Sentences with phrase «cloud feedback model»

Not exact matches

Even models that correctly capture cloud behavior may fail to fully account for other climate feedbacks from factors like changing snow and sea ice cover, atmospheric water vapor content, and temperature.

A Columbia Engineering team led by Pierre Gentine, professor of earth and environmental engineering, and Adam Sobel, professor of applied physics and applied mathematics and of earth and environmental sciences, has developed a new approach, opposite to climate models, to correct climate model inaccuracies using a high - resolution atmospheric model that more precisely resolves clouds and convection (precipitation) and parameterizes the feedback between convection and atmospheric circulation.

Some of these feedback processes are poorly understood — like how climate change affects clouds — and many are difficult to model, therefore the climate's propensity to amplify any small change makes predicting how much and how fast the climate will change inherently difficult.

In order to evaluate this uncertainty, Lauer et al. (2010) used 16 GCMs and the International Pacific Research Center (IPRC) Regional Atmospheric Model (iRAM) described in Lauer et al. (2009) to simulate clouds and cloud — climate feedbacks in the tropical and subtropical eastern Pacific region.

In this work, using a simple time - dependent 1D model, we demonstrate that radiative cloud feedback can drive spontaneous atmospheric variabilities in both temperature and cloud structures under conditions appropriate for brown dwarfs and directly imaged EGPs.

Several models see a positive feedback of clouds when the temperatures increase, but this seems to be wrong, at least in the tropics and the Arctic, where clouds form a strong negative feedback.

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).

Dufresne, 2005: Marine boundary - layer clouds at the heart of tropical cloud feedback uncertainties in climate models.

Cess, R.D., et al., 1989: Interpretation of cloud - climate feedback as produced by 14 atmospheric general circulation models.

Jerome Fast has lead a team of PNNL scientists that have contributed a gas - phase chemistry mechanism, an sectional aerosol model, cloud chemistry, cloud - aerosol interactions, and radiative feedback processes into the chemistry version of Weather Research and Forecasting (WRF - chem) model.

Yao, 2005a: Evaluation of regional cloud feedbacks using single - column models.

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.

Peters, M.E., and C.S. Bretherton, 2005: A simplified model of the Walker circulation with an interactive ocean mixed layer and cloud - radiative feedbacks.

They got 10 pages in Science, which is a lot, but in it they cover radiation balance, 1D and 3D modelling, climate sensitivity, the main feedbacks (water vapour, lapse rate, clouds, ice - and vegetation albedo); solar and volcanic forcing; the uncertainties of aerosol forcings; and ocean heat uptake.

However, in view of the fact that cloud feedbacks are the dominant contribution to uncertainty in climate sensitivity, the fact that the energy balance model used by Schmittner et al can not compute changes in cloud radiative forcing is particularly serious.

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.»

This session invites both modeling and observational presentations, from cloud - scale understandings to large - scale circulation and moisture feedback.

A model without any dynamics in the atmosphere doesn't even have the right information in it to even have a chance of doing cloud feedbacks correctly.

Tropical Water Vapor and Cloud Feedbacks in Climate Models: A Further Assessment Using Coupled Simulations.

«Cloud climate feedback constitutes the most important uncertainty in climate modelling, and currently even its sign is still unknown.

Secondly, if the potential cloud response is related to changes in circulation caused by the TSI or an ozone related change, then it isn't an extra forcing at all — it is part of the feedback, and should already be incorporated in models.

In general, models suggest that they are a positive feedback — i.e. there is a relative increase in high clouds (which warm more than they cool) compared to low clouds (which cool more than they warm)-- but this is quite variable among models and not very well constrained from data.

A conceptual model is presented where, through a number of synergistic processes and positive feedbacks, changes in the ultraviolet / blue flux alter the dimethyl sulphide flux to the atmosphere, and in turn the number of cloud condensation nuclei, cloud albedo, and thus sea surface temperature.

«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.

In the recently published report of the intergovernmental panel on climate change (IPCC), 6 out of 20 climate models showed a positive and 14 a negative cloud radiative feedback in a doubled CO2 scenario.»

My own amateur suspicions are that Rampal and Kay have already highlighted two of the major model failings in regards to sea - ice modelling: ice mechanics and kinematics and cloud forcing / feedbacks.

There is no question that cloud feedbacks are uncertain and that modeling them is hard — I don't think that anyone has ever seriously claimed otherwise.

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.

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.»

Some features among all classes of models are robust, but it is often the questions that can't be answered with such simple approaches (what is the sign of the cloud feedback?

Five models predict net cooling, five predict net warming due to cloud feedbacks.

Also who is to say that the models have water vapor / cloud feedbacks correct too?

[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.

Since many of these processes result in non-symmetric time, location and temperature dependant feedbacks (eg water vapor, clouds, CO2 washout, condensation, ice formation, radiative and convective heat transfer etc) then how can a model that uses yearly average values for the forcings accurately reflect the results?

The longwave component of cloud feedbacks being positive tends to be a robust result of models, and model spread is primarily from the shortwave part of the response.

So a reasonable null hypothesis is that reducing the spatial area in which such clouds reside will result in a near zero feedback, with the residual probably quite model and cloud property dependent.

With «mean climate», surely the model ensemble mean is meant, however the «real data» to base the tuning on by definition is restricted to the single realisation of Earth's climate (including cloud cover caused by, for instance, multi-decadal oscillations instead of AGW feedback).

1) Attempts to constrain ECS with shortwave cloud feedback alone (e.g. Sherwood et al. 2014) miss the possibility that real - world longwave feedbacks could lie outside that spanned by the model ensemble (the infamous unknown unknowns).

The models are wrong on clouds and the feedback of water vapor.

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).

RE # 24, Ferdinand you state, «Several models see a positive feedback of clouds when the temperatures increase, but this seems to be wrong, at least in the tropics and the Arctic, where clouds form a strong negative feedback.»

The largest problem in current climate models is the feedback from cloud cover.

Several models see a positive feedback of clouds when the temperatures increase, but this seems to be wrong, at least in the tropics and the Arctic, where clouds form a strong negative feedback.

-- These storms should penetrate higher as climate warms according to the models, a positive feedback, and satellite data looking at cloud height changes over El Nino time scales show something similar and show the models getting that about right also, for physical reasons we think we understand

Water vapour feedbacks are very robust across different models — cloud feedbacks less so of course.

Now we have a couple of mechanisms that seem realistic for several specific types of changes such as the cloud height feedback, and we have some observational confirmation of a general latitudinal pattern of feedbacks that many models seem to get, more so than was the case a decade ago.

They got 10 pages in Science, which is a lot, but in it they cover radiation balance, 1D and 3D modelling, climate sensitivity, the main feedbacks (water vapour, lapse rate, clouds, ice - and vegetation albedo); solar and volcanic forcing; the uncertainties of aerosol forcings; and ocean heat uptake.

For instance, see Clement et al., Observational and Model Evidence for Positive Low - Level Cloud Feedback, Science 2009.

Therefore, the allowable CO2 concentration also correlates with the strength of the low - cloud feedback in climate models (see our paper for a figure).

I also start using the high - resolution PyCLES model for process - oriented analysis of low - cloud feedback.