Abstract

Using an 8-day series (18–26 August 1975) of multivariate statistical analyses of European radiosonde data together with a measure of analysis error, we have estimated error statistics from 959 Nimbus 6 temperature profiles for 10 isobaric layers in the troposphere and lower stratosphere. The mean error or bias is largest near the tropopause (+0.9°C) but changes sign several times in the vertical so that the integrated mean error for the atmospheric column 1000–70 mb is small (−0.1°C). The root-mean-square error peaks at the tropopause (2.9°C) with a minimum in the midtroposphere (1.0°C). In all layers, the horizontal correlation of retrieval error shows little systematic dependence on direction but strong dependence on distance. The correlation is greater than 0.50 at distances less than 400 km and less than 0.10 at 800 km and beyond, and it can be approximated by a Gaussian curve. The vertical correlations are greatest between adjacent layers (∼0.50); negative correlations exist between layers on opposite sides of the tropopause. This information is useful in any statistical objective analysis which accounts for observational error.

Abstract

Progress toward understanding the causes of and physical mechanisms involved in the 1988 North American drought is reported. An earlier study demonstrated that major sea surface temperature (SST) anomalies in the tropical Pacific Ocean, in association with the 1988 La Niña, may have disrupted atmospheric heating patterns by changing the location and intensity of the intertropical convergence zone and that such heating anomalies could have initiated the circulation anomalies across North America responsible for the drought. A key issue of when the drought circulation anomalies developed and their relation to changes in tropical Pacific SSTs is examined. Although unusually dry soil moisture and heat waves persisted into August, the anomalous atmospheric conditions that brought on the drought occurred in April, May, and June of 1988. The evolution of the Pacific SSTs and tropical convection, as revealed by outgoing longwave radiation, is shown to be consistent with the development of the conditions favorable for initiating the drought circulation pattern in April through June of 1988. On the equator at 110°W, SST anomalies exceeded −2.75°C in only April, May, and June and were largest (−4.1°C) in May 1988. The issues of how the 1988 La Niña differed from those in the past and the importance of the whole SST field in determining the anomalous diabatic heating are also discussed. Diagnostic calculations of atmospheric diabatic heating confirm that atmospheric heating anomalies existed in the tropical Pacific in association with the major SST anomalies during this time. The link between the anomalous heating and the tropical SSTs supports the view that influences external to the atmosphere were important and that the drought was not generated solely by mechanisms internal to the atmosphere. The distribution of diagnosed heating anomalies over North America, together with a planetary wave model response to idealized forcing, is described to clarify the possible role of soil moisture anomalies in perpetuating the drought. It is argued that feedback-caused soil moisture anomalies may have been secondary sources for the drought circulation but could not have been the primary instigator. For the most part, other diagnosed heating anomalies during the drought are found to have little influence on the North American region. Criteria to help judge the ability of general circulation models to simulate the drought are discussed.

Abstract

A conceptual framework is developed for quantifying the relationship between low-frequency variability and extreme events. In this framework, variability is decomposed into low-frequency and synoptic components using complementary 10-day low-pass and high-pass filters, and a distinction is made between two ways that low-frequency variability influences extremes: the additive effect, which neglects the dependence of synoptic variability on the low-frequency state, and the multiplicative effect, which is due to the dependence of synoptic variability on the low-frequency state. The influence of various factors on the relationship between low-frequency variability and extreme events is decomposed and quantified by generating a series of simple synthetic datasets based on different assumptions about low-frequency and synoptic variability and their relationship.

These techniques are used to study the relationship between low-frequency variability and extreme westerly wind events in three datasets, an 1158-yr GCM simulation and two reanalysis datasets, with similar results for all three. Geographical variations in the low-frequency–extreme relationship are only partially explained by geographical variations in the low-frequency–synoptic variance ratio; the non-Gaussianity of low-frequency and synoptic variability and the relationship between synoptic variance and the low-frequency state are also found to be important. The simple synthetic datasets that include these factors provide good estimates of the magnitude and probability of extremes. Implications for predictability and applications to more complex low-frequency–extreme relationships are discussed.

Abstract

A multivariate statistical analysis procedure has been developed for estimating geopotential height h and wind (u, v) on a global latitude-longitude grid. Estimates are obtained by modifying the “first guess” from a prediction model by a linear combination of forecast errors deduced from observed data. Because the scheme is multivariate, the regression coefficients (weights) are matrices, which depend upon covariance among forecast errors in h, u and v. These covariances are modeled mathematically with geostrophic constraints. In the tropics, however, only the wind field is analyzed, covariances are modeled under the constraint of nondivergence, and heights are obtained from a balance equation. At high latitudes, analyses are performed in polar stereographic coordinates.

The objective analysis scheme fits observed data as well as the “Cressman scheme” that was used operationally at the National Meteorological Center until recently and also as well as a skilled analyst. In data-rich areas, the analyses are insensitive to the type of fist guess. Realistic ageostrophic and divergent components are present in the analyzed winds, and the kinetic energy spectrum at 40°N is reasonable at zonal wavenumbers less than 20. When both wind and height observations are plentiful, two univariate schemes (one for height, one for wind) fit the data as well as the multivariate scheme, but forecasts based upon the latter are consistently better. Experiments suggest that for a fixed amount of initial data, small gains in forecast accuracy can be made by improving the analysis procedure.

Abstract

No abstract available.

Abstract

A global, spectral, primitive equation model is developed to study the seasonal climatology of the large-scale features of the atmosphere. The model resolution is five equally-spaced sigma levels in the vertical and triangular truncation at wavenumber 10 in the horizontal. Included in the model are: orography; time-varying (but prescribed) sea-surface temperatures, snowcover, and solar declination angle; parameterizations for radiation, convection, condensation, diffusion, and surface transports; and a surface heat budget. The external seasonal forcing of the model atmosphere is composed of sinusoidal time variations in the incoming solar radiation and latitude of the snowline and more complicated variations in the albedo of the snow and the sea-surface temperatures. A five-year seasonal simulation has been analyzed. The model reasonably reproduces the general features of the observed atmospheric circulation, seasonal cycles, interannual variations and hemispheric differences. The success of this low-resolution model in simulating the large-scale features of the atmospheric seasonal cycle illustrates the usefulness of such models for climate studies in conjunction with high-resolution general circulation model simulations.

Abstract

In this paper, an attempt is made to estimate possible sensitivities of El Niño-Southern Oscillation (ENSO)-related effects in a climate with increased carbon dioxide (CO₂). To illustrate this sensitivity, results are shown from two different interactive ocean-atmosphere model configurations and an atmospheric model with prescribed heating anomalies. In the first, an atmospheric general circulation model (GCM) is coupled to a global coarse-grid dynamical ocean GCM (coupled model). In the second, the same atmospheric model is coupled to a simple nondynamic slab-ocean mixed-layer model (mixed-layer model). In the third, an atmospheric model is run in perpetual January mode with observed sea surface temperatures (SSTs) and prescribed tropical tropospheric heating anomalies (prescribed-heating model). Results from the coupled model show that interannual SST variability (with warm and cold events relative to the mean SST) continues to occur in the tropics with a doubling of CO₂. This variability is superimposed on mean SSTs in the tropical eastern Pacific that are higher by about 1°. The pattern of precipitation and soil-moisture anomalies in the tropics is similar in model warm events with present amounts of CO₂ (1 × CO₂) and in warm events with instantaneously doubled CO₂ (2 × CO₂). When a warm-event SST anomaly is superimposed, the rise in mean SST in the tropical eastern Pacific from the doubling of CO₂ leads to increased evaporation and low-level moisture convergence, greater precipitation over the SST anomaly, and an intensification of atmospheric anomalies in the tropics involved with the anomalous large-scale east-west (Walker) circulation. Consequently, differences of precipitation and soil moisture between 1 × CO₂ and 2 × CO₂ warm events show that most anomalously dry areas become drier (implying risk of increased drought in those regions in 2 × CO₂ Warm events) and anomalously wet areas wetter in the coupled model. In the extratropics, the increased CO₂ causes a large change in the midlatitude atmospheric circulation. This is associated with an alteration of extratropical teleconnections in 2 × CO warm events compared to 1 × CO₂ warm events in a relative sense, with more zonally symmetric anomalies in sea level pressure and 200- mb height. Similar results in the tropics and extratropics are obtained for the mixed-layer model with warm-event SST anomalies in the tropical Pacific prescribed for 1 × CO₂ and 2 × CO₂ mean climates, and from the prescribed-heating model with anomalous heat sources in the tropical troposphere analogous to those in 1 × CO₂ and 2 × CO₂ warm events. This study is a precursor to future higher-resolution model studies that could also address possible changes in ENSO but with better representation of coupled mechanisms thought to contribute to ENSO.

Abstract

Perpetual January experiments have been performed using versions of the NCAR Community Climate Model (CCM) with and without mountains. Features of the mean simulations of the “no mountains” experiment are compared with those of the standard CCM, the “mountains” experiment. The stationary waves in the “no mountains” experiment have smaller amplitudes than those in the “mountains” case, especially for zonal wavenumbers 2 and 3. The mean zonal wind in the “no mountains” case also has weaker horizontal gradients than in the “mountains” case.

The response of these two versions of the CCM to equatorial Pacific sea surface temperature (SST) anomalies is investigated. Two anomalies are considered for each model configuration. Differences in the responses of the two models to the same anomalous forcing are discussed. The Northern Hemisphere midlatitude response of the “no mountains” model has nearly the same spatial scale as that of the “mountains” model, but details of the shape of the response pattern are different. The amplitude of the response in the Northern Hemisphere is weaker in the “no mountains” case than in the “mountains” case by about a factor of 2. On the other hand, the response in the Southern Hemisphere is stronger in the “no mountains” case than in the “mountains” case. It is shown that this is consistent with the interpretation that the Pacific/North American (PNA) teleconnection pattern extracts energy from the mean zonal flow by barotropic conversion. The importance of barotropic conversion in the Southern Hemisphere is also demonstrated.

A linear barotropic vorticity equation model is used to compare the response to localized tropical forcing in each of the two basic states, for “mountains” and “no mountains,” produced by the CCM. When forced in the vicinity of the SST anomaly, the linear model shows a sensitivity to the state about which it is linearized that is similar to the sensitivity shown by the CCM to its climatic state. This sensitivity is shown to be influenced by barotropic conversion processes, which in turn are influenced by the basic state configuration. Furthermore, calculations indicate that forcing in virtually any region of the tropics tends to produce a stronger (weaker) Northern (Southern) Hemisphere response for the “mountains” basic state than the “no mountains” basic state. It is also shown that anomalous upper troposphere convergence around Indonesia may be contributing to the CCM response to the eastern Pacific SST anomalies being considered in this study.

We conclude that the stationary waves in each CCM simulation affect the midlatitude response of that model to tropical forcing anomalies.

Abstract

A three-dimensional (3D), multivariate, statistical objective analysis scheme (referred to as optimum interpolation or OI) has been developed for use in numerical weather prediction studies with the FGGE data. Some novel aspects of the present scheme include 1) a multivariate surface analysis over the oceans, which employs an Ekman balance instead of the usual geostrophic relationship, to model the pressure-wind error cross correlations, and 2) the capability to use an error correlation function which is geographically dependent.

A series of 4-day data assimilation experiments are conducted to examine the importance of some of the key features of the OI in terms of their effects on forecast skill, as well as to compare the forecast skill using the OI with that utilizing a successive correction method (SCM) of analysis developed earlier. For the three cases examined, the forecast skill is found to be rather insensitive to varying the error correlation function geographically. However, significant differences are noted between forecasts from a two-dimensional (2D) version of the OI and those from the 3D OI, with the 3D OI forecasts exhibiting better forecast skill. The 3D OI forecasts are also more accurate than those from the SCM initial conditions.

The 3D OI with the multivariate oceanic surface analysis was found to produce forecasts which were slightly more accurate, on the average, than a univariate version.