Michaelyya/contrarian-claims huggingface dataset:
This report relates to climate change scenarios based on computer modelling. Models involve simplifications of the real processes that are not fully understood. Accordingly, no responsibility is accepted by the CSIRO for the accuracy of forecasts or predictions inferred from this report or for any persons interpretations, deductions, conclusions or actions in reliance on this report.
Climate models are portrayed by the media and by political entities like the IPCC as splendid tools for forecasting future climate. The climate science community, however, is well aware that climate models are deeply flawed. Rarely, if ever, are the models chronic problems presented to the public and policymakers. In this post, I cited scientific studies that showed that the models are flawed at simulating, for example:
The two main points here are (1) the observations are much cooler than the mid-range of the model spread and are at the minimum of the model simulations and (2) the satellite adjustment for surface comparisons is exceptionally good. The implication of (1) is that the best estimates of the IPCC models are too warm, or that they are too sensitive to CO2 emissions.
Climate models are unable to replicate the observed behavior in the patterns of changes in the daily temperature range. Instead, the models warm daily high temperatures too fastprobably because they dont account properly for cloud dynamics.
The left panel shows the models predictions to 2050. The right panel shows the discrepancy of half a Celsius degree between prediction and reality since 2005.
* Continuing scientific dispute exists over whether observations are confirming or disconfirming key short-run predictions of climate models such as an increase in tropospheric water vapor and an increase in tropical tropospheric surface temperatures relative to tropical surface temperatures;
Computer simulations of the climate, referred to as general circulation models (GCMs), can be used to assess the sensitivity of climate to changes that might result from increased greenhouse gases. However, because physical feedbacks between Earths atmosphere (including clouds), the ocean, and the biosphere remain incomplete in the models, their use as a tool is limited.
The most important measure of model skill is of course its ability to predict previously unmeasured (or unnoticed) phenomena or connections in ways that are more accurate than some simpler heuristic. Many examples exist, from straightforward predictions (ahead of time) of the likely impact of the Pinatubo eruption (Hansen et al 1992), the skillful projection of the last three decades of warming (Hansen et al 1988; Hargreaves 2010) and correctly predicting the resolution of disagreements between different sources of observation data e.g., between ocean and land temperature reconstructions in the last glacial period (Rind and Peteet 1985), or the satellite and surface temperature records in the 1990s (Mears et al 2003; Thorne et al 2011). Against this must be balanced predictions that did not match subsequent observationsfor instance the underestimate of the rate of Arctic sea ice loss in CMIP3 (Stroeve et al 2007).
The successful launch of this new atmospheric monitoring system and its detailed diagnosis of clouds will be a very important achievement. The need for this data shows that the understanding and skillful prediction of the climate system has more uncertainties than has been recognized in multi-decadal climate projections, such as reported by the U.S. National Assessment of the Potential Consequences of Climate Variability and Change and the Intergovernmental Panel on Climate Change assessments.
Imagine that! No consensus , not even in terms of the sign of the longwave cloud feedback. Perhaps the GCMs really are cursed. And to rid existing models of that possibility, Huang concludes his paper by stating that "more studies are still required to understand how clouds modify global warming, even with regard to the longwave aspect alone."
Aside from that, I think some of the criticism is a bit exaggerated. But it really depends on what you want to look at. Some models are worse than others, but there are some models which are used in seasonal prediction and are able to provide some description of ENSO albeit with biases. My own analysis of various GCMs also suggest that they reproduce the characteristics of the NAO too.
5. These results are the sensitivity of the models with respect to their own outputs , not the sensitivity of the real earth. It is their internal sensitivity.
I don??t know if the authors used NCAR-PCM or NCAR-CCSM3. But note that NCAR-CCSM3 shows more warming than typical of models. I would suggest that if NCAR-CCSM3 was chosen, one might need to take the projections about impact of farming with a grain of salt. That said: it??s possible precipitation is the greater effect and my comparison of models shown above can??t tell us anything about that.
Is it really possible that the simple no-change forecast of 21st Century temperatures is better than the IPCC projections from expensive and complex computer models?
Pat Michaels is in San Francisco this week attending the annual meeting of the American Geophysical Union (AGU) and presenting a poster detailing the widening mismatch between observations of the earths temperature and climate model projections of its behavior. Since most global warming concern (including that behind regulatory action) stems from the projections of climate models as to how the earths temperature will evolve as we emit greenhouse gases into the atmosphere (as a result of burning fossil fuels to produce energy), it is important to keep a tab on how the model projections are faring when compared with reality. That they are faring not very well should be more widely knownPat will spread the word while there.
However, it should be understood that RCMs operate under a set of boundary conditions set by whatever GCM is being used. Hence, if the GCM does not do an adequate job of reproducing the climate signal of a particular region, the RCM will simply mimic those inaccuracies and biases, and propagate the uncertainties even further, albeit at a regional scale.
Even when climate alarmists cheat by cherry-picking the data they use to show climate models are consistent with observed temperatures, their efforts fail. John Christy of the University of Alabama at Huntsville shows climate models project from 157 percent to 297 percent more warming than has been recorded by any data set in the global mid-troposphere where the models indicate greenhouse gases should amplify warming the most. Models projected 230 percent more warming than the median value of the combined observational data sets. READ MORE
World leaders are making critical decisions based upon predictions of General Circulation Models or Global Climate Models GCMs) that humans are causing global climate change or global warming. Global climate models attempt to describe the earth's climate and are used in variety of applications. These include the investigation of the possible causes of climate change and the simulation of past and future climates. But these models are limited in important ways.
As they describe it, they explored "the potential skill of Coupled Model Intercomparison Project (CMIP5) decadal hindcasts from 9 GCMs and their ensembles over the period 1960-2010 from a hydrological perspective." And what did they thereby find? Quoting them again, they say that "the CMIP5 decadal simulations of rainfall exhibit very low skill when assessed across Australia and across a range of time-scales from yearly to longer which extend to the entire decade." And it is this finding that leads them to ultimately conclude that the current state of precipitation predictability "is not enough to drive impact models at decadal timescales and to influence policy and decision making."
Dr Benny Peiser, of Lord Lawsons Global Warming Policy Foundation, argued todays report from the Intergovernmental Panel on Climate Change (IPCC) is based on flawed models which do not give an accurate prediction of a future change in temperature.
Uncertainty about important feedback mechanisms is one reason why the ultimate goal of climate modeling forecasting reliably the future of key variables such as temperature and rainfall patterns is not realizable.
Too, we dont have accurate actual measurements of all locations, levels, and components (like temperature, moisture, wind speed, and so on) of the atmosphere, land, ocean (and outer space). The number of measurements we do have is minuscule compared to the actual size of the earth. So it is hard to reconcile the output of models with what the actual state of the earth is, though, of course, this is a necessary step. The process whereby the models are adjusted so that they conform to both the actual measurements and to the scientists expectations is called tuning . All models undergo extensive tuning until the majority of it users are satisfied of the output.
In his book, Climate Change: A Natural Hazard, William Kininmonth, former head of Australias National Climate Centre says, The simple one-dimensional energy balance model used by the IPCC to justify its radiative forcing hypothesis is unrealistic in its portrayal of processes at the earth-atmosphere interface. The IPCC model suggests that the heat and latent energy exchange between the underlying surface and the atmosphere is a direct response to the imbalance of solar energy and terrestrial radiation at the surface. Such a proposal is at odds with the physics of the surface energy exchange processes. Its one of many errors made to achieve a result; actions that are the opposite of even poor science.
The first peer reviewed study determines that today's climate models will never be able to predict the climate. Essentially, climate models assume linear climate relationships yet the real-world climate is non-linear and chaotic - defying intermediate and long-term predictive "expertise" with predictable regularity.
In the absence of model verification or falsification, Stainforth et al. (2007) describes the challenges of building confidence in climate models. Owing to the long time scales present, particularly in the ocean component, there is no possibility of atrue cycle of model improvement and confirmation, particularly since the life cycle of an individual model version (order of a few years) is substantially less than the simulation period (order of centuries). Model projections of future climate states relate to a state of the system that has not been previously experienced. Hence it is impossible to calibrate the model for the future climate state or confirm the usefulness of the forecast.
Most of the fundamental problems in our still immature understanding of climate have remained unresolved for decades. Some cannot be resolved at all. The U.N.?s climate panel admitted in 2001 what has been known for 50 years: Because climate is a ?coupled, non-linear, chaotic object,? reliable long-term climate prediction is impossible.
I welcome articles from you that show the IPCC models have skill on the multi-decadal time periods. Until you (or others) do, all the IPCC-type models can tell us is that the climate system is sensitive to the addition of CO2, other greenhouse gases, aerosols and land use/land cover change. We do not need a multi-hundred million dollar modeling program to provide regional and local impact scenarios when the models have shown no skill at doing this on multi-decadal time scales (even when run in a hindcast mode).
However, for the 15-year trend interval corresponding to the latest observation period 1998-2012, only 2% of the 62 CMIP5 and less than 1% of the 189 CMIP3 trend computations are as low as or lower than the observed trend. Applying the standard 5% statistical critical value, we conclude that the model projections are inconsistent with the recent observed global warming over the period 1998- 2012.
???To date, not one of theirs and other computer models proving global warming have been accurate.?? ??? Alan Caruba
A paper published today in the Journal of Climate finds that ensembles of climate models used by the IPCC to predict future climate change"may lead to overly confident climate predictions." The authors find that many models share the same computer code, have the same limitations, and "tend to be fairly similar," resulting in confirmation bias. Indeed, empirical observations have shown far less warming than the "90% confident" IPCC models in AR4, as shown in this poster by John Christy:
Given the inadequacies of current climate models, how should we interpret the multi-model ensemble simulations of the 21 st century climate used in the IPCC assessment reports? This ensemble-of-opportunity is comprised of models with generally similar structures but different parameter choices and calibration histories. McWilliams (2007) and Parker (2010) argue that current climate model ensembles are not designed to sample representational uncertainty in a thorough or strategic way. Stainforth et al. (2007) argue that model inadequacy and an inadequate number of simulations in the ensemble preclude producing meaningful probability density functions (PDFs) from the frequency of model outcomes of future climate. Nevertheless, as summarized by Parker (2010), it is becoming increasingly common for results from individual multi-model and perturbed-physics simulations to be transformed into probabilistic projections of future climate, using Bayesian and other techniques. Parker argues that the reliability of these probabilistic projections is unknown, and in many cases they lack robustness. Knutti et al. (2008) argues that the real challenge lies more in how to interpret the PDFs rather whether they should be constructed in the first place. Stainforth et al. (2007) warns against over interpreting current model results since they could be contradicted by the next generation of models, undermining the credibility of the new generation of model simulations.
Trying to take the planetary energy balance equationand torture it into something that it was never intended to do is pointless and misleading at best. The planetary energy balance equation is the simplest possible climate model. On the other hand there are the general circulation global climate models, which are of enormous complexity. In between is a whole class of energy balance climate models, which were very popular in the 1950s 1980s, the Wikipedia gives a good introduction to this class of models, which includes box models, radiative-convective models, higher dimension versions of the zero-dimensional planetary energy balance model, and Earth system models of intermediate complexity (statistical/dynamical models). Different models can be used depending the nature of questions asked and the pertinent time scales.
Model simulations are able to show how a possible future might look like. However, it is very difficult regionally to make reliable conclusions as we run against the limits of what is possible with climate models today. You can only fathom the possibilities.
We couldnt say it any better once again, the real world is not cooperating with expectations from predictions from numerical climate models for a world of never-seen-before environmental conditions resulting from ever-increasing atmospheric greenhouse gas concentrations.
In his new paper, Lovejoy applies the same approach to the 15-year period after 1998, during which globally averaged temperatures remained high by historical standards, but were somewhat below most predictions generated by the complex computer models used by scientists to estimate the effects of greenhouse-gas emissions.
And the NRC reminds us that there are other fundamental uncertainties as well. There is a "wide range of uncertainty inherent in current model predictions of global climate change" that can only be reduced by "major advances in understanding and modeling" of greenhouse gas emissions, the role of aerosols in the global climate system, and the incredibly complex "feedback" mechanisms in the Earth's climate.
The models say the Earth system ought to accrue energy at the rate of 3W/m 2 . Instead the best estimate we have of recent energy balance suggests we??ve been losing energy at a rate of about 0.1 W/m 2 (Knox and Douglass 2010). The models don??t match the observations.
Simulations using several IPCC-class general circulation models (GCMs) are used to demonstrate that the adjustment to climate change itself takes place over decadal timescales, meaning that ENSO response is not statistically significant during the 21st century. This implies that current model intercomparisonexperiments are insufficient to measure the true range of ENSO climate sensitivity. However, significant changes to atmospheric teleconnectionsmay take place within the 21st century: the NCARCommunity Climate System Model version 4 (CCSM4), for example, predicts harsher winters in the Southwestern US during La Nia and weaker Australian teleconnections during both El Nio and La Nia.
3) Chaotic Uncertainty. It is well understood that climate is a chaotic object and climate models reflect that property. The outcome of a particular model run (???simulation??) depends sensitively on the initial conditions; even minute changes can lead to greatly differing outcomes. For example, the five runs of a Japanese MRI model show temperature trends that differ by almost a factor of 10, an order of magnitude. (If more runs had been performed, the spread would have been even greater.) One can show that taking the mean of an ensemble of more than 10 runs leads to an asymptotic value for the trend. However, most modelers face constraints on time and money and are not able to carry out so many runs. For example, of the 22 models in the IPCC compilation of ???20 CEN?? there are 5 single run models, 5 two-run models, and only 7 models with four or more runs.
When the data sets are compared to model projections for the tropical mid-troposphere, where climate models say a tropical hot spot should emerge, the models do even worse, overstating the warming by between 169 and 414 percent overestimating the median of the observational data sets for the tropics by 330 percent.
Even in the absence of external forcing, climate models often exhibit long-term trends that cannot be attributed to natural variability . This so called climate drift arises for various reasons including: perturbations to the climate system on coupling component models together and deficiencies in model physics and numerics.To illustrate this we have focussed on simulated trends over the second half of the 20th century Below depths of 1000 to 2000m, drift dominates over any forced trend in most regions. As such steric sea-level is strongly affected and for some models and regions the sea-level trend direction is reversed.
GoshWhere Did All of Those Expensive Climate Models Go Wrong?
Pearce also tries to suggest that forecasting climate change is like forecasting the seasons i.e., easy. Unfortunately, Edward Lorenz proved that claim to be false very nearly half a century ago, in the landmark paper in a climatological journal that founded chaos theory. Lorenz demonstrated that unless we knew the values of the millions of parameters that define the climate to a precision that is not and can never be in practice attainable the long-range prediction of climatic behavior would be impossible by any method. Yet the UNs case for climate alarm is based almost exclusively on the output of models that have been demonstrated, time and again, to fail. Not one of the models on which the UN relies, for instance, had predicted that there would be 15 years without any statistically-significant global warming the years 1995-2009 inclusive.
Callaghan and Power's analysis of their lengthy and comprehensive new data base reveals results that appear to be totally at odds with the contentions of the IPCC, which are based more on the output of numerical models of the atmosphere than on real-world observations. And their results also highlight the fact that even real-world observations may be misleading, especially if they do not cover a long enough time period to reveal the oscillatory nature of various aspects of earth's climate. References
This is yet another reason why we need to move away from using multi-decadal global climate models as the tool to communicate to policymakers. They are NOT skillful multi-decadal projection tools even in terms of global average radiative forcing.
How Have Climate Models Done Predicting Cloud Coverage? Not Too Well
Just add sea ice onto the growing list of variables that are simulated poorly by the IPCCs climate models. Over the past few months, weve illustrated and discussed that the climate models stored in the CMIP5 archive for the upcoming 5 th Assessment Report (AR5) cannot simulate observed:
In the end the authors brand this unsolved mystery the ?Holocene temperature conundrum.? We have heard this song before, usually from climate modelers asking for more grant money to ?improve? their wonky programs. There may be some wiggle room in the interpretation of the proxy data but the new study, M13, is supposed to be the latest and most accurate temperature history available. This points directly back at the models as the weak link in the story. ?If the M13 reconstruction is correct, it will imply major biases across the current generation of climate models,? Liu et al conclude. This is more than just a conundrum for climate scientists?those computer models are the embodiment of climate science's understanding of how climate works here on planet Earth. If the models do not work, climate science has gotten it wrong.
I invite anyone to defend this perspective, and we will present as a guest weblog post. From my perspective, if a global climate model cannot simulate current climate, as well as changes in the climate system, accurately it cannot produce accurate projections of climate in the coming decades.
The authors write that "how well the CMIP5 GCMs simulate climate at regional scales is of great interest to both researchers and resource managers," because, as they say in stating the obvious, "a model that fails to reproduce aspects of the past climate will be less likely to produce a correct projection of future climate."
??And that strength of models is, if you still do not consider adequately the effect of clouds? They can not explain the polar climate? They can not predict the next hurricane season?
The dynamical trends were similar in the observations and the model simu- lations, except that the dynamical effect was much larger in the observations than in both model experiments. In other words, the dynamical effects are clearly present in the state-of-the-art global climate models, but they are much too small to match the data.
As for some other specifics, the two researchers determined that (1) "long-term mean vertically integrated cloud fields have quite significant deficiencies in all the CMIP5 model simulations," that (2) "both the CMIP5 and CMIP3 models display a clear bias in simulating too high LWP in mid-latitudes," that (3) "this bias is not reduced in the CMIP5 models," that (4) there have been "little to no changes in the skill of reproducing the observed LWP and CA ," that (5) "inter-model differences are still large in the CMIP5 simulations," and that (6) "there is very little to no improvement apparent in the tropical and subtropical regions in CMIP5."
While climate models are supposed to be snapshots of the Earths real climate, the mismatch between observed and modeled climate behavior shows that even the best models are not accurate and are likely overestimating outcomes. Gerald North, a Texas A&M atmospheric sciences and oceanography professor, estimates that climate models overestimate warming by roughly 50 percent.
(And what fundamental feedback in the tropical Pacific are climate models still unable to simulate correctly? Thats right: Bjerknes feedback. See Bellenger et al (2013) .)