Climate alarmists have been pointing to the output of computer models for decades, insisting that the models could predict the future. The Earth is warming uncontrollably, they claim, and human CO2 emissions are the proximate cause. Others have argued that CO2 is not a powerful enough forcing factor to cause such a calamity. Still, the climate change catastrophists point to their models, claiming that the models do not lie. Now, according to data released by NASA, it seems that climate models not only can not predict the future, they do not even echo current conditions correctly. A new paper says climate scientists have misdiagnosed the surface temperature feedbacks and more heat is being radiated back into space than the models allow for. We have all been subjected to three decades of climate change malpractice.
According to Roy W. Spencer and William D. Braswell, both from the Earth System Science Center (ESSC) at the University of Alabama in Huntsville, the magnitude of the surface temperature response of the climate system to an imposed radiative energy imbalance—the climate system's sensitivity—remains just as uncertain today as it was decades ago. In a stunning paper, “On the Misdiagnosis of Surface Temperature Feedbacks from Variations in Earth’s Radiant Energy Balance,” published in the journal Remote Sensing, climate science's lack of progress in improving climate model accuracy is rooted in the complexity of the system being modeled and the inability to quantify feedbacks in the real climate system. The article's abstract nicely sums up the author's findings:
The sensitivity of the climate system to an imposed radiative imbalance remains the largest source of uncertainty in projections of future anthropogenic climate change. Here we present further evidence that this uncertainty from an observational perspective is largely due to the masking of the radiative feedback signal by internal radiative forcing, probably due to natural cloud variations. That these internal radiative forcings exist and likely corrupt feedback diagnosis is demonstrated with lag regression analysis of satellite and coupled climate model data, interpreted with a simple forcing-feedback model. While the satellite-based metrics for the period 2000–2010 depart substantially in the direction of lower climate sensitivity from those similarly computed from coupled climate models, we find that, with traditional methods, it is not possible to accurately quantify this discrepancy in terms of the feedbacks which determine climate sensitivity. It is concluded that atmospheric feedback diagnosis of the climate system remains an unsolved problem, due primarily to the inability to distinguish between radiative forcing and radiative feedback in satellite radiative budget observations.
They believe that the primary difficulty in diagnosing climate feedback is the “contamination of the feedback signature” by unknown radiative factors that are internally generated. An example of one such ‘unforced’ feedback is natural variation in cloud cover. “In simple terms, radiative changes resulting from temperature change (feedback) cannot be easily disentangled from those causing a temperature change (forcing),” the author's state.
To perform their analysis, the authors did lagged regressions between surface temperature and net radiative flux time series. The resulting regression coefficients are shown in the figure below. Computations for global anomalies (a) and anomalies based upon only data over the global ice-free oceans (b) are shown separately.
Lagged regressions between the surface temperature and the Net radiative flux.
The most obvious conclusions from figure above is that the satellite observations and climate models display markedly different time-dependent behaviors. Differences in temperature versus radiation variations are particularly noticeable over the oceans. Note the change in sign of the radiative imbalances depending upon whether radiation leads or lags temperature. Clearly, the models disagree with the real world measurements. The data used for this study were monthly global average anomalies in surface temperatures from HadCRUT3, and radiative fluxes from Terra CERES SSF Edition 2.5, for the period March 2000 through June 2010.
Climate sensitivity is generally defined as how much the planet will warm if the level of atmospheric CO2 is doubled. Spencer and Braswell's observations make a mockery of attempts to summarize climate change as a single number. Of course, this is not the first article to suggest that climate models are not an accurate representation of the real climate system (see “The Sensitive Kind”).
Recent investigations have shown that models cannot predict catastrophic change, now Spencer and Braswell have shown that the models do not even get day to day variation correct. In the article's concluding discussion Spencer and Braswell characterize their findings this way:
A simple forcing-feedback model shows that this is the behavior expected from radiatively forced temperature changes, and it is consistent with energy conservation considerations. In such cases it is difficult to estimate a feedback parameter through current regression techniques.
In contrast, predominately non-radiatively forced temperature changes would allow a relatively accurate diagnosis of the feedback parameter at zero time lag using regression since most radiative variability would be due to feedback. Unfortunately, this appears not to be the situation in either the satellite observations or the coupled climate models.
This leads the author's conclude that there exists a “rather large discrepancy” between the radiative signatures displayed by the real climate system, as measured by actual satellite data, versus the climate models. What does this say about estimating climate sensitivity, one of the favorite games of climate scientists? “While this discrepancy is nominally in the direction of lower climate sensitivity of the real climate system, there are a variety of parameters other than feedback affecting the lag regression statistics which make accurate feedback diagnosis difficult.” In other words, though it's hard to put a number on it, their analysis says it is lower than the figures used by the alarmists.
“The satellite observations suggest there is much more energy lost to space during and after warming than the climate models show,” Spencer said in a University of Alabama press release. “There is a huge discrepancy between the data and the forecasts that is especially big over the oceans.”
“At the peak, satellites show energy being lost while climate models show energy still being gained,” Spencer said. So the models are wrong, but can they be fixed with just a few more tweaks as the climate modelers claim? “The main finding from this research is that there is no solution to the problem of measuring atmospheric feedback, due mostly to our inability to distinguish between radiative forcing and radiative feedback in our observations.” In other words, the models will continue to be wrong.
The results here once again highlight the inadequacy of current day climate science: the models are wrong; our knowledge of the Earth system is filled with gaps and errors; and the complexity of the system overwhelms any simple attempt to make accurate predictions. This study also illustrates the primacy of real data over computer generated guesswork. Global warming zealots are faced with a hard choice—continue to insist their models are accurate and give up all claim to being real scientists, or admit that their predictions of future climate calamity are baseless.
When a medical doctor misdiagnoses a condition and goes ahead with a course of treatment that harms the patient it is called malpractice. A world spanning cadre of climate quacks have misdiagnosed Earth's climate system and have urged governments to take draconian actions to “cure” the malady of global warming. It is high time these scientific incompetents be charged with climate change malpractice and punished for the damage they have done.
Be safe, enjoy the interglacial and stay skeptical.