data from three locations in the greater Cape Town region showed mixed trends in extreme rainfall where extreme rainfall was taken as that in the 90th percentile. One station, located in the lee of topography, showed a statistically significant increase in the intensity of extreme rainfall and another, at a relatively topography-free location, a significant decrease. The third station showed no significant trend. Decadal changes in monthly precipitation show a shift in the start and end of the extreme rainfall season to starting later in winter and continuing into the early spring. The station with the significant increase in extreme rainfall intensity also showed an increase in 99th percentile rainfall intensity. Synoptic states associated with extreme rainfall in the greater Cape Town region were then examined. These were identified as mid-latitude cyclones with centers at relatively low latitudes. They were characterized by strong pressure gradients at the surface and in the upper air high as well as high regional humidities. Precipitation characteristics of the frontal systems ranged from precipitation that fell over a number of days in relatively low daily amounts to very heavy precipitation that fell in one day. Over the twenty-three year test period examined, there are change