Sentences with phrase «model biases»

But naturally one must first adjust for best estimate / judgement of model bias before combining evidence.

What does model bias mean, and what implication does that have for my risk assessment?

The inverse relationship between model bias and projection, and the role of model resolution are discussed.

The raw output of the model is adjusted for known model bias towards high extent and thickness.

It is still not well understood how model biases in simulation of modern climate affect climate sensitivity.

Regional climate model bias correction improved the estimates on changes to future mean runoff

The mean ice extent in September, averaged across all ensemble members, corrected for forward model bias.

Hiroyuki Murakami, Pang - Chi Hsu, Osamu Arakawa, Tim Li, Influence of Model Biases on Projected Future Changes in Tropical Cyclone Frequency of Occurrence.

A follow - up study will assess the impacts of climate change on explosive cyclones, and evaluate how model biases presented in this study affect the projections.

Some common model biases in the Southern Ocean have been identified, resulting in some uncertainty in oceanic heat uptake and transient climate response.

Regional climate model bias correction improved the estimates on changes to future mean runoff

Therefore, I see no justification for using observed values of those aspects to adjust model - predicted warming to correct model biases relating to those aspects, which is in effect what BC17 does.

This approach allows us to examine what the models say about future warming relative to future emissions, without bringing in any potential model bias simulated over the historical period.

«Evolution has given us what we call a good model bias,» Pitkow said.

Consistent model biases among the simulations driven by a set of alternative forcings suggest that uncertainty in the forcing plays only a relatively minor role.

Focus: Long - standing model biases (at least a few of them); Understand how model errors or shortcomings impact projections and predictions; Gain physical understanding of the climate system through model development.

Focus: Initiative # 5: «What key model biases should we aim at reducing in priority?»

Other factors that will affect the forecast skill of a realistically initialized CESM include ensemble size, internal model biases, initialization method, and the low resolution adopted here.

This Perspective considers the issues of bias correction and makes recommendations for research to overcome model biases.

Minimize model biases, especially biases that are known to correlate with the climate response of models.

Expand the use of eddy - resolving models, particularly in regional / process studies designed to: i) test the robustness of AMOC variability mechanisms identified in coarser GCMs or idealized models; ii) address the origins of persistent model bias in the North Atlantic region (e.g., Gulf Stream separation and the North Atlantic Current path); and iii) assess the role of ocean turbulence in AMOC variability.

The 5th — 95th percentile temperature range across the models (shown by solid contours with labels) in these regions is smaller and has a different spatial pattern, suggesting that these simulated patterns of warming are not largely controlled by model biases.

Inferences on sub-continental scales are indicative rather than definitive because of the absence of locally important forcings and processes in model simulations, as well as model biases

For all the ensemble members, we used one regression model using 27 years of past model data and NSIDC Merged SMMR and SSM / I sea ice concentration data to estimate and correct for systematic model bias.

The mean minimum ice extent in September, averaged across all ensemble members and corrected for forward model bias, is our projected ice extent.

What is the impact of model biases on the ability to capture AMOC variability and teleconnections?

FMI has been involved in research project, which evaluated the simulations of long - range transport of BB aerosol by the Goddard Earth Observing System (GEOS - 5) and four other global aerosol models over the complete South African - Atlantic region using Cloud - Aerosol Lidar with Orthogonal Polarization (CALIOP) observations to find any distinguishing or common model biases.

Climate scenarios make implicit or explicit assumptions about the extrapolation of climate model biases from current to future time periods.

Progress in the longer term depends on identifying and correcting model biases, accumulating as complete a set of historic observations as possible, and developing improved methods of detection and correction of observational biases.»

The links between model biases and the underlying assumptions of the shallow cumulus scheme are further diagnosed with the aid of large - eddy simulations and aircraft measurements, and by suppressing the triggering of the deep convection scheme.

Although the precise causes of such differences are unclear, model biases in lower stratospheric temperature trends are likely to be reduced by more realistic treatment of stratospheric ozone depletion and volcanic aerosol forcing.

The next steps are to identify the causes of model bias and develop improved reanalysis products that better inform climate models.

The model bias, known as the «Gulf Stream separation problem,» is a result of the models» coarse resolution.

The team evaluated the simulated streamflow globally against the observations from 1,674 major river gauge stations worldwide and systematically examined possible sources of model biases, which can impact the range of answers in simulations.

«Given the current uncertainties in both the reconstruction and model sensitivity, however, this model - data discrepancy could be attributed to either the seasonal bias in the SST reconstructions or the model bias in regional and seasonal climate sensitivity.

Focus: Role of local vs large - scale or remotely forced changes in driving regional changes; Identify robust responses; Interpret uncertain components; Assess the impact of model biases or shortcomings on regional responses

And how do the model biases in the ITCZ arise?