Contradicting the TOOTSIF alarmists, sea levels may not be rising. Indeed, though Bangladesh has long been the sea-rise poster country, new data "shows that Bangladesh's landmass is increasing, contradicting forecasts that the South Asian nation will be under the waves by the end of the century." According to Planet Gore's Chris Horner:
Oh, and you know those global computer simulations IPCC relied on to predict warming? According to an article by Greek scientists published in Hydrological Sciences Journal, the "general circulation models" can't predict annual or longer changes (at 682):
source: Planet Gore
It turns out that the genii at the IPCC never considered that rivers silt up. This should not be surprising: leading sea-level rise expert Nils-Axel Mörner noted that the IPCC’s SLR panel is stacked with people who aren’t sea-level rise experts.
The current scientific scene is dominated by the hypothesis that climate is deterministically predictable, combined with the belief that GCMs suitably implement this hypothesis and produce credible projections of future climate. As this hypothesis and this belief are widely accepted in a variety of scientific disciplines, including hydrology and water resources science, technology and management, and are used as a foundation upon which diverse impact studies are built, there is an urgent need to assess the credibility of climatic models. To date, the required attention has not been paid and many studies have built upon climatic projections without such prior assessment. This study compares observed, long climatic time series with GCM-produced time series in past periods in an attempt to trace elements of falsifiability, which is an important concept in science (according to Popper, 1983, “[a] statement (a theory, a conjecture) has the status of belonging to the empirical sciences if and only if it is falsifiable”).Agreed.
In all examined cases, GCMs generally reproduce the broad climatic behaviours at different geographical locations and the sequence of wet/dry or warm/cold periods at a monthly scale. Specifically, the correlation of modelled time series with historical ones is fair and the resulting coefficient of efficiency seems satisfactory. However, where tested, replacement of the modelled time series with a series of monthly averages (same for all years) resulted in higher efficiency.
At the annual and the climatic (30-year) scales, GCM interpolated series are irrelevant to reality. GCMs do not reproduce natural over-year fluctuations and, generally, underestimate the variance and the Hurst coefficient of the observed series. Even worse, when the GCM time series imply a Hurst coefficient greater than 0.5, this results from a monotonic trend, whereas in historical data the high values of the Hurst coefficient are a result of large-scale over-year fluctua-tions (i.e. successions of upward and downward “trends”). The huge negative values of coef-ficients of efficiency show that model predictions are much poorer than an elementary prediction based on the time average. This makes future climate projections at the examined locations not credible. Whether or not this conclusion extends to other locations requires expansion of the study, which we have planned. However, the poor GCM performance in all eight locations examined in this study allows little hope, if any. An argument that the poor performance applies merely to the point basis of our comparison, whereas aggregation at large spatial scales would show that GCM outputs are credible, is an unproved conjecture and, in our opinion, a false one.
As Wolf Howling reports, scientists recently examined hundreds of years of ship logs from Britain’s Royal Navy dating back to the 1600s and found: much the same weather patterns as today. This further undermines claims about recent and man-made warming.