My small business currently produces a new variety of climate forecasts, centered in New Mexico and reaching across most of the western United States. These forecasts are informed by my research at the University of New Mexico as well as a long history of successful collaborations with scientists at New Mexico’s two major national laboratories.
My firm achieves high forecast accuracy by focusing on correlations which work. In that research we have found that although New Mexico climate shows little correlation to greenhouse gas emissions, there are extraordinary correlations here to ocean indexes.
The correlations between New Mexico moisture and the Pacific Decadal Oscillation (PDO) can reach up to 90 percent. A similar high correlation exists between New Mexico temperatures and the Atlantic Multidecadal Oscillation (AMO).
Also, pointing towards a physical causation basis, we have found equally significant correlations between the sun and another relevant climatic variable. We now use that to additionally forecast the PDO with high accuracy.
To raise awareness of our new solution, I’ve compared our forecast results to those from numerous climate forecasting vendors, both private and governmental, and I published a survey on the topic at my Web site.
The premier United Nations Intergovernmental Panel on Climate Change climate model ensembles were included in this comparison. Unfortunately, as the survey documents, key elements of the actual IPCC climate simulation skill results were replaced every few years with observed data.
This guaranteed the perception of a high forecasting skill. Without those adjustments, the actual model results would show global temperatures rapidly rising like helium balloons to absurdly high temperatures.
The fact that these government subsidized models cannot accurately simulate a single year of our past climate appears to only be disclosed within the fine print. Needless to say, a model that cannot accurately simulate a year of Earth’s recent climate cannot be relied upon to accurately forecast Earth’s climate for even a year into the future.
However, these models are the primary foundation for nearly all of the alarming multiple-decade climate forecasts which are distributed for public consumption and for use by other scientists.
Accordingly I am concerned by the assertions of Los Alamos National Laboratory ecologist Nathan McDowell, many of which were featured by the Albuquerque Journal in a recent article.
McDowelll appears to relate that his experimental studies confirm that all evergreen trees (conifers) in New Mexico and throughout the Southwest will disappear in as little as a few decades. His study attributes this primarily to anthropogenic greenhouse gas emissions.
His experiments involve withholding sufficient water from conifers until they die. But killing a few trees by deliberately depriving them of water does no more to prove a pending megadrought than dissolving shellfish in a tank of hydrochloric acid would prove that the world’s oceans are acidifying.
It turns out – again from reading the fine print – that his leap from these experiments to the assertion of nearly continental-scale deforestation, is only supported by his minimal references to the flawed IPCC model temperature projections.
My company’s forecast techniques are anchored by the discovery that New Mexico sits at a grand nexus of climate connections to ocean drivers and the sun. These climate driving features are consistently disregarded by other regional climate change scientists.
Future Treemaggedons in our state are certainly possible, since they have occurred in the past. But natural oscillations in moisture will be the cause.
Moreover, these climate oscillations can now be forecast with a fidelity never before seen. Thus, when significant droughts or surpluses of moisture are on our horizon, we now have more effective tools to anticipate this in advance.
If I’m not mistaken, most would prefer climate forecasts which are plausible, accurate, transparent and affordable. My service offers all of those, and thereby poses a significant, and as yet unanswered challenge to the current hyperbolic climate change forecasting paradigm.