Sentences with phrase «risk modeling outputs»

Not exact matches

Murali Haran, a professor in the department of statistics at Penn State University; Won Chang, an assistant professor in the department of mathematical sciences at the University of Cincinnati; Klaus Keller, a professor in the department of geosciences and director of sustainable climate risk management at Penn State University; Rob Nicholas, a research associate at Earth and Environmental Systems Institute at Penn State University; and David Pollard, a senior scientist at Earth and Environmental Systems Institute at Penn State University detail how parameters and initial values drive an ice sheet model, whose output describes the behavior of the ice sheet through time.

The approach is a simplified catastrophe risk assessment, to calculate the direct costs of storm surges under scenarios of sea level rise, coupled to an economic input — output (IO) model.

For this reason, it is considered good practice to use output from multiple models to explore a range of scientifically plausible futures — to account for an envelope of future climate risk, rather than a single future pathway.

Output from global circulation models indicates that climate variability will continue to be an important characteristic of the region in the future [52], but that climate change may increase the risk of extreme climatic events such as multi-decade droughts and extreme winter precipitation [53], [54].

Integrated physiological and economic models (e.g., Fischer et al., 2005a) allow holistic simulation of climate change effects on agricultural productivity, input and output prices, and risk of hunger in specific regions, although these simulations rely on a small set of component models.

As estimates of the greenhouse gas attributable change in rainfall risk may depend on the model datasets considered, it is also useful to consider model outputs from several datasets and using various estimates of counterfactual surface conditions to establish robust attribution statements for extreme rainfall events.

So it then occurred to me that if you are comparing the output from a climate model with actual measures at a grid level there was a risk that both datasets would suffer from spatial autocorrelation, and this should be tested for before using standard statistics as validation of the model output.