Sentences with phrase «many uncertain parameters»

«They involve relatively complex processes and rather constrained sets of uncertain parameters.»

The nature of the tuning also matters: allowing an uncertain parameter to vary within reasonable bounds and picking the value that gives the best result, is quite different to inserting completely artificial fluxes to correct for biases.

Eight test cases were constructed and an ensemble of USHCN analyses were undertaken, varying uncertain parameters within the algorithm.

The EnKF is used to assimilate seasonally averaged observational data into the model, thereby generating an ensemble of runs with a range of values for the uncertain parameters, all reasonably compatible with present - day climatology.

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.

Lastly, while there are wide variations in plausible scenarios around the future of the homeownership rate, they require many uncertain parameters, which makes it difficult to say with much confidence how the homeownership rate will evolve.

Not exact matches

«The precise parameters of the U.K.'s future relationship with the European Union remained highly uncertain and it seemed likely that asset prices would remain sensitive to perceived developments in the outlook in the months ahead,» the Bank of England said through the minutes of the policy committee's meeting.

Although estimates of this parameter, based on observations, are very uncertain, observers generally quote values in the range from 50 to 90 kilometres per second per megaparsec.

In that survey, it was almost universal that groups tuned for radiation balance at the top of the atmosphere (usually by adjusting uncertain cloud parameters), but there is a split on pratices like using flux corrections (2 / 3rds of groups disagreed with that).

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.

However, underlying parameters and their precise effects are often uncertain, leading to insufficient parameterization and overall model uncertainty.

The challenge is to produce models that can be tested, rather than derived parameters that have uncertain interpretation.

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

Typically there is also a single parameter controlling the rate of ocean heat uptake, and forcings (from aerosols in particular, this forcing being quite uncertain) can of course readily be adjusted.

It is pretty clear that the model for the process governing Sun spot occurrence is the correct one, even if the parameterization is somewhat statistically uncertain (and even if some parameters may be randomly or deterministically varying slowly and / or narrowly in time, as well as the precise frequency distribution of noise energy, though we really only care about that within a narrow band around the resonances).

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.

Indefinite time scales, natural contributions, many adjustable parameters, uncertain response to CO2, averaging of model outputs, non linearity, chaos and the absence of successful predictions are all reasons to continue to challenge the present models.

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.

Because of the sensitivity of the shelter level temperature to parameters and forcing, especially to uncertain turbulence parameterization in the SNBL, there should be caution about the use of minimum temperatures as a diagnostic global warming metric in either observations or models.»

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.

Increasing the Hurst parameter always makes averages more uncertain, but beyond a certain point it actually makes trends less uncertain.

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.

Even the most fundamental quantitative parameter of all, the forcing factor relating the increase in mean temperature to a doubling of CO2, lies somewhere between 1 and 3 degrees, and is thus uncertain to within a factor of 3.