Sentences with phrase «constrain model climate»

The researchers explain that these new findings can help constrain model climate projections over the Arctic region.

Not exact matches

Instead of looking at the differences in the climate effects of two sources that add the same total energy to the climate system, we constrained the model so those sources had the same climate effects.

Wigley et al. (1997) pointed out that uncertainties in forcing and response made it impossible to use observed global temperature changes to constrain ECS more tightly than the range explored by climate models at the time (1.5 °C to 4.5 °C), and particularly the upper end of the range, a conclusion confirmed by subsequent studies.

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.

Reduction of these uncertainties will be crucial for evaluating and better constraining climate models.

«We use a massive ensemble of the Bern2.5 D climate model of intermediate complexity, driven by bottom - up estimates of historic radiative forcing F, and constrained by a set of observations of the surface warming T since 1850 and heat uptake Q since the 1950s... Between 1850 and 2010, the climate system accumulated a total net forcing energy of 140 x 1022 J with a 5 - 95 % uncertainty range of 95 - 197 x 1022 J, corresponding to an average net radiative forcing of roughly 0.54 (0.36 - 0.76) Wm - 2.»

«A deeper look at the differences between the different land surface and Earth system models may help better constrain the response of mid-latitude ecosystems to climate variability.»

There are tons of studies — ranging from paleoclimate studies to studies of volcanic effects, etc. that constrain climate response and which generally yield results consistent with the models.

Cloud responses are more uncertain and that feeds in to the uncertainty in overall climate sensitivity — but the range in the AR4 models (2.1 to 4.5 deg C for 2xCO2) can't yet be constrained by paleo - climate results which have their own uncertainties.

This kind of forecast doesn't depend too much on the models at all — it is mainly related to the climate sensitivity which can be constrained independently of the models (i.e. via paleo - climate data), moderated by the thermal inertia of the oceans and assuming the (very likely) continuation of CO2 emissions at present or accelerated rates.

Optimization of climate models raises important questions about whether tuning methods a priori constrain the model results in unintended ways that would affect our confidence in climate projections.

Constraining climate sensitivity isn't a matter of a few «calculations,» but of considering known or modeled climate forcings and responses.

Permafrost modeling studies typically indicate a potential release of in the neighborhood ~ 200 PgC as carbon dioxide equivalent by 2100, though poorly constrained, but comparable to other biogeochemical and climate - ecosystem related feedbacks, such as the additional CO2 released by the warming of terrestrial soils.

Models also differ significantly in the degree of CO2 fertilisation they allow, and the extent to which CO2 responses are constrained by nutrient availability; the extent to which CO2 concentrations affect the global distribution of C3 and C4 photosynthetic pathways; and the impacts of climate, CO2 and land management on the tree - grass balance.

More broadly, the time is ripe to develop «learning climate models,» which from the outset incorporate the capacity to learn closure parameters (latent variables) from observations or from supercolumn simulations that are conducted as needed to constrain uncertain processes.

By constraining A through observations (grey bar), this example suggests that some models are more realistic (models 14 -15-17-18-22) and, by inference, provide more realistic future climate sensitivities.

The most popular observationally - constrained method of estimating climate sensitivity involves comparing data whose relation to S is too complex to permit direct estimation, such as temperatures over a spatio - temporal grid, with simulations thereof by a simplified climate model that has adjustable parameters for setting S and other key climate properties.

The day - by - day, month - by - month, year - by - year, etc. sequencing of values, however, will not correspond to observations, since climate models solve a «boundary value problem» and are not constrained to reproduce the timing of natural climate variability (e.g., El Niño - Southern Oscillation) in the observational record.

- ARAMATE (The reconstruction of ecosystem and climate variability in the north Atlantic region using annually resolved archives of marine and terrestrial ecosystems)- CLIM - ARCH-DATE (Integration of high resolution climate archives with archaeological and documentary evidence for the precise dating of maritime cultural and climatic events)- CLIVASH2k (Climate variability in Antarctica and Southern Hemisphere in the past 2000 years)- CoralHydro2k (Tropical ocean hydroclimate and temperature from coral archives)- Global T CFR (Global gridded temperature reconstruction method comparisons)- GMST reconstructions - Iso2k (A global synthesis of Common Era hydroclimate using water isotopes)- MULTICHRON (Constraining modeled multidecadal climate variability in the Atlantic using proxies derived from marine bivalve shells and coralline algae)- PALEOLINK (The missing link in the Past — Downscaling paleoclimatic Earth System Models)- PSR2k (Proxy Surrogate Reconstructclimate variability in the north Atlantic region using annually resolved archives of marine and terrestrial ecosystems)- CLIM - ARCH-DATE (Integration of high resolution climate archives with archaeological and documentary evidence for the precise dating of maritime cultural and climatic events)- CLIVASH2k (Climate variability in Antarctica and Southern Hemisphere in the past 2000 years)- CoralHydro2k (Tropical ocean hydroclimate and temperature from coral archives)- Global T CFR (Global gridded temperature reconstruction method comparisons)- GMST reconstructions - Iso2k (A global synthesis of Common Era hydroclimate using water isotopes)- MULTICHRON (Constraining modeled multidecadal climate variability in the Atlantic using proxies derived from marine bivalve shells and coralline algae)- PALEOLINK (The missing link in the Past — Downscaling paleoclimatic Earth System Models)- PSR2k (Proxy Surrogate Reconstructclimate archives with archaeological and documentary evidence for the precise dating of maritime cultural and climatic events)- CLIVASH2k (Climate variability in Antarctica and Southern Hemisphere in the past 2000 years)- CoralHydro2k (Tropical ocean hydroclimate and temperature from coral archives)- Global T CFR (Global gridded temperature reconstruction method comparisons)- GMST reconstructions - Iso2k (A global synthesis of Common Era hydroclimate using water isotopes)- MULTICHRON (Constraining modeled multidecadal climate variability in the Atlantic using proxies derived from marine bivalve shells and coralline algae)- PALEOLINK (The missing link in the Past — Downscaling paleoclimatic Earth System Models)- PSR2k (Proxy Surrogate ReconstructClimate variability in Antarctica and Southern Hemisphere in the past 2000 years)- CoralHydro2k (Tropical ocean hydroclimate and temperature from coral archives)- Global T CFR (Global gridded temperature reconstruction method comparisons)- GMST reconstructions - Iso2k (A global synthesis of Common Era hydroclimate using water isotopes)- MULTICHRON (Constraining modeled multidecadal climate variability in the Atlantic using proxies derived from marine bivalve shells and coralline algae)- PALEOLINK (The missing link in the Past — Downscaling paleoclimatic Earth System Models)- PSR2k (Proxy Surrogate Reconstructclimate variability in the Atlantic using proxies derived from marine bivalve shells and coralline algae)- PALEOLINK (The missing link in the Past — Downscaling paleoclimatic Earth System Models)- PSR2k (Proxy Surrogate Reconstruction 2k)

The «CSALT» model makes false sun - climate geometric assumptions that are strictly ruled out by law - constrained observations.

Carbon budgets have been estimated by a number of different methods, including complex ESMs (shown in yellow), simple climate models employed by Integrated Assessment Models (IAMs, shown in red), and by using observational data on emissions and warming through present to «constrain» the ESM results (shown in models employed by Integrated Assessment Models (IAMs, shown in red), and by using observational data on emissions and warming through present to «constrain» the ESM results (shown in Models (IAMs, shown in red), and by using observational data on emissions and warming through present to «constrain» the ESM results (shown in blue).

They generally use correlations between the response of climate models to increasing greenhouse gas (GHG) concentrations and a quantity in principle observable in the present climate (e.g., an amplitude of natural fluctuations) to constrain ECS given measurements of the present - day observable.

While regional climate downscaling yields higher spatial resolution, the downscaling is strongly dependent on the lateral boundary conditions and the methods used to constrain the regional climate model variables to the coarser spatial scale information from the parent global models.

If the models can not even accurately simulate current climate statistics when they are not constrained by real world data, the expense to run them to produce detailed spatial maps is not worthwhile.

But even with perfect models, climate predictability will always be constrained by unknown evolutions of climate forcings, both anthropogenic and non-anthropogenic.

Reduction of these uncertainties will be crucial for evaluating and better constraining climate models.

Climate projections have been remarkably difficult to constrain by comparing the simulated climatological state from different models with observations, in particular for small ensembles with structurally different models.

They discuss the part played by water vapour and cloud cover and summarise their conclusions as follows» Moreover it is not yet clear which tests are critical for constraining future projections.Consequently a set of model metrics that might be used to narrow the range of plausible climate change feedbacks and climate sensitivity has yet to be developed.»

«We use a massive ensemble of the Bern2.5 D climate model of intermediate complexity, driven by bottom - up estimates of historic radiative forcing F, and constrained by a set of observations of the surface warming T since 1850 and heat uptake Q since the 1950s... Between 1850 and 2010, the climate system accumulated a total net forcing energy of 140 x 1022 J with a 5 - 95 % uncertainty range of 95 - 197 x 1022 J, corresponding to an average net radiative forcing of roughly 0.54 (0.36 - 0.76) Wm - 2.»

In a system such as the climate, we can never include enough variables to describe the actual system on all relevant length scales (e.g. the butterfly effect — MICROSCOPIC perturbations grow exponentially in time to drive the system to completely different states over macroscopic time) so the best that we can often do is model it as a complex nonlinear set of ordinary differential equations with stochastic noise terms — a generalized Langevin equation or generalized Master equation, as it were — and average behaviors over what one hopes is a spanning set of butterfly - wing perturbations to assess whether or not the resulting system trajectories fill the available phase space uniformly or perhaps are restricted or constrained in some way.

The results should enable scientists to better constrain the effects of El Niños in climate models.

«The large - scale winds would look better because the release of latent heat drives a lot of those winds, and climate sensitivity would be better constrained because not only is the base state highly dependent on convective parameterization but the model predictions for future climate change are also very sensitive to that as well.»

With respect to Zhao (2016) he works out: «The problem is that, while it may be possible to find some properties of the climate simulation that look better in one of these models than the others, the biases in other parts of the model affecting the same metric can make it hard to make a convincing case that you have constrained cloud feedback.

Using proxy - data from the LGM at low and high latitudes we constrain the set of realistic model versions and thus climate sensitivity.

The «POGA - H» model also uses historical data for climate forcing, but constrains sea surface temperature in the tropical eastern Pacific (the ENSO region) to follow their historical values.

It is direct corollary of this this if you constrain the model with SSTs that contain climate variations significantly different from the climate they are designed to replicate they will produce different output.

These methods do not use physical information provided by climate models regarding the expected response magnitudes to constrain the estimated responses to the forcings.

of the present climate to constrain model projections of ECS.

Knowing that the spread in ECS is mostly related to uncertainties in low - cloud feedback, it seems obvious that constraining how low clouds respond to global warming can reduce the spread of climate sensitivity among models.

Therefore, we can use the covariation of TLC reflection with temperature obtained from observations of the present climate to constrain model projections of ECS.

In the late 1990s, signs of climate feedback started to be constrained by using climate models along with observations (e.g., Hall and Manabe, 1999).

Meehl et al. [2007] report that the 5 — 95 % range of equilibrium climate sensitivity in CMIP3 models is 2.1 — 4.4 K. Over the last three decades, a lot of attention has been given to ΔT2x but it is still relatively poorly constrained.

The authors tried to constrain the global - mean future precipitation change simulated by the set of climate models participating in the CMIP2 model intercomparison project through observable temperature variability and a simple energetic framework.

Climate philosophy, including models, is potentially science when constrained to a limited but variable frame of reference (i.e. scientific domain) in both time and space, where phenomena can observed, reproduced, and characterized through deduction.

Another approach is to «grow» the sediment column through geologic time to obtain an initial condition for a climate change perturbation scenario (Archer et al., 2012), but uncertainties in various model parameters, such as the methane production rate and the fate of bubbles in the sediment column, prevent a well - constrained model forecast of the methane hydrate response to climate warming.

That conclusion, in conjunction with a climate model incorporating only the most fundamental processes, constrains average fast - feedback climate sensitivity to be in the upper part of the sensitivity range that is normally quoted [1,48,99], i.e. the sensitivity is greater than 3 °C for 2 × CO2.

Climate envelope models do not simulate dynamic population or migration processes, and results are typically constrained to the regional level, so that the implications for biodiversity at the global level are difficult to infer (Malcolm et al., 2002a).

«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).»

We can rigidly constrain climate models using earth orientation data.

Hansen's climate analyses have been based not only on the very basic physics that goes into climate model design, but on the detailed studies of the geological ice core and isotope records that are used to constrain and confirm climate model sensitivity.