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Ming Ji and Thomas M. Smith

Abstract

Two 11-yr Pacific Ocean simulations using an ocean general circulation model are compared with corresponding ocean analyses and with in situ observations from moorings and island tide gauges. The ocean simulations were forced by combining the climatological wind stress of Hellerman and Rosenstein with wind stress anomalies obtained from (a) The Florida State University surface wind analysis and (b) a two-member ensemble from an atmospheric model simulation. The ocean analyses were obtained by assimilating observed surface and subsurface temperatures into an ocean GCM, forced with the same wind stress anomaly fields used in the simulations.

The difference in thermocline depth between simulation and analysis using the same wind stress forcing is large in the off-equatorial regions near the North Equatorial Counter Current trough and in the South Pacific, suggesting that the mean climatological stress fields may be in error. The simulation results using the atmospheric GCM stress anomalies failed to show anomalous interannual sea level responses in the eastern equatorial Pacific, indicating that there are significant errors in the AGCM stress anomalies due to errors in the atmospheric model. The analyses show significant improvement over the comparable simulations when compared with the tide gauge data, indicating that assimilation of subsurface oceanic thermal data can compensate for stress-forcing errors and model errors on interannual timescales. However, the more accurate stress-forcing field leads to a better ocean analysis, indicating that the present density of temperature data is not sufficient to determine the ocean state.

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Ji-Eun Kim and M. Joan Alexander

Abstract

Tropical precipitation characteristics are investigated using the Tropical Rainfall Measuring Mission (TRMM) 3-hourly estimates, and the result is compared with five reanalyses including the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim), Modern Era Retrospective Analysis for Research and Applications (MERRA), National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) reanalysis (NCEP1), NCEP–U.S. Department of Energy (DOE) reanalysis (NCEP2), and NCEP–Climate Forecast System Reanalysis (CFSR). Precipitation characteristics are evaluated in terms of the mean, convectively coupled equatorial wave activity, frequency characteristics, diurnal cycle, and seasonality of regional precipitation variability associated with submonthly scale waves. Generally the latest reanalyses such as ERA-Interim, MERRA, and CFSR show better performances than NCEP1 and NCEP2. However, all the reanalyses are still different from observations. Besides the positive mean bias in the reanalyses, a spectral analysis revealed that the reanalyses have overreddened spectra with persistent rainfall. MERRA has the most persistent rainfall, and CFSR appears to have the most realistic variability. The diurnal cycle in NCEP1 is extremely exaggerated relative to TRMM. The low-frequency waves with the period longer than 3 days are relatively well represented in ERA-Interim, MERRA, and CFSR, but all the reanalyses have significant deficiencies in representing convectively coupled equatorial waves and variability in the high-frequency range.

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T. M. Smith, A. G. Barnston, M. Ji, and M. Chelliah

Abstract

The value of assimilated subsurface oceanic data to statistical predictions of interannual variability of sea surface temperature (SST) at the National Centers for Environmental Prediction (NCEP) is shown. Subsurface temperature data for the tropical Pacific Ocean come from assimilated ocean analysis from July 1982 to June 1993 and from a numerical model forced by observed surface wind stress from 1961 to June 1982. The value of subsurface oceanic data on the operational NCEP canonical correlation analysis (CCA) forecasts of interannual SST variability is assessed. The CCA is first run using only sea level pressure and SST as predictors, and then the subsurface data are added. It is found that use of the subsurface data improves the forecast for lead times of six months or longer, with some seasonal dependence in the improvements. Forecasts of less than six months are not helped by the subsurface data. Greatest improvements occur for forecasts of boreal winter to spring conditions, with less improvements for the rest of the year.

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A. Kumar, M. Hoerling, M. Ji, A. Leetmaa, and P. Sardeshmukh

Abstract

This study investigates the predictability of seasonal mean circulation anomalies associated purely with the influence of anomalous sea surface temperatures (SSTs). Within this framework, seasonal mean atmospheric anomalies on a case by case basis are understood to consist of a potentially predictable boundary-forced component and an unpredictable naturally varying component. The predictive capability of an atmospheric general circulation model (AGCM) for seasonal timescales should therefore be assessed in terms of the average skill over many cases, since it is only then that the boundary-forced predictable signal in observations can be identified.

To illustrate, experiments for 1982–1993 using two versions of an AGCM are presented. The models, referred to here as MRF8 and MRF9, differ in the parameterization of a single process. Each model is run nine times for the 12 years using different initial conditions but identical observed global SSTS. The nine-member ensemble mean anomalies for each season in 1982–1993 are compared with observed anomalies over the Pacific–North American (PNA) region.

Several different measures of the impact of SST boundary forcing on the extratropical flow suggest that MRF9 is a better model for seasonal prediction purposes. The two AGCMs have substantially different zonal-mean climatologies in the Tropics and subtropics, with MRF9 significantly better. It is argued that the improved mean flow in MRF9 enhances its midlatitude sensitivity to tropical forcing. The results highlight the importance of continued GCM development and give reason to hope that an even better model would lead to further improved forecasts of seasonal anomalies over the PNA sector.

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J.-I. Yano, P. M. M. Soares, M. Köhler, and A. Deluca
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Ji-Young Gu, A. Ryzhkov, P. Zhang, P. Neilley, M. Knight, B. Wolf, and Dong-In Lee

Abstract

The ability of C-band polarimetric radar to account for strong attenuation/differential attenuation is demonstrated in two cases of heavy rain that occurred in the Chicago, Illinois, metropolitan area on 5 August 2008 and in central Oklahoma on 10 March 2009. The performance of the polarimetric attenuation correction scheme that separates relative contributions of “hot spots” (i.e., strong convective cells) and the rest of the storm to the path-integrated total and differential attenuation has been explored. It is shown that reliable attenuation correction is possible if the radar signal is attenuated by as much as 40 dB. Examination of the experimentally derived statistics of the ratios of specific attenuation Ah and differential attenuation A DP to specific differential phase K DP in hot spots is included in this study. It is shown that these ratios at C band are highly variable within the hot spots. Validation of the attenuation correction algorithm at C band has been performed through cross-checking with S-band radar measurements that were much less affected by attenuation. In the case of the Oklahoma storm, a comparison was made between the data collected by closely located C-band and S-band polarimetric radars.

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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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M. Joan Alexander, David A. Ortland, Alison W. Grimsdell, and Ji-Eun Kim

Abstract

Using an idealized model framework with high-frequency tropical latent heating variability derived from global satellite observations of precipitation and clouds, the authors examine the properties and effects of gravity waves in the lower stratosphere, contrasting conditions in an El Niño year and a La Niña year. The model generates a broad spectrum of tropical waves including planetary-scale waves through mesoscale gravity waves. The authors compare modeled monthly mean regional variations in wind and temperature with reanalyses and validate the modeled gravity waves using satellite- and balloon-based estimates of gravity wave momentum flux. Some interesting changes in the gravity spectrum of momentum flux are found in the model, which are discussed in terms of the interannual variations in clouds, precipitation, and large-scale winds. While regional variations in clouds, precipitation, and winds are dramatic, the mean gravity wave zonal momentum fluxes entering the stratosphere differ by only 11%. The modeled intermittency in gravity wave momentum flux is shown to be very realistic compared to observations, and the largest-amplitude waves are related to significant gravity wave drag forces in the lowermost stratosphere. This strong intermittency is generally absent or weak in climate models because of deficiencies in parameterizations of gravity wave intermittency. These results suggest a way forward to improve model representations of the lowermost stratospheric quasi-biennial oscillation winds and teleconnections.

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J.-I. Yano, M. Vlad, S. H. Derbyshire, J.-F. Geleyn, and K. Kober
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Yongkang Xue, Jinjun Ji, Shufen Sun, Guoxiong Wu, K-M. Lau, Isabelle Poccard, Hyun-Suk Kang, Renhe Zhang, John C. Schaake, Jian Yun Zhang, and Yanjun Jiao

Abstract

This is an exploratory study to investigate the spatial and temporal characteristics of east China’s (EC) river runoff and their relationship with precipitation and sea surface temperature (SST) at the continental scale. Monthly mean data from 72 runoff stations and 160 precipitation stations in EC, covering a period between 1951 and 1983, are used for this study. The station river runoff data have been spatially interpolated onto 1° grid boxes as runoff depth based on an extracted drainage network. Comparing runoff depth with precipitation shows that seasonal variation in runoff is consistent with the development of the summer monsoon, including the delayed response of runoff in several subregions. The dominant spatial scales and temporal patterns of summer runoff and precipitation are studied with empirical orthogonal function (EOF) analysis and wavelet analyses. The analyses show interannual, biennial, and longer-term variations in the EOF modes. South–north dipole anomaly patterns for the first two runoff EOF’s spatial distributions have been identified. The first/second runoff principal components (PCs) are highly correlated with the second/first precipitation PCs, respectively. The summer runoff’s EOF PCs also show significant correlations with the multivariate El Niño–Southern Oscillation index (MEI) of the summer and winter months, while the summer precipitation PCs do not. Statistic analysis shows that EOF1 of runoff and EOF2 of precipitation are related to El Niño, while EOF2 of runoff and EOF1 of precipitation are related to a dipole SST anomaly over the northwestern Pacific. The interdecadal relationship between summer runoff, precipitation, and SST variability is further studied by singular value decomposition (SVD) analysis. Pronounced warming (SST) and drying (runoff) trends in first SVD PCs have been identified. These SVDs are used to reconstruct a decadal anomaly pattern, which produces flooding in part of the Chang Jiang River basin and dryness in the northern EC, consistent with observations.

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