Sentences with phrase «on uncertainty in climate models»

Some of my «opponents» don't see the contradiction when they rely on uncertainty in climate models to «disprove» the general consensus, and yet are content to rely on innuendo to rubbish the scientists.

Not exact matches

Clouds also are the largest source of uncertainty in present climate models, according to the latest report of the Intergovernmental Panel on Climate climate models, according to the latest report of the Intergovernmental Panel on Climate Climate Change.

For example, when examining hurricanes and typhoons, the lack of a high - quality, long - term historical record, uncertainty regarding the impact of climate change on storm frequency and inability to accurately simulate these storms in most global climate models raises significant challenges when attributing assessing the impact of climate change on any single storm.

However, he says, «Aerosol effects on climate are one of the main uncertainties in climate models.

Understanding how well climate models represent these processes will help reduce uncertainties in the model projections of the effects of global warming on the world's water cycle.

Dr. Yun Qian, atmospheric and climate modeling scientist at Pacific Northwest National Laboratory, was invited to organize and direct an international workshop on «Uncertainty Quantification in Climate Modeling and Projection» in Trieste,climate modeling scientist at Pacific Northwest National Laboratory, was invited to organize and direct an international workshop on «Uncertainty Quantification in Climate Modeling and Projection» in Triestemodeling scientist at Pacific Northwest National Laboratory, was invited to organize and direct an international workshop on «Uncertainty Quantification in Climate Modeling and Projection» in Trieste,Climate Modeling and Projection» in TriesteModeling and Projection» in Trieste, Italy.

Given that clouds are known to be the primary source of uncertainty in climate sensitivity, how much confidence can you place in a study based on a model that doesn't even attempt to simulate clouds?

The model results (which are based on driving various climate models with estimated solar, volcanic, and anthropogenic radiative forcing changes over this timeframe) are, by in large, remarkably consistent with the reconstructions, taking into account the statistical uncertainties.

There are uncertainties in parts of the general circulation models used to forecast future climate, but thousands of scientists have made meticulous efforts to make sure that the processes are based on observations of basic physics, laboratory measurements, and sound theoretical calculations.

When faced with durable uncertainty on many fronts — in the modeling of the atmosphere, in data delineating past climate changes, and more — pushing ever harder to boost clarity may be scientifically important but is not likely to be very relevant outside a small circle of theorists.

Although there is still some disagreement in the preliminary results (eg the description of polar ice caps), a lot of things appear to be quite robust as the climate models for instance indicate consistent patterns of surface warming and rainfall trends: the models tend to agree on a stronger warming in the Arctic and stronger precipitation changes in the Topics (see crude examples for the SRES A1b scenarios given in Figures 1 & 2; Note, the degrees of freedom varies with latitude, so that the uncertainty of these estimates are greater near the poles).

Some attribution assessments that link events to dynamically driven changes in circulation have been criticized on the grounds that small signal - to - noise ratios, modeling deficiencies, and uncertainties in the effects of climate forcings on circulation render conclusions unreliable and prone to downplaying the role of anthropogenic change.

And of course there's still substantial uncertainty in climate models at the regional scale in war - prone places (again, a prime example is the set of countries along the southern fringe of the Sahara Desert, where models still clash on which areas will grow drier or wetter; see my Somalia posts.)

Kevin Trenberth of the National Center for Atmospheric Research made this point powerfully last year in an important piece in the journal Nature Reports / Climate Change warning that more uncertainty, not less, would likely result from a push to enrich climate models used for the next report from the Intergovernmental Panel on Climate Climate Change warning that more uncertainty, not less, would likely result from a push to enrich climate models used for the next report from the Intergovernmental Panel on Climate climate models used for the next report from the Intergovernmental Panel on Climate Climate Change:

In the rekognition of the uncertainties, the IPCC Good - Practice - Guidance - Paper on using climate model results offers some wise advice (first bullet point under section 3.5 on p. 10): the local climate change scenarios should be based on (i) historical change, (ii) process change (e.g. changes in the driving circulation), (iii) global climate change projected by GCMs, and (iv) downscaled projected changIn the rekognition of the uncertainties, the IPCC Good - Practice - Guidance - Paper on using climate model results offers some wise advice (first bullet point under section 3.5 on p. 10): the local climate change scenarios should be based on (i) historical change, (ii) process change (e.g. changes in the driving circulation), (iii) global climate change projected by GCMs, and (iv) downscaled projected changin the driving circulation), (iii) global climate change projected by GCMs, and (iv) downscaled projected change.

More complex metrics have also been developed based on multiple observables in present day climate, and have been shown to have the potential to narrow the uncertainty in climate sensitivity across a given model ensemble (Murphy et al., 2004; Piani et al., 2005).

But even as the certainty of the models improved, Exxon focused instead on their uncertainty in its campaign to delay climate action.

(2007) • Contribution of Renewables to Energy Security (2007) • Modelling Investment Risks and Uncertainties with Real Options Approach (2007) • Financing Energy Efficient Homes Existing Policy Responses to Financial Barriers (2007) • CO2 Allowance and Electricity Price Interaction - Impact on Industry's Electricity Purchasing Strategies in Europe (2007) • CO2 Capture Ready Plants (2007) • Fuel - Efficient Road Vehicle Non-Engine Components (2007) • Impact of Climate Change Policy Uncertainty on Power Generation Investments (2006) • Raising the Profile of Energy Efficiency in China — Case Study of Standby Power Efficiency (2006) • Barriers to the Diffusion of Solar Thermal Technologies (2006) • Barriers to Technology Diffusion: The Case of Compact Fluorescent Lamps (2006) • Certainty versus Ambition — Economic Efficiency in Mitigating Climate Change (2006) • Sectoral Crediting Mechanisms for Greenhouse Gas Mitigation: Institutional and Operational Issues (2006) • Sectoral Approaches to GHG Mitigation: Scenarios for Integration (2006) • Energy Efficiency in the Refurbishment of High - Rise Residential Buildings (2006) • Can Energy - Efficient Electrical Appliances Be Considered «Environmental Goods»?

Although mainstream scientists do identify considerable uncertainties in their climate predictions, which are based on computer models, they are increasingly confident that global warming is a serious problem and often say that the uncertainties do not justify inaction.

Yeh, X. Fu, and E.A.B. Eltahir, 2015: Uncertainty in modeled and observed climate change impacts on American Midwest hydrology.

Contribution from working group I to the fifth assessment report by IPCC TS.5.4.1 Projected Near - term Changes in Climate Projections of near - term climate show small sensitivity to Green House Gas scenarios compared to model spread, but substantial sensitivity to uncertainties in aerosol emissions, especially on regional scales and for hydrological cycle varClimate Projections of near - term climate show small sensitivity to Green House Gas scenarios compared to model spread, but substantial sensitivity to uncertainties in aerosol emissions, especially on regional scales and for hydrological cycle varclimate show small sensitivity to Green House Gas scenarios compared to model spread, but substantial sensitivity to uncertainties in aerosol emissions, especially on regional scales and for hydrological cycle variables.

The structural uncertainties above are not expressed in trivial intermodel variability, but lie at the core of IPCC climate modeling, including its reliance on the radiative forcing paradigm.

Estimates from proxy data1 (for example, based on sediment records) are shown in red (1800 - 1890, pink band shows uncertainty), tide gauge data in blue for 1880 - 2009,2 and satellite observations are shown in green from 1993 to 2012.3 The future scenarios range from 0.66 feet to 6.6 feet in 2100.4 These scenarios are not based on climate model simulations, but rather reflect the range of possible scenarios based on other kinds of scientific studies.

Those opposing policies on the basis of uncertainties about models often fail to acknowledge that the models could be wrong not only in overstating the impacts of climate change but also in greatly understating climate impacts.

In his talk, «Statistical Emulation of Streamflow Projections: Application to CMIP3 and CMIP5 Climate Change Projections,» PCIC Lead of Hydrological Impacts, Markus Schnorbus, explored whether the streamflow projections based on a 23 - member hydrological ensemble are representative of the full range of uncertainty in streamflow projections from all of the models from the third phase of the Coupled Model Intercomparison ProjecIn his talk, «Statistical Emulation of Streamflow Projections: Application to CMIP3 and CMIP5 Climate Change Projections,» PCIC Lead of Hydrological Impacts, Markus Schnorbus, explored whether the streamflow projections based on a 23 - member hydrological ensemble are representative of the full range of uncertainty in streamflow projections from all of the models from the third phase of the Coupled Model Intercomparison Projecin streamflow projections from all of the models from the third phase of the Coupled Model Intercomparison Project.

The disagreement was always about the scope and depth of natural variability, on the point where data adjustments become statistical manipulations, on gaps and uncertainties in data, on the proper use and limitations of climate models and on chaos in climate and models.

I won't repeat what I said on an earlier forum, but a quick look at Paul Williams» presentation on numerical errors in climate modeling shows a host of issues that would lead me to assign a rather high uncertainty to the model results, and then we have the uncertainties in the physical models themselves.

On p601, they state that «Models continue to have significant limitations, such as in their representation of clouds, which lead to uncertainties in the magnitude and timing, as well as regional details, of predicted climate change.»

These NAO «book - ends» provide an estimate of the 5 — 95 % range of uncertainty in projected trends due to internal variability of the NAO based on observations superimposed upon model estimates of human - induced climate change.

In most cases, these range from about 2 to 4.5 C per doubled CO2 within the context of our current climate — with a most likely value between 2 and 3 C. On the other hand, chapter 9 describes attempts ranging far back into paleoclimatology to relate forcings to temperature change, sometimes directly (with all the attendant uncertainties), and more often by adjusting model parameters to determine the climate sensitivity ranges that allow the models to best simulate data from the past — e.g., the Last Glacial Maximum (LGM).

But we know that the mechanisms responsible for the variation of Ts are different in internal variability on these time scales and in forced climate change, then my questions is that: is it possible that the spread in ECS might not be so directly caused by low - cloud feedback, although the low cloud feedback is a very good indictor for the model uncertainty?

Recent climate models reproduce well the observed tends of the cryosphere; they have uncertainties about future clouds that appear in the uncertainties displayed on the results of the models.

While climate contrarians like Richard Lindzen tend to treat the uncertainties associated with clouds and aerosols incorrectly, as we noted in that post, they are correct that these uncertainties preclude a precise estimate of climate sensitivity based solely on recent temperature changes and model simulations of those changes.

This scale factor was based on simulations with an early climate model [3,92]; comparable forcings are found in other models (e.g. see discussion in [93]-RRB-, but results depend on cloud representations, assumed ice albedo and other factors; so the uncertainty is difficult to quantify.

«The assessment is supported additionally by a complementary analysis in which the parameters of an Earth System Model of Intermediate Complexity (EMIC) were constrained using observations of near - surface temperature and ocean heat content, as well as prior information on the magnitudes of forcings, and which concluded that GHGs have caused 0.6 °C to 1.1 °C (5 to 95 % uncertainty) warming since the mid-20th century (Huber and Knutti, 2011); an analysis by Wigley and Santer (2013), who used an energy balance model and RF and climate sensitivity estimates from AR4, and they concluded that there was about a 93 % chance that GHGs caused a warming greater than observed over the 1950 — 2005 period; and earlier detection and attribution studies assessed in the AR4 (Hegerl et al., 2007b).&rModel of Intermediate Complexity (EMIC) were constrained using observations of near - surface temperature and ocean heat content, as well as prior information on the magnitudes of forcings, and which concluded that GHGs have caused 0.6 °C to 1.1 °C (5 to 95 % uncertainty) warming since the mid-20th century (Huber and Knutti, 2011); an analysis by Wigley and Santer (2013), who used an energy balance model and RF and climate sensitivity estimates from AR4, and they concluded that there was about a 93 % chance that GHGs caused a warming greater than observed over the 1950 — 2005 period; and earlier detection and attribution studies assessed in the AR4 (Hegerl et al., 2007b).&rmodel and RF and climate sensitivity estimates from AR4, and they concluded that there was about a 93 % chance that GHGs caused a warming greater than observed over the 1950 — 2005 period; and earlier detection and attribution studies assessed in the AR4 (Hegerl et al., 2007b).»

The uncertainties in climate projections originate in the representation of processes such as clouds and turbulence that are not resolvable on the computational grid of global models.

My take on the uncertainty issue is that the difference in +1 C (mild) +3 C (climate model) and +10 C (outlier) scenarios are really just different people's intuition, all with valid scientific arguments that rationalize them.

The Institute also announced that it has scheduled an ongoing attack on the report of the Intergovernmental Panel on Climate Change, promising a «Fraser Institute Supplementary Analysis Series» on topics such as «Fundamental Uncertainties in Climate Modelling.»

Zhang, M., S. Klein, D. Randall, R. Cederwall, and A. Del Genio, 2005: Introduction to special section on «Toward Reducing Cloud - Climate Uncertainties in Atmospheric General Circulation Models».

The U.K. government's chief scientific advisor, John Beddington, has acknowledged some climate scientists exaggerated the impact of global warming and called for more honesty in explaining to the public the inherent uncertainties of predictions based on computer climate models, adding: «I don't think it's healthy to dismiss proper skepticism.»

Although the most advanced theoretical climate models still leave uncertainty, particularly about the sign and magnitudes of the effects, on GHG feedbacks, of some low - and high - clouds, a consensus began to develop that threats of resulting increases in global temperature — and the very large risks associated with their possible consequences — deserved substantial increase in attention.

As valuation depends strongly on the transient climate response, uncertainty in sensitivity is based on the range in a recent study of the AR5 models (1.3 — 3.15 °C; (Shindell 2014)-RRB- relative to the mean of those models (1.8 °C, hence − 28 % / +75 %; those models also exhibited a mean ECS of 3.2 °C).