Sentences with phrase «models and observations force»

In previous posts we have stressed that discrepancies between models and observations force scientists to re-examine the foundations of both the modelling and the interpretation of the data.

Not exact matches

Themes: Aerosols, Arctic and Antarctic climate, Atmospheric Science, Climate modelling, Climate sensitivity, Extreme events, Global warming, Greenhouse gases, Mitigation of Climate Change, Present - day observations, Oceans, Paleo - climate, Responses to common contrarian arguments, The Practice of Science, Solar forcing, Projections of future climate, Climate in the media, Meeting Reports, Miscellaneous.

Figure 1: Annual average TOA shortwave cloud forcing for present - day conditions from 16 IPCC AR4 models and iRAM (bottom center) compared with CERES satellite observations (bottom right)

The discovery was that Procyon, one of the most studied stars, shows no pulsations at all, which contradicts 20 years of ideas and observations, and forced astronomers to rethink their models for stars.

Phytoplankton ecology, photophysiology, bio-optical oceanography, modeling of marine primary production, physical forcing and regulation of phytoplankton biomass and production, flow cytometry, instrument development for autonomous phytoplankton observation, and remote sensing of phytoplankton.

Knutti, R., T.F. Stocker, F. Joos, and G.K. Plattner, 2002: Constraints on radiative forcing and future climate change from observations and climate model ensembles.

There is medium confidence that the GMST trend difference between models and observations during 1998 — 2012 is to a substantial degree caused by internal variability, with possible contributions from forcing error and some CMIP5 models overestimating the response to increasing greenhouse - gas forcing.»

The figure below, taken from the 2007 IPCC report, shows model runs with only natural forcings; model runs with all forcings; and observations of surface temperatures for the whole globe — land areas and ocean areas.

In contrast to this observational approach, Schmittner (as well as a number of other papers, like reference # 4 in this post) take advantage of both models and observations, and try to use the observations to constrain which feedback parameters in the model are consistent (e.g., an overly sensitive model with the same forcings as another model will produce too big of a temperature change than observations allow).

This method tries to maximize using pure observations to find the temperature change and the forcing (you might need a model to constrain some of the forcings, but there's a lot of uncertainty about how the surface and atmospheric albedo changed during glacial times... a lot of studies only look at dust and not other aerosols, there is a lot of uncertainty about vegetation change, etc).

While complex, the take - aways are that the observations, shown in panels H and I, are much more similar to the all - forcings model runs (panel A) and the anthropogenic forcings - only runs (panel D) than to the runs using either all natural forcings (E) or only specific natural forcings (volcanoes only in F, solar only in G).

However, satellite observations are notably cooler in the lower troposphere than predicted by climate models, and the research team in their paper acknowledge this, remarking: «One area of concern is that on average... simulations underestimate the observed lower stratospheric cooling and overestimate tropospheric warming... These differences must be due to some combination of errors in model forcings, model response errors, residual observational inhomogeneities, and an unusual manifestation of natural internal variability in the observations.»

Another approach uses the response of climate models, most often simple climate models or Earth System Models of Intermediate Complexity (EMICs, Table 8.3) to explore the range of forcings and climate parameters that yield results consistent with observations (Andronova and Schlesinger, 2001; Forest et al., 2002; Harvey and Kaufmann, 2002; Knutti et al., 2002, 2003; Forest et al., models, most often simple climate models or Earth System Models of Intermediate Complexity (EMICs, Table 8.3) to explore the range of forcings and climate parameters that yield results consistent with observations (Andronova and Schlesinger, 2001; Forest et al., 2002; Harvey and Kaufmann, 2002; Knutti et al., 2002, 2003; Forest et al., models or Earth System Models of Intermediate Complexity (EMICs, Table 8.3) to explore the range of forcings and climate parameters that yield results consistent with observations (Andronova and Schlesinger, 2001; Forest et al., 2002; Harvey and Kaufmann, 2002; Knutti et al., 2002, 2003; Forest et al., Models of Intermediate Complexity (EMICs, Table 8.3) to explore the range of forcings and climate parameters that yield results consistent with observations (Andronova and Schlesinger, 2001; Forest et al., 2002; Harvey and Kaufmann, 2002; Knutti et al., 2002, 2003; Forest et al., 2006).

«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.»

I understand the argument that past projections are based on estimated future forcings which can change, but this amounts to the same things as tuning hindcasts and declaring matching a hindcast to observations as a validation of your model.

We have many studies presenting the projections from GCMs under various forcing scenarios where unforced variability is simulated, and we have a few studies (not many I think) which have a model reproduce the * actual * forcings and unforced variability and see how well the output matches observations (a recent one by Yu Kosaka and Shang - Ping Xie being a case in point).

In this case, there has been an identification of a host of small issues (and, in truth, there are always small issues in any complex field) that have involved the fidelity of the observations (the spatial coverage, the corrections for known biases), the fidelity of the models (issues with the forcings, examinations of the variability in ocean vertical transports etc.), and the coherence of the model - data comparisons.

You can also account for possible errors in the amplitudes of the external forcing and the model response by scaling the signal patterns to best match the observations without influencing the attribution from fingerprinting methods, and this provides a more robust framework for attributing signals than simply looking at the time history of global temperature in models and obs and seeing if they match up or not.

I actually believe there are positive and negative forces, but based on the data models and observations the positives are outweighing the negatives.

However, the observations are well within the spread of the models and so could easily be within the range of the forced trend + simulated internal variability.

* Indeed, possible errors in the amplitudes of the external forcing and a models response are accounted for by scaling the signal patterns to best match observations, and thus the robustness of the IPCC conclusion is not slaved to uncertainties in aerosol forcing or sensitivity being off.

And yes, there is such evidence — in the predicted response to volcanic forcing, the ozone hole, orbital variations, the sun, paleo - lake outbursts, the response to ENSO etc. that all show models matching the observations skillfully (which is not to say they match perfectly).

Oops sorry — the one you should start with is Reto Knutti, Thomas Stocker, Fortunat Joos & Gian - Kasper Plattner, Constraints on radiative forcing and future climate change from observations and climate model ensmbles, Nature 416, 18 April 2002.

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.

It is argued that uncertainty, differences and errors in sea ice model forcing sets complicate the use of models to determine the exact causes of the recently reported decline in Arctic sea ice thickness, but help in the determination of robust features if the models are tuned appropriately against observations.

Using comprehensive data sets of observations made between 1979 and 2001 of sea ice thickness, draft, extent, and speeds, we find that it is possible to tune model parameters to give satisfactory agreement with observed data, thereby highlighting the skill of modern sea ice models, though the parameter values chosen differ according to the model forcing used.

Summary for Policymakers Chapter 1: Introduction Chapter 2: Observations: Atmosphere and Surface Chapter 3: Observations: Ocean Chapter 4: Observations: Cryosphere Chapter 5: Information from Paleoclimate Archives Chapter 6: Carbon and Other Biogeochemical Cycles Chapter 7: Clouds and Aerosols Chapter 8: Anthropogenic and Natural Radiative Forcing Chapter 8 Supplement Chapter 9: Evaluation of Climate Models Chapter 10: Detection and Attribution of Climate Change: from Global to Regional Chapter 11: Near - term Climate Change: Projections and Predictability Chapter 12: Long - term Climate Change: Projections, Commitments and Irreversibility Chapter 13: Sea Level Change Chapter 14: Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Supplement Technical Summary

Are the observations of initial and boundary forcings of ocean models adequate to provide good simulations?

The agreement of this model with observations is particularly good and perhaps partly fortuitous, given that there is still uncertainty both in the climate sensitivity and in the amplitudes of the aerosol and solar forcings.

A detailed reanalysis is presented of a «Bayesian» climate parameter study (Forest et al., 2006) that estimates climate sensitivity (ECS) jointly with effective ocean diffusivity and aerosol forcing, using optimal fingerprints to compare multi-decadal observations with simulations by the MIT 2D climate model at varying settings of the three climate parameters.

There might be value in a more generic piece focusing on this kind of tension, which is going to be with us for some time — the slow steady responses predicted by current models, the concern that some observations of faster and bigger changes might actually be the greenhouse gas - forced signal and not just internal variability, the patience required before new observations and better theories / models sort things out.

Many different models have now demonstrated that our understanding of current forcings, long - term observations of the land surface and ocean temperature changes and the canonical estimates of climate forcing are all consistent within the uncertainties.

Just to clarify: It is important to distinguish between attributing the differences between models (with anthropogenic forcing) and observations to natural variability and attributing the 33 + year decline in sea ice to anthropogenic forcing.

Like I say, you see a richness of behaviour in the models including in some occasions behaviour that at first sight looks not dissimilar to that highlighted in the observations by the Thompson paper and this on top of the «external control» as we called it in our 2000 paper in Science of the external forcings in a particular model which drives much of the multi-decadal hemispheric response in these models and which, in terms of the overall global warming response, is dominated by greenhouse gases.

See Stowasser & Hamilton, Relationship between Shortwave Cloud Radiative Forcing and Local Meteorological Variables Compared in Observations and Several Global Climate Models, Journal of Climate 2006; Lauer et al., The Impact of Global Warming on Marine Boundary Layer Clouds over the Eastern Pacific — A Regional Model Study, Journal of Climate 2010.

But this means that they could always «force» a model to reproduce a particular set of observations by telling it exactly what those observations are and making it flexible enough to fit them exactly.

Penn State and the University of Hawaii both shared a grant of $ 770,000 for a research project called «Improved Projections of the Climate Response to Anthropogenic Forcing: Combining Paleoclimate Proxy and Instrumental Observations with an Earth System Model».

According to Saha et al. (2010), for land - surface analysis, the model - generated precipitation is replaced by the a mix of observation - based (CMAP and CPCU) and model - generated precipitation as forcing data.

If only GHG forcing is used, without aerosols, the surface temperature in the last decade or so is about 0.3 - 0.4 C higher than observations; adding in aerosols has a cooling effect of about 0.3 - 0.4 C (and so cancelling out a portion of the GHG warming), providing a fairly good match between the climate model simulations and the observations.

Radiative transfer models use fundamental physical equations and observations to translate this increased downward radiation into a radiative forcing, which effectively tells us how much increased energy is reaching the Earth's surface.

Its seven chapters discuss the global climate models, forcings and feedbacks, solar forcing of the climate, and observations on temperature, the icecaps, the water cycle and oceans, and weather.»

The proposition to be proved (# 7) is assumed in premise # 3 by virtue of kludging of the model parameters and the aerosol forcing to agree with the 20th century observations of surface temperature.

The high confidence level ascribed by the IPCC provides bootstrapped plausibility to the uncertain temperature observations, uncertain forcing, and uncertain model sensitivity, each of which has been demonstrated in the previous sections to have large uncertainties that were not accounted for in the conclusion.

Here we apply such a method using near surface air temperature observations over the 1851 — 2010 period, historical simulations of the response to changing greenhouse gases, aerosols and natural forcings, and simulations of future climate change under the Representative Concentration Pathways from the second generation Canadian Earth System Model (CanESM2).

I'm puzzled by your assignment of only a 30 percent probability to the proposition that «Global climate model simulations that include anthropogenic forcing (greenhouse gases and pollution aerosol) provide better agreement with historical observations in the second half of the 20th century than do simulations with only natural forcing (solar and volcanoes).»

Modelling and observations presented above show that differences higher than the error of measurement are observed between the model based on surface forcing (observed SAT plus assumed POM) and observation.

with respect to «It is seen from the figure with both natural and human forcing that climate models simulations agree with observations very well during the period 1970 - 2000.»

Global climate model simulations that include anthropogenic forcing (greenhouse gases and pollution aerosol) provide better agreement with historical observations in the second half of the 20th century than do simulations with only natural forcing (solar and volcanoes).

So I asked Mr. Knappenberger to test the models» agreement with long - term observations using a new «third» scenario in which internal variability once again «enhances» the «externally forced trend» and global warming resumes at the 1984 - 1998 rate of 0.265 ºC / decade.

The method combines the results of long - term atmospheric reanalyses downscaled with a stochastic statistical method and homogenized station observations to derive the meteorological forcing needed for hydrological modeling.