February 28, 2007
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Global Climate Change Digest
A Guide to Information on Greenhouse Gases and Ozone Depletion
Published July 1988 through June 1999
FROM VOLUME 1, NUMBER 5, NOVEMBER 1988
"Recurrence Analysis of Climate Sensitivity Experiments," H.
Von Storch, F.W. Zwiers (Can. Clim. Ctr., 4905 Dufferin St., Downsview, Ontario
M3H 5T4, Can.), J. Clim., 1(2), 157-171, Feb. 1988.
Proposes a new concept called recurrence in statistical analysis of climate
sensitivity experiments, that can identify the parts of the response which are
likely to recur with an a priori likelihood each time a new experimental
realization is obtained. Suggests a variety of statistical tests to assess
recurrence by means of limited samples. A recurrence analysis of sea surface
temperature (SST) with several El Niño SST anomalies indicate that
equatorial Pacific SST anomalies are of rather limited predictive value even if
the anomalies are very strong.
"Infrared Radiation Models for Atmospheric Ozone," D.P. Kratz
(NASA Goddard Space Flight Ctr., Greenbelt MD 20771), R.D. Cess, J. Geophys.
Res., 93(D6), 7047-7054, June 20, 1988.
Discusses a hierarchy of line-by-line, narrow-band and broadband infrared
radiation models for ozone. Measurement of atmospheric ozone fluxes by the
narrow-band (Malkmus) model is in near-precise agreement with the line-by-line
model. This provides a test for the narrow-band Curtis-Godson scaling which
leads to errors in fluxes of up to 10%, due to the altitude dependence of the
ozone mixing ratio. Slightly greater error arises with the broadband model.
"Effect of Computed Horizontal Diffusion Coefficients on
Two-Dimensional N2O Model Distributions," C.H. Jackman (Atmos. Chem., NASA
Goddard Space Flight Ctr., Greenbelt MD 20771), P.A. Newman et al., ibid.,
93(5), 5213-5219, May 20, 1988.
Two sets of horizontal diffusion coefficients Kyy and Kyz were computed:
from National Meteorological Center temperature data using quasi-geostrophic
potential vorticity, and self-consistent Kyy values from computed residual
circulation. The use of either set of these diffusion coefficients produced
substantial changes in the N2O distribution, especially in the middle to upper
stratosphere and at high latitudes in the winter. The changes lead to enhanced
transport of N2O to higher latitudes, with the set of self-consistent Kyy values
transporting the greatest amount of N2O. The enhanced transport increases the
lifetime of N2O because the photochemical lifetime is longer at high altitudes.
"Sensitivity of Soil Moisture to Doubling of Carbon Dioxide in
Climate Model Experiments. Part I. North America," W.W. Kellogg (NCAR, POB
3000, Boulder CO 80307), Z-C. Zhao, J. Clim., 348-366, Apr. 1988.
Results from five current models show considerable difference between the
soil moisture formulations and the resulting outputs of these five models.
General conclusions drawn are 1) agreement between models is considerably better
in the wintertime than in the summertime; 2) in winter there may be an increase
in soil moisture in North America at high latitudes and an onset of drier
conditions in the southern states and Mexico; 3) in summer there may be a
tendency toward drier conditions in the midwest with wetter conditions along the
Gulf Coast and West Coast of the U.S. and Canada. Most of these results are
consistent with past warmer periods.
"Modelling Tropical Deforestation: A Study of GCM Land-Surface
Parameterizations," R.E. Dickinson (NCAR, Boulder CO 80307), A.
Henderson-Sellers, Q. J. R. Meteorol. Soc., 114B(480), 439-462,
In a 13-month integration that assumes that all of the Amazon tropical
forest in South America is replaced by impoverished grassland, surface
hydrological and temperature effects dominate the response. Reduced mixing and
less interception and evaporation from the canopy cause runoff to increase and
surface temperature to rise by 3-5 ° K. The land-surface model, driven in a
stand-alone mode by prescribed atmospheric conditions and with an imposed
seasonal cycle of rainfall, mimics the seasonal cycle of soil moisture and
runoff found in the Community Climate Model (CCM).
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