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Stephen H. Schneider

Abstract

The effect of variation in cloudiness on the climate is considered in terms of 1) a relation between the radiation balance of the earth-atmosphere system and variations in the amount of cloud cover or effective cloud top height, 2) the effect on the surface temperature of variations in cloudiness, and 3) the dynamic coupling or “feedback” effects relating changes in surface temperature to the formation of clouds. The first two points are studied by numerical integration of a simple radiation flux model, and the third point is discussed qualitatively. Global-average radiation balance calculations show that an increase in the amount of low and middle level cloud cover (with cloud top height and cloud albedo fixed) decreases the surface temperature. But, this result for the global-average case does not hold near polar regions, where the albedo of the cloudy areas can he comparable to (or even smaller than) the albedo of the snow-covered cloudless areas, and where, especially in the winter season, the amount of incoming solar radiation at high latitudes is much less than the global-average value of insolation. The exact latitude at which surface cooling changes to surface warming from a given increase in cloud cover amount depends critically upon the local values of the cloud albedo and the albedo of the cloudless areas that are used in the calculation. However, an increase in effective cloud top height (with cloud cover and cloud albedo fixed) increases the surface temperature at all latitudes.

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Stephen H. Schneider
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Stephen H. Schneider

Abstract

A number of estimates of global surface temperature sensitivity to a doubling of atmospheric carbon dioxide to 600 ppm are collected here and critically reviewed. The assumptions and formulations that lead to differences between certain models' estimates are explained in some detail. Based on current understanding of climate theory and modeling it is concluded that a state-of-the-art order-of-magnitude estimate for the global surface temperature increase from a doubling of atmospheric C02 content is between 1.5 and 3 K with an amplification of the global average increase in polar zones. It is pointed out, however, that this estimate may prove to be high or low by several-fold as a result of climatic feedback mechanisms not properly accounted for in state-of-the-art models.

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Clifford Mass and Stephen H. Schneider

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Over the years many claims have been made of relationships between climate and volcanic dust veils or sunspots. Sunspot proponents have claimed significant climatic variations on the time scales of 11 years, 22 years, and other multiples of the duration of the sunspot cycle (e.g., King, 1973a,b). Current hypotheses of a 22-year drought cycle in the Great Plains area is a contemporary example of such speculations (Thompson, 1973). Supporters of volcanic dust effects point to reportedly marked temperature drops alter large volcanic eruptions followed by a gradual return to preexplosion levels [e.g., Mitchell (1961) or Lamb (1970) or more recently, Oliver (1976)].

For the case of volcanic dust a plausible physical mechanism has always been at hand: the absorptive and scattering properties of volcanic particles. However, proposed mechanisms for the suggested sunspot-climate link have remained highly speculative, underlining the need for particularly critical assessments when such relationships are suspected. In this article we will attempt such an assessment for both sunspots and volcanic dust by examining all of the continuous, long-term temperature histories of more than 85 years held in the data library of the National Center for Atmospheric Research. By compositing and spectrally analyzing the observed records themselves, as well as comparing them with calculated temperature histories, we attempt to appraise the validity of certain proposed relationships as well as to determine any indications of previously undiscovered ones. Our results suggest that a volcanic signal can be weakly detected but that a sunspot influence cannot be reliably inferred from these data.

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Stephen G. Warren and Stephen H. Schneider

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Stephen G. Warren and Stephen H. Schneider

Abstract

The energy-transport parameterization of Budyko (1969), which was devised to parameterize mean annual net radiation as a function of zonally averaged surface temperature, is subjected to verification with seasonal transport data in order to evaluate its validity for climatic change experiments. It is found that Budyko's linear parameterization is able to describe the annual zonal heat transport divergence for all latitudes and also the seasonal cycle of heat transport divergence at high latitudes (ϕ > 50°), but has no predictive ability for the seasonal deviation from annual average in lower latitudes.

The parameterization of infrared flux at the top of the atmosphere as a linear function of zonal surface temperature is tested using seasonal data for latitude zones in which the seasonal cycle of temperature has a large amplitude. The temperature coefficients for the different zones examined are found to differ from each other by as much as a factor of 2.

This uncertainty, together with the uncertainty in the strength of the ice-albedo-temperature positive feedback, propagates to an uncertainty in the sensitivity of model global climate to changes in the solar constant. The reduction in solar output required by a simple climate model to generate an ice-covered earth falls roughly in the range of 2 to 21% because of uncertainties in these two radiative coefficients alone. Uncertainty in the transport parameterization would further increase this range.

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Stephen H. Schneider and Robert E. Dickinson

Abstract

The neglect of multiple reflections between clouds and the earth's surface leads to an underestimate of the downward flux of solar radiation reaching the earth's surface. This underestimate is most pronounced in regions of persistent cloud cover and high surface albedos—the snow- and ice-covered regions of the high latitude zone, for example. Since the rate of snow melt (and thus snow albedo) depends upon the downward flux, neglect of multiple reflection is most serious in climatic models that predict snow and ice cover. Two simple algebraic expressions to account for multiple reflections in climate models are given and are shown to be limiting cases of a more general formula. Since this more general formula depends on the spatial distribution of subgrid-scale cloud cover amounts, an unambiguous definition of cloud amount over a GCM-scale grid square cannot be given, even if perfect knowledge of the optical properties of the subgrid-scale clouds were in hand. However, the uncertainties in downward solar flux at the earth's surface (or the albedo of the combined cloudiness-surface system) introduced by lack of knowledge of the two-dimensional geometric distribution of fractional cloud cover are shown to be generally less than 10%, most likely less than the errors in predicting cloud cover amounts or from the neglect of three-dimensional effects of finite-sized clouds.

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Robert M. Chervin and Stephen H. Schneider

Abstract

General Circulation Models (GCM's) of the earth's atmosphere have been frequently used to test different mechanisms for climate change. Typically, these tests involve comparing the statistics of the model with a prescribed change of a variable, system parameter or boundary condition, to the statistics from an unperturbed control case. However, it is not uncommon for the prescribed change experiment statistics to be comparable to statistics from a random perturbation experiment. Consequently, it is essential to determine the inherent “noise climatology” of GCM's in order to distinguish between signal and noise in climate experiments. We have examined the time-averaged response of the NCAR GCM to random perturbations in the initial conditions, while leaving all boundary conditions fixed. The dependence on time averaging interval of the noise level of a number of globally- and zonally-averaged GCM variables has been computed and gives an indication of how long to time average in order to reduce noise levels by a given amount. Additionally, we have found that it is important to delay for several simulated weeks the process of compiling climatological statistics from perturbed runs. Furthermore, the global distribution of noise levels reveals that certain regions are more prone to inherent variability for a given variable than are other areas. Also, we show that analysis of noise characteristics can be a useful diagnostic tool. However, different random perturbations do not reproduce the same noise level distribution, which implies that a Monte Carlo approach with more independent samples may he necessary for a more definite determination of the noise levels of GCM-generated statistics. Unfortunately, generating more samples means using more computer time, and that can be a fairly imposing barrier to the use of a GCM in climate experiments.

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Linda O. Mearns, Richard W. Katz, and Stephen H. Schneider

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Robert M. Chervin, W. Lawrence Gates, and Stephen H. Schneider

Abstract

We have tested the response of certain versions of atmospheric general circulation models (GCM's) to random perturbations in initial conditions while leaving external and boundary conditions unaltered. By means of time averaging and global space averaging, we have investigated the reduction of the level of noise inherent in the models. In this way, comparisons of the magnitude of the models' response to random perturbations with their response to advertent changes can be used to determine the significance of prescribed (non-random) perturbation experiments. This paper is intended to present preliminary results from two GCM's (NCAR and Rand) and may be useful for those contemplating using GCM's for climate or perturbation experiments.

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