977 resultados para seasonal climate prediction
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Mineral dust has a large impact on regional and global climate, depending on its particle size. Especially in the Atlantic Ocean downwind of the Sahara, the largest dust source on earth, the effects can be substantial but are poorly understood. This study focuses on seasonal and spatial variations in particle size of Saharan dust deposition across the Atlantic Ocean, using an array of submarine sediment traps moored along a transect at 12° N. We show that the particle size decreases downwind with increased distance from the Saharan source, due to higher gravitational settling velocities of coarse particles in the atmosphere. Modal grain sizes vary between 4 and 33 µm throughout the different seasons and at five locations along the transect. This is much coarser than previously suggested and incorporated into climate models. In addition, seasonal changes are prominent, with coarser dust in summer, and finer dust in winter and spring. Such seasonal changes are caused by transport at higher altitudes and at greater wind velocities during summer than in winter. Also the latitudinal migration of the dust cloud, associated with the Intertropical Convergence Zone, causes seasonal differences in deposition as the summer dust cloud is located more to the north, and more directly above the sampled transect. Furthermore, increased precipitation and more frequent dust storms in summer coincide with coarser dust deposition. Our findings contribute to understanding Saharan dust transport and deposition relevant for the interpretation of sedimentary records for climate reconstructions, as well as for global and regional models for improved prediction of future climate.
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Thesis (Master's)--University of Washington, 2016-06
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Elevated ocean temperatures can cause coral bleaching, the loss of colour from reef-building corals because of a breakdown of the symbiosis with the dinoflagellate Symbiodinium. Recent studies have warned that global climate change could increase the frequency of coral bleaching and threaten the long-term viability of coral reefs. These assertions are based on projecting the coarse output from atmosphere-ocean general circulation models (GCMs) to the local conditions around representative coral reefs. Here, we conduct the first comprehensive global assessment of coral bleaching under climate change by adapting the NOAA Coral Reef Watch bleaching prediction method to the output of a low- and high-climate sensitivity GCM. First, we develop and test algorithms for predicting mass coral bleaching with GCM-resolution sea surface temperatures for thousands of coral reefs, using a global coral reef map and 1985-2002 bleaching prediction data. We then use the algorithms to determine the frequency of coral bleaching and required thermal adaptation by corals and their endosymbionts under two different emissions scenarios. The results indicate that bleaching could become an annual or biannual event for the vast majority of the world's coral reefs in the next 30-50 years without an increase in thermal tolerance of 0.2-1.0 degrees C per decade. The geographic variability in required thermal adaptation found in each model and emissions scenario suggests that coral reefs in some regions, like Micronesia and western Polynesia, may be particularly vulnerable to climate change. Advances in modelling and monitoring will refine the forecast for individual reefs, but this assessment concludes that the global prognosis is unlikely to change without an accelerated effort to stabilize atmospheric greenhouse gas concentrations.
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We present AUSLEM (AUStralian Land Erodibility Model), a land erodibility modelling system that utilizes a rule-set of surficial and climatic thresholds applied through a Geographic Information System (GIs) modelling framework to predict landscape susceptibility to wind erosion. AUSLEM is distinctive in that it quantitatively assesses landscape susceptibility to wind erosion at a 5 x 5 km. spatial resolution on a monthly time-step across Australia. The system was implemented for representative wet (1984), dry (1994), and average rainfall (1997) years with corresponding low, high and moderate dust storm day frequencies. Results demonstrate that AUSLEM can identify landscape erodibility, and provide an interpretation of the physical nature and distribution of erodible landscapes in Australia. Further, results offer an assessment of the dynamic tendencies of erodibility in space and time in response to the El Nino Southern Oscillation (ENSO) and seasonal synoptic scale climate variability. A comparative analysis of AUSLEM output with independent national and international wind erosion, atmospheric aerosol and dust event records indicates a high level of model competency. (c) 2006 Elsevier B.V. All rights reserved.
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Understanding how climate change will affect the distribution and the phenology of plants is becoming an increasingly important topic in ecological studies. In response to climate warming, there are documented upward shift and alterations of phenology and physiology of several plant species. Despite this, the effects of climate change on plant regeneration from seeds have largely been neglected. However, regeneration from seeds, a key event in the plant life cycle, could be significantly affected by climate warming. In this regard, we investigated how climatic changes will affect the seasonal dynamics of seed germination and seedling survival in two different alpine context. The first part refers to five species inhabiting a snowbed located at the Gavia pass (Parco Naturale dello Stelvio). Here, plants were exposed, in the field, to natural conditions and to artificial warming using Open Top Chambers proposed by the ITEX (International Tundra Experiment). The germination curves of seeds produced were compared in order to highlight differences in seed germination ecology and in seed physiology induced by the climate warming. In the second part, we considered two tree species that form the treeline in Davos (Switzerland). As a surrogate of climate warming we used the natural thermal gradient driven by the altitude and we compared the germination behavior of the species studied in three sites at three different elevations in order to evaluate the likelihood of treeline shift under the predicted climate warming.
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Animals and plants in temperate regions must adapt their life cycle to pronounced seasonal variation. The research effort that has gone into studying these cyclical life history events, or phenological traits, has increased greatly in recent decades. As phenological traits are often correlated to temperature, they are relevant to study in terms of understanding the effect of short term environmental variation as well as long term climate change. Because of this, changes in phenology are the most obvious and among the most commonly reported responses to climate change. Moreover, phenological traits are important for fitness as they determine the biotic and abiotic environment an individual encounters. Fine-tuning of phenology allows for synchronisation at a local scale to mates, food resources and appropriate weather conditions. On a between-population scale, variation in phenology may reflect regional variation in climate. Such differences can not only give insights to life cycle adaptation, but also to how populations may respond to environmental change through time. This applies both on an ecological scale through phenotypic plasticity as well as an evolutionary scale through genetic adaptation. In this thesis I have used statistical and experimental methods to investigate both the larger geographical patterns as well as mechanisms of fine-tuning of phenology of several butterfly species. The main focus, however, is on the orange tip butterfly, Anthocharis cardamines, in Sweden and the United Kingdom. I show a contrasting effect of spring temperature and winter condition on spring phenology for three out of the five studied butterfly species. For A. cardamines there are population differences in traits responding to these environmental factors between and within Sweden and the UK that suggest adaptation to local environmental conditions. All populations show a strong negative plastic relationship between spring temperature and spring phenology, while the opposite is true for winter cold duration. Spring phenology is shifted earlier with increasing cold duration. The environmental variables show correlations, for example, during a warm year a short winter delays phenology while a warm spring speeds phenology up. Correlations between the environmental variables also occur through space, as the locations that have long winters also have cold springs. The combined effects of these two environmental variables cause a complex geographical pattern of phenology across the UK and Sweden. When predicting phenology with future climate change or interpreting larger geographical patterns one must therefore have a good enough understanding of how the phenology is controlled and take the relevant environmental factors in to account. In terms of the effect of phenological change, it should be discussed with regards to change in life cycle timing among interacting species. For example, the phenology of the host plants is important for A. cardamines fitness, and it is also the main determining factor for oviposition. In summary, this thesis shows that the broad geographical pattern of phenology of the butterflies is formed by counteracting environmental variables, but that there also are significant population differences that enable fine-tuning of phenology according to the seasonal progression and variation at the local scale.
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The relationship between the daily deposition of soredia of Hypogymnia physodes (L.) Nyl. and local climatic records was studied in the field during three periods at a site in Seattle, WA, U.S.A: (1) 11 August – 16 September 1986 (Study A); (2) 16 December – 11 January 1987 (Study B) and (3) 8 July 1988 – 30 January 1989 (Study C). The soredia were trapped on adhesive strips placed at various locations on a Prunus blireiana L. tree for 24 hr periods. A correlation matrix of the data from all three studies revealed a negative correlation between soredial deposition and relative humidity; and a positive correlation with rainfall and temperature. A multiple regression and forward stepwise regression analysis selected relative humidity as the most significant climatic variable, i.e. more soredia tended to be deposited when relative humidity was low. Analysis of individual studies by multiple regression revealed: (1) no significant relationships between soredial deposition and climate in Study A; (2) positive relationships with temperature and wind speed in Study B and (3) positive relationships with wind speed and rainfall in the summer/autumn months of Study C; in the winter months no relationships with climate were found because few soredia were deposited. The data suggest that in the field seasonal photoperiod differences combined with moderately high temperatures and high relative humidity may promote soredial formation and accumulation on thalli prior to soredia dispersal. In addition, low relative humidity may promote soredial release while wind and raindrops may be possible agents of dispersal.
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This research investigates the contribution that Geographic Information Systems (GIS) can make to the land suitability process used to determine the effects of a climate change scenario. The research is intended to redress the severe under representation of Developing countries within the literature examining the impacts of climatic change upon crop productivity. The methodology adopts some of the Intergovernmental Panel on Climate Change (IPCC) estimates for regional climate variations, based upon General Circulation Model predictions (GCMs) and applies them to a baseline climate for Bangladesh. Utilising the United Nations Food & Agricultural Organisation's Agro-ecological Zones land suitability methodology and crop yield model, the effects of the scenario upon agricultural productivity on 14 crops are determined. A Geographic Information System (IDRISI) is adopted in order to facilitate the methodology, in conjunction with a specially designed spreadsheet, used to determine the yield and suitability rating for each crop. A simple optimisation routine using the GIS is incorporated to provide an indication of the 'maximum theoretical' yield available to the country, should the most calorifically significant crops be cultivated on each land unit both before and after the climate change scenario. This routine will provide an estimate of the theoretical population supporting capacity of the country, both now and in the future, to assist with planning strategies and research. The research evaluates the utility of this alternative GIS based methodology for the land evaluation process and determines the relative changes in crop yields that may result from changes in temperature, photosynthesis and flooding hazard frequency. In summary, the combination of a GIS and a spreadsheet was successful, the yield prediction model indicates that the application of the climate change scenario will have a deleterious effect upon the yields of the study crops. Any yield reductions will have severe implications for agricultural practices. The optimisation routine suggests that the 'theoretical maximum' population supporting capacity is well in excess of current and future population figures. If this agricultural potential could be realised however, it may provide some amelioration from the effects of climate change.
Detecting Precipitation Climate Changes: An Approach Based on a Stochastic Daily Precipitation Model
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2002 Mathematics Subject Classification: 62M10.
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In 2002, 2003 and 2004, we took macoinvertebrate samples on a total of 36 occasions at the Badacsony bay of Lake Balaton. Our sampling site was characterised by areas of open water (in 2003 and 2004 full of reed-grass) as well as by areas covered by common reed (Phragmites australis) and narrowleaf cattail (Typha angustifolia). Samples were taken both from water body and benthic ooze by use of a stiff hand net. We have gained our data from processing 208 individual samples. We took samples frequently from early spring until late autumn for a deeper understanding of the processes of seasonal dynamics. The main seasonal patterns and temporal changes of diversity were described. We constructed a weather-dependent simulation model of the processes of seasonal dynamics in the interest of a possible further utilization of our data in climate change research. We described the total number of individuals, biovolume and diversity of all macroinvertebrate species with a single index and used the temporal trends of this index for simulation modelling. Our discrete deterministic model includes only the impact of temperature, other interactions might only appear concealed. Running the model for different climate change scenarios it became possible to estimate conditions for the 2070-2100 period. The results, however, should be treated very prudently not only because our model is very simple but also because the scenarios are the results of different models.
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A climate envelope model was run on the distribution of four coniferous species (European silver fir, European larch, Norway spruce, and Swiss pine). The model was supported by EUFORGEN area database, ArcGIS 10 and PAST software, andREMO climate model. Prediction periods were 2011-40 and 2041-70.
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Knowledge on the expected effects of climate change on aquatic ecosystems is defined by three ways. On the one hand, long-term observation in the field serves as a basis for the possible changes; on the other hand, the experimental approach may bring valuable pieces of information to the research field. The expected effects of climate change cannot be studied by empirical approach; rather mathematical models are useful tools for this purpose. Within this study, the main findings of field observations and their implications for future were summarized; moreover, the modelling approaches were discussed in a more detailed way. Some models try to describe the variation of physical parameters in a given aquatic habitat, thus our knowledge on their biota is confined to the findings based on our present observations. Others are destined for answering special issues related to the given water body. Complex ecosystem models are the keys of our better understanding of the possible effects of climate change. Basically, these models were not created for testing the influence of global warming, rather focused on the description of a complex system (e. g. a lake) involving environmental variables, nutrients. However, such models are capable of studying climatic changes as well by taking into consideration a large set of environmental variables. Mostly, the outputs are consistent with the assumptions based on the findings in the field. Since synthetized models are rather difficult to handle and require quite large series of data, the authors proposed a more simple modelling approach, which is capable of examining the effects of global warming. This approach includes weather dependent simulation modelling of the seasonal dynamics of aquatic organisms within a simplified framework.
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Pavement performance is one of the most important components of the pavement management system. Prediction of the future performance of a pavement section is important in programming maintenance and rehabilitation needs. Models for predicting pavement performance have been developed on the basis of traffic and age. The purpose of this research is to extend the use of a relatively new approach to performance prediction in pavement performance modeling using adaptive logic networks (ALN). Adaptive logic networks have recently emerged as an effective alternative to artificial neural networks for machine learning tasks. ^ The ALN predictive methodology is applicable to a wide variety of contexts including prediction of roughness based indices, composite rating indices and/or individual pavement distresses. The ALN program requires key information about a pavement section, including the current distress indexes, pavement age, climate region, traffic and other variables to predict yearly performance values into the future. ^ This research investigates the effect of different learning rates of the ALN in pavement performance modeling. It can be used at both the network and project level for predicting the long term performance of a road network. Results indicate that the ALN approach is well suited for pavement performance prediction modeling and shows a significant improvement over the results obtained from other artificial intelligence approaches. ^
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Precipitation and temperature in Florida responds to climate teleconnections from both the Pacific and Atlantic regions. In this region south of Lake Okeechobee, encompassing NWS Climate Divisions 5, 6, and 7, modern movement of surface waters are managed by the South Florida Water Management District and the US Army Corps of Engineers for flood control, water supply, and Everglades restoration within the constraints of the climatic variability of precipitation and evaporation. Despite relatively narrow, low-relief, but multi-purposed land separating the Atlantic Ocean from the Gulf of Mexico, South Florida has patterns of precipitation and temperature that vary substantially on spatial scales of 101–102 km. Here we explore statistically significant linkages to precipitation and temperature that vary seasonally and over small spatial scales with El Niño-Southern Oscillation (ENSO), the Atlantic Multidecadal Oscillation (AMO), and the Pacific Decadal Oscillation (PDO). Over the period from 1952 to 2005, ENSO teleconnections exhibited the strongest influence on seasonal precipitation. The Multivariate ENSO Index was positively correlated with winter (dry season) precipitation and explained up to 34 % of dry season precipitation variability along the southwest Florida coast. The AMO was the most influential of these teleconnections during the summer (wet season), with significant positive correlations to South Florida precipitation. These relationships with modern climate parameters have implications for paleoclimatological and paleoecological reconstructions, and future climate predictions from the Greater Everglades system.