Bayes ' Theorem- a law in probability theory which relates the probability of a hypothesis given observed evidence to its inverse, the probability of that evidence given the hypothesis. Climate Sensitivity- The global mean temperature response in Kelvin to an instantaneous doubling of carbon dioxide) CMIP-3- The Coupled Model Inter-comparison Project, a set of coordinated experiments using GCMs from the world’s major modeling centers Detection and Attribution- a process whereby spatial ‘fingerprints ’ associated with individual climate forcing factors are identified and used to relate forcing signals in a period of climate change. Empirical Model- A model which makes no attempt to justify its representations of the system with any phys...
Climate output from general circulation models (GCMs) is being used with increasing frequency to exp...
This is the second of three parts of an introduction to the philosophy of climate science. In this s...
A Bayesian statistical model is proposed that combines information from a multi-model ensemble of at...
The Earth's average temperature has risen by 1.4°F over the past century, and computer models projec...
This project asks: what might we learn from today’s climate models? This is a tremendously important...
Despite great advances in understanding of the earth’s climate, our estimate of the global temperatu...
Although scientists now ac-cept global warming as in-controvertible, humans con-tinue to alter the c...
Large-scale climate models are validated by comparing the model’s mean and variability to observatio...
Since 1850 the global surface temperature (GST) has warmed by about 0.9 oC. The CMIP5 general circul...
Summary. Posterior distributions for the joint projections of future temperature and precip-itation ...
Many scientific studies warn of a rapid global climate change during the next century. These changes...
Content: Application Of Statistics To Modeling The Earth's Climate System NCAR Colloquium - 6 to 19 ...
empirical Bayes, ensemble of opportunity, general circulation model (GCM), multi-model ensemble, reg...
A Bayesian approach is applied to the observed global surface air temperature ( SAT) changes using m...
This is the second of three parts of an introduction to the philosophy of climate science. In this s...
Climate output from general circulation models (GCMs) is being used with increasing frequency to exp...
This is the second of three parts of an introduction to the philosophy of climate science. In this s...
A Bayesian statistical model is proposed that combines information from a multi-model ensemble of at...
The Earth's average temperature has risen by 1.4°F over the past century, and computer models projec...
This project asks: what might we learn from today’s climate models? This is a tremendously important...
Despite great advances in understanding of the earth’s climate, our estimate of the global temperatu...
Although scientists now ac-cept global warming as in-controvertible, humans con-tinue to alter the c...
Large-scale climate models are validated by comparing the model’s mean and variability to observatio...
Since 1850 the global surface temperature (GST) has warmed by about 0.9 oC. The CMIP5 general circul...
Summary. Posterior distributions for the joint projections of future temperature and precip-itation ...
Many scientific studies warn of a rapid global climate change during the next century. These changes...
Content: Application Of Statistics To Modeling The Earth's Climate System NCAR Colloquium - 6 to 19 ...
empirical Bayes, ensemble of opportunity, general circulation model (GCM), multi-model ensemble, reg...
A Bayesian approach is applied to the observed global surface air temperature ( SAT) changes using m...
This is the second of three parts of an introduction to the philosophy of climate science. In this s...
Climate output from general circulation models (GCMs) is being used with increasing frequency to exp...
This is the second of three parts of an introduction to the philosophy of climate science. In this s...
A Bayesian statistical model is proposed that combines information from a multi-model ensemble of at...