Efficiently Constraining Climate Sensitivity with Ensembles
... model error implies that we cannot expect our ensemble members to simulate the LGM data to within its observational errors, and applying such a “perfect model” constraint would result in an implausibly narrow posterior ensemble. The accuracy of our temperature estimate is limited by the representati ...
... model error implies that we cannot expect our ensemble members to simulate the LGM data to within its observational errors, and applying such a “perfect model” constraint would result in an implausibly narrow posterior ensemble. The accuracy of our temperature estimate is limited by the representati ...
High Dimensional Inference - uf statistics
... This talk focuses on classification with microarray gene expression data which is one of the leading examples behind the recent surge of interests in high dimensional data analysis. It is now known that feature selection is crucial and often necessary in high dimensional classification problems. In ...
... This talk focuses on classification with microarray gene expression data which is one of the leading examples behind the recent surge of interests in high dimensional data analysis. It is now known that feature selection is crucial and often necessary in high dimensional classification problems. In ...
Regional Modeling. - Advanced Study Program
... • High resolution useful when there are high resolution forcings: complex topography, complex coastlines, islands, heterogeneous landuse • Consider also statistical downscaling (Wilby) • More guidance on web at: ...
... • High resolution useful when there are high resolution forcings: complex topography, complex coastlines, islands, heterogeneous landuse • Consider also statistical downscaling (Wilby) • More guidance on web at: ...
INTERDYNAMIK thematic workshop on vegetation dynamics
... The calibration of models against pollen data is a complex problem. It was discussed whether the models should be compared to the data at the species, PFT or biome level, but the workshop did not arrive at a conclusive answer. Most DGVMs were developed with the modern climate in mind, and in this co ...
... The calibration of models against pollen data is a complex problem. It was discussed whether the models should be compared to the data at the species, PFT or biome level, but the workshop did not arrive at a conclusive answer. Most DGVMs were developed with the modern climate in mind, and in this co ...
Framing Document - American Physical Society
... The overall uncertainty of the annually averaged global ocean mean [heat flux] for each term is expected to be in the range 10–20%. In the case of the latent heat flux term, this corresponds to an uncertainty of up to 20 W m–2. In comparison, changes in global mean values of individual heat flux com ...
... The overall uncertainty of the annually averaged global ocean mean [heat flux] for each term is expected to be in the range 10–20%. In the case of the latent heat flux term, this corresponds to an uncertainty of up to 20 W m–2. In comparison, changes in global mean values of individual heat flux com ...
Low Rank Language Models for Small Training Sets
... in prohibitively large files when the -gram order or vocabulary size is large. To interpolate higher -gram order LRLMs will require either development of a new format or implementation of interpolation within LRLM training itself. In addition to implementation issues, our initial experiments did not ...
... in prohibitively large files when the -gram order or vocabulary size is large. To interpolate higher -gram order LRLMs will require either development of a new format or implementation of interpolation within LRLM training itself. In addition to implementation issues, our initial experiments did not ...
Solutions to Nonlinear Equations
... Nonlinear Equations: Roots – Objective is to find a solution of F(x) = 0 Where F is a polynomial or a transcendental function, given explicitly. – Exact solutions are not possible for most equations. – A number x ± e, ( e > 0 ) is an approximate solution of the equation if there is a solution in th ...
... Nonlinear Equations: Roots – Objective is to find a solution of F(x) = 0 Where F is a polynomial or a transcendental function, given explicitly. – Exact solutions are not possible for most equations. – A number x ± e, ( e > 0 ) is an approximate solution of the equation if there is a solution in th ...
FAQ 7.1 | How Do Clouds Affect Climate and Climate Change
... Clouds strongly affect the current climate, but observations alone cannot yet tell us how they will affect a future, warmer climate. Comprehensive prediction of changes in cloudiness requires a global climate model. Such models simulate cloud fields that roughly resemble those observed, but importan ...
... Clouds strongly affect the current climate, but observations alone cannot yet tell us how they will affect a future, warmer climate. Comprehensive prediction of changes in cloudiness requires a global climate model. Such models simulate cloud fields that roughly resemble those observed, but importan ...
Model confirmation in climate economics
... technologies. Thus as TFP grows, and the technologies of production become more advanced, fewer capital and labour inputs are required to generate a given level of economic output. A specific model of the time dependence of TFP is assumed in DICE. This model depends on free parameters that can be es ...
... technologies. Thus as TFP grows, and the technologies of production become more advanced, fewer capital and labour inputs are required to generate a given level of economic output. A specific model of the time dependence of TFP is assumed in DICE. This model depends on free parameters that can be es ...
Lead time = 3
... Other tropical ocean SST - difficult Remote tropical atmospheric teleconnections Climate change - largest for temperature Local land surface conditions - soil moisture, snow Atmospheric composition - difficult Volcanic eruptions - important for large events Mid-latitude ocean temperatures - longer t ...
... Other tropical ocean SST - difficult Remote tropical atmospheric teleconnections Climate change - largest for temperature Local land surface conditions - soil moisture, snow Atmospheric composition - difficult Volcanic eruptions - important for large events Mid-latitude ocean temperatures - longer t ...
Atmospheric and Oceanic Studies
... the atmosphere. Topics include application of numerical and statistical models, diagnosis of vertical motion, development of midlatitude synoptic systems, mesoscale phenomena associated with cyclones, convective systems, and radar applications. Laboratories include extensive practice in forecasting ...
... the atmosphere. Topics include application of numerical and statistical models, diagnosis of vertical motion, development of midlatitude synoptic systems, mesoscale phenomena associated with cyclones, convective systems, and radar applications. Laboratories include extensive practice in forecasting ...
23 January 1979 25 January 1979 30 January 1979 3 March 1979
... A question of scale Earth scientists now use probes stuck into the ground to measure soil moisture in small, shallow, widely scattered areas, just one meter square. At the other extreme, satellites measure average soil moisture over large areas, roughly 30 kilometers square. Neither approach provide ...
... A question of scale Earth scientists now use probes stuck into the ground to measure soil moisture in small, shallow, widely scattered areas, just one meter square. At the other extreme, satellites measure average soil moisture over large areas, roughly 30 kilometers square. Neither approach provide ...
Drought assessment and trends analysis from 20th century
... of the atmosphere, and the atmospheric model component of those CMIP5 models. The observations used in this study are gridded monthly temperature and precipitation data prepared by the Climatic Research Unit (CRU) time-series 3.1 dataset (www.cru.uea.ac.uk/cru/data/hrg/) from the University of East ...
... of the atmosphere, and the atmospheric model component of those CMIP5 models. The observations used in this study are gridded monthly temperature and precipitation data prepared by the Climatic Research Unit (CRU) time-series 3.1 dataset (www.cru.uea.ac.uk/cru/data/hrg/) from the University of East ...
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.