The role of mountains in maintaining extensive midlatitude arid regions in the N.H. was investigated using the GFDL GCM with and without orography. In the integration with mountains, dry climate were simulated over central Asia and the interior of North America, agreeing with the observed climate. In contrast, moist climates were simulated in the same regions in an integration without mountains. During all seasons but summer, large amplitude stationary waves occur in response to the Tibetan Plateau and Rocky Mountains. The midlatitude dry regions are located upstream of the troughs of these waves, where general subsidence and relatively infrequent storm development occur and precipitation is thus inhibited. The results of this experiment suggest that midlatitude dryness is largely due to the existence of orography, an alternative to the traditional explanation that distance from oceanic moisture sources, accented locally by the presence of mountain barriers upwind, is the major cause of midlatitude dry regions. Paleoclimatic evidence of less aridity during the late Tertiary, before substantial uplift of the Rocky Mountains and Tibetan Plateau is believed to have occurred, supports this possibility.

A comparison of climate feedback processes in GCMs finds a threefold variations in climate sensitivity, most of which is attributable to model differences in depiction of cloud feedback. Improvement in the treatment of clouds is of course urged with the reminder that cloud feedback is the consequence of all interacting physical and dynamical processes in a GCM. The result of these processes in to produce changes in temperature, moisture distribution, and clouds which are integrated into the radiation response termed cloud feedback.

The major source of cloud-condensation nuclei (CCN) over the oceans appears to be dimethylsulphide, which is produced by planktonic algae in sea water and oxidizes in the atmosphere to form a sulphate aerosol. Because the reflectance (albedo) of clouds (and thus the Earth's radiation budget) is sensitive to CCN density, biological regulation of the climate is possible through the effects of temperature and sunlight on phytoplankton population and dimethylsulphide production. To counteract the warming due to doubling of atmospheric CO2, an approximate doubling of CCN would be needed.

Robust estimation of power spectra, coherences, and transfer functions is investigated in the context of geophysical data processing. The methods described are frequency-domain extensions of current techniques from the statistical literature and are applicable in cases where section-averaging methods would be used with data that are contaminated by local nonstationarity or isolated outliers. The paper begins with a review of robust estimation theory, emphasizing statistical principles and the maximum likelihood or M-estimators. These are combined with section-averaging spectral techniques to obtain robust estimates of power spectra, coherences, and transfer functions in an automatic, data-adaptive fashion. Because robust methods implicitly identify abnormal data, methods for monitoring the statistical behavior of the estimation process using quantile-quantile plots are also discussed. The results are illustrated using a variety of examples from electromagnetic geophysics.

A linearized, steady state, primitive equation model is used to simulation the climatological zonal asymmetries (stationary eddies) in the wind and temperature fields of the 18,000 YBP climate during winter. We compare these results with the eddies simulated in the ice age experiments of Broccoli and Manabe (1987), who used CLIMAP boundary conditions and reduced atmospheric CO2 in an atmospheric general circulation model (GCM) coupled with a static mixed layer ocean model. The agreement between the models is good, indicating that the linear model can be used to evaluate the relative influences of orography, diabatic heating, and transient eddy heat and momentum transports in generating stationary waves.

Recently, additional ozone production mechanisms have been proposed to resolve the ozone deficit problem, which arises from greater ozone destruction than production in seceral photochemical models of the upper stratosphere and lower mesosphere. A detailed ozone model budget analysis was performed with simultaneous observations of O3, HCl, H2O, CH4, NO and NO2 from the Halogen Occultation Experiment (HALOE) on the Upper Atmosphere Research Satellite (UARS) under conditions with the strongest photochemical control of ozone. The results indicate that an ozone deficit may not exist. On the contrary, the use of currently recommended photochemical parameters leads to insufficient ozone destruction in the model.

The recognition of the central role of radiative processes in many proposed climate change mechanisms and the perception of possibly significant uncertainties in the estimates of these fundamental processes led the Joint Scientific Committee of the World Climate Research Programme and the International Radiation Commission of the International Association of Meteorology and Atmospheric Physics to initiate the international Intercomparison of Radiation Codes in Climate Models (ICRCCM). The results from model calculations with specified clear-and-cloudy conditions show that many radiation algorithms may have unidentifiable but large errors that may significantly affect the conclusions of the studies for which they are used. This is true for climate modeling but may also be the case for other applications such as the estimation of radiation fluxes at the sruface from satellite observations. As the study has progressed over a 4-year period, there has been a narrowing of results as errors were found in some codes and as the understanding of many modeling problems increased. Many of the results, particulary for clear-sky conditions, indicate that we are close to the range of (relative) accuracy for calculating flux quantities necessary for many climate programs. However, not all models will give such accuracy. It is recommended that the ICRCCM test cases be used to test radiation algorithms prior to their application to climate-related problems. THe participants feel that the rather large discrepancies revealed during ICRCCM cannot be decisively resolved by further calculation. Therefore the group recommends the organization of a program to simultaneously measure spectral radiance at high spectral resolution along with the atmospheric data necessary to calculate radiances.

Discusses many examples from the paleoclimatic literature of very rapid climate change and possible mechanisms.

A general method is described for constructing simple dynamical models to approximate complex dynamical systems with many degrees of freedom. The technique can be applied to intepret sets of observed time series or numerical simulations with high-resolution models, or to relate observation and simulations. The method is based on a project of the complete system on to a smaller number of ``principal interaction patterns (PIPs). The coefficients of the PIP expansion are assumed to be governed by a dynamic model containing a small number of adjustable parameters. The optimization of the dynamical model, which in the general case can be both nonlinear and time-dependent, is carried out simultaneously with the construction of the optimal set of interaction patterns. In the linear case the PIPs reduce to the eigenoscillations of a first-order linear vector process with stochastic forcing (principal oscillation patterns, or POPs). POPs are linearly related to the ``principal prediction patterns'' used in linear forecasting applications. The POP analysis can also be applied as a diagnostic tool to compress the extensive information contained in the high-dimensional cross-spectral covariance matrix representing the complete second-moment structure of the system.

An optimal linear filter (fingerprint) is derived for the detection of a given, time-dependent, multivariate climate change signal in the presence of natural climate variability noise. Application of the fingerprint to the observed (or model simulated) climate data yields a climate change detection variable (detector) with maximal signal-to-noise ratio. The optimal fingerprint is given by the product of the assumed signal pattern and the inverse of the climate variability covariance matrix. The data can consist of any, not necessarily dynamically complete, climate dataset for which estimates of the natural variability covariance matrix exist. The single-pattern analyis readily generalizes to the multipattern case of a climate change signal lying in a prescribed (in practice relatively low dimensional) signal pattern space: the single-pattern result is simply applied separately to each individual base pattern spanning the the signal pattern space. Multipattern detection methods can be applied either to test the statistical significance of individual components of a predicted multicomponent climate change response, using a multivariate test. Both detection modes make use of the same set of detectors. The difference in direction of the assumed signal pattern and computed optimal fingerprint vector allows alternative interpretations of the estimated signal associated with the set of optimal detectors. The present analysis yields an estimated signal lying in the assumed signal space, whereas an earlier analysis of the time-independent detection problem by Hasselmann yielded an estimated signal in the computed fingerprint space. The different interpretations can be explained by different choices of the metric used to related the signal space to the fingerprint space (inverse covariance matrix versus standard Euclidean metric, respectively). Two simple natural variability models are considered: a space-time separability model, and an expansion in terms of POPs (principal oscillation patterns). For each model the application of the optimal fingerprint method is illustrated by an example.

A benchmark calculation is proposed for evaluating the dynamical cores of atmospheric general circulation models independently of the physical parameterizations. The test focuses on the long-term statistical properties of a fully developed general circulation; thus, it is particularly appropriate for intercomparing the dynamics used in climate models. To illustrate the use of the benchmark, two very different atmospheric dynamical cores-one spectral, one finite difference-are compared. It is found that the long-term statistics produced by the two models are very similar. Selected results from these calculations are presented to initiate the comparison.

A proxy temperature record from the Vostok ice core is created using oxygen isotope and deuterium ratios. This is compared to ocean sediment core data. A detailed discussion of the differences between the proxy records is included.

The TOPEX/POSEIDON satellite altimeter mission has measured global mean sea level every 10 days over the last 2 years with a precision of 4 millimeters, which approaches the requirements for climate change research. The estimated rate of sea level change is +3.9 +/- 0.8 millimeters per year. A substantial portion of this trend may represent a short-term variation unrelated to the long-term signal expected from global warming. For this reason, and because the long-term measurement accuracy requires additional monitoring, a longer time series is necessary before climate change signals can be unequivocally detected.

Available from Prof. A. Ohmura, ETH Zurich, CH-8057 Zurich, Switzerland

A gravitationally self-consistent theory of postglacial relative sea level change is used to infer the variation of surface ice and water cover since the Last Glacial Maximum (LGM). The results show that LGM ice Volume was approximately 35 percent lower than suggested by the CLIMAP reconstruction and the maximum heights of the main Laurentian and Fennoscandian ice complexes are inferred to have been commensurately lower with respect to sea level. Use of these Ice Age boundary conditions in atmospheric general circulation models will yield climates that differ significantly from those previously inferred on the basis of the CLIMAP data set.

Understanding the natural variability of cliamte is important for predicting its near-term evolution. Models of the oceans' thermohaline and wind-driven circulation show low-frequency oscillations. Long instrumental records can help validate the oscillatory behavior of these models. Singular spectrum analysis applied to the 335-year-long central England temperature (CET) record has identified climate oscillations with interannual (7- to 8-year) and interdecadal (15- to 25-year) periods, probably related to the North Atlantic's wind-driven and thermohaline circulation, respectively, Statistical prediction of oscillatory variability shows CETs decreasing toward the end of this decade and rising again into the middle of the next.

The Cooperative Holocene Mapping Project (COHMAP) assembled a global array of well-dated paleoclimatic data and used the NCAR CCM to identify and evaluate causes and mechanisms of climate change over the period. Seven sets of simulation experiments were carried out for the 18,000 year period, focusing on monsoonal and North Atlantic climates. The GCM experiments employed boundary conditions for northern hemisphere solar radiation, land ice volume, atmospheric CO2 concentration, and sea surface temperature appropriate for the period of the simulations. [From Handel and Risbey (1992).]

Here we study the low-frequency variability of the tropical Indian and Pacific basins with a new statistical technique. Bayesian oscillation patterns (BOP). To describe the climatic system in this region, zonal wind and sea surface temperature (SST) are the selected variables. Their variabililty can be explained in terms of a reduced number of frequencies and spatial patterns, which are identified for each field via a statistical procedure. A predictive scheme is devised and applied in two forecast experiments using the patterns and frequencies thus identified.

In addition to the well-known warming of  0.5 deg. C since the middle of the 19th century, global-mean surface temperature records display substantial variability on timescales of a century or less. Accurate prediction of future temperature change requires an understanding of the causes of this variability; possibilities included external factors, such as increasing greenhouse-gas concentrations and anthropogenic sulfate aerosols, and internal factors, both predictable (such as El Nino) and unpredictable (noise). Here we apply singular spectrum analysis (SSA) to four global-mean temperature records and identify a temperature oscillation with a period of 65-70 years. SSA of the surface temperature records for 11 geographical regions shows that the 65-70 year oscillation is the statistical result of 50-88 year oscillations for the North Atlantic Ocean and its bounding Northern Hemisphere continents. These oscillations have obscured the greenhouse warming signal in the North Atlantic and North America. Comparison with previous observations and model simulations suggests that the oscillation arises from predictable internal variability of the ocean-atmosphere system.

The factors that caontorl the flux of energy across a latitude belt in the atmosphere/ocean system are determined. The results show that, as long as a hemisphere is in equilibrium and as long as the structure of the system is dominated by the planetary scale, the total flux is constrained to peak near 35 deg. lat., the flux per unit area to peak near 45 deg. lat., and the magnitude of the flux is determined primarily by the solar constant, the size of the earth, the tilt of the earth's axis, and the hemispheric mean albedo. The magnitude of the flux is insensitive to the structure and dynamics of the atmosphere/ocean system because the high efficiency of the dynamical transport mechanisms and because of the negative correlation between local planetary albedo and local thermal emissions to space.

On the basis of land station station from the N.H., it was determined that roughly half of the temporal variance of monthly mean hemispheric mean anomalies in surface air temperature during the period from 1900 through 1990 were linearly related to the amplitude of a distinctive spatial pattern in which the oceans are anomalously cold and the continents are anomalously warm poleward of 40 degrees north when the hemisphere is warm. Apart from an upward trend since 1975, to which El Nino has contributed, the amplitude time series associated with this pattern resembles seasonally dependent white noise. It is argued that the variability associated with this pattern is dynamically induced and is not necessarily an integral part of the fingerprint of global warming.