Modeling Earth`s future
... results from integrated assessment models generally cannot be relied upon if gross simplifications of component systems such as the atmosphere have been made. ...
... results from integrated assessment models generally cannot be relied upon if gross simplifications of component systems such as the atmosphere have been made. ...
Trends in Arctic sea ice extent from CMIP5, CMIP3 and observations
... [5] Here, we use two metrics to evaluate the performance of climate models contributing to the Phase 5 (CMIP5) project. The distribution of simulated extents over the period of observations is used to assess how well the models capture the observed state of the ice cover. Trends in simulated ice ext ...
... [5] Here, we use two metrics to evaluate the performance of climate models contributing to the Phase 5 (CMIP5) project. The distribution of simulated extents over the period of observations is used to assess how well the models capture the observed state of the ice cover. Trends in simulated ice ext ...
A modelling framework for assessing adaptive management options
... realized through linkage of the point models with agro-ecological databases in a GIS environment. This will allow various upscaling procedures, both data input aggregation for regional models or model output interpolation using representative sites or calculations units (e.g. derived agro-ecological ...
... realized through linkage of the point models with agro-ecological databases in a GIS environment. This will allow various upscaling procedures, both data input aggregation for regional models or model output interpolation using representative sites or calculations units (e.g. derived agro-ecological ...
Differences in spatial predictions among species distribution
... designation of land in conservation planning. Many studies have compared the prediction accuracy of different SDM modeling methods, but few have quantified the similarity among prediction maps. There has also been little systematic exploration of how the relative importance of different predictor va ...
... designation of land in conservation planning. Many studies have compared the prediction accuracy of different SDM modeling methods, but few have quantified the similarity among prediction maps. There has also been little systematic exploration of how the relative importance of different predictor va ...
Neuro-Fuzzy System Optimized Based Quantum Differential
... data, [7] said Subtractive clustering, is a fast, one-pass algorithm for estimating the number of clusters and the cluster centers in a set of data and can be used to initialize iterative optimization-based clustering methods (Fuzzy CMean) and model identification methods (like ANFIS). 2.3 ANFIS Str ...
... data, [7] said Subtractive clustering, is a fast, one-pass algorithm for estimating the number of clusters and the cluster centers in a set of data and can be used to initialize iterative optimization-based clustering methods (Fuzzy CMean) and model identification methods (like ANFIS). 2.3 ANFIS Str ...
Climate change consequences on the biome distribution in tropical
... scenarios is associated to different projections from different AOGCMs. The projected temperature warming for South America range from 1° to 4°C for emissions scenarios B1 and from 2° to 6°C for A2. The analysis is much more complicated for rainfall changes. Different climate models show distinct pa ...
... scenarios is associated to different projections from different AOGCMs. The projected temperature warming for South America range from 1° to 4°C for emissions scenarios B1 and from 2° to 6°C for A2. The analysis is much more complicated for rainfall changes. Different climate models show distinct pa ...
In relation to written expression for our discipline, how do we define
... where r is the radius, V is the volume, and h is the height. Let π = 3.14159 (or the value in the calculator). TASK: ...
... where r is the radius, V is the volume, and h is the height. Let π = 3.14159 (or the value in the calculator). TASK: ...
Regional Modelling of Vegetation Distributions
... these models – even for current climate and CO2 – do not represent well the vegetation (structure or processes) in Australia, even at a continental scale. Consequently, these models are of no use for predicting the finer-scale patterns of vegetation of even quite large regions where immediate knowle ...
... these models – even for current climate and CO2 – do not represent well the vegetation (structure or processes) in Australia, even at a continental scale. Consequently, these models are of no use for predicting the finer-scale patterns of vegetation of even quite large regions where immediate knowle ...
Notes on Probabilistic Graphical Models 1
... The framework of probabilistic graphical models provides a general approach for this task. The approach is modelbased, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be u ...
... The framework of probabilistic graphical models provides a general approach for this task. The approach is modelbased, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be u ...
Tierney 1..8 - Science Advances
... rising greenhouse gases and temperature, we investigate the climate model projections under the high-emissions Representative Concentration Pathway (RCP) 8.5 scenario for the 21st century. In this case, the same set of models overwhelmingly (≥90% in some regions) predict wetter conditions as greenho ...
... rising greenhouse gases and temperature, we investigate the climate model projections under the high-emissions Representative Concentration Pathway (RCP) 8.5 scenario for the 21st century. In this case, the same set of models overwhelmingly (≥90% in some regions) predict wetter conditions as greenho ...
eScience
... Even with the incredible speed of today’s supercomputers, climate models still simplify some small-scale physical processes, such as clouds. Any one simulation requires a large number of interacting parameters and for many of these we do not know precisely which value is most realistic. This means t ...
... Even with the incredible speed of today’s supercomputers, climate models still simplify some small-scale physical processes, such as clouds. Any one simulation requires a large number of interacting parameters and for many of these we do not know precisely which value is most realistic. This means t ...
Selecting Ensemble Members to Provide Regional Climate Change
... An approach pioneered in the United Kingdom by the Hadley Centre (Murphy et al. 2004) and climate prediction.net (Stainforth et al. 2005) has been the use of ensembles that systematically explore the implication of known uncertainties in model parameters. Parameters are identified that are known to ...
... An approach pioneered in the United Kingdom by the Hadley Centre (Murphy et al. 2004) and climate prediction.net (Stainforth et al. 2005) has been the use of ensembles that systematically explore the implication of known uncertainties in model parameters. Parameters are identified that are known to ...
Greenhouse warming and the 21st Century hydroclimate of
... he 24 climate models used as part of the Intergovernmental Panel on Climate Change Assessment Report Four (IPCC AR4) robustly predict that southwestern North America (SWNA), a region defined as stretching form the high Plains to the Pacific Ocean and from the latitude of the California-Oregin border ...
... he 24 climate models used as part of the Intergovernmental Panel on Climate Change Assessment Report Four (IPCC AR4) robustly predict that southwestern North America (SWNA), a region defined as stretching form the high Plains to the Pacific Ocean and from the latitude of the California-Oregin border ...
Large scale 1D-1D surface modelling tool for urban water - I
... small stream running through the developed area. Combinations of upstream urban overflow and a grassland drainage system generate a risk for overland flooding in the village and following material damage. Downstream the village the stream conveys into a larger urban area with high damage risk when f ...
... small stream running through the developed area. Combinations of upstream urban overflow and a grassland drainage system generate a risk for overland flooding in the village and following material damage. Downstream the village the stream conveys into a larger urban area with high damage risk when f ...
Numerical weather prediction
Numerical weather prediction uses mathematical models of the atmosphere and oceans to predict the weather based on current weather conditions. Though first attempted in the 1920s, it was not until the advent of computer simulation in the 1950s that numerical weather predictions produced realistic results. A number of global and regional forecast models are run in different countries worldwide, using current weather observations relayed from radiosondes, weather satellites and other observing systems as inputs.Mathematical models based on the same physical principles can be used to generate either short-term weather forecasts or longer-term climate predictions; the latter are widely applied for understanding and projecting climate change. The improvements made to regional models have allowed for significant improvements in tropical cyclone track and air quality forecasts; however, atmospheric models perform poorly at handling processes that occur in a relatively constricted area, such as wildfires.Manipulating the vast datasets and performing the complex calculations necessary to modern numerical weather prediction requires some of the most powerful supercomputers in the world. Even with the increasing power of supercomputers, the forecast skill of numerical weather models extends to about only six days. Factors affecting the accuracy of numerical predictions include the density and quality of observations used as input to the forecasts, along with deficiencies in the numerical models themselves. Post-processing techniques such as model output statistics (MOS) have been developed to improve the handling of errors in numerical predictions.A more fundamental problem lies in the chaotic nature of the partial differential equations that govern the atmosphere. It is impossible to solve these equations exactly, and small errors grow with time (doubling about every five days). Present understanding is that this chaotic behavior limits accurate forecasts to about 14 days even with perfectly accurate input data and a flawless model. In addition, the partial differential equations used in the model need to be supplemented with parameterizations for solar radiation, moist processes (clouds and precipitation), heat exchange, soil, vegetation, surface water, and the effects of terrain. In an effort to quantify the large amount of inherent uncertainty remaining in numerical predictions, ensemble forecasts have been used since the 1990s to help gauge the confidence in the forecast, and to obtain useful results farther into the future than otherwise possible. This approach analyzes multiple forecasts created with an individual forecast model or multiple models.