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T. P. Barnett
,
K. Arpe
,
L. Bengtsson
,
M. Ji
, and
A. Kumar

Abstract

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 conditional on the tropical SST.

Taken together with earlier studies, the present results suggest that the original set of AMIP integrations (single 10-yr runs) is not adequate to reliably test the participating models’ simulations of interannual climate variability in the midlatitudes.

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S. Hasselmann
,
K. Hasselmann
,
J. H. Allender
, and
T. P. Barnett

Abstract

Four different parameterizations of the nonlinear energy transfer S nl in a surface wave spectrum are in investigated. Two parameterizations are based on a relatively small number of parameters and are useful primarily for application in parametrical or hybrid wave models. In the first parameterization, shape-distortion parameters are introduced to relate the distribution S nl for different values of the peak-enhancement parameter γ. The second parameterization is based on an EOF expansion of a set of S nl computed for a number of different spectral distributions. The remaining two parameterizations represent operator forms that contain the same number of free parameters as used to describe he wave spectrum. Such parameterizations with a matched number of input and output parameters are required for numerical stability in high-resolution discrete spectral models. A cubic, fourth-order diffusion-operator expression derived by a local-interaction expansion is found to be useful for understanding many of the properties of S nl , but is regarded as too inaccurate in detail for application in most wave models. The best results are achieved with a discrete-interaction operator parameterization, in which a single interaction configuration, together with its mirror image (representing a two-dimensional continuum of interactions with respect to a variable reference wavenumber scale and direction) is used to simulate the net effect of the full five-dimensional interaction continuum.

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T. P. Barnett
,
R. A. Knox
, and
R. A. Weller

Abstract

During January and February 1974 the NORPAX POLE experiment was carried out in the central Pacific to begin collection of data needed to design a large-scale ocean/atmosphere monitoring program. This paper describe features of the ocean temperature field observed during POLE within a region of about 400 km in diameter centered near 35°N, 155°W. The temperature field, which was approximately stationary during the month-long experiment, was dominated by a strong north-south gradient as expected. The east-west gradient was negligible. Superimposed on this mean field was energetic noise with typical rms isotherm displacements of 25 m near the bottom of the mixed layer. The characteristic horizontal scale of this noise was 50 km near the surface although the field appeared to be anisotropic. The energy, scale length and degree of anisotropy all decrease with depth. The implications of these observations to a sampling strategy are discussed as are other conclusions drawn from a statistical analysis of the temperature data.

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Russ Davis
,
T. P. Barnett
, and
C. S. Cox

Abstract

Variability of near-surface Currents over a 20-Day period in a 15O km diameter region of the central North Pacific is described using vertical profiles from a current meter and the tracks of 25 drifting buoys. Energetic fluctuations of order 0.10 m s−1 having time scales of a few days and vertical scales in excess of 100 m were found, apparently coherent with the wind forcing. Buoy tracks disclose a small-scale (<15 km) short-period (less than a few days) variability with speeds of the order 0.05 m s−1 and an energetic mesoscale motion with speeds of the order 0.07 m s−1, space scales of the order 40 km and time scales exceeding 20 days. Additionally, the difference between the mean current observed over the experiment. having a speed of aboutO.03 m s−1, and the climatological norm inferred from ship-drift. with a speed of about 0.10 m s−1, suggests a larger scale variability not adequately resolved.

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S. E. Pazan
,
T. P. Barnett
,
A. M. Tubbs
, and
D. Halpern

Abstract

The global band wind analysis produced by the Fleet Numerical Oceanography Center (FNOC) has been compared with direct buoy observations of winds in the Central Equatorial Experiment. Using the six months of available data it is concluded that the FNOC winds are generally lower in variability than the actual observations. In most cases the spectral coherence between the wind products and the buoy observations is not good. Discrepancies in model results versus buoy observations do not appear to be related to lack of real-time input data for analysis.

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T. P. Barnett
,
N. Graham
,
S. Pazan
,
W. White
,
M. Latif
, and
M. Flügel

Abstract

A hybrid coupled model (HCM) of the tropical ocean–atmosphere system is described. The ocean component is a fully nonlinear ocean general circulation model (OGCM). The atmospheric element is a statistical model that specifies wind stress from ocean-model sea surface temperatures (SST). The coupled model demonstrates a chaotic behavior during extended integration that is related to slow changes in the background mean state of the ocean. The HCM also reproduces many of the observed variations in the tropical Pacific ocean-atmosphere system.

The physical processes operative in the model together describe a natural mode of climate variability in the tropical Pacific ocean–atmosphere system. The mode is composed of (i) westward-propagating Rossby waves and (ii) an equatorially confined air–sea element that propagates eastward. Additional results showed that the seasonal dependence of the anomalous ocean–atmosphere coupling was vital to the model's ability to both replicate and forecast key features of the tropical Pacific climate system.

A series of hindcast and forecast experiments was conducted with the model. It showed real skill in forecasting fall/winter tropical Pacific SST at a lead time of up to 18 months. This skill was largely confined to the central equatorial Pacific, just the region that is most prominent in teleconnections with the Northern Hemisphere during winter. This result suggests the model forecasts of winter SST at leads times of at least 6 months are good enough to be used with atmospheric models (statistical or OGCM) to attempt long-range winter forecasts for the North American continent. This suggestion is confirmed in Part II of this paper.

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T. P. Barnett
,
W. C. Patzert
,
S. C. Webb
, and
B. R. Bean

Two different satellite estimates of sea-surface temperature (SST) have been compared with observed temperature sections in the central tropical Pacific Ocean. The satellite products were found to be biased with respect to the observations by approximately 1–4°C. The bias field had a strong latitudinal and longitudinal structure. The spatial structure of this field and the large magnitude of errors in estimates of SST, if a normal situation, preclude the use of the satellite products by themselves in climatological studies of the area. However, if some means can be found to remove the bias from the satellite products then they will be marginally useful in the study of interannual variations of SST in the tropical Pacific.

The errors associated with the estimates of satellite SST are strongly linked to cloud cover and the amount of water vapor in the atmosphere, indicating present methods of correcting for these types of contamination are inadequate. The errors also depend on the number of observations that have gone into the satellite estimate of SST.

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T. P. Barnett
,
R. W. Preisendorfer
,
L. M. Goldstein
, and
K. Hasselmann

Abstract

Methods of estimating the significance of optimal regression models selected from a model hierarchy proposed by Barnett and Hasselmann (1979) are reexamined allowing for the multiple-candidate nature of the selection criteria. It is found that the single-candidate models' significance value previously used can over- or underestimate the true multiple-candidate significance level of the selected model depending on the selection criteria used. A number of possible selection strategies to remove these problems are discussed and evaluated both theoretically and by Monte Carlo simulators.

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T. P. Barnett
,
K. Hasselmann
,
M. Chelliah
,
T. Delworth
,
G. Hegerl
,
P. Jones
,
E. Rasmusson
,
E. Roeckner
,
C. Ropelewski
,
B. Santer
, and
S. Tett

This paper addresses the question of where we now stand with respect to detection and attribution of an anthropogenic climate signal. Our ability to estimate natural climate variability, against which claims of anthropogenic signal detection must be made, is reviewed. The current situation suggests control runs of global climate models may give the best estimates of natural variability on a global basis, estimates that appear to be accurate to within a factor of 2 or 3 at multidecadal timescales used in detection work.

Present uncertainties in both observations and model-simulated anthropogenic signals in near-surface air temperature are estimated. The uncertainty in model simulated signals is, in places, as large as the signal to be detected. Two different, but complementary, approaches to detection and attribution are discussed in the context of these uncertainties.

Applying one of the detection strategies, it is found that the change in near-surface, June through August air temperature field over the last 50 years is generally different at a significance level of 5% from that expected from model-based estimates of natural variability. Greenhouse gases alone cannot explain the observed change. Two of four climate models forced by greenhouse gases and direct sulfate aerosols produce results consistent with the current climate change observations, while the consistency of the other two depends on which model's anthropogenic fingerprints are used. A recent integration with additional anthropogenic forcings (the indirect effects of sulfate aerosols and tropospheric ozone) and more complete tropospheric chemistry produced results whose signal amplitude and pattern were consistent with current observations, provided the model's fingerprint is used and detection carried out over only the last 30 years of annually averaged data. This single integration currently cannot be corroborated and provides no opportunity to estimate the uncertainties inherent in the results, uncertainties that are thought to be large and poorly known. These results illustrate the current large uncertainty in the magnitude and spatial pattern of the direct and indirect sulfate forcing and climate response. They also show detection statements depend on model-specific fingerprints, time period, and seasonal character of the signal, dependencies that have not been well explored.

Most, but not all, results suggest that recent changes in global climate inferred from surface air temperature are likely not due solely to natural causes. At present it is not possible to make a very confident statement about the relative contributions of specific natural and anthropogenic forcings to observed climate change. One of the main reasons is that fully realistic simulations of climate change due to the combined effects of all anthropogenic and natural forcings mechanisms have yet to be computed. A list of recommendations for reducing some of the uncertainties that currently hamper detection and attribution studies is presented.

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T. Das
,
H. G. Hidalgo
,
D. W. Pierce
,
T. P. Barnett
,
M. D. Dettinger
,
D. R. Cayan
,
C. Bonfils
,
G. Bala
, and
A. Mirin

Abstract

This study examines the geographic structure of observed trends in key hydrologically relevant variables across the western United States at ⅛° spatial resolution during the period 1950–99. Geographical regions, latitude bands, and elevation classes where these trends are statistically significantly different from trends associated with natural climate variations are identified. Variables analyzed include late-winter and spring temperature, winter-total snowy days as a fraction of winter-total wet days, 1 April snow water equivalent (SWE) as a fraction of October–March (ONDJFM) precipitation total [precip(ONDJFM)], and seasonal [JFM] accumulated runoff as a fraction of water-year accumulated runoff. Observed changes were compared to natural internal climate variability simulated by an 850-yr control run of the finite volume version of the Community Climate System Model, version 3 (CCSM3-FV), statistically downscaled to a ⅛° grid using the method of constructed analogs. Both observed and downscaled model temperature and precipitation data were then used to drive the Variable Infiltration Capacity (VIC) hydrological model to obtain the hydrological variables analyzed in this study. Large trends (magnitudes found less than 5% of the time in the long control run) are common in the observations and occupy a substantial part (37%–42%) of the mountainous western United States. These trends are strongly related to the large-scale warming that appears over 89% of the domain. The strongest changes in the hydrologic variables, unlikely to be associated with natural variability alone, have occurred at medium elevations [750–2500 m for JFM runoff fractions and 500–3000 m for SWE/Precip(ONDJFM)] where warming has pushed temperatures from slightly below to slightly above freezing. Further analysis using the data on selected catchments indicates that hydroclimatic variables must have changed significantly (at 95% confidence level) over at least 45% of the total catchment area to achieve a detectable trend in measures accumulated to the catchment scale.

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