Separating Signal and Noise in Climate Warming
ScienceDaily (Nov. 17, 2011) — In order to separate human-caused global warming from the "noise" of purely natural climate fluctuations, temperature records must be at least 17 years long, according to climate scientists.
A National Oceanic and Atmospheric Administration (NOAA) weather satellite. (Credit: Image courtesy of NASA)
To address criticism of the reliability of thermometer records of surface warming, Lawrence Livermore National Laboratory scientists analyzed satellite measurements of the temperature of the lower troposphere (the region of the atmosphere from the surface to roughly five miles above) and saw a clear signal of human-induced warming of the planet.
Satellite measurements of atmospheric temperature are made with microwave radiometers, and are completely independent of surface thermometer measurements. The satellite data indicate that the lower troposphere has warmed by roughly 0.9 degrees Fahrenheit since the beginning of satellite temperature records in 1979. This increase is entirely consistent with the warming of Earth's surface estimated from thermometer records.
Recently, a number of global warming critics have focused attention on the behavior of Earth's temperature since 1998. They have argued that there has been little or no warming over the last 10 to 12 years, and that computer models of the climate system are not capable of simulating such short "hiatus periods" when models are run with human-caused changes in greenhouse gases.
"Looking at a single, noisy 10-year period is cherry picking, and does not provide reliable information about the presence or absence of human effects on climate," said Benjamin Santer, a climate scientist and lead author on an article in the Nov. 17 online edition of the Journal of Geophysical Research (Atmospheres).
Many scientific studies have identified a human "fingerprint" in observations of surface and lower tropospheric temperature changes. These detection and attribution studies look at long, multi-decade observational temperature records. Shorter periods generally have small signal to noise ratios, making it difficult to identify an anthropogenic signal with high statistical confidence, Santer said.
"In fingerprinting, we analyze longer, multi-decadal temperature records, and we beat down the large year-to-year temperature variability caused by purely natural phenomena (like El NiÃ'±os and La NiÃ'±as). This makes it easier to identify a slowly-emerging signal arising from gradual, human-caused changes in atmospheric levels of greenhouse gases," Santer said.
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JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 116, D22105, 19 PP., 2011
doi:10.1029/2011JD016263
Separating signal and noise in atmospheric temperature changes: The importance of timescale
Key Points
Models run with human forcing can produce 10-year periods with little warming
S/N ratios for tropospheric temp. are ∼1 for 10-yr trends, ∼4 for 32-yr trends
Trends >17 yrs are required for identifying human effects on tropospheric temp
B. D. Santer
Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, California, USA
C. Mears
Remote Sensing Systems, Santa Rosa, California, USA
C. Doutriaux
Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, California, USA
P. Caldwell
Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, California, USA
P. J. Gleckler
Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, California, USA
T. M. L. Wigley
National Center for Atmospheric Research, Boulder, Colorado, USA
S. Solomon
Department of Atmospheric and Oceanic Sciences, University of Colorado at Boulder, Boulder, Colorado, USA
N. P. Gillett
Canadian Centre for Climate Modelling and Analysis, Environment Canada, Victoria, British Columbia, Canada
D. Ivanova
Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, California, USA
T. R. Karl
National Climatic Data Center, National Oceanic and Atmospheric Administration, Asheville, North Carolina, USA
J. R. Lanzante
Geophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration, Princeton, New Jersey, USA
G. A. Meehl
National Center for Atmospheric Research, Boulder, Colorado, USA
P. A. Stott
Met Office Hadley Centre, Exeter, UK
K. E. Taylor
Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, California, USA
P. W. Thorne
National Climatic Data Center, National Oceanic and Atmospheric Administration, Asheville, North Carolina, USA
M. F. Wehner
Lawrence Berkeley National Laboratory, Berkeley, California, USA
F. J. Wentz
Remote Sensing Systems, Santa Rosa, California, USA
We compare global-scale changes in satellite estimates of the temperature of the lower troposphere (TLT) with model simulations of forced and unforced TLT changes. While previous work has focused on a single period of record, we select analysis timescales ranging from 10 to 32 years, and then compare all possible observed TLT trends on each timescale with corresponding multi-model distributions of forced and unforced trends. We use observed estimates of the signal component of TLT changes and model estimates of climate noise to calculate timescale-dependent signal-to-noise ratios (S/N). These ratios are small (less than 1) on the 10-year timescale, increasing to more than 3.9 for 32-year trends. This large change in S/N is primarily due to a decrease in the amplitude of internally generated variability with increasing trend length. Because of the pronounced effect of interannual noise on decadal trends, a multi-model ensemble of anthropogenically-forced simulations displays many 10-year periods with little warming. A single decade of observational TLT data is therefore inadequate for identifying a slowly evolving anthropogenic warming signal. Our results show that temperature records of at least 17 years in length are required for identifying human effects on global-mean tropospheric temperature.
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