Sentences with phrase «different climate model simulations»

We employed two different climate model simulations: (1) the simulation of the NCAR CSM 1.4 coupled atmosphere - ocean General Circulation Model (GCM) analyzed by Ammann et al (2007) and (2) simulations of a simple Energy Balance Model (EBM).

Not exact matches

To see whether this increase in crops has influenced the region's unusual weather, researchers at the Massachusetts Institute of Technology in Cambridge used computers to model five different 30 - year climate simulations, based on data from 1982 to 2011.

Then they plugged that into simulations that took into account climate models and two different carbon emissions scenarios identified by the Intergovernmental Panel on Climate climate models and two different carbon emissions scenarios identified by the Intergovernmental Panel on Climate Climate Change.

«The burned simulations are based on three different climate and wildfire scenarios, and we also used three different erosion models,» said Sankey.

To understand the role of human - induced climate change in these new records they compare simulations of the Earth's climate from nine different state - of - the - art climate models and the very large ensemble of climate simulations provided by CPDN volunteers for the weather@home ANZ experiments for the world with and without human - induced climate change.

Using thus 10 different climate models and over 10,000 simulations for the weather@home experiments alone, they find that breaking the previous record for maximum mean October temperatures in Australia is at least six times more likely due to global warming.

Caldeira and Myhrvold analyzed more than 50 climate simulations, which were performed using 20 different climate models for the Climate Model Intercomparison Project, Phase 5 (climate simulations, which were performed using 20 different climate models for the Climate Model Intercomparison Project, Phase 5 (climate models for the Climate Model Intercomparison Project, Phase 5 (Climate Model Intercomparison Project, Phase 5 (CMIP5).

Based on transient climate model simulations of glacial - interglacial transitions (rather than «snapshots» of different modeled climate states), Ganopolski and Roche (2009) proposed that in addition to CO2, changes in ocean heat transport provide a critical link between northern and southern hemispheres, able to explain the apparent lag of CO2 behind Antarctic temperature.

Figure 1.4 http://cybele.bu.edu/courses/gg312fall02/chap01/figures/figure1.4.gif shows the natural variability of the annual mean surface temperature on several different spatial scales from a climate model simulation for 200 years.

Projections for the these variables are given for different model simulations of climate scenarios.

Some of them are optimal fingerprint detection studies (estimating the magnitude of fingerprints for different external forcing factors in observations, and determining how likely such patterns could have occurred in observations by chance, and how likely they could be confused with climate response to other influences, using a statistically optimal metric), some of them use simpler methods, such as comparisons between data and climate model simulations with and without greenhouse gas increases / anthropogenic forcing, and some are even based only on observations.

http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-16-0628.1 In our discussion exploring the (very minor) differences in results when using different datasets we said: - «Dataset creation approaches that infill missing data areas may give overconfidence to climate changes in regions where there are no direct measurements, when compared with model simulations that have data in those regions.»

The disagreement arises from different assessments of the value and importance of particular classes of evidence as well as disagreement about the appropriate logical framework for linking and assessing the evidence — 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.

Ensemble simulations conducted with EMICs (Renssen et al., 2002; Bauer et al., 2004) and coupled ocean - atmosphere GCMs (Alley and Agustsdottir, 2005; LeGrande et al., 2006) with different boundary conditions and freshwater forcings show that climate models are capable of simulating the broad features of the observed 8.2 ka event (including shifts in the ITCZ).

Due to the important role of ozone in driving temperature changes in the stratosphere as well as radiative forcing of surface climate, several different groups have provided databases characterizing the time - varying concentrations of this key gas that can be used to force global climate change simulations (particularly for those models that do not calculate ozone from photochemical principles).

In climate science, Myanna Lahsen in her text «Seductive simulations» has used it to explain different group's attitudes towards models.

Both periods had a substantially different climate compared to the present, and there is relatively good information from data synthesis and model simulation experiments (Braconnot et al., 2004; Cane et al., 2006).

To see whether this increase in crops has influenced the region's unusual weather, researchers at the Massachusetts Institute of Technology in Cambridge used computers to model five different 30 - year climate simulations, based on data from 1982 to 2011.

«all of the coupled climate models used in the IPCC AR4 reproduce the time series for the 20th century of globally averaged surface temperature anomalies; yet they have different feedbacks and sensitivities and produce markedly different simulations of the 21st century climate.»

Ensembles made with the same model but different initial conditions only characterize the uncertainty associated with internal climate variability, whereas multi-model ensembles including simulations by several models also include the impact of model differences.

Just looking at the AR4 and early AR5 simulations, it looks as if the different climate models give a wide range of answers.

A model simulation study shows that different diurnal cycles of precipitation are consistent with radically different present and future climate characteristics.

Coupled simulations, using six different models to determine the ocean biological response to climate warming between the beginning of the industrial revolution and 2050 (Sarmiento et al., 2004), showed global increases in primary production of 0.7 to 8.1 %, but with large regional differences, which are described in Chapter 4.

To understand the role of human - caused global warming in the new records we compare simulations of the Earth's climate from nine different models from around the world.

An estimate of the forced response in global mean surface temperature, from simulations of the 20th century with a global climate model, GFDL's CM2.1, (red) and the fit to this evolution with the simplest one - box model (black), for two different relaxation times.

However, even state - of - the - art climate models (GCMs) have systematic errors in simulation of different climate characteristics, which are often much larger than observations uncertainties (Covey et al. 2003).

The comparison of different computer simulations of climate change impacts is at the heart of the ISIMIP project (Inter-Sectoral Impacts Modelling Intercomparison Project) comprising about 100 modelling groups wModelling Intercomparison Project) comprising about 100 modelling groups wmodelling groups worldwide.

They will focus on simulations that explore how the scale of the model affects clouds and atmospheric particles in different climate regimes.

Clearly, the reality of the world is vastly different from what is portrayed by the IPCC and the world's climate alarmists, based on simulations produced by state - of - the - art climate models.

Crop model simulations were conducted for the baseline climate and for each of the three climate scenarios, with and without CO2 enrichment (to estimate the relative contributions of CO2 and climate to crop yield changes), and assuming different levels of adaptation capacity.

I examined simulations from 34 different climate models, each run with projected increases in greenhouse gas concentrations.

To better assess confidence in the different model estimates of climate sensitivity, two kinds of observational tests are available: tests related to the global climate response associated with specified external forcings (discussed in Chapters 6, 9 and 10; Box 10.2) and tests focused on the simulation of key feedback processes.

What appears to have happened, based on global climate model simulations run by Shakun et al., is not all that different from our previous explanation of the supposed CO2 lag - just a bit more nuanced.

Assessments of our relative confidence in climate projections from different models should ideally be based on a comprehensive set of observational tests that would allow us to quantify model errors in simulating a wide variety of climate statistics, including simulations of the mean climate and variability and of particular climate processes.

One approach is to use an MME, which consists of simulations contributed by different models of climate research institutes from around the world, often referred to as an «ensemble of opportunity».

In the present study, simulations of the present - day climate by two kinds of climate model ensembles, multi-model ensembles (MMEs) of CMIP3 and single model ensembles (SMEs) of structurally different climate models, HadSM3 / CM3, MIROC3.2, and NCAR CAM3.1, are investigated through the rank histogram approach.

Say I have data on average precipitation for the last 30 years in the Southwest United States, as well as simulations from 20 different climate models of current and future precipitation in the same region, and I want to know what the expected change in precipitation will be at the end of this century under a specific emissions scenario.

For example, Stainforth et al. (2005) have shown that many different combinations of uncertain model sub-grid scale parameters can lead to good simulations of global mean surface temperature, but do not lead to a robust result for the model's climate sensitivity.

Next, the magnitudes and patterns of climate change from high - end model simulations are examined and compared with the remaining projections, to see whether the behaviour of these two classes of model is very different.

They are intended to be scenario simulations, illustrating the response of the climate system to a range of different emission scenarios, with all other factors (like volcanoes, solar, landcover) remaining the same (although some models are starting to put in interactive vegetation).

The authors used a large set of simulations from 18 different climate models (from CMIP5).