Thus far, no other candidate has said they're going to make climate change their top priority.
Ever notice how politicians' plans are always far out in the future? Sure, 35 years is within the scope of of most of our lives, but usually they are well past the time that the politicians proposing them will be around to face the consequences. We hear the same thing all the time about balancing the budget and paying down the deficit ever since Reagan, but neither one has happened yet.
Amen to that.
If we are gonna claim to be serious about cutting emissions, France has already proven the technology to do so has already existed for a long time. We can start funding the deployment of nuclear power on a large scale now. The technology all existed to transition years ago already when France did it and used it to this day to sell energy to the rest of Europe.
Meandering mouth service to researching solar or wind or some other solution isn't bad per se, but it is absolutely inadequate to stop there. There are real concrete actions that can be taken today by anyone that is truly motivated and convinced of the importance to do so.
In Chapter 9 of the IPCC AR5 Report The current status of tuning model performance notes the following, including a half dozen citations of peer-reviewed articles verifying the statement: Model tuning aims to match observed climate system behaviour and so is connected to judgements as to what constitutes a skilful representation of the Earth’s climate. For instance, maintaining the global mean top of the atmosphere (TOA) energy balance in a simulation of pre-industrial climate is essential to prevent the climate system from drifting to an unrealistic state. The models used in this report almost universally contain adjustments to parameters in their treatment of clouds to fulfil this important constraint of the climate system (Watanabe et al., 2010; Donner et al., 2011; Gent et al., 2011; Golaz et al., 2011; Martin et al., 2011; Hazeleger et al., 2012; Mauritsen et al., 2012; Hourdin et al., 2013).
For reference, TOA energy imbalance is the entire driving force of long term climate change, and models still have to hand-tune cloud parameters to get TOA energy correct.
Without getting TOA energy correct, the only thing the models are telling us is how climate components respond to changing TOA energy. They do NOT tell us the much more important element of how TOA energy will respond in the future. The current state of climate models is a necessary step to getting there, and is step forwards from the past practice of directly inserting energy when needed, but let's not go claiming we already are there. The facts and evidence state that we aren't. Any actual modellers claiming that they can predict future TOA energy, and thus macro climate trends, is being dishonest and there's a reason you don't see any of them quoted as such. Instead you see their papers quoted as the IPCC did above, and other folks are the ones coming in and crowing about the importance of the climate model's prediction without grasping the underlying factors that are still being tooled.
We are already a long way down this slope. The day that the notion of consensus was picked up was the day a knife was stuck into the heart of climate science and the religion of climate science was birthed. The state of things today leaves almost no possible future I can imagine that doesn't leave this entire timeframe and 'debate' as a huge black mark on science. The number of conditions, uncertainties, and qualifiers that have been left out of statements now, not only by the press but by journal authors themselves on blogs and other media, leaves almost no future were the public can't point backwards and cry that the 'consensus' from today didn't cause extinction of corals or polar bears or wipe miles off the Floride coastline by 2050...
Why not let the pope in on it all too at this point.
ISIS is crawling with Baathist leaders, not Al-Qaeda. In case you forgot. The baathists were Sadaam Hussein's group in Iraq. Guess who ousted them from leadership and simply disbanded the military with no continuation or accountability? The same ones who thought they would be greeted as liberators with no plan after "blow 'em up, they got WMDs!" -- little bush and his warmonger buddies.
Where did you get your drudge fox impression of the situation?
How can you know that and still miss the obvious? You correctly note that a lot of former Baathists are working with ISIS. Presumably from your tone you also are not a fan of ISIS and believe that them expanding their control and influence is a bad thing.
Can I suggest taking the next step and asking we ponder what Iraq might be like if those guys controlled the entire country and whether or not you think that would be a positive change?
The reason I ask, is because that WAS the situation before bush and his warmonger buddies ousted them. No question Bush and co fouled things up from day one, but the actual decision to remove Saddam and the Baathists was hardly a bad thing. ISIS has yet to touch the atrocities that Saddam perpetrated while in power, and yes, keeping the remnants of his diseased regime out of power is certainly important and another reason to resist ISIS expansion. Just don't try and over simplify things to the point you start making the absolutely idiotic wish that Bush hadn't screwed up Iraq with his horrible war. Iraq was already an awful place long before, and the former Baathists that are working with ISIS are the biggest part of that.
But isn't the TOA energy budget one of the most nailed down parameters, given that it is directly measured all over the place at all times by satellites? Unlike all the murky atmospheric transactions which have to be inferred and estimated and calculated from spotty measurements of varying characteristics?
Yes and no. The biggest distinction is between observation and modelling. We have a lot of direct observations of TOA energy that are more or less in agreement(more later), but in climate models the ability to simulate the observed TOA energy today and to maintain a neutral TOA energy when in equilibrium still requires tuning parameters like clouds by hand. Without that tuning, the TOA energy budget drifts off and all the model results drift out along with it. One of the reasons is that models leak or create energy at cell boundaries, which is to say that yes, conservation of energy is imperfect in the majority of current climate models. The other component of drift results from uncertainties in processes and parameters as the model runs.
Reality has dictated though that modellers jobs weren't hard enough with only the above to worry about. In reality, even the observational TOA energy imbalance is subject to a lot of instrumental uncertainty. The two main observational records used are from direct satellite measurements, but calibration issues for sensitive equipment on multiple satellites across the sky leave some uncertainty present. I don't have the exact numbers up in front of me, but the order of magnitude is approximately such that the total radiation coming in and going out at TOA is like 250W/m-2 going out, and 253W/M-2 going out. The error from calibration estimates vary, but something on the order of 1% or so is there, which sounds pretty tight. The trick for climate modellers is that the NET energy imbalance is 253-250, so 3W/M-2, where a 1% error adds up to +/-2.5W/M-2. Ocean heat content numbers are even tougher because you are trying to measure average temperature across the oceans and add up how much energy that is and watch for the delta from year to year, so an even tougher nut than the satellite data. The only good news for the poor guys trying to model this all is that ocean heat content estimates and satellite observation estimates line up within their margins of error. So we are pretty confident in an observed TOA energy imbalance of about 1W/M-2, but modelling that small an overall difference is very small eye of the needle to try and hit.
We need to continue to invest (massively) in climate research. At the same time, because there is uncertainty about model predictions we have to assume that the outcome could be worse the predictions, and begin mitigating against those outcomes immediately.
It's a pity that model outcomes could not be more certain.
Why can't introduction to logic be mandatory education in our world already?
If we have the choice between preparing for catastrophe or not, and the possibility of catastrophe is unknown it is NOT proven that action must be taken.
For example, I can posit that there is an unknown possibility of a catastrophic earth impact from some massive rock out in space. That is an accurate enough statement, but it is not proof that we must immediately start a multi-billion dollar program to combat the threat of our extinction.
Climate change is in fact a bit different still, because contrary to what alarmists want to believe, the science is pretty clear that catastrophe is NOT coming down on our heads anywhere within the next 100 years. Best estimates from the IPCC's model projections of 2100(which yes have major uncertainty) is 1.5C higher than current(I'm going for memory so forgive if I miss a little on the scenario 4.5 projection), and a sea level rise of about 0.4m. That is over 100 years a temperature increase of 1.5C and sea level rise of 0.4m. Before crying wolf about how bad that really is, lets look backwards in time. Back in 1901 when we didn't have cars, or planes or pretty much the whole of modern technology, we can look at the climate change our great grand parents faced. In 100 years they would have to cope with a temperature rise of 1.1C and sea level rise of 0.19m. Far from being in a post-apocolyptic dystopia, the global standard of living has never been better. The biggest threat to mankind is still the damage and abuse we dish out upon one another. From the IPCC predictions for climate change, I'm gonna go ahead and predict that in 100 years the greatest threat to mankind is still the abuse we inflict on one another. You know, not much different than the last 2 millennia...
>Considering we don't know what the temperature will be tomorrow, or whether it will rain at my house, I'm pretty sure we don't know what the climate will be in 100 years. So, not settled in my book.
That's a ridiculously stupid claim to make. Climate is a LOT simpler than weather. Many, many orders of magnitude simpler. Why ? Because climate is an average.
... Climate is an average of weather over long periods (30 years typically). That's a LOT simpler to predict than the individual weather points that make it up.
Apples meet oranges. With weather the prediction is based on well known conditions in the surrounding areas. We then are able to map out the likely changes for the next couple of days. The more days you go out, the less certain things become. The trick though is that when predicting tomorrow's weather, you are working with a very complete set of initial conditions.
Compare that to climate in 100 years or 300 years. You have the initial conditions still, but mapping out what planetary ice, plants, ocean currents and water vapor are gonna do to the TOA energy balance that drives climate decade after decade is hardly simple. Compare to a 5 day weather forecast, it is as a matter of fact much more challenging. Add onto that the fact that weather models can be tested against NEW data almost weekly, while climate models need to wait decades for actual true NEW data to compare projections against.
If you want to predict the average of weather, that's different than predicting changes to the climate. Average weather is as simple as observing something global average temperature next year will be much like this year +/- 0.5C. You can even confidently declare that global average temperature 25 years from today will again be the same as this year +/- 1.0 C. Climate scales stretch out to hundreds of years where the overall energy imbalance can swing things a couple of degrees. As I quoted directly above you, that projection of TOA energy is still an unsolved problem that requires corrections for modelled hindcasts to be reasonable.
Climate projections are every bit as challenging as weather projections, and when taken in context climate modelling faces many unique challenges that weather does not. Most importantly that weather models can be tested and refined on a much shorter time frame and against many, many more datasets.
More data is always good, but presenting any uncertainty and conditions on predictions is vital. Not only so we make properly informed decisions, but also so we don't tarnish trust by misrepresented predictions.
Climate models are really great science, but are also really ripe for this sort of problematic viewing from the public. Not just the laymen, but informed and educated public as well. To just quickly read and peruse climate model summaries you'd get the impression that confidence in models is really high. The reality is that confidence in PORTIONS of the models is really high. The whole however still has a long ways to go.
The IPCC fifth assessment report in chapter 9 notes the following: Model tuning aims to match observed climate system behaviour and so is connected to judgements as to what constitutes a skilful representation of the Earth’s climate. For instance, maintaining the global mean top of the atmosphere (TOA) energy balance in a simulation of pre-industrial climate is essential to prevent the climate system from drifting to an unrealistic state. The models used in this report almost universally contain adjustments to parameters in their treatment of clouds to fulfil this important constraint of the climate system (Watanabe et al., 2010; Donner et al., 2011; Gent et al., 2011; Golaz et al., 2011; Martin et al., 2011; Hazeleger et al., 2012; Mauritsen et al., 2012; Hourdin et al., 2013).
That's taken context and backed up by over a dozen citations to relevant journal articles on model tuning. The short version is that tuning Top Of Atmosphere energy is still a required step to avoid climate models running out to unrealistic states. The journal articles all confirm this. With TOA energy being the ultimate overall driving force behind climate change, our predictions are still subject to the fact we aren't yet able to predict TOA energy. Without that we can make guesses what TOA energy might do, but the confidence in them is nothing like the confidence in other components of climate. Failing to qualify this though could leave us 20 years from now pointing at the AR5 projections and asking what went so terribly wrong with them, and the answer is that they had things largely right, save that TOA energy rose faster or slower than anticipated. That's in essence already the conversation over the IPCC First assessment projections from the 20+ years ago.
Mary Schweitzer's been working on this since 1993 when she found soft-tissue in a fossil and nobody would believe that's what she'd found. Her latest paper is getting fragments of the protein chains from a T-Rex. Back in 1993 she was told that any soft tissue at all was entirely and completely impossible. Don't know how realistic it is, but I really want to believe some manner of circumstances allow T-Rex clones in the future.
They want their headline back. Mary Schweitzer already made the same discovery in 1993, and she's been fighting for more than 2 decades to get her findings past the "consensus" that such long preservation was impossible. It seemed like she had gotten her findings verified again by 2000 but I guess it's still only now becoming generally accepted. Really unfortunate it can still take that long for a major discovery to become accepted.
Then rigorously show how this is the case. It's really that simple. The fact it has not been done yet should tell you something.
I'm not sure of the 'this' that needs to be proven, but I'm going to interpret it as the GP statement scientists don't have the climate models rightas something that isn't nuts or crazy.
From Chapter 9 of the IPCC AR5, complete with more than a half dozen citations: For instance, maintaining the global mean top of the atmosphere (TOA) energy balance in a simulation of pre-industrial climate is essential to prevent the climate system from drifting to an unrealistic state. The models used in this report almost universally contain adjustments to parameters in their treatment of clouds to fulfil this important constraint of the climate system (Watanabe et al., 2010; Donner et al., 2011; Gent et al., 2011; Golaz et al., 2011; Martin et al., 2011; Hazeleger et al., 2012; Mauritsen et al., 2012; Hourdin et al., 2013). And later the IPCC goes on to note: Model tuning directly influences the evaluation of climate models, as the quantities that are tuned cannot be used in model evaluation. Quantities closely related to those tuned will provide only weak tests of model performance.
So to recap, the singular driving force of all climate change, the energy imbalance at Top Of Atmosphere, is NOT an emergent property of the underlying climate models, but instead still requires hand tuning to avoid running out to an unrealistic state. More over, by the IPCC's own standards, quantities closely related to those tuned are only weak tests of model performance. How many components of the climate aren't at least weakly related to TOA energy?
It keeps going though, as if you hand adjust clouds or other parameters to balance energy, if your results were off you'd expect to get the macro of energy balance that was tuned correct on the mean, but because of compensating errors too high here and too low there. Read the entire IPCC chapter linked above and count the number of times compensating errors are observed in the unknown parameters like clouds.
If you further your reading beyond the IPCC chapter and read the linked journals you'll even find that climate models still regularly, as in more often than not, don't pass the conservation of energy test, it's even stated as part of the reason that tuning TOA energy is still necessary until bugs in code or algorithms can catch the leaking energy or additional energy that coming out of the ether.
All that said, climate models truly are still good tools. The steps taken above are still good steps, and I honestly and truly mean that. They all still contribute to testing theories and ideas of how components of our climate function and are vital tool to furthering our understanding. At the exact same time though, to declare that lacking confidence in future prediction from them today is nuts or crazy is just wrong. There's very good reason to place very big caveats and conditions on the projections currently being generated. In particular hindcast skill should NOT be expected to be a very good indicator of predictive skill at all.
The Wind Shear's Full Scale Rolling Road was built in 2008, but I guess that also isn't currently under construction so you still might cling to that...
The underlying important point remains. When it comes to engineering planes and automobiles, computer modelling is heavily used, but the use of real world tests like wind tunnels are also still in use because the models are not yet perfect.
But the modelers argue that this really wasn't a failure, because their predictions served as worst-case scenarios that mobilized international efforts.
so.... how about those climate models out there????
So, you're saying that climate models that do not reflect the mobilization of international efforts mean that we should not attempt to push for international efforts to ensure that those worst-case predictions do not happen?
Climate science is always evolving. Scientists learn more about the planet and how different aspects of our planet's behavior interact, and they discover new aspects through this process. I don't think there's a lot of argument that humans are taking huge carbon deposits that are the result of plants using carbon from the air as building material in their structures and reintroducing that carbon into the atmosphere again. The debate is what that does to climate.
I think the more salient point is our call to action should take into account uncertainties within the models. If the actions we call people to are costly, people should reasonably expect that the evidence brought forward is certain enough to justify the cost...
Modelling climate is insanely challenging as the scope is our entire planet, and the components involved number in the hundreds or thousands, and the interactions between them are again almost universally dependant upon one another. Climate models are a great tool for us to investigate and test theories about those systems and their interactions. We are getting pretty good at sorting the important/dominant components from the less significant ones. That said, there IS still a long ways to go. I would strongly advocate for continued study and development of climate modelling. I would also strongly caution against placing high confidence in specific model projections out into the future. Evidence follows:
Climate models fail the conservation of energy test. That's pretty fundamental, and models very widely still 'leak' energy. From Mauritsen et al. Among the model simulations whose data were available at the time of this analysis, there is a tendency for drift in the CMIP5 models to be less pronounced than in some of the CMIP3 models, and there is a reduction in the number of warm and cold biased models in CMIP5. Only a few models are close to zero imbalance, or likely to relax to near-zero imbalance. If a model equilibrates at a positive radiation imbalance it indicates that it leaks energy, which appears to be the case in the majority of models, and if the equilibrium balance is negative it means that the model has artificial energy sources.
Climate model tuning today normally uses adjustments to cloud parameters to balance the Top of Atmosphere energy. The single central driving force behind climate change still gets tuned by hand and is not yet an emergent property of the underlying understanding or simulation of the system. From Chapter 9 of the IPCC AR5, complete with more than a half dozen citations to articles on model tuning confirming exactly this. For instance, maintaining the global mean top of the atmosphere (TOA) energy balance in a simulation of pre-industrial climate is essential to prevent the climate system from drifting to an unrealistic state. The models used in this report almost universally contain adjustments to parameters in their treatment of clouds to fulfil this important constraint of the climate system (Watanabe et al., 2010; Donner et al., 2011; Gent et al., 2011; Golaz et al., 2011; Martin et al., 2011; Hazeleger et al., 2012; Mauritsen et al., 2012; Hourdin et al., 2013).
It's tempting to take the above and declare all climate models are bunk and toss them out, which would be very bad. Climate models are doing an important job of
You shouldn't have stopped reading with Mann et al's reply, go ahead and read McShane and Wyner's rebuttal [e-publications.org].
The rebuttal is reasonably long (27 pages, not including the details), and I admit I only read some of it. However, I did read that part, and also some commentary on the rebuttal. The commentary seems to affirm that Mann's criticism on that issue was valid and the rebuttal's characterisation was inaccurate.
Regardless of whether it was reasonable for Mann et al 2008 (M08) to exclude those data sets, they did. Any attempt to criticize the statistical method used in M08 would benefit from separating the concerns of what data to include and how the data should be analysed, so the analysis should use the original input data and focus on the difference in the results produced by the methods. Unfortunately for McShane and Wyner, it seems that if you only change the methods or only change the input data, the results are less significant.
The commentary you link is to a blog post by Mann himself in which, unsurprisingly, he declares himself the winner. I'd encourage you to read the full rebuttal despite it's length. Having read both it's full details and Mann's blog post after it seems rather clear Mann is glossing over some very clear and specific details that are devastating to his claims.
I did read the discussion, it seems McShane and Wyner have contributed some useful analysis and some not very useful analysis. Unfortunately, some of their conclusions are tainted by failure to follow the same procedures as Mann et al 2008 when claiming that they (effectively) did. For example, they chose to use tree ring proxies that were excluded by M08 and claimed that their exclusion was ad hoc and thus unsupportable. However, the major exclusive criteria seems to be fairly simple: proxies that contain fewer than 8 individual trees are too unreliable for inclusion (individual trees exert too much influence over the proxy average and can produce significant anomalous results). Re-running their analysis with that one change seems to flip their results on their head. Instead of reducing the confidence in anomalous warming in the late 20th century to 80% certainty, it increases the confidence to 99% certainty.
I think that shows a basic problem with the methodology of the McShane and Wyner analysis. They changed the input data at the same time as they changed the analysis method, thus conflating the two different changes together. We all know that in an experiment you want to change only one variable at time, right? Similarly, I think that they should have used the exact same data when they challenged the analysis method and challenged the data selection methods in a separate paper if they felt that issue was important enough to warrant a challenge.
You shouldn't have stopped reading with Mann et al's reply, go ahead and read McShane and Wyner's rebuttal. They are rather more emphatic about Mann's failure to use statistics properly. Where Mann's reply consisted of larger hand waving and very general criticism like what data to include, McShane's rebuttal is heavy on detail and specifics. For example, the critique of McShane not filtering or excluding the data they used correctly is directly and exhaustive addressed: SMR allege that we have applied the various methods in Sections 4 and 5 of our paper to an inappropriately large group of 95 proxies which date back to 1000 AD (93 when the Tiljander lightsum and thicknessmm series are removed due to high correlation as in our paper; see footnote 11). In contrast, the reconstruction of Mann et al. (2008) is applied to a smaller set of 59 proxies (57 if the two Tiljander series mentioned previously are removed; 55 if all four Tiljander series are excluded because they are ”potentially contaminated”). The process by which the complete set of 95/93 proxies is reduced to 59/57/55 is only suggestively described in an online supplement to Mann et al. (2008). As statisticians we can only be skeptical of such improvisation, especially since the instrumental calibration period contains very few independent degrees of freedom. Consequently, the application of ad hoc methods to screen and exclude data increases model uncertainty in ways that are unmeasurable and uncorrectable.
Moreover, our interpretation of SMR Figure 1 is quite different.... Smoothing exaggerates the difference and requires careful adjustment of fit statistics such as standard errors, adjustments which are lacking in SMR and which are in general known only under certain restrictive conditions. In contrast, consider the right panel of Figure 1 which is a reproduction of SMR Figure 1a without smoothing. The difference between a given model fit to the full dataset or the reduced data set is clearly dwarfed by the annual variation of the fit;... In short, while SMR allege that we use the ”wrong” data, the result remains the same (also see SI).
It is really worth reading the detailed reasons I glossed over as well, as it contributes to the veracity and accuracy of their critique of Mann's usage of statistics.
McIntyre & McKitrick aren't statisticians at all, so no argument there. Science shouldn't be about credentials, but if it is...
McShane and Wyner ARE statisticians and they published this paper in The Annals of Applied Statistics regarding Mann's statistic uasge for proxy reconstruction methods. The abstract follows, mostly because it pretty much speaks for itself: Predicting historic temperatures based on tree rings, ice cores, and other natural proxies is a difficult endeavor. The relationship between proxies and temperature is weak and the number of proxies is far larger than the number of target data points. Furthermore, the data contain complex spatial and temporal dependence structures which are not easily captured with simple models.
In this paper, we assess the reliability of such reconstructions and their statistical significance against various null models. We find that the proxies do not predict temperature significantly better than random series generated independently of temperature. Furthermore, various model specifications that perform similarly at predicting temperature produce extremely different historical backcasts. Finally, the proxies seem unable to forecast the high levels of and sharp run-up in temperature in the 1990s either in-sample or from contiguous holdout blocks, thus casting doubt on their ability to predict such phenomena if in fact they occurred several hundred years ago.
We propose our own reconstruction of Northern Hemisphere average annual land temperature over the last millennium, assess its reliability, and compare it to those from the climate science literature. Our model provides a similar reconstruction but has much wider standard errors, reflecting the weak signal and large uncertainty encountered in this setting.
Mann et al. of course filed a rebuttal, which more or less amounts to declaring that statisticians know nothing about handling climate data, and furthermore that McShane and Wyner used completely inappropriate statistical methods.
I've read the rebuttals and McShane and crew seem to be the most on the ball in the exchange from my reading, with Mann et al's arguments seeming to be tangential to the central meat of the article and concerns identified, but go read it yourself.
Forget the climate models, just explain why if its not getting any hotter all the world's glaciers, ice fields, as ice caps are melting.
Who ever said it wasn't getting warmer? Sure wasn't me. Instrumental records since 1900 have shown a global upward trend. Sea level has been steadily rising over that same time.
You've maybe mistaken me for someone that wants to reject the science on this or something?
What I pointed to in the scientific literature was that climate models still aren't predicting TOA energy accurately and so they are still in the general practice of hand tuning parameters like clouds to correct it. Climate models are still terrifically useful for learning more about our climate, they are in fact fundamental to that end. I'm merely also noting that since TOA energy dominates long term climate trends, until models can predict it with being tuned by hand we can't rely on climate models for long term trends, or at the very least not without a lot of caveats and conditions.
Not sure what your point is. The models are useful, but don't pay attention to them because they have (what would seem to be typical) modeing issues,... so they're not useful? Seems muddled.
As you say yourself, the models are useful for understanding trends and influences, and continue to improve.
Note that the reason you're able to point out a citation on this is because the IPCC (and others) are pointing it out for all to be aware of. It's not like they're not aware of this or trying to hide it.
By your statement, "You can't take a variable you've hand tuned and claim anything about it's predictive powers," you may be missing the point. If the TOA adjustment is to align the model state at some point in time or points in time to real data by "adjustments to parameters in their treatment of clouds," then how does that invalidate trend and sensitivity predictions of the overall model?
What do you think could be done better? Is there a better method to set these parameters other than TOA balance?
And, more importantly, do you have evidence that the model predictions are heavily sensitive to the sort (or magnitude) of adjustments being made? (I don't know myself, but you seem awfully concerned about the TOA adjustment in particular, so is there an analysis of this you can point to?)
TOA energy IS climate change. Increased CO2(and any other greenhouse gases) act by trapping more radiation coming in at the top of atmosphere boundary. Current climate models, as per the IPCC and 8 cited articles, drift to unrealistic states UNLESS they are tuned by hand for more realistic TOA.
Yes, my concerns are backed up by the papers if you go read them. I can give you a quick link to the one by Golaz in which they test 3 different equally valid seeming cloud tunings to compare the results and they conclude: CM3w predicts the most realistic 20th century warming. However, this is achieved with a small and less desirable threshold radius of 6.0 m for the onset of precipitation. Conversely, CM3c uses a more desirable value of 10.6 m but produces a very unrealistic 20th century temperature evolution. This might indicate the presence of compensating model errors.
Choosing the most realistic tuning setting for clouds led the model to fail to reproduce the the last century of warming accurately. Choosing a less realistic one gave a better result. For further reference and those that won't read the whole article, ALL 3 tuning settings chosen additionally were chosen on condition of balancing TOA energy correctly.
What is more, this assessment is assuming that all the other forcings not being tuned are correct, because in practice the tuning for TOA could be correcting for other erroneous values across the models regarding energy transfer patterns. If that were the case, simply balancing the overall global TOA would lead to accurate macros, but possibly because of compensating errors... Like the ones that Golaz noted. Also like the observation made throughout the IPCC report on cloud behaviour in models...
Why does it matter? Because it's the driving force of the climate. The climate models ARE still good at helping us understand the climatic responses to the driving. Regrettably though as long as we are hand-correcting the driving variable, we can't say too much from the models about future driving from TOA.
This hardly seems a controversial observation at this point.
we will take out what we don't like and put it what fits our agenda.
Alternately, if the data suddenly changes about the same time as you changed your data-gathering methodology, there's no real need for a big conspiracy theory to explain what happened. But what do I know, I actually read the fine article.
Exactly, like in Michael Mann's "hockey stick" reconstructions and the data-gathering methodology changes at the year 1900 the sudden change in trend obviously is a result of the methodology change as well, and not something else....
For everyone worrying about our pending demise in 100 years based on climate models I would urge you to step back from the ledge.
Climate models are a great tool for expanding our understanding of climate processes and their interactions. They have been invaluable in gaining new knowledge and testing theories to better know how our climate behaves. At the same time, they also have a long ways to go.
The most basic primer on climate is that the greenhouse effect is basically the trapping of energy by gases in our atmosphere. The most basic and fundamental measure of this is the difference between energy coming in and energy leaving at the Top Of Atmosphere. This is more commonly referred to as TOA energy balance. This energy imbalance though is very small compared to the overall energy in and out, so measuring it is hard, let alone simulating it. Thus, parameters in climate models are hand tuned until TOA energy matches known and observed trends. This is a necessary step so that all the other modelled processes in the simulation operate under conditions that are reasonable and accurate and we can then compare their behaviour to the real world.
The alarmists, and maybe even some that don't count themselves such, will take huge issue with my next statement. With the climate models hand tuning TOA energy in order to avoid unrealistic conditions, and with TOA energy dominating long term climate trends, the climate models utility to long term predictions right now is poor at best and near nil at worst. You can't take a variable you've hand tuned and claim anything about it's predictive powers.
If you think I'm off my rocker, here's the IPCC saying the same thing: For instance, maintaining the global mean top of the atmosphere (TOA) energy balance in a simulation of pre-industrial climate is essential to prevent the climate system from drifting to an unrealistic state. The models used in this report almost universally contain adjustments to parameters in their treatment of clouds to fulfil this important constraint of the climate system (Watanabe et al., 2010; Donner et al., 2011; Gent et al., 2011; Golaz et al., 2011; Martin et al., 2011; Hazeleger et al., 2012; Mauritsen et al., 2012; Hourdin et al., 2013). and later.... Model tuning directly influences the evaluation of climate models, as the quantities that are tuned cannot be used in model evaluation. Quantities closely related to those tuned will provide only weak tests of model performance. I needn't point out that TOA energy is closely related to pretty near everything in our climate.
you don't really understand anything about measurements, physics, or science if you have to ask this question. Perhaps you should learn what data correction commonly means before you just rely on your ignorance to confirm your political position. hint: its basically impossible to measure what we want to measure, but we can develop very good ideas from first principles about the differences between what we want to measure and what we actually measure. Thus, we can make corrections. First principles that have been effectively validated by 100s of years of scientific endeavor. (Heat transfer, thermodynamics, EM theory, take it down to quantum mechanics, if you want. etc). From basic tested physics theories, we can apply thousands of corrections, but there are several trade-offs, diminishing returns at play. Sometimes those may change over time. Sometimes we missed an important one. Shit happens. Deal with it. Or you can just remain willfully ignorant, which seems to suit you better.
That must be why every damn time temperature data is "corrected" it's in ways that support Chicken Little, alarmist AGW hypotheses.
Where the hell are the corrections in the other direction?
Hell, where are the scientific outliers? Why isn't there one published climate model out there that significantly differs from the "settled" consensus? Where's the model that actually produced predictions that matched the 15-year hiatus in measured warming? Why are all the predictions we see published closer together than the margin of error in the measurements?
Even Einstein himself famously thought quantum mechanics was BS. But nope - we have NONE of that with AGW.
For climate models they are so tightly grouped partly because of model tuning. One of the steps in preparing climate models is tuning parameters to get the correct/observed Top Of Atmosphere(TOA) energy balance. Most commonly parameters for clouds are adjusted until the climate model's TOA energy results match the known values. Without doing that, the energy imbalance rapidly drives the climate into unrealistic states. That is straightforward as incoming/outgoing TOA energy is of course the singular long term driver of climate change. Of course, with it being almost universal practice to hand tune TOA energy to the same trends and values it shouldn't be too surprising that on the macro level, the climate models follow the same trends...
Now just watch some idiot alarmist come call me out for lying or something. The IPCC fifth assessment report states the following in Chapter 9: Model tuning aims to match observed climate system behaviour and so is connected to judgements as to what constitutes a skilful representation of the Earth’s climate. For instance, maintaining the global mean top of the atmosphere (TOA) energy balance in a simulation of pre-industrial climate is essential to prevent the climate system from drifting to an unrealistic state. The models used in this report almost universally contain adjustments to parameters in their treatment of clouds to fulfil this important constraint of the climate system (Watanabe et al., 2010; Donner et al., 2011; Gent et al., 2011; Golaz et al., 2011; Martin et al., 2011; Hazeleger et al., 2012; Mauritsen et al., 2012; Hourdin et al., 2013).
That's the IPCC saying exactly what I just did, and a list of fully 8 different peer-reviewed journal articles backing their specific statement up. You want to call me out as wrong or cherry picking then provide something significantly more substantial than the above references if you want to have any leg to stand on.
Climate models have been a great means of testing and expanding our understanding of how our climate functions and interacts. That said, the limits on our climate models are GROSSLY underestimated by a great many people.
Why would I say that? Here's a quote from the IPCC's fifth assessment report: For instance, maintaining the global mean top of the atmosphere (TOA) energy balance in a simulation of pre-industrial climate is essential to prevent the climate system from drifting to an unrealistic state. The models used in this report almost universally contain adjustments to parameters in their treatment of clouds to fulfil this important constraint of the climate system (Watanabe et al., 2010; Donner et al., 2011; Gent et al., 2011; Golaz et al., 2011; Martin et al., 2011; Hazeleger et al., 2012; Mauritsen et al., 2012; Hourdin et al., 2013).
Before I'm get accused of cherry picking quotes, notice that the statement is backed by reference to 8 separate peer-reviewed journal articles on the subject of climate model tuning. You can use google scholar to look up the articles if you want to point out any misrepresentation by the IPCC, but to save time I've read them myself and the characterization is accurate.
That is to say that TOA energy is hand tuned as a general practice to prevent the models running into unrealistic states. The parameter most commonly used to tune it is the function of clouds. To be fair, the tuning is also restricted to values that are 'reasonable', meaning in keeping with existing observational limits if there are any for the parameters being tuned. The Golaz et al article though notes that even within those limits the choice of equally valid and realistic parameters can make a big difference in predictions, in their case a near complete failure to reproduce recent warming in simulation by changing cloud parameters between equally realistic values.
As I said, climate models are great for advancing and test our understanding of interactions of components of the climate. That comes with the huge caveat though that TOA Energy is the absolute driving factor of long term warming/cooling, and climate models absolutely do NOT predict it correctly as in my understanding from multiple journal articles on the subject, hand tuning is required to prevent unrealistic TOA values over even observed time frames.
But that is counter balanced by owing it to their editors and share holders to skip those few minutes and facts to publish an article that will catch more readers eyes.
They can still do that, while reporting accurately. They just need to include a disclaimer in the article that there was no peer review, and it likely total nonsense. Responsible publications have articles about unconfirmed preliminary research all the time, they are just careful to label it as such.
Guess I needed a sarcasm sign with your response and a down vote too. What happened to everyone's sense of humor?
There is a link to climate change. The solution to the ozone problem is a proof that we can do it. Now I am not saying that waning off from CO2 dumping is going to be as relatively easy as CFCs, but it is at least as important.
Sulphur is a similar proof that global cooperation can fix damage done to our atmosphere.
As important by what metric? Yes, we have a very good instrumental record of warming global temperature for nearly a century. Yes, we have a record about half as long showing CO2 concentrations rising. Yes, we have trivial physics to show that CO2 absolutely traps radiation and contributes to warming. Yes, we know that our actions have been contributing CO2 to the atmosphere over those time frames. Please point me to more sources, but this is about the extent of the very strongly agreed items on climate change. Further facts all start deriving from climate models, or statistically reconstructed models of proxy data. How do we quantitatively define the importance of reducing our CO2 emissions from this?
We need to know the cost we will bear from continuing temperature changes if we carry on our merry way. We need to know the reduction of those costs if we take a certain set of actions today or in the near future. We then can compare the costs of those actions today against the saved costs in the future and make an informed decision.
I hate to be that guy, but our climate models today are NOT sufficient for assessing this. Plenty of the guys working on the models will call me out, and maybe you should listen to them instead of me because they are after all the experts and know their work and field better than I. Scientifically speaking though, please also look at the facts I base my statement upon. The IPCC states the following information on climate models in general: -Model tuning aims to match observed climate system behaviour and so is connected to judgements as to what constitutes a skilful representation of the Earth’s climate. For instance, maintaining the global mean top of the atmosphere (TOA) energy balance in a simulation of pre-industrial climate is essential to prevent the climate system from drifting to an unrealistic state. -The models used in this report almost universally contain adjustments to parameters in their treatment of clouds to fulfil this important constraint of the climate system (Watanabe et al., 2010; Donner et al., 2011; Gent et al., 2011; Golaz et al., 2011; Martin et al., 2011; Hazeleger et al., 2012; Mauritsen et al., 2012; Hourdin et al., 2013). -Model tuning directly influences the evaluation of climate models, as the quantities that are tuned cannot be used in model evaluation.
Now, if you read those points something comes out and screams problem doesn't it? The heart and soul of all climate change is the increase or decrease of energy at the Top Of Atmosphere. The climate models nearly universally modify cloud effects to get their hindcasting of the energy at TOA correct. If they don't do this tuning, the models drift to an unrealistic state. The quantities that are tuned also should NOT be used in evaluation of the models. So TOA energy imbalance is one of the things that practically by DEFINITION the models are not meant to be able to be evaluated upon, let alone predictive for.
Let me humbly suggest that models that still aren't up to projecting TOA energy aren't exactly cut out for long term predictions of CO2 or any other impacts on TOA energy, which is THE centrally component of the greenhouse effect. The models are tackling problems like what happens to temperature and precipitation patterns under certain changes to TOA energy, but that's not the problem we are most interested in when assessing what level of energy changes CO2 is causing for us.
If I'm wrong or insane anywhere please, please correct me and point me in a good direction for some reasons I'm off base here. I'm a Comp Sci grad so I get computer modelling and the above concerns I've outlined seem terribly fundamental and after searching for a long time I can't find anywhere that the central concern I've outlined is meaningfully addressed.
Here's the trick: You and I know this, but the average schlub out there does not.
Science journalists should be better than "average schlubs". They owe it to their readers to do at least a few minutes of fact checking before publishing.
But that is counter balanced by owing it to their editors and share holders to skip those few minutes and facts to publish an article that will catch more readers eyes.
Slashdot Mirror
Thus far, no other candidate has said they're going to make climate change their top priority.
Ever notice how politicians' plans are always far out in the future? Sure, 35 years is within the scope of of most of our lives, but usually they are well past the time that the politicians proposing them will be around to face the consequences. We hear the same thing all the time about balancing the budget and paying down the deficit ever since Reagan, but neither one has happened yet.
Amen to that.
If we are gonna claim to be serious about cutting emissions, France has already proven the technology to do so has already existed for a long time. We can start funding the deployment of nuclear power on a large scale now. The technology all existed to transition years ago already when France did it and used it to this day to sell energy to the rest of Europe.
Meandering mouth service to researching solar or wind or some other solution isn't bad per se, but it is absolutely inadequate to stop there. There are real concrete actions that can be taken today by anyone that is truly motivated and convinced of the importance to do so.
In Chapter 9 of the IPCC AR5 Report The current status of tuning model performance notes the following, including a half dozen citations of peer-reviewed articles verifying the statement:
Model tuning aims to match observed climate system behaviour and so is connected to judgements as to what constitutes a skilful representation of the Earth’s climate. For instance, maintaining the global mean top of the atmosphere (TOA) energy balance in a simulation of pre-industrial climate is essential to prevent the climate system from drifting to an unrealistic state. The models used in this report almost universally contain adjustments to parameters in their treatment of clouds to fulfil this important constraint of the climate system (Watanabe et al., 2010; Donner et al., 2011; Gent et al., 2011; Golaz et al., 2011; Martin et al., 2011; Hazeleger et al., 2012; Mauritsen et al., 2012; Hourdin et al., 2013).
For reference, TOA energy imbalance is the entire driving force of long term climate change, and models still have to hand-tune cloud parameters to get TOA energy correct.
Without getting TOA energy correct, the only thing the models are telling us is how climate components respond to changing TOA energy. They do NOT tell us the much more important element of how TOA energy will respond in the future. The current state of climate models is a necessary step to getting there, and is step forwards from the past practice of directly inserting energy when needed, but let's not go claiming we already are there. The facts and evidence state that we aren't. Any actual modellers claiming that they can predict future TOA energy, and thus macro climate trends, is being dishonest and there's a reason you don't see any of them quoted as such. Instead you see their papers quoted as the IPCC did above, and other folks are the ones coming in and crowing about the importance of the climate model's prediction without grasping the underlying factors that are still being tooled.
We are already a long way down this slope. The day that the notion of consensus was picked up was the day a knife was stuck into the heart of climate science and the religion of climate science was birthed. The state of things today leaves almost no possible future I can imagine that doesn't leave this entire timeframe and 'debate' as a huge black mark on science. The number of conditions, uncertainties, and qualifiers that have been left out of statements now, not only by the press but by journal authors themselves on blogs and other media, leaves almost no future were the public can't point backwards and cry that the 'consensus' from today didn't cause extinction of corals or polar bears or wipe miles off the Floride coastline by 2050...
Why not let the pope in on it all too at this point.
Apparently you do not have a fucking clue.
ISIS is crawling with Baathist leaders, not Al-Qaeda. In case you forgot. The baathists were Sadaam Hussein's group in Iraq. Guess who ousted them from leadership and simply disbanded the military with no continuation or accountability? The same ones who thought they would be greeted as liberators with no plan after "blow 'em up, they got WMDs!" -- little bush and his warmonger buddies.
Where did you get your drudge fox impression of the situation?
How can you know that and still miss the obvious? You correctly note that a lot of former Baathists are working with ISIS. Presumably from your tone you also are not a fan of ISIS and believe that them expanding their control and influence is a bad thing.
Can I suggest taking the next step and asking we ponder what Iraq might be like if those guys controlled the entire country and whether or not you think that would be a positive change?
The reason I ask, is because that WAS the situation before bush and his warmonger buddies ousted them. No question Bush and co fouled things up from day one, but the actual decision to remove Saddam and the Baathists was hardly a bad thing. ISIS has yet to touch the atrocities that Saddam perpetrated while in power, and yes, keeping the remnants of his diseased regime out of power is certainly important and another reason to resist ISIS expansion. Just don't try and over simplify things to the point you start making the absolutely idiotic wish that Bush hadn't screwed up Iraq with his horrible war. Iraq was already an awful place long before, and the former Baathists that are working with ISIS are the biggest part of that.
But isn't the TOA energy budget one of the most nailed down parameters, given that it is directly measured all over the place at all times by satellites? Unlike all the murky atmospheric transactions which have to be inferred and estimated and calculated from spotty measurements of varying characteristics?
Yes and no. The biggest distinction is between observation and modelling. We have a lot of direct observations of TOA energy that are more or less in agreement(more later), but in climate models the ability to simulate the observed TOA energy today and to maintain a neutral TOA energy when in equilibrium still requires tuning parameters like clouds by hand. Without that tuning, the TOA energy budget drifts off and all the model results drift out along with it. One of the reasons is that models leak or create energy at cell boundaries, which is to say that yes, conservation of energy is imperfect in the majority of current climate models. The other component of drift results from uncertainties in processes and parameters as the model runs.
Reality has dictated though that modellers jobs weren't hard enough with only the above to worry about. In reality, even the observational TOA energy imbalance is subject to a lot of instrumental uncertainty. The two main observational records used are from direct satellite measurements, but calibration issues for sensitive equipment on multiple satellites across the sky leave some uncertainty present. I don't have the exact numbers up in front of me, but the order of magnitude is approximately such that the total radiation coming in and going out at TOA is like 250W/m-2 going out, and 253W/M-2 going out. The error from calibration estimates vary, but something on the order of 1% or so is there, which sounds pretty tight. The trick for climate modellers is that the NET energy imbalance is 253-250, so 3W/M-2, where a 1% error adds up to +/-2.5W/M-2. Ocean heat content numbers are even tougher because you are trying to measure average temperature across the oceans and add up how much energy that is and watch for the delta from year to year, so an even tougher nut than the satellite data. The only good news for the poor guys trying to model this all is that ocean heat content estimates and satellite observation estimates line up within their margins of error. So we are pretty confident in an observed TOA energy imbalance of about 1W/M-2, but modelling that small an overall difference is very small eye of the needle to try and hit.
Indeed.
We need to continue to invest (massively) in climate research. At the same time, because there is uncertainty about model predictions we have to assume that the outcome could be worse the predictions, and begin mitigating against those outcomes immediately.
It's a pity that model outcomes could not be more certain.
Why can't introduction to logic be mandatory education in our world already?
If we have the choice between preparing for catastrophe or not, and the possibility of catastrophe is unknown it is NOT proven that action must be taken.
For example, I can posit that there is an unknown possibility of a catastrophic earth impact from some massive rock out in space. That is an accurate enough statement, but it is not proof that we must immediately start a multi-billion dollar program to combat the threat of our extinction.
Climate change is in fact a bit different still, because contrary to what alarmists want to believe, the science is pretty clear that catastrophe is NOT coming down on our heads anywhere within the next 100 years. Best estimates from the IPCC's model projections of 2100(which yes have major uncertainty) is 1.5C higher than current(I'm going for memory so forgive if I miss a little on the scenario 4.5 projection), and a sea level rise of about 0.4m. That is over 100 years a temperature increase of 1.5C and sea level rise of 0.4m. Before crying wolf about how bad that really is, lets look backwards in time. Back in 1901 when we didn't have cars, or planes or pretty much the whole of modern technology, we can look at the climate change our great grand parents faced. In 100 years they would have to cope with a temperature rise of 1.1C and sea level rise of 0.19m. Far from being in a post-apocolyptic dystopia, the global standard of living has never been better. The biggest threat to mankind is still the damage and abuse we dish out upon one another. From the IPCC predictions for climate change, I'm gonna go ahead and predict that in 100 years the greatest threat to mankind is still the abuse we inflict on one another. You know, not much different than the last 2 millennia...
>Considering we don't know what the temperature will be tomorrow, or whether it will rain at my house, I'm pretty sure we don't know what the climate will be in 100 years. So, not settled in my book.
That's a ridiculously stupid claim to make. Climate is a LOT simpler than weather. Many, many orders of magnitude simpler. Why ? Because climate is an average.
Climate is an average of weather over long periods (30 years typically). That's a LOT simpler to predict than the individual weather points that make it up.
Apples meet oranges. With weather the prediction is based on well known conditions in the surrounding areas. We then are able to map out the likely changes for the next couple of days. The more days you go out, the less certain things become. The trick though is that when predicting tomorrow's weather, you are working with a very complete set of initial conditions.
Compare that to climate in 100 years or 300 years. You have the initial conditions still, but mapping out what planetary ice, plants, ocean currents and water vapor are gonna do to the TOA energy balance that drives climate decade after decade is hardly simple. Compare to a 5 day weather forecast, it is as a matter of fact much more challenging. Add onto that the fact that weather models can be tested against NEW data almost weekly, while climate models need to wait decades for actual true NEW data to compare projections against.
If you want to predict the average of weather, that's different than predicting changes to the climate. Average weather is as simple as observing something global average temperature next year will be much like this year +/- 0.5C. You can even confidently declare that global average temperature 25 years from today will again be the same as this year +/- 1.0 C. Climate scales stretch out to hundreds of years where the overall energy imbalance can swing things a couple of degrees. As I quoted directly above you, that projection of TOA energy is still an unsolved problem that requires corrections for modelled hindcasts to be reasonable.
Climate projections are every bit as challenging as weather projections, and when taken in context climate modelling faces many unique challenges that weather does not. Most importantly that weather models can be tested and refined on a much shorter time frame and against many, many more datasets.
More data is always good, but presenting any uncertainty and conditions on predictions is vital. Not only so we make properly informed decisions, but also so we don't tarnish trust by misrepresented predictions.
Climate models are really great science, but are also really ripe for this sort of problematic viewing from the public. Not just the laymen, but informed and educated public as well. To just quickly read and peruse climate model summaries you'd get the impression that confidence in models is really high. The reality is that confidence in PORTIONS of the models is really high. The whole however still has a long ways to go.
The IPCC fifth assessment report in chapter 9 notes the following:
Model tuning aims to match observed climate system behaviour and so is connected to judgements as to what constitutes a skilful representation of the Earth’s climate. For instance, maintaining the global mean top of the atmosphere (TOA) energy balance in a simulation of pre-industrial climate is essential to prevent the climate system from drifting to an unrealistic state. The models used in this report almost universally contain adjustments to parameters in their treatment of clouds to fulfil this important constraint of the climate system (Watanabe et al., 2010; Donner et al., 2011; Gent et al., 2011; Golaz et al., 2011; Martin et al., 2011; Hazeleger et al., 2012; Mauritsen et al., 2012; Hourdin et al., 2013).
That's taken context and backed up by over a dozen citations to relevant journal articles on model tuning. The short version is that tuning Top Of Atmosphere energy is still a required step to avoid climate models running out to unrealistic states. The journal articles all confirm this. With TOA energy being the ultimate overall driving force behind climate change, our predictions are still subject to the fact we aren't yet able to predict TOA energy. Without that we can make guesses what TOA energy might do, but the confidence in them is nothing like the confidence in other components of climate. Failing to qualify this though could leave us 20 years from now pointing at the AR5 projections and asking what went so terribly wrong with them, and the answer is that they had things largely right, save that TOA energy rose faster or slower than anticipated. That's in essence already the conversation over the IPCC First assessment projections from the 20+ years ago.
Mary Schweitzer's been working on this since 1993 when she found soft-tissue in a fossil and nobody would believe that's what she'd found. Her latest paper is getting fragments of the protein chains from a T-Rex. Back in 1993 she was told that any soft tissue at all was entirely and completely impossible. Don't know how realistic it is, but I really want to believe some manner of circumstances allow T-Rex clones in the future.
They want their headline back. Mary Schweitzer already made the same discovery in 1993, and she's been fighting for more than 2 decades to get her findings past the "consensus" that such long preservation was impossible. It seemed like she had gotten her findings verified again by 2000 but I guess it's still only now becoming generally accepted. Really unfortunate it can still take that long for a major discovery to become accepted.
Then rigorously show how this is the case. It's really that simple. The fact it has not been done yet should tell you something.
I'm not sure of the 'this' that needs to be proven, but I'm going to interpret it as the GP statement scientists don't have the climate models rightas something that isn't nuts or crazy.
From Chapter 9 of the IPCC AR5, complete with more than a half dozen citations:
For instance, maintaining the global mean top of the atmosphere (TOA) energy balance in a simulation of pre-industrial climate is essential to prevent the climate system from drifting to an unrealistic state. The models used in this report almost universally contain adjustments to parameters in their treatment of clouds to fulfil this important constraint of the climate system (Watanabe et al., 2010; Donner et al., 2011; Gent et al., 2011; Golaz et al., 2011; Martin et al., 2011; Hazeleger et al., 2012; Mauritsen et al., 2012; Hourdin et al., 2013).
And later the IPCC goes on to note:
Model tuning directly influences the evaluation of climate models, as the quantities that are tuned cannot be used in model evaluation. Quantities closely related to those tuned will provide only weak tests of model performance.
So to recap, the singular driving force of all climate change, the energy imbalance at Top Of Atmosphere, is NOT an emergent property of the underlying climate models, but instead still requires hand tuning to avoid running out to an unrealistic state. More over, by the IPCC's own standards, quantities closely related to those tuned are only weak tests of model performance. How many components of the climate aren't at least weakly related to TOA energy?
It keeps going though, as if you hand adjust clouds or other parameters to balance energy, if your results were off you'd expect to get the macro of energy balance that was tuned correct on the mean, but because of compensating errors too high here and too low there. Read the entire IPCC chapter linked above and count the number of times compensating errors are observed in the unknown parameters like clouds.
If you further your reading beyond the IPCC chapter and read the linked journals you'll even find that climate models still regularly, as in more often than not, don't pass the conservation of energy test, it's even stated as part of the reason that tuning TOA energy is still necessary until bugs in code or algorithms can catch the leaking energy or additional energy that coming out of the ether.
All that said, climate models truly are still good tools. The steps taken above are still good steps, and I honestly and truly mean that. They all still contribute to testing theories and ideas of how components of our climate function and are vital tool to furthering our understanding. At the exact same time though, to declare that lacking confidence in future prediction from them today is nuts or crazy is just wrong. There's very good reason to place very big caveats and conditions on the projections currently being generated. In particular hindcast skill should NOT be expected to be a very good indicator of predictive skill at all.
The Wind Shear's Full Scale Rolling Road was built in 2008, but I guess that also isn't currently under construction so you still might cling to that...
The underlying important point remains. When it comes to engineering planes and automobiles, computer modelling is heavily used, but the use of real world tests like wind tunnels are also still in use because the models are not yet perfect.
But the modelers argue that this really wasn't a failure, because their predictions served as worst-case scenarios that mobilized international efforts.
so.... how about those climate models out there????
So, you're saying that climate models that do not reflect the mobilization of international efforts mean that we should not attempt to push for international efforts to ensure that those worst-case predictions do not happen?
Climate science is always evolving. Scientists learn more about the planet and how different aspects of our planet's behavior interact, and they discover new aspects through this process. I don't think there's a lot of argument that humans are taking huge carbon deposits that are the result of plants using carbon from the air as building material in their structures and reintroducing that carbon into the atmosphere again. The debate is what that does to climate.
I think the more salient point is our call to action should take into account uncertainties within the models. If the actions we call people to are costly, people should reasonably expect that the evidence brought forward is certain enough to justify the cost...
Modelling climate is insanely challenging as the scope is our entire planet, and the components involved number in the hundreds or thousands, and the interactions between them are again almost universally dependant upon one another. Climate models are a great tool for us to investigate and test theories about those systems and their interactions. We are getting pretty good at sorting the important/dominant components from the less significant ones. That said, there IS still a long ways to go. I would strongly advocate for continued study and development of climate modelling. I would also strongly caution against placing high confidence in specific model projections out into the future. Evidence follows:
Climate models fail the conservation of energy test. That's pretty fundamental, and models very widely still 'leak' energy.
From Mauritsen et al.
Among the model simulations whose data were available at the time of this analysis, there is a tendency for drift in the CMIP5 models to be less pronounced than in some of the CMIP3 models, and there is a reduction in the number of warm and cold biased models in CMIP5. Only a few models are close to zero imbalance, or likely to relax to near-zero imbalance. If a model equilibrates at a positive radiation imbalance it indicates that it leaks energy, which appears to be the case in the majority of models, and if the equilibrium balance is negative it means that the model has artificial energy sources.
Climate model tuning today normally uses adjustments to cloud parameters to balance the Top of Atmosphere energy. The single central driving force behind climate change still gets tuned by hand and is not yet an emergent property of the underlying understanding or simulation of the system.
From Chapter 9 of the IPCC AR5, complete with more than a half dozen citations to articles on model tuning confirming exactly this.
For instance, maintaining the global mean top of the atmosphere (TOA) energy balance in a simulation of pre-industrial climate is essential to prevent the climate system from drifting to an unrealistic state. The models used in this report almost universally contain adjustments to parameters in their treatment of clouds to fulfil this important constraint of the climate system (Watanabe et al., 2010; Donner et al., 2011; Gent et al., 2011; Golaz et al., 2011; Martin et al., 2011; Hazeleger et al., 2012; Mauritsen et al., 2012; Hourdin et al., 2013).
It's tempting to take the above and declare all climate models are bunk and toss them out, which would be very bad. Climate models are doing an important job of
You shouldn't have stopped reading with Mann et al's reply, go ahead and read McShane and Wyner's rebuttal [e-publications.org].
The rebuttal is reasonably long (27 pages, not including the details), and I admit I only read some of it. However, I did read that part, and also some commentary on the rebuttal. The commentary seems to affirm that Mann's criticism on that issue was valid and the rebuttal's characterisation was inaccurate.
Regardless of whether it was reasonable for Mann et al 2008 (M08) to exclude those data sets, they did. Any attempt to criticize the statistical method used in M08 would benefit from separating the concerns of what data to include and how the data should be analysed, so the analysis should use the original input data and focus on the difference in the results produced by the methods. Unfortunately for McShane and Wyner, it seems that if you only change the methods or only change the input data, the results are less significant.
The commentary you link is to a blog post by Mann himself in which, unsurprisingly, he declares himself the winner. I'd encourage you to read the full rebuttal despite it's length. Having read both it's full details and Mann's blog post after it seems rather clear Mann is glossing over some very clear and specific details that are devastating to his claims.
I did read the discussion, it seems McShane and Wyner have contributed some useful analysis and some not very useful analysis. Unfortunately, some of their conclusions are tainted by failure to follow the same procedures as Mann et al 2008 when claiming that they (effectively) did. For example, they chose to use tree ring proxies that were excluded by M08 and claimed that their exclusion was ad hoc and thus unsupportable. However, the major exclusive criteria seems to be fairly simple: proxies that contain fewer than 8 individual trees are too unreliable for inclusion (individual trees exert too much influence over the proxy average and can produce significant anomalous results). Re-running their analysis with that one change seems to flip their results on their head. Instead of reducing the confidence in anomalous warming in the late 20th century to 80% certainty, it increases the confidence to 99% certainty.
I think that shows a basic problem with the methodology of the McShane and Wyner analysis. They changed the input data at the same time as they changed the analysis method, thus conflating the two different changes together. We all know that in an experiment you want to change only one variable at time, right? Similarly, I think that they should have used the exact same data when they challenged the analysis method and challenged the data selection methods in a separate paper if they felt that issue was important enough to warrant a challenge.
You shouldn't have stopped reading with Mann et al's reply, go ahead and read McShane and Wyner's rebuttal. They are rather more emphatic about Mann's failure to use statistics properly. Where Mann's reply consisted of larger hand waving and very general criticism like what data to include, McShane's rebuttal is heavy on detail and specifics. For example, the critique of McShane not filtering or excluding the data they used correctly is directly and exhaustive addressed:
SMR allege that we have applied the various methods in Sections 4 and 5 of our paper to an inappropriately large group of 95 proxies which date back to 1000 AD (93 when the Tiljander lightsum and thicknessmm series are removed due to high correlation as in our paper; see footnote 11). In contrast, the reconstruction of Mann et al. (2008) is applied to a smaller set of 59 proxies (57 if the two Tiljander series mentioned previously are removed; 55 if all four Tiljander series are excluded because they are ”potentially contaminated”). The process by which the complete set of 95/93 proxies is reduced to 59/57/55 is only suggestively described in an online supplement to Mann et al. (2008). As statisticians we can only be skeptical of such improvisation, especially since the instrumental calibration period contains very few independent degrees of freedom. Consequently, the application of ad hoc methods to screen and exclude data increases model uncertainty in ways that are unmeasurable and uncorrectable.
Moreover, our interpretation of SMR Figure 1 is quite different. ... ...
Smoothing exaggerates the difference and requires careful adjustment of fit statistics such as standard errors, adjustments which are lacking in SMR and which are in general known only under certain restrictive conditions. In contrast, consider the right panel of Figure 1 which is a reproduction of SMR Figure 1a without smoothing. The difference between a given model fit to the full dataset or the reduced data set is clearly dwarfed by the annual variation of the fit;
In short, while SMR allege that we use the ”wrong” data, the result remains the same (also see SI).
It is really worth reading the detailed reasons I glossed over as well, as it contributes to the veracity and accuracy of their critique of Mann's usage of statistics.
Those claims would be more interesting if some references were provided. For example, I seem to remember some people who are often referred to as statisticians (actually a minerals prospector and an economist) doing something similar, but it turns out instead of "proving" that the hockey stick wasn't real, they proved that they couldn't follow the documented procedures.
McIntyre & McKitrick aren't statisticians at all, so no argument there. Science shouldn't be about credentials, but if it is...
McShane and Wyner ARE statisticians and they published this paper in The Annals of Applied Statistics regarding Mann's statistic uasge for proxy reconstruction methods. The abstract follows, mostly because it pretty much speaks for itself:
Predicting historic temperatures based on tree rings, ice cores, and other natural proxies is a difficult endeavor. The relationship between proxies and temperature is weak and the number of proxies is far larger than the number of target data points. Furthermore, the data contain complex spatial and temporal dependence structures which are not easily captured with simple models.
In this paper, we assess the reliability of such reconstructions and their statistical significance against various null models. We find that the proxies do not predict temperature significantly better than random series generated independently of temperature. Furthermore, various model specifications that perform similarly at predicting temperature produce extremely different historical backcasts. Finally, the proxies seem unable to forecast the high levels of and sharp run-up in temperature in the 1990s either in-sample or from contiguous holdout blocks, thus casting doubt on their ability to predict such phenomena if in fact they occurred several hundred years ago.
We propose our own reconstruction of Northern Hemisphere average annual land temperature over the last millennium, assess its reliability, and compare it to those from the climate science literature. Our model provides a similar reconstruction but has much wider standard errors, reflecting the weak signal and large uncertainty encountered in this setting.
Mann et al. of course filed a rebuttal, which more or less amounts to declaring that statisticians know nothing about handling climate data, and furthermore that McShane and Wyner used completely inappropriate statistical methods.
I've read the rebuttals and McShane and crew seem to be the most on the ball in the exchange from my reading, with Mann et al's arguments seeming to be tangential to the central meat of the article and concerns identified, but go read it yourself.
Forget the climate models, just explain why if its not getting any hotter all the world's glaciers, ice fields, as ice caps are melting.
Who ever said it wasn't getting warmer? Sure wasn't me. Instrumental records since 1900 have shown a global upward trend. Sea level has been steadily rising over that same time.
You've maybe mistaken me for someone that wants to reject the science on this or something?
What I pointed to in the scientific literature was that climate models still aren't predicting TOA energy accurately and so they are still in the general practice of hand tuning parameters like clouds to correct it. Climate models are still terrifically useful for learning more about our climate, they are in fact fundamental to that end. I'm merely also noting that since TOA energy dominates long term climate trends, until models can predict it with being tuned by hand we can't rely on climate models for long term trends, or at the very least not without a lot of caveats and conditions.
Not sure what your point is. The models are useful, but don't pay attention to them because they have (what would seem to be typical) modeing issues,... so they're not useful? Seems muddled.
As you say yourself, the models are useful for understanding trends and influences, and continue to improve.
Note that the reason you're able to point out a citation on this is because the IPCC (and others) are pointing it out for all to be aware of. It's not like they're not aware of this or trying to hide it.
By your statement, "You can't take a variable you've hand tuned and claim anything about it's predictive powers," you may be missing the point. If the TOA adjustment is to align the model state at some point in time or points in time to real data by "adjustments to parameters in their treatment of clouds," then how does that invalidate trend and sensitivity predictions of the overall model?
What do you think could be done better? Is there a better method to set these parameters other than TOA balance?
And, more importantly, do you have evidence that the model predictions are heavily sensitive to the sort (or magnitude) of adjustments being made? (I don't know myself, but you seem awfully concerned about the TOA adjustment in particular, so is there an analysis of this you can point to?)
TOA energy IS climate change. Increased CO2(and any other greenhouse gases) act by trapping more radiation coming in at the top of atmosphere boundary. Current climate models, as per the IPCC and 8 cited articles, drift to unrealistic states UNLESS they are tuned by hand for more realistic TOA.
Yes, my concerns are backed up by the papers if you go read them. I can give you a quick link to the one by Golaz in which they test 3 different equally valid seeming cloud tunings to compare the results and they conclude:
CM3w predicts the most realistic 20th century warming. However, this is achieved with a small and less desirable threshold radius of 6.0 m for the onset of precipitation. Conversely, CM3c uses a more desirable value of 10.6 m but produces a very unrealistic 20th century temperature evolution. This might indicate the presence of compensating model errors.
Choosing the most realistic tuning setting for clouds led the model to fail to reproduce the the last century of warming accurately. Choosing a less realistic one gave a better result. For further reference and those that won't read the whole article, ALL 3 tuning settings chosen additionally were chosen on condition of balancing TOA energy correctly.
What is more, this assessment is assuming that all the other forcings not being tuned are correct, because in practice the tuning for TOA could be correcting for other erroneous values across the models regarding energy transfer patterns. If that were the case, simply balancing the overall global TOA would lead to accurate macros, but possibly because of compensating errors... Like the ones that Golaz noted. Also like the observation made throughout the IPCC report on cloud behaviour in models...
Why does it matter? Because it's the driving force of the climate. The climate models ARE still good at helping us understand the climatic responses to the driving. Regrettably though as long as we are hand-correcting the driving variable, we can't say too much from the models about future driving from TOA.
This hardly seems a controversial observation at this point.
we will take out what we don't like and put it what fits our agenda.
Alternately, if the data suddenly changes about the same time as you changed your data-gathering methodology, there's no real need for a big conspiracy theory to explain what happened. But what do I know, I actually read the fine article.
Exactly, like in Michael Mann's "hockey stick" reconstructions and the data-gathering methodology changes at the year 1900 the sudden change in trend obviously is a result of the methodology change as well, and not something else....
For everyone worrying about our pending demise in 100 years based on climate models I would urge you to step back from the ledge.
Climate models are a great tool for expanding our understanding of climate processes and their interactions. They have been invaluable in gaining new knowledge and testing theories to better know how our climate behaves. At the same time, they also have a long ways to go.
The most basic primer on climate is that the greenhouse effect is basically the trapping of energy by gases in our atmosphere. The most basic and fundamental measure of this is the difference between energy coming in and energy leaving at the Top Of Atmosphere. This is more commonly referred to as TOA energy balance. This energy imbalance though is very small compared to the overall energy in and out, so measuring it is hard, let alone simulating it. Thus, parameters in climate models are hand tuned until TOA energy matches known and observed trends. This is a necessary step so that all the other modelled processes in the simulation operate under conditions that are reasonable and accurate and we can then compare their behaviour to the real world.
The alarmists, and maybe even some that don't count themselves such, will take huge issue with my next statement. With the climate models hand tuning TOA energy in order to avoid unrealistic conditions, and with TOA energy dominating long term climate trends, the climate models utility to long term predictions right now is poor at best and near nil at worst. You can't take a variable you've hand tuned and claim anything about it's predictive powers.
If you think I'm off my rocker, here's the IPCC saying the same thing:
For instance, maintaining the global mean top of the atmosphere (TOA) energy balance in a simulation of pre-industrial climate is essential to prevent
the climate system from drifting to an unrealistic state. The models used in this report almost universally contain adjustments to parameters in their treatment of clouds to fulfil this important constraint of the climate system (Watanabe et al., 2010; Donner et al., 2011; Gent et al., 2011; Golaz et al., 2011; Martin et al., 2011; Hazeleger et al., 2012; Mauritsen et al., 2012; Hourdin et al., 2013).
and later....
Model tuning directly influences the evaluation of climate models, as the quantities that are tuned cannot be used in model evaluation. Quantities closely related to those tuned will provide only weak tests of model performance.
I needn't point out that TOA energy is closely related to pretty near everything in our climate.
you don't really understand anything about measurements, physics, or science if you have to ask this question. Perhaps you should learn what data correction commonly means before you just rely on your ignorance to confirm your political position. hint: its basically impossible to measure what we want to measure, but we can develop very good ideas from first principles about the differences between what we want to measure and what we actually measure. Thus, we can make corrections. First principles that have been effectively validated by 100s of years of scientific endeavor. (Heat transfer, thermodynamics, EM theory, take it down to quantum mechanics, if you want. etc). From basic tested physics theories, we can apply thousands of corrections, but there are several trade-offs, diminishing returns at play. Sometimes those may change over time. Sometimes we missed an important one. Shit happens. Deal with it. Or you can just remain willfully ignorant, which seems to suit you better.
That must be why every damn time temperature data is "corrected" it's in ways that support Chicken Little, alarmist AGW hypotheses.
Where the hell are the corrections in the other direction?
Hell, where are the scientific outliers? Why isn't there one published climate model out there that significantly differs from the "settled" consensus? Where's the model that actually produced predictions that matched the 15-year hiatus in measured warming? Why are all the predictions we see published closer together than the margin of error in the measurements?
Even Einstein himself famously thought quantum mechanics was BS. But nope - we have NONE of that with AGW.
For climate models they are so tightly grouped partly because of model tuning. One of the steps in preparing climate models is tuning parameters to get the correct/observed Top Of Atmosphere(TOA) energy balance. Most commonly parameters for clouds are adjusted until the climate model's TOA energy results match the known values. Without doing that, the energy imbalance rapidly drives the climate into unrealistic states. That is straightforward as incoming/outgoing TOA energy is of course the singular long term driver of climate change. Of course, with it being almost universal practice to hand tune TOA energy to the same trends and values it shouldn't be too surprising that on the macro level, the climate models follow the same trends...
Now just watch some idiot alarmist come call me out for lying or something. The IPCC fifth assessment report states the following in Chapter 9:
Model tuning aims to match observed climate system behaviour and so is connected to judgements as to what constitutes a skilful representation of the Earth’s climate. For instance, maintaining the global mean top of the atmosphere (TOA) energy balance in a simulation of pre-industrial climate is essential to prevent the climate system from drifting to an unrealistic state. The models used in this report almost universally contain adjustments to parameters in their treatment of clouds to fulfil this important constraint of the climate system (Watanabe et al., 2010; Donner et al., 2011; Gent
et al., 2011; Golaz et al., 2011; Martin et al., 2011; Hazeleger et al., 2012; Mauritsen et al., 2012; Hourdin et al., 2013).
That's the IPCC saying exactly what I just did, and a list of fully 8 different peer-reviewed journal articles backing their specific statement up. You want to call me out as wrong or cherry picking then provide something significantly more substantial than the above references if you want to have any leg to stand on.
Climate models have been a great means of testing and expanding our understanding of how our climate functions and interacts. That said, the limits on our climate models are GROSSLY underestimated by a great many people.
Why would I say that? Here's a quote from the IPCC's fifth assessment report:
For instance, maintaining the global mean top of the atmosphere (TOA) energy balance in a simulation of pre-industrial climate is essential to prevent
the climate system from drifting to an unrealistic state. The models used in this report almost universally contain adjustments to parameters in their treatment of clouds to fulfil this important constraint of the climate system (Watanabe et al., 2010; Donner et al., 2011; Gent et al., 2011; Golaz et al., 2011; Martin et al., 2011; Hazeleger et al., 2012; Mauritsen et al., 2012; Hourdin et al., 2013).
Before I'm get accused of cherry picking quotes, notice that the statement is backed by reference to 8 separate peer-reviewed journal articles on the subject of climate model tuning. You can use google scholar to look up the articles if you want to point out any misrepresentation by the IPCC, but to save time I've read them myself and the characterization is accurate.
That is to say that TOA energy is hand tuned as a general practice to prevent the models running into unrealistic states. The parameter most commonly used to tune it is the function of clouds. To be fair, the tuning is also restricted to values that are 'reasonable', meaning in keeping with existing observational limits if there are any for the parameters being tuned. The Golaz et al article though notes that even within those limits the choice of equally valid and realistic parameters can make a big difference in predictions, in their case a near complete failure to reproduce recent warming in simulation by changing cloud parameters between equally realistic values.
As I said, climate models are great for advancing and test our understanding of interactions of components of the climate. That comes with the huge caveat though that TOA Energy is the absolute driving factor of long term warming/cooling, and climate models absolutely do NOT predict it correctly as in my understanding from multiple journal articles on the subject, hand tuning is required to prevent unrealistic TOA values over even observed time frames.
But that is counter balanced by owing it to their editors and share holders to skip those few minutes and facts to publish an article that will catch more readers eyes.
They can still do that, while reporting accurately. They just need to include a disclaimer in the article that there was no peer review, and it likely total nonsense. Responsible publications have articles about unconfirmed preliminary research all the time, they are just careful to label it as such.
Guess I needed a sarcasm sign with your response and a down vote too. What happened to everyone's sense of humor?
There is a link to climate change. The solution to the ozone problem is a proof that we can do it.
Now I am not saying that waning off from CO2 dumping is going to be as relatively easy as CFCs, but it is at least as important.
Sulphur is a similar proof that global cooperation can fix damage done to our atmosphere.
As important by what metric? Yes, we have a very good instrumental record of warming global temperature for nearly a century. Yes, we have a record about half as long showing CO2 concentrations rising. Yes, we have trivial physics to show that CO2 absolutely traps radiation and contributes to warming. Yes, we know that our actions have been contributing CO2 to the atmosphere over those time frames. Please point me to more sources, but this is about the extent of the very strongly agreed items on climate change. Further facts all start deriving from climate models, or statistically reconstructed models of proxy data. How do we quantitatively define the importance of reducing our CO2 emissions from this?
We need to know the cost we will bear from continuing temperature changes if we carry on our merry way. We need to know the reduction of those costs if we take a certain set of actions today or in the near future. We then can compare the costs of those actions today against the saved costs in the future and make an informed decision.
I hate to be that guy, but our climate models today are NOT sufficient for assessing this. Plenty of the guys working on the models will call me out, and maybe you should listen to them instead of me because they are after all the experts and know their work and field better than I. Scientifically speaking though, please also look at the facts I base my statement upon. The IPCC states the following information on climate models in general:
-Model tuning aims to match observed climate system behaviour and so is connected to judgements as to what constitutes a skilful representation of the Earth’s climate. For instance, maintaining the global mean top of the atmosphere (TOA) energy balance in a simulation of pre-industrial climate is essential to prevent the climate system from drifting to an unrealistic state.
-The models used in this report almost universally contain adjustments to parameters in their treatment of clouds to fulfil this important constraint of the climate system (Watanabe et al., 2010; Donner et al., 2011; Gent et al., 2011; Golaz et al., 2011; Martin et al., 2011; Hazeleger et al., 2012; Mauritsen et al., 2012; Hourdin et al., 2013).
-Model tuning directly influences the evaluation of climate models, as the quantities that are tuned cannot be used in model evaluation.
Now, if you read those points something comes out and screams problem doesn't it? The heart and soul of all climate change is the increase or decrease of energy at the Top Of Atmosphere. The climate models nearly universally modify cloud effects to get their hindcasting of the energy at TOA correct. If they don't do this tuning, the models drift to an unrealistic state. The quantities that are tuned also should NOT be used in evaluation of the models. So TOA energy imbalance is one of the things that practically by DEFINITION the models are not meant to be able to be evaluated upon, let alone predictive for.
Let me humbly suggest that models that still aren't up to projecting TOA energy aren't exactly cut out for long term predictions of CO2 or any other impacts on TOA energy, which is THE centrally component of the greenhouse effect. The models are tackling problems like what happens to temperature and precipitation patterns under certain changes to TOA energy, but that's not the problem we are most interested in when assessing what level of energy changes CO2 is causing for us.
If I'm wrong or insane anywhere please, please correct me and point me in a good direction for some reasons I'm off base here. I'm a Comp Sci grad so I get computer modelling and the above concerns I've outlined seem terribly fundamental and after searching for a long time I can't find anywhere that the central concern I've outlined is meaningfully addressed.
Here's the trick: You and I know this, but the average schlub out there does not.
Science journalists should be better than "average schlubs". They owe it to their readers to do at least a few minutes of fact checking before publishing.
But that is counter balanced by owing it to their editors and share holders to skip those few minutes and facts to publish an article that will catch more readers eyes.