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Bretherton et al. (1992) have pointed out that for most problems involving interseasonal and longer timescale variability of the atmosphere and oceans, prefiltering data is necessary for canonical correlation analysis (CCA) to perform comparably to certain other multivariate statistical schemes like singular value decomposition. This approach to CCA for climate problems was first suggested by Barnett and Preisendorfer (1987, hereafter BP), who filtered multiple fields of time series by replacing them with a truncated set of their principal components (PCs). Subsequent to its introduction, the BP method was applied extensively to linear climate prediction at the National Weather Service (Barnston 1994), which in turn has led to several further applications, both published (Barnston and Smith 1996, hereafter BS; He and Barnston 1996; Barnston and He 1996) and in progress. The CCA procedure produces an orthogonal hierarchy of predictor–predictand pattern pairs that represent in turn the maximum amount of cross correlation between the predictor and predictand fields, the second largest portion, etc. The transformation matrix relating the ordered predictor and predictand patterns can be used to optimally linearly specify the latter from the former. Its use (specifically the BP method) was adopted by us to address variants of the specification (i.e., simultaneous relationships) problems studied by BS (see Smith and Livezey 1998, hereafter SL, and Livezey and Smith 1998, hereafter LS2). In the course of this work it became apparent that the application of PC prefiltering

Observations show snowpack has declined across much of the western United States over the period 1950–99. This reduction has important social and economic implications, as water retained in the snowpack from winter storms forms an important part of the hydrological cycle and water supply in the region. A formal model-based detection and attribution (D–A) study of these reductions is performed. The detection variable is the ratio of 1 April snow water equivalent (SWE) to water-year-to-date precipitation (P), chosen to reduce the effect of P variability on the results. Estimates of natural internal climate variability are obtained from 1600 years of two control simulations performed with fully coupled ocean–atmosphere climate models. Estimates of the SWE/P response to anthropogenic greenhouse gases, ozone, and some aerosols are taken from multiple-member ensembles of perturbation experiments run with two models. The D–A shows the observations and anthropogenically forced models have greater SWE/P reductions than can be explained by natural internal climate variability alone. Model-estimated effects of changes in solar and volcanic forcing likewise do not explain the SWE/P reductions. The mean model estimate is that about half of the SWE/P reductions observed in the west from 1950 to 1999 are the result of climate changes forced by anthropogenic greenhouse gases, ozone, and aerosols. Lawrence Livermore National Laboratory///Estados Unidos Scripps Institution of Oceanography//SIO/Estados Unidos Ministry of Education, Culture, Sports, Science and Technology///Japón California Energy Commission///Estados Unidos Program of Climate Model Diagnosis and Intercomparison/[DOE-W-7405-ENG-48]/PCMDI/Estados Unidos UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigaciones Geofísicas (CIGEFI)

Potential predictability of low-frequency climate changes in the North Pacific depends on two main factors. The first is the sensitivity of the atmosphere to ocean-induced anomalies at the sea surface in midlatitudes. The second is the degree of teleconnectivity of the tropical low-frequency variability to midlatitudes. In contrast to the traditional approach of prescribing sea surface temperature (SST) anomalies, the response of a coupled atmospheric general circulation (CCM3)–mixed layer ocean model to oceanic perturbations of the mixed layer heat budget is examined. Since positive oceanic heat flux perturbations partially increase SST anomalies (locally), and partially are vented directly into the atmosphere, expressing boundary forcing on the atmosphere by prescribing upper-ocean heat flux anomalies allows for better understanding of the physical mechanism of low-frequency variability in midlatitudes. In the framework of this approach SST is considered to be a part of the adjustment of the co...

Running correlations between pairs of stochastic time series are typically characterized by low-frequency evolution. This simple result of sampling variability holds for climate time series but is not often recognized for being merely noise. As an example, this paper discusses the historical connection between El Nino–Southern Oscillation (ENSO) and average Indian rainfall (AIR). Decades of strong correlation (∼−0.8) alternate with decades of insignificant correlation, and it is shown that this decadal modulation could be due solely to stochastic processes. In fact, the specific relationship between ENSO and AIR is significantly less variable on decadal timescales than should be expected from sampling variability alone.

The effect of sampling error in surface air temperature observations is assessed for detection and attribution of an anthropogenic signal. This error arises because grid-box values are based on varying densities of station and marine data. An estimate of sampling error is included in the application of an optimal detection and attribution method based on June–August trends over 50 yr. The detection and attribution method is applied using both the full spatial pattern of observed trends and spatial patterns from which the global mean warming has been subtracted. Including the effect of sampling error is found to increase the uncertainty in estimates of the greenhouse gas–plus–sulfate aerosol signal from observations by less than 2%–6% for recent trend patterns (1949–98), and 3%–8% for signal estimates from observations in the first half of the twentieth century. Random instrumental error shows even smaller effects. However, the effects of systematic instrumental errors, such as changes in measurem...

The common variance between 100-yr-long control runs from 11 coupled global climate models (CGCMs) has been studied by use of common empirical orthogonal functions (EOFs). The results suggest that there is a considerable disparity between the CGCMs estimates of internal variability. About one-half of this difference can be attributed to model drift or other low-frequency variations in several of the models. However, even after accounting for this effect, it was found that the models can easily differ by a factor of 2 or more for the energy levels in different EOF mode (wave) numbers. Comparison with observations showed that no one model consistently reproduced the observed partial eigenvalue spectrum. Again, differences between observed and model energy levels were commonly a factor of 2 or more. It is speculated that at least some of the disagreement is due to the relative coarse resolution of the models used in this study. Separate analysis of a 1000-yr control run of the Geophysical Fluid Dyna...

The signature of ENSO in the wintertime frequencies of heavy precipitation and temperature extremes is derived from both observations and atmospheric general circulation model output for the contiguous United States. ENSO signals in the frequency of occurrence of heavy rainfall are found in the Southeast, Gulf Coast, central Rockies, and the general area of the Mississippi–Ohio River valleys. Strong, nonlinear signals in extreme warm temperature frequencies are found in the southern and eastern United States. Extreme cold temperature frequencies are found to be less sensitive to ENSO forcing than extreme warm temperature frequencies. Observed ENSO signals in extreme temperature frequencies are not simply manifestations of shifts in mean seasonal temperature. These signals in the wintertime frequency of extreme rainfall and temperature events appear strong enough to be useful in long-range regional statistical prediction. Comparisons of observational and model results show that the model climate i...

When long integrations of climate models forced by observed boundary conditions are compared against observations, differences appear that have spatial and temporal coherence. These differences are due to several causes, the largest of which are fundamental model errors and the internal variability inherent in a GCM integration. Uncertainties in the observations themselves are small in comparison. The present paper constitutes a first attempt to compare the time dependence of these spatial difference patterns with the time dependence of simulated spatial patterns of climate change associated with anthropogenic sources. The analysis procedure was to project the model minus observed near-surface temperature difference fields onto estimates of the anthropogenic “signal” (in this case the response to greenhouse-gas and sulfate-aerosol forcing). The temporal behavior of this projection was then compared with the estimated temporal evolution of the anthropogenic signal. Such comparisons were performed ...

The characteristic space–time scales of surface solar radiation fields measured by the 111-instrument MESONET in Oklahoma are estimated after removal of the diurnal cycle. These estimates of “within-day” variability are used to deduce the representativeness of surface solar radiation measurements made at the central ARM measurement site as a function of time-averaging interval. Nomograms of the relation between point measurements and area averages are given for different space–time-averaging intervals. Examples from the nomograms show, for instance, that under conditions of low mean radiation (cloudy days), the central site point measurements are representative of a spatial area the size of a T42 GCM grid box (280 km × 280 km) if one uses hourly averages and is willing to accept a correlation of 0.45 between area average and point measurement. The point data represent a 60 km × 60 km region at a 0.90 correlation level if a 5-min time average is used. The characteristic timescale for the within-da...

Ensembles of extended Atmospheric Model Intercomparison Project (AMIP) runs from the general circulation models of the National Centers for Environmental Prediction (formerly the National Meteorological Center) and the Max-Planck Institute (Hamburg, Germany) are used to estimate the potential predictability (PP) of an index of the Pacific–North America (PNA) mode of climate change. The PP of this pattern in “perfect” prediction experiments is 20%–25% of the index’s variance. The models, particularly that from MPI, capture virtually all of this variance in their hindcasts of the winter PNA for the period 1970–93. The high levels of internally generated model noise in the PNA simulations reconfirm the need for an ensemble averaging approach to climate prediction. This means that the forecasts ought to be expressed in a probabilistic manner. It is shown that the models’ skills are higher by about 50% during strong SST events in the tropical Pacific, so the probabilistic forecasts need to be conditio...

The dynamics and predictability of decadal climate variability over the North Pacific and North America are investigated by analyzing various observational datasets and the output of a state of the art coupled ocean–atmosphere general circulation model that was integrated for 125 years. Both the observations and model results support the picture that the decadal variability in the region of interest is based on a cycle involving unstable ocean–atmosphere interactions over the North Pacific. The period of this cycle is of the order of a few decades. The cycle involves the two major circulation regimes in the North Pacific climate system, the subtropical ocean gyre, and the Aleutian low. When, for instance, the subtropical ocean gyre is anomalously strong, more warm tropical waters are transported poleward by the Kuroshio and its extension, leading to a positive SST anomaly in the North Pacific. The atmospheric response to this SST anomaly involves a weakened Aleutian low, and the associated fluxes at the air–sea interface reinforce the initial SST anomaly, so that ocean and atmosphere act as a positive feedback system. The anomalous heat flux, reduced ocean mixing in response to a weakened storm track, and anonmalous Ekman heat transport contribute to this positive feedback. The atmospheric response, however, consists also of a wind stress curl anomaly that spins down the subtropical ocean gyre, thereby reducing the poleward heat transport and the initial SST anomaly. The ocean adjusts with some time lag to the change in the wind stress curl, and it is this transient ocean response that allows continuous oscillations. The transient response can be expressed in terms of baroclinic planetary waves, and the decadal timescale of the oscillation is therefore determined to first order by wave timescales. Advection by the mean currents, however, is not negligible. The existence of such a cycle provides the basis of long-range climate forecasting over North America at decadal timescales. At a minimum, knowledge of the present phase of the decadal mode should allow a “now-cast” of expected climate “bias” over North America, which is equivalent to a climate forecast several years ahead.

The relative roles of heat and freshwater fluxes in forcing the tropical Pacific on interannual timescales are investigated using sophisticated atmospheric and oceanic general circulation models. Interannual density flux anomalies due to anomalous precipitation and shortwave and longwave radiation are highly correlated since they all depend on clouds. Their respective contributions to the anomalous surface density flux are of comparable magnitude, with precipitation and longwave anomalies opposing shortwave radiation. This implies that anomalous radiation and precipitation associated with the eastward shift of the centers of deep convection during El Nino change the density flux little since they largely balance. This near cancellation also causes the evaporative component to dominate interannual anomalies of the density flux in the eastern Pacific and in the Indian Ocean and implies that anomalous net surface density fluxes there can be approximated by anomalous evaporation alone. However, in th...

Ensemble forecasts of global climate conditions during the seven largest Pacific warm/cold events of the last 23 years have been made with a new two-tiered climate forecast technique. The internal model variability (IMV) within the atmospheric model used in the forecasts was large, approximately ⅓ to ½ the variability obtained from the same model forced by observed SST over the last 20 yr. This variability can lead to a wide range of realistic-looking forecasts, even when the equatorial SST forcing was nil. For example, single model forecasts forced by climatological SST produced excellent simulations of the extreme cold event of 1988/89 and the large warm event of 1982/83. Clearly, single simulations are totally inadequate for forecasting and sensitivity studies. The 10 member ensembles used in this study appear to be the minimum for reliable forecasts/sensitivity studies in midlatitudes, although fewer realizations may be needed in the Tropics. The long-range forecasts are shown to be insensiti...

Long-range sea surface temperature forecasts from two different coupled ocean-atmosphere models of the tropical Pacific are used in conjunction with statistical models relating winter Northern Hemisphere 700-mb height and tropical SST to forecast the former field at a lead time of two seasons in advance. The forecasts show considerable skill over large areas, with a regional distribution of predictive performance that is consistent with the observed contemporaneous relation between the two fields. Comparable skills for lead time of a year or more in advance seem likely.

Three numerical experiments have been conducted to explore the relative roles of midlatitude and tropical SSTs in producing atmospheric variability. In these experiments, anomalous observed SSTs were prescribed in 1) the tropical oceans only, 2) the midlatitude oceans only, and 3) globally. These simulations were conducted with the same atmospheric model and covered the period 1970–88. Although each simulation reproduced some aspects of the observed circulation variability, the results from the two experiments including tropical SSTs performed best by most measure particularly in showing temporal signals that agreed with those seen in the observations. The results indicate that the response of the observed atmospheric circulation to North Pacific SST anomalies is much smaller and much less systematic than the response to tropical SSTs. It is suggested that the apparent links between North Pacific SSTs and the observed winter circulation am due primarily to the effects on oceanic forcing by the re...

Two different sea-level pressure datasets for the strip between 20 o and 40 o S are intercompared. At large space-time scales the sets are virtually identical. However, for studies of short-term, regional changes caution must be exercised because the datasets may differ substantially

Radiosonde data from a global 118-station network are used to determine the spatial and temporal scales of variability of tropospheric water vapor. Various sources of possible error and bias in the data are analyzed. Changes in instrumentation at U.S. stations are shown to have a considerable influence on the record; information on comparable changes in other countries is not readily available. Mean monthly data are shown to be acceptable at tropical nations but not at high-latitude stations, where the nonlinear dependence of saturation vapor pressure on temperature, coupled with large temperature ranges, leads to biases of up to 10% in mean monthly specific humidity. A series of three empirical orthogonal function analyses (for the tropics, North America, and the globe) of specific humidity at the surface, 850-mb, 700-mb, and 500-mb levels is presented. All three show evidence of a shift in the specific humidity field in the winter of 1976/77, with generally lower values from the beginning of th...