Sentences with phrase «ensemble of climate model simulations»

A large ensemble of climate model simulations suggests that the frequency of extreme wet - to - dry precipitation events will increase by 25 % to 100 % across California due to anthropogenic forcing.

We prefer this result since it was based on a comprehensive analysis using extensive proxy data from both land and ocean in combination with an ensemble of climate model simulations, in a paper focused entirely on LGM cooling.

Between 801 and 1800 ce, the surface cooling trend is qualitatively consistent with an independent synthesis of terrestrial temperature reconstructions, and with a sea surface temperature composite derived from an ensemble of climate model simulations using best estimates of past external radiative forcings.

it says «Large ensembles of climate model simulations have shown that the ability of models to simulate present climate has value in constraining climate sensitivity.».

Not exact matches

The first is the development of a comprehensive, closely coordinated ensemble of simulations from 18 modeling groups around the world for the historical and future evolution of the earth's climate.

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.

Murphy, J.M., et al., 2004: Quantification of modelling uncertainties in a large ensemble of climate change simulations.

A large ensemble of Earth system model simulations, constrained by geological and historical observations of past climate change, demonstrates our self ‐ adjusting mitigation approach for a range of climate stabilization targets ranging from 1.5 to 4.5 °C, and generates AMP scenarios up to year 2300 for surface warming, carbon emissions, atmospheric CO2, global mean sea level, and surface ocean acidification.

M2009 use a simplified carbon cycle and climate model to make a large ensemble of simulations in which principal uncertainties in the carbon cycle, radiative forcings, and climate response are allowed to vary, thus yielding a probability distribution for global warming as a function of time throughout the 21st century.

Recently I have been looking at the climate models collected in the CMIP3 archive which have been analysed and assessed in IPCC and it is very interesting to see how the forced changes — i.e. the changes driven the external factors such as greenhouse gases, tropospheric aerosols, solar forcing and stratospheric volcanic aerosols drive the forced response in the models (which you can see by averaging out several simulations of the same model with the same forcing)-- differ from the internal variability, such as associated with variations of the North Atlantic and the ENSO etc, which you can see by looking at individual realisations of a particular model and how it differs from the ensemble mean.

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).

Natural variability from the ensemble of 587 21 - year - long segments of control simulations (with constant external forcings) from 24 Coupled Model Intercomparison Project phase 3 (CMIP3) climate models is shown in black and gray.

To ensure their models are accurate, Ault said researchers distinguished and separated normal climatic variability from long - term atmospheric alterations, by using a new ensemble of climate change simulations.

IPCC relied on climate models (CMIP5), the hypotheses under test if you will, to exclude natural variability: «Observed Global Mean Surface Temperature anomalies... lie well outside the range of Global Mean Surface Temperature anomalies in CMIP5 simulations with natural forcing only, but are consistent with the ensemble of CMIP5 simulations including both anthropogenic and natural forcing...» (Ref.: Working Group I contribution to fifth assessment report by IPCC.

Ensemble - A group of parallel model simulations used for climate projections.

Van Haren et al (2012) also nicely illustrate the dependence of regional skill on lateral boundary conditions: simulations of (historic) precipitation trends for Europe failed to match the observed trends when lateral boundary conditions were provided from an ensemble of CMIP3 global climate model simulations, while a much better correspondence with observations was obtained when reanalyses were used as boundary condition.

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 difEnsembles 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 difensembles including simulations by several models also include the impact of model differences.

In this way, we can obtain the expected range of projected climate trends using the interannual statistics of the observed NAO record in combination with the model's radiatively - forced response (given by the ensemble - mean of the 40 simulations).

We make use of a 40 - member ensemble of climate change simulations under historical and RCP8.5 radiative forcing scenarios for the period 1920 — 2100 conducted with the Community Earth System Model Version 1 (CESM1; Hurrell et al. 2013).

They ran an ensemble of simulations with a climate model of intermediate complexity to evaluate the causes of past climate changes.

The fact that the CMIP simulations ensemble mean can reproduce the 1970 — 2010 US SW temperature increase without inclusion of the AMO (the AMO is treated as an intrinsic natural climate vari - ability that is averaged out by taking an ensemble mean of individual simulations) suggests that the CMIP5 models» predicted US SW temperature sensitivity to the GHG has been significantly (by about a factor of two) overestimated.

«The fact that the CMIP simulations ensemble mean can reproduce the 1970 — 2010 US SW temperature increase without inclusion of the AMO (the AMO is treated as an intrinsic natural climate variability that is averaged out by taking an ensemble mean of individual simulations) suggests that the CMIP5 models» predicted US SW temperature sensitivity to the GHG has been significantly (by about a factor of two) overestimated.»

Where precision is an issue (e.g., in a climate forecast), only simulation ensembles made across systematically designed model families allow an estimate of the level of relevant irreducible imprecision... In each of these model — ensemble comparison studies, there are important but difficult questions: How well selected are the models for their plausibility?

Stainforth et al. (2007) argue that model inadequacy and an inadequate number of simulations in the ensemble preclude producing meaningful probability PDFs from the frequency of model outcomes of future climate.

M2009 use a simplified carbon cycle and climate model to make a large ensemble of simulations in which principal uncertainties in the carbon cycle, radiative forcings, and climate response are allowed to vary, thus yielding a probability distribution for global warming as a function of time throughout the 21st century.

Alternatively, an automated procedure based on a cluster initialization algorithm is proposed and applied to changes in 27 climate extremes indices between 1986 — 2005 and 2081 — 2100 from a large ensemble of phase 5 of the Coupled Model Intercomparison Project (CMIP5) simulations.

Simulations by regional climate models show good agreement with observations in the seasonal and spatial variability of the joint distribution, especially when an ensemble of simulationSimulations by regional climate models show good agreement with observations in the seasonal and spatial variability of the joint distribution, especially when an ensemble of simulationssimulations was used.

In CMIP5 there is no correlation between aerosol forcing and sensitivity across the ensemble, so the implication that aerosol forcing affects the climate sensitivity in such «forward» calculations is false... The spread of model climate sensitivities is completely independent of historical simulations.»

Continuous daily streamflow simulations from 1976 to 2100 (Alfieri et al. 2015a), forcing a distributed hydrological model (Lisflood, van der Knijff et al. 2010) with an ensemble of seven EURO - CORDEX (Jacob et al. 2014) RCP 8.5 downscaled regional climate scenarios over Europe.

It is true that the model replications of past conditions are not perfect, which is to be expected given the chaotic variations of the climate about its now - changing baseline; however, the ensemble of model simulations has been tested against previously observed perturbations to climate (such as the response to volcanic eruptions) and overall they correspond well with what is observed to occur.

The influence of reduced solar forcing (grand solar minimum or geoengineering scenarios like solar radiation management) on the Atlantic Meridional Overturning Circulation (AMOC) is assessed in an ensemble of atmosphere — ocean — chemistry — climate model simulations.

These fingerprints will be based on a large, multi-model ensemble of state - of - the - art climate model simulations.

«A strong warming and severe drought predicted on the basis of the ensemble mean of the CMIP climate models simulations is supported by our regression analysis only in a very unlikely case of the continually increasing AMO at a rate similar to its 1970 — 2010 increase» 7

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.

In this study, we primarily investigate the reliability of the climatology (long - term mean of model simulation) of large - scale features of climate model ensembles, but we also consider the trend for surface air temperature where transient simulations are available (that is, for the coupled ocean — atmosphere models).

Nature provides only one single realization of many possible realizations of temperature variability over time from a whole distribution of possible realizations of a chaotic system for the given climate conditions, whereas the ensemble mean of models is an average over many of the possible realizations (117 model simulations in this case).

To answer this question, large ensemble simulations of regional climate models will be carried out for an East Asian domain for two worlds: (1) Real world condition for which the observed sea surface temperatures will be prescribed and (2) Counter-factual world condition for which we will use adjusted sea surface temperatures obtained by removing human - induced ocean warming patterns.

In a new study published in the Journal of Climate, the Community Earth System Model Large Ensemble (CESM - LENS) of simulations is used to explore how various characteristics of the mid-latitude atmospheric circulation (zonal flow, synoptic blockings, jet stream meanders) evolve along the course of the 21st century under the RCP8.5 scenario of anthropogenic emissions.