Sentences with phrase «modeled effects of clouds»

Our computer models do not model the effects of clouds well.

It all depends on how you model the effects of clouds.

Not exact matches

The model calculations, which are based on data from the CLOUD experiment, reveal that the cooling effects of clouds are 27 percent less than in climate simulations without this effect as a result of additional particles caused by human activity: Instead of a radiative effect of -0.82 W / m2 the outcome is only -0.60 W / m2.

The model calculations show that the effects as a result of changes in ionisation through the sun are too small to make a significant contribution to cloud formation.

Mission leaders were relieved and eager to begin their studies of cloud and haze effects, which «constitute the largest uncertainties in our models of future climate — that's no exaggeration,» says Jens Redemann, an atmospheric scientist at NASA's Ames Research Center in Mountain View, California, and the principal investigator for ObseRvations of Aerosols above CLouds and their IntEractionS (ORACLES).

In a recent study, for instance, well - respected climate models were shown to have completely opposing estimates for the overall effect of the clouds and smoke in the southeast Atlantic: Some found net warming, whereas others found cooling.

CUMULUS CAUSALITY In «A Formula for Economic Calamity,» by David H. Freedman, David Colander of Middlebury College asserts that climate models often have no terms to account for the effects of clouds.

Using a NASA computer model, Oman tracked the worldwide effects of the sulfate aerosol cloud that formed following the Laki eruption.

Which of these effects dominates depends on the type, distribution and altitude of the clouds — difficult for climate models to predict.

The information could also feed into climate models to help understand the effects of clouds and aerosols on Earth's energy balance.

The other two shortlisted missions — which had been whittled down from an original list of over 20 possibilities — were CoReH2O, which sought to model the water balance in glaciers and snow - covered areas, and PREMIER, which aimed to study chemical processes in the upper troposphere and lower stratosphere and the radiative effects of clouds.

Among the most uncertain elements in climate models are the effects of aerosols and their interactions with clouds — just the things involved in albedo modification — she says.

Reporting in the Nov. 14 issue of the journal Science, University of California, Berkeley, climate scientist David Romps and his colleagues look at predictions of precipitation and cloud buoyancy in 11 different climate models and conclude that their combined effect will generate more frequent electrical discharges to the ground.

This fog layer is induced by the large nighttime precipitation, missed by current climate models, which underestimated the effect of clouds and precipitation.

His model also makes specific predictions about the effect these clouds will have on the planet's climate and the types of information that future telescopes, like the James Webb Space Telescope, will be able to gather.

The multi-scale aerosol - climate model, an extension of a multi-scale modeling framework, examined specific aerosol - cloud interactions and their effects on the Earth's energy budget, one of the toughest climate forecasting problems.

Sally, who was nominated by Dr. Beat Schmid, Associate Director, Atmospheric Sciences and Global Change Division, was honored for her exceptional contribution in the field of atmospheric science, particularly in her efforts to improve understanding of the radiative effect of clouds and aerosols on the Earth's atmosphere and their representation in climate models.

An adjustment is necessary because as climate models are continually evaluated against observations evidence has become emerged that the strength of their aerosol - cloud interactions are too strong (i.e. the models» «aerosol indirect effect» is larger than inferred from observations).

When the CLIMAP data proved to be wrong, and was replaced by more reliable estimates showing a substantial tropical surface temperature drop, Lindzen had to abandon his then - current model and move on to other forms of mischief (first the «cumulus drying» negative water vapor feedback mechanism, since abandoned, and now the «Iris» effect cloud feedback mechanism).

Possible reasons include increased oceanic circulation leading to increased subduction of heat into the ocean, higher than normal levels of stratospheric aerosols due to volcanoes during the past decade, incorrect ozone levels used as input to the models, lower than expected solar output during the last few years, or poorly modeled cloud feedback effects.

Lin, W.Y., and M.H. Zhang, 2004: Evaluation of clouds and their radiative effects simulated by the NCAR Community Atmospheric Model against satellite observations.

This result suggests that models may not yet adequately represent the long - term feedbacks related to ocean circulation, vegetation and associated dust, or the cryosphere, and / or may underestimate the effects of tropical clouds or other short - term feedback processes.»

New modelling methods provide more precise information than ever on the effect of landforms on cloud formation

And as this knowledge is disseminated and better understood, eventually we'll have better models, and can achieve more widespread adoption of them - if the solar / cosmic ray / cloud mechanism is significant it could explain why temperatures in neither hemisphere are proceeding upwards lock - step with IPCC forecasts - and opening the door for a more accurate and widespread acknowledgement of CO2 effects on temperature and climate.

But, as far as I can see, the «attacks» by vested interests are not even able to make legitimate points (e.g. uncertainty about the effects of clouds or aerosols in climate models).

«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.

Steve Reynolds, The fact that the sign is not known for clouds may indicate a variety of things: 1) lack of data, 2) the net effect may be near zero, 3) the mechanism may be obscure or there may be competing mechanisms that make it difficult to determine how to include the effects in the model.

Data from satellite observations «suggest that greenhouse models ignore negative feedback produced by clouds and by water vapor, that diminish the warming effects» of human carbon dioxide emissions.

Eventually, when we know more about the effects of the mechanisms involved, fluctuations in cosmic rays could be incorporated in helping model cloud albedo changes.

This result suggests that models may not yet adequately represent the long - term feedbacks related to ocean circulation, vegetation and associated dust, or the cryosphere, and / or may underestimate the effects of tropical clouds or other short - term feedback processes.»

Taking into account the obscuring effects of high cloud, it was found that thick low clouds decreased by more than 20 % in the eastern tropical Pacific... In contrast, most increase in low cloud amount due to doubled CO2 simulated by the NCAR and GFDL models occurs in the subtropical subsidence regimes associated with a strong atmospheric stability.»

[Response: These feedbacks are indeed modelled because they depend not on the trace greenhouse gas amounts, but on the variation of seasonal incoming solar radiation and effects like snow cover, water vapour amounts, clouds and the diurnal cycle.

The top panel shows the direct effects of the individual components, while the second panel attributes various indirect factors (associated with atmospheric chemistry, aerosol cloud interactions and albedo effects) and includes a model estimate of the «efficacy» of the forcing that depends on its spatial distribution.

They may also help researchers understand the effects of cloud cover, which also creates diffuse light and represents the biggest source of uncertainty in climate models, he says.

A good friend of mine (who's a scientist in an area far removed from climate) was arguing some time ago that the climate models are useless because they don't include clouds at all or in such a crude way that their effect is totally meaningless.

So, the question of whether or not more of these clouds would be formed, along with the question of their net effect (given that they reflect sunlight from above, but also trap heat from below), gives rise to some degree of imprecision when it comes to the degree of warming predicted by models.

When the CLIMAP data proved to be wrong, and was replaced by more reliable estimates showing a substantial tropical surface temperature drop, Lindzen had to abandon his then - current model and move on to other forms of mischief (first the «cumulus drying» negative water vapor feedback mechanism, since abandoned, and now the «Iris» effect cloud feedback mechanism).

Similarily, the uncertainty in aerosols is mostly in the data (and then cloud effects), the «masking» effect is a result of the model's physics it isn't just parameterized and fed in.

I write it off as a very real effect that is not well characterized by the models, probably because these models don't model with enough accuracy the effect of the additional aerosol particles on cloud production to properly account for it's full effect on temperature.

You claim that what you're doing shows the effect of cloud forcing uncertainty in the models, but if those results are plainly not really representative of what would happen in the models, then there's a disconnect.

In models that include indirect effects, different treatments of the indirect effect are used, including changing the albedo of clouds according to an off - line calculation (e.g., Tett et al., 2002) and a fully interactive treatment of the effects of aerosols on clouds (e.g., Stott et al., 2006b).

In addition, some models include the indirect effects of tropospheric sulphate aerosols on clouds (e.g., Tett et al., 2002), whereas others consider only the direct radiative effect (e.g., Meehl et al., 2004).

These models suggest that if the net effect of ocean circulation, water vapour, cloud, and snow feedbacks were zero, the approximate temperature response to a doubling of carbon dioxide from pre-industrial levels would be a 1oC warming.

(Phys.org)-- The first study that combines different scales — cloud - sized and earth - sized — in one model to simulate the effects of Asian pollution on the Pacific storm track shows that Asian pollution can influence weather...

Climate models that include these aerosol - cloud interactions fail to include a number of buffering responses, such as rainfall scavenging of the aerosols and compensating dynamical effects (which would reduce the magnitude of the aci cooling effect).

If, for instance, CO2 concentrations are doubled, then the absorption would increase by 4 W / m2, but once the water vapor and clouds react, the absorption increases by almost 20 W / m2 — demonstrating that (in the GISS climate model, at least) the «feedbacks» are amplifying the effects of the initial radiative forcing from CO2 alone.

Could tropical cloud feedbacks, or the coupling to ENSO, amplify the effects of low latitude hydrological responses to high - latitude anomalies in these models?

The effect of large - scale model time step and multiscale coupling frequency on cloud climatology, vertical structure, and rainfall extremes in a superparameterized global climate model.

In this paper we explore the effect of reducing the large - scale model time step, which has the byproduct of increasing the frequency with which the planetary vs. cloud resolving scales are allowed to interact.

''... the warming is only missing if one believes computer models where so - called feedbacks involving water vapor and clouds greatly amplify the small effect of CO2.»