Climate Change

What follows suggests that climate models may not be as accurate as many people, including some scientists insist.  Roy Spencer is very controversial, but the point he makes in the following articles seems important.

Roy W. Spencer received his Ph.D. in meteorology at the University of Wisconsin-Madison in 1981. Before becoming a Principal Research Scientist at the University of Alabama in Huntsville in 2001, he was a Senior Scientist for Climate Studies at NASA’s Marshall Space Flight Center, where he and Dr. John Christy received NASA’s Exceptional Scientific Achievement Medal for their global temperature monitoring work with satellites. Dr. Spencer’s work with NASA continues as the U.S. Science Team leader for the Advanced Microwave Scanning Radiometer flying on NASA’s Aqua satellite. He has provided congressional testimony several times on the subject of global warming.

On the Misdiagnosis of Surface Temperature Feedbacks from Variations in Earth’s Radiant Energy Balance†

Roy W. Spencer * and William D. Braswell

Published: 25 July 2011

Abstract

 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.

The role of ENSO in global ocean temperature changes during 1955–2011 simulated with a 1D climate model

Roy W. Spencer, William D. Braswell

Abstract

Global average ocean temperature variations to 2,000 m depth during 1955–2011 are simulated with a 40 layer 1D forcing-feedback-mixing model for three forcing cases. The first case uses standard anthropogenic and volcanic external radiative forcings. The second adds non-radiative internal forcing (ocean mixing changes initiated in the top 200 m) proportional to the Multivariate ENSO Index (MEI) to represent an internal mode of natural variability. The third case further adds ENSO-related radiative forcing proportional to MEI as a possible natural cloud forcing mechanism associated with atmospheric circulation changes. The model adjustable parameters are net radiative feedback, effective diffusivities, and internal radiative (e.g., cloud) and non-radiative (ocean mixing) forcing coefficients at adjustable time lags. Model output is compared to Levitus ocean temperature changes in 50 m layers during 1955–2011 to 700 m depth, and to lag regression coefficients between satellite radiative flux variations and sea surface temperature between 2000 and 2010. A net feedback parameter of 1.7Wm−2 K−1 with only anthropogenic and volcanic forcings increases to 2.8Wm−2 K−1 when all ENSO forcings (which are one-third radiative) are included, along with better agreement between model and observations. The results suggest ENSO can influence multi-decadal temperature trends, and that internal radiative forcing of the climate system affects the diagnosis of feedbacks. Also, the relatively small differences in model ocean warming associated with the three cases suggests that the observed levels of ocean warming since the 1950s is not a very strong constraint on our estimates of climate sensitivity.