Sentences with phrase «climate model biases»

Firstly, climate model biases are still substantial, and may well be systemically related to the use of deterministic bulk - formula closure - this is an area where a much better basic understanding is needed.

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

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

Not exact matches

«Being based on climate records, this approach avoids any biases that might affect the sophisticated computer models that are commonly used for understanding global warming.»

A warm bias in sea surface temperature in most global climate models is due to a misrepresentation of the coastal separation position of the Gulf Stream, which extends too far north of Cape Hatteras, North Carolina.

Using 19 climate models, a team of researchers led by Professor Minghua Zhang of the School of Marine and Atmospheric Sciences at Stony Brook University, discovered persistent dry and warm biases of simulated climate over the region of the Southern Great Plain in the central U.S. that was caused by poor modeling of atmospheric convective systems — the vertical transport of heat and moisture in the atmosphere.

A main point in conducting the experiments was to show that climate models contain a bias that could be corrected.

The models are all over the place, and most climate models yield patterns with some bias either in geographical character, amplitude or time scales.

In fact, it increasingly appears that, if there is any systemic bias in the climate models, it's that they understate the gravity of the situation.

In order to do model versus proxy comparisons, it is necessary to understand how proxies filter and reflect the climate system, and to account of those processes and any potential biases.

Large warm bias can hinder models» fidelity of climate simulations and their future projections.

Researchers proposed a framework that holds promise for addressing several challenges and biases in climate models related to cloud size, interactions among clouds, and evolution of clouds over time.

Scientists at Lawrence Livermore National Laboratory within the Atmospheric, Earth, and Energy Division, along with collaborators from the U.K. Met Office and other modeling centers around the world, organized an international multi-model intercomparison project, name CAUSES (Clouds Above the United States and Errors at the Surface), to identify possible causes for the large warm surface air temperature bias seen in many weather forecast and climate model simulations.

«By comparing the response of clouds and water vapor to ENSO forcing in nature with that in AMIP simulations by some leading climate models, an earlier evaluation of tropical cloud and water vapor feedbacks has revealed two common biases in the models: (1) an underestimate of the strength of the negative cloud albedo feedback and (2) an overestimate of the positive feedback from the greenhouse effect of water vapor.

«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.

Crichton seems unaware that the discussion of climate model validation is a common feature of publications utilizing these models and model errors and biases are often explicitly quantified and described.

Hence the biases in climate models in reproducing the basic conditions ripe for tornadoes is not as well reproduced as one would like, and how one «corrects» for those biases (downscales) is subject to problems.

Several other groups have evaluated the impact of coupling specific models of carbon to climate models but clear results are difficult to obtain because of inevitable biases in both the terrestrial and atmospheric modules (e.g., Delire et al., 2003).

Others include, the role of the Sun (being the main heat source), the vast oceans which cover over 70 % of the Earth's surface (and the natural factors which determine the storage and release of CO2 back into the atmosphere), water - vapour being the dominant greenhouse gas comprising 98 % of the atmosphere, the important role of low - level clouds which is thought to be a major factor in determining the natural variation of climate temperatures (P.S. Significantly, computer - models are unable to replicate cloud - formation and coverage — which again — injects bias into model).

Of course, nobody is going to point out that the «bias» is against a bunch of worthless rubbish climate models.

Stick to your climate fiction and religious affinity, but do nt bother us with your patological convictions based on biased climate computer models which is spurious and swamped with wishful thinking.

IN this case, Judith's explains her own «bias» (what could be fairer that that) thusly: «my reasoning is weighted heavily in favor of observational evidence and understanding of natural internal variability of the climate system, whereas the IPCC's reasoning is weighted heavily in favor of climate model simulations and external forcing of climate change.»

This Nature Climate Change paper concluded, based purely on simulations by the GISS - E2 - R climate model, that estimates of the transient climate response (TCR) and equilibrium climate sensitivity (ECS) based on observations over the historical period (~ 1850 to recent times) were biasClimate Change paper concluded, based purely on simulations by the GISS - E2 - R climate model, that estimates of the transient climate response (TCR) and equilibrium climate sensitivity (ECS) based on observations over the historical period (~ 1850 to recent times) were biasclimate model, that estimates of the transient climate response (TCR) and equilibrium climate sensitivity (ECS) based on observations over the historical period (~ 1850 to recent times) were biasclimate response (TCR) and equilibrium climate sensitivity (ECS) based on observations over the historical period (~ 1850 to recent times) were biasclimate sensitivity (ECS) based on observations over the historical period (~ 1850 to recent times) were biased low.

These systems likely contribute to an observed regional trend of increasing extreme rainfall, and poor prediction of them likely contributes to a warm, dry bias in climate models downstream of the Sierras de Córdoba in a key agricultural region.

Instead of involving a choice of whether to keep or discard an observation based upon a prior expectation, we hypothesize that this selection bias involves the «survival» of climate models from generation to generation, based upon their warming rate.

Levine, R.C., Turner, A.G., Marathayil, D. and Martin, G.M. (accepted Dec 2012), The role of northern Arabian Sea surface temperature biases in CMIP5 model simulations and future predictions of Indian summer monsoon rainfall, in press, Climate Dynamics., DOI 10.1007 / s00382 -012-1656-x link

For this purpose, we instructed them to indicate their level of agreement or disagreement with statements such as «the scientists who did the study were biased,» «computer models like those relied on in the study are not a reliable basis for predicting the impact of CO2 on the climate,» and «more studies must be done before policymakers rely on the findings» of the study etc..

I show that cloud error is highly correlated among CMIP5 climate models, which implies that the (+ / --RRB- 4 W / m ^ 2 reflects a theory - bias.

To use just one example, consider climate models and the bias in experimental design.

In fact, it increasingly appears that, if there is any systemic bias in the climate models, it's that they understate the gravity of the situation.

My impression from outside is that the statistical analyses are weak, the climate models are simplistic and overinfluenced by selection and publication biases, the theoretic underpinning is extraordinarily shakey and the belief engine is overrevved with the popularity of certain «star performers» and the Romantic desire for a Paradise Lost that never existed.

The author's points on non-linearity and time delays are actually more relevant to the discussion in other presentations when I talked about whether the climate models that show high future sensitivities to CO2 are consistent with past history, particularly if warming in the surface temperature record is exaggerated by urban biases.

I note that 1) they can't easily go back and re-run all the climate model simulations with more accurate forcings, and 2) if they did, the climate models would be biased much too high relative to measured temperatures over the last decade, effectively proving that the modeled climate sensitivities are too high.

Psychologists studying climate communication make two additional (and related) points about why the warming - snow link is going to be exceedingly difficult for much of the public to accept: 1) people's confirmation biases lead them to pay skewed attention to weather events, in such a way as to confirm their preexisting beliefs about climate change (see p. 4 of this report); 2) people have mental models of «global warming» that tend to rule out wintry impacts.

Consequently, Callendar's error or bias has been a basis for accumulating other errors and biases in modern - day climate models.

«You can't fix the climate model simulation via «bias removal» — you should fix what is wrong with the model physics,» he said in an email.

A consolidated estimate of ocean surface fluxes based on multiple reanalyses also helps understand biases in ENSO predictions and simulations from climate models.

His climate investigations are conducted in the limited spare time available to a parent, and are currently focussed in two areas; coverage bias in the instrumental temperature record, and simple response - function climate models.

Lyman and colleagues combined different ocean monitoring groups» data sets, taking into account different sources of bias and uncertainty — due to researchers using different instruments, the lack of instrument coverage in the ocean, and different ways of analyzing data used among research groups — and put forth a warming rate estimate for the upper ocean that it is more useful in climate models.

By 2015 and especially 2020, it will be obvious to anyone with a brain that the Alarmists have got it wrong, as the climate continues not to play along with their simplistic, biased computer models.

Research interests: Physics - dynamics interaction and multi-scale physics in climate models, double ITCZ bias dynamics, coupled ITCZ shift dynamics.

``... since uncertainty is a structural component of climate and hydrological systems, Anagnostopoulos et al. (2010) found that large uncertainties and poor skill were shown by GCM predictions without bias correction... it can not be addressed through increased model complexity....

(3) Satellite temperature data says that climate models are warming too fast in the troposphere, therefore model projections of precipitation change are systematically biased.

«When initialized with states close to the observations, models «drift» towards their imperfect climatology (an estimate of the mean climate), leading to biases in the simulations that depend on the forecast time.

A recent study by Cowtan et al. (paper here) suggests that accounting for these biases between the global temperature record and those taken from climate models reduces the divergence in trend between models and observations since 1975 by over a third.

I have been worrying that even common properties of all present climate models and models than can be developed in near future may common bias towards such stability that is not necessarily true for the real Earth system.

Second, results from climate model simulations are not evidence of anything but the biases, beliefs, and mistakes of the programmers.

And in that sense, a climate model is nothing more or less than a conceptual model, necessary to give a frame of reference to the state of the real world, which can never be generated by observations alone (which are incomplete, may be inconsistent, contradicting each other, biased, non-representative,...).

Could unrecognized systemic bias from excluded or unrecognized physics be causing the major disconnect between observations of climate sensitivity and projections from global climate models?

Most of the theoretical claims embodied in the climate models are based on empirical evidence: biases, beliefs and mistakes are only a subset of those.