Sentences with phrase «uncertain climate parameters»

Not exact matches

More broadly, the time is ripe to develop «learning climate models,» which from the outset incorporate the capacity to learn closure parameters (latent variables) from observations or from supercolumn simulations that are conducted as needed to constrain uncertain processes.

Assessing the role of uncertain precipitation estimates on the robustness of hydrological model parameters under highly variable climate conditions.

In perturbed physics projections, «a single model structure is used and perturbations are made to uncertain physical parameters within that structure...» [5] That is, a perturbed physics experiment shows the variation in climate projections as model parameters are varied step-wise across their physical uncertainty.

Even when updated by fnite Bayesian learning, uncertain structural parameters induce a critical ìtail fatteningî of posterior - predictive distributions, Such fattened tails have strong implications for situations, like climate change, where a catastrophe is theoretically possible because prior knowledge can not place su cents ciently narrow bounds on overall damages.

This may be partly because the SMEs were constructed by perturbing uncertain physical parameters thought to affect climate sensitivity which are mainly related to clouds.

Climate IAMs have «hundreds of input parameters, each of which is highly uncertain in the long run.»

However, climates at high latitude are known to be very sensitive to orbital parameters affecting insolation (Ravelo et al., 2004), and thus proxy estimates with uncertain age constraints are not directly comparable to model simulations that typically span hundreds of years.

For example, Stainforth et al. (2005) have shown that many different combinations of uncertain model sub-grid scale parameters can lead to good simulations of global mean surface temperature, but do not lead to a robust result for the model's climate sensitivity.

In UKCIP08, for example, we are handling this problem by combining results from two different types of ensemble data: One is a systematic sampling of the uncertainties in a single model, obtained by changing uncertain parameters that control the climate system; the other is a multi-model ensemble obtained by pooling results from alternative models developed at different international centers.