You have failed once more to show that the climate is influenced in any way by changes in emissions of trace gases. I have already shown that your model is defective. You have never subjected it to the necessary discipline of validation which requires successful prediction of a range of future climate properties. Mere simulation of past climate does not constitute evidence. Evaluation, Detection and Attribution is an excessively complex system of organised guesswork where the series of likelihoods and confidences are made by people who are paid to produce them and have a conflict of interest.
“A common confusion between weather and climate arises when scientists are asked how they can predict climate 50 years from now when they cannot predict the weather a few weeks from now. The chaotic nature of weather makes it unpredictable beyond a few days. Projecting changes in climate (i.e., long-term average weather) due to changes in atmospheric composition or other factors is a very different and much more manageable issue”.
The chaotic nature of the atmosphere means that even the tiniest error in the depiction of ‘initial conditions' typically leads to inaccurate forecasts beyond a week or so. This is the so-called ‘butterfly effect’.
Climate scientists do not attempt or claim to predict the detailed future evolution of the weather over coming seasons, years or decades. There is, on the other hand, a sound scientific basis for supposing that aspects of climate can be predicted, albeit imprecisely, despite the butterfly effect. For example, increases in long-lived atmospheric greenhouse gas concentrations tend to increase surface temperature in future decades. Thus information from the past can and does help predict future climate.
It is useful for purposes of analysis and description to consider the pre-industrial climate system as being in a state of climatic equilibrium with a fixed atmospheric composition and an unchanging Sun.
Some types of naturally occurring so-called ‘internal’ variability can—in theory at least—extend the capacity to predict future climate. Internal climatic variability arises from natural instabilities in the climate system. If such variability includes or causes extensive, long-lived, upper ocean temperature anomalies, this will drive changes in the overlying atmosphere, both locally and remotely. The El Niño-Southern Oscillation phenomenon is probably the most famous example of this kind of internal variability. Variability linked to the El Niño-Southern Oscillation unfolds in a partially predictable fashion. The butterfly effect is present, but it takes longer to strongly influence some of the variability linked to the El Nino-Southern Oscillation
E. A. Ripley, O. W. Archibold December 2002Accuracy of Canadian short- and medium- range weather forecasts Weather ,
Article first published online: 12 JAN 2007DOI: 10.1256/wea.245.01
They found that temperature forecasts had a bias of ±1ºC and were rarely better than ±2ºC.
The British Met Office -at http://www.metoffice.gov.uk/about-us/who/accuracy/forecasts
comes to a similar conclusion.
“Warming of the climate system is unequivocal, and since the 1950s, many of the observed changes are unprecedented over decades to millennia”.
Global mean surface air temperatures over land and oceans have increased over the last 100 years.
It is certain that Global Mean Surface Temperature has increasedsince the late 19th century.
“This has been much discussed and it is robust and meaningful”
“The globally averaged combined land and ocean surface temperature data as calculated by a linear trend, show a warming of 0.85 [0.65 to 1.06] C, over the period 1880 to 2012, when multiple independently produced datasets exist. The total increase between the average of the 1850-1900 period and the 2003-2012 period is 0.78 [0.72 to 0.85] °C, based on the single longest dataset available".
The observed recent warming hiatus, defined as the reduction in GMST trend during 1998–2012 as compared to the trend during 1951–2012, is attributable in roughly equal measure to a cooling contribution from internal variability and a reduced trend in external forcing (expert judgement, medium confidence).
We emphasize throughout the chapter that one cannot derive a global record of sea level from individual records, and that regional variability can strongly modulate the global signal.