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Robert E. Livezey and Jae-Kyung E. Schemm

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Jae-Kyung E. Schemm and Alan J. Faller

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The NMC Barotropic-Mesh Model has been used to test a statistical correction procedure, designated as M-II, that was developed in Schemm et al. In the present application, statistical corrections at 12 h resulted in significant reductions of the mean-square errors of both vorticity, ζ, and ∇2 h, where h is the 850–500 mb thickness. Predictions to 48 h demonstrated the feasibility of applying corrections at every 12 h in extended forecasts.

In addition to these improvements, however, the statistical corrections resulted in a shift of error from smaller to larger-scale motions, improving the smallest scales dramatically but deteriorating the largest scales. This effect is shown to be a consequence of randomization of the residual errors by the regression equations and can be corrected by spatially high-pass filtering the field of corrections before they are applied.

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Charles Jones and Jae-Kyung E. Schemm

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Kingtse C. Mo, Jae-Kyung E. Schemm, and Soo-Hyun Yoo

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Composites based on observations and model outputs from the Climate Variability and Predictability (CLIVAR) drought experiments were used to examine the impact of El Niño–Southern Oscillation (ENSO) and the Atlantic multidecadal oscillation (AMO) on drought over the United States. Because drought implies persistent dryness, the 6-month standardized precipitation index, standardized runoff index, and soil moisture anomalies are used to represent drought. The experiments were performed by forcing an AGCM with prescribed sea surface temperature anomalies (SSTAs) superimposed on the monthly mean SST climatology. Four model outputs from the NCEP Global Forecast System (GFS), NASA’s Seasonal-to-Interannual Prediction Project, version 1 (NSIPP1), GFDL’s global atmospheric model, version 2.1 (AM2.1), and the Lamont-Doherty Earth Observatory (LDEO)/NCAR Community Climate System Model, version 3 (CCM3) were analyzed in this study. Each run lasts from 36 to 51 yr.

The impact of ENSO on drought over the United States is concentrated over the Southwest, the Great Plains, and the lower Colorado River basin, with cold (warm) ENSO events favoring drought (wet spells). Over the East Coast and the Southeast, the impact of ENSO is small because the precipitation responses to ENSO are opposite in sign for winter and summer. For these areas, a prolonged ENSO does not always favor either drought or wet spells.

The direct influence of the AMO on drought is small. The major influence of the AMO is to modulate the impact of ENSO on drought. The influence is large when the SSTAs in the tropical Pacific and in the North Atlantic are opposite in phase. A cold (warm) event in a positive (negative) AMO phase amplifies the impact of the cold (warm) ENSO on drought. The ENSO influence on drought is much weaker when the SSTAs in the tropical Pacific and in the North Atlantic are in phase.

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Kingtse C. Mo, Lindsey N. Long, and Jae-Kyung E. Schemm

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Atmosphere–land–ocean coupled model simulations are examined to diagnose the ability of models to simulate drought and persistent wet spells over the United States. A total of seven models are selected for this study. They are three versions of the NCEP Climate Forecast System (CFS) coupled general circulation model (CGCM) with a T382, T126, and T62 horizontal resolution; GFDL Climate Model version 2.0 (CM2.0); GFDL CM2.1; Max Planck Institute (MPI) ECHAM5; and third climate configuration of the Met Office Unified Model (HadCM3) simulations from the World Climate Research Programme (WCRP) Coupled Model Intercomparison Project phase 3 (CMIP3) experiments.

Over the United States, drought and persistent wet spells are more likely to occur over the western interior region, while extreme events are less likely to persist over the eastern United States and the West Coast. For meteorological drought, which is defined by precipitation (P) deficit, the east–west contrast is well simulated by the CFS T382 and the T126 models. The HadCM3 captures the pattern but not the magnitudes of the frequency of occurrence of persistent extreme events. For agricultural drought, which is defined by soil moisture (SM) deficit, the CFS T382, CFS T126, MPI ECHAM5, and HadCM3 models capture the east–west contrast.

The models that capture the west–east contrast also have a realistic P climatology and seasonal cycle. ENSO is the dominant mode that modulates P over the United States. A model needs to have the ENSO mode and capture the mean P responses to ENSO in order to simulate realistic drought. To simulate realistic agricultural drought, the model needs to capture the persistence of SM anomalies over the western region.

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Duane E. Waliser, Charles Jones, Jae-Kyung E. Schemm, and Nicholas E. Graham

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In this study, a statistical model is developed that exploits the slow evolution of the Madden–Julian oscillation (MJO) to predict tropical rainfall variability at long lead times (i.e., 5–20 days). The model is based on a field-to-field decomposition that uses previous and present pentads of outgoing longwave radiation (OLR; predictors) to predict future pentads of OLR (predictands). The model was developed using 30–70-day bandpassed OLR data from 1979 to 1989 and validated on data from 1990 to 1996. For the validation period, the model exhibits temporal correlations to observed bandpassed data of about 0.5–0.9 over a significant region of the Eastern Hemisphere at lead times from 5 to 20 days, after which the correlation drops rapidly with increasing lead time. Correlations against observed total anomalies are on the order of 0.3–0.5 over a smaller region of the Eastern Hemisphere.

Comparing the skill values from the above OLR-based model, along with those from an identical statistical model using reanalysis-derived 200-mb zonal wind anomalies, to the skill values of 200-mb zonal wind predictions from the National Centers for Environmental Prediction’s Dynamic Extended Range Forecasts shows that the statistical models appear to perform considerably better. These results indicate that considerable advantage might be afforded from the further exploration and eventual implementation of MJO-based statistical models to augment current operational long-range forecasts in the Tropics. The comparisons also indicate that there is considerably more work to be done in achieving the likely forecast potential that dynamic models might offer if they could suitably simulate MJO variability.

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Seung-On Hwang, Jae-Kyung E. Schemm, Anthony G. Barnston, and Won-Tae Kwon

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Canonical correlation analysis (CCA), a linear statistical method designed to find correlated patterns between predictor and predictand fields, is applied to the eastern Asian region of Korea and Japan. The cross-validation technique is used to estimate the levels and sources of forecast skill for 3-month-averaged surface air temperature and total precipitation for the 37-yr time period of 1961–97. Quasi-global SST, Northern Hemisphere 700-hPa height, and prior values of the predictand field itself are used as predictor fields in an attempt to maximize the strength of the predictive relationships. The global SST field turns out generally to contribute the most to the final skill, with the exception of the winter season, in which the geopotential height field contributes most. The highest skill for temperature forecasts occurs in early spring, with relative insensitivity to the forecast lead time. This skill is statistically significant, averaging over 0.3 but including higher values locally. A secondary seasonal skill maximum appears in late summer. The forecast skill of precipitation is not high overall but is relatively highest in early winter, with area-averaged skill of nearly 0.2. Diagnostic analysis of the CCA loading patterns indicates that the strongest predictive mode for temperature is a long-term warming trend that pervades the entire seasonal cycle and contributes to skill during both the cold and the warm peak skill seasons. Interannual temperature fluctuations related to ENSO are captured in the second mode. Because of the dominant role of trends, CCA skill decreases substantially when the datasets are detrended. Forecast skill for precipitation is found to be due to a combination of interdecadal variations and ENSO-related variations.

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Anthony G. Barnston, Nicolas Vigaud, Lindsey N. Long, Michael K. Tippett, and Jae-Kyung E. Schemm

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The Madden–Julian oscillation (MJO) is known to exert some control on the variations of North Atlantic tropical cyclone (TC) activity within a hurricane season. To explore the possibility of better TC predictions based on improved MJO forecasts, retrospective hindcast data on MJO and on TC activity are examined both in the current operational version of the CFSv2 model (T126 horizontal resolution) and a high-resolution (T382) experimental version of CFS. Goals are to determine how well each CFS version reproduces reality in 1) predicting MJO and 2) reproducing observed relationships between MJO phase and TC activity. For the operational CFSv2, skill of forecasts of TC activity is evaluated directly.

Both CFS versions reproduce MJO behavior realistically and also roughly approximate observed relationships between MJO phase and TC activity. Specific biases in the high-resolution CFS are identified and their causes explored. The high-resolution CFS partially reproduces an observed weak tendency for TC activity to propagate eastward during and following the high-activity MJO phases. The operational (T126) CFSv2 shows useful skill (correlation >0.5) in predicting the MJO phase and amplitude out to ~3 weeks. A systematic error of slightly too slow MJO propagation is detected in the operational CFSv2, which still shows usable skill (correlation >0.3) in predicting weekly variations in TC activity out to 10–14 days. A conclusion is that prediction of intraseasonal variations of TC activity by CFSv2 is already possible and implemented in real-time predictions. An advantage of the higher resolution in the T382 version is unable to be confirmed.

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Kyong-Hwan Seo, Jae-Kyung E. Schemm, Wanqiu Wang, and Arun Kumar

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Observational evidence has indicated the important role of the interaction of the atmosphere with the sea surface in the development and maintenance of the tropical intraseasonal oscillation (ISO). However, improvements in ISO simulations with fully coupled atmosphere–ocean general circulation models are limited and model dependent. This study further examines the effect of air–sea coupling and the basic-state sea surface temperature (SST) associated with the boreal summer intraseasonal oscillation (BSISO) in a 21-yr free run with the recently developed NCEP coupled Climate Forecast System (CFS) model. For this, the CFS run is compared with an Atmospheric Model Intercomparison Project–type long-term simulation forced by prescribed SST in the NCEP Global Forecast System (GFS) model and flux-corrected version of CFS (referred to as CFSA). The GFS run simulates significantly unorganized BSISO convection anomalies, which exhibit an erroneous standing oscillation. The CFS run with interactive air–sea coupling has limited improvements, including the generation of intraseasonal SST variation preceding the convection anomaly by ∼10 days. However, this simulation still does not show the observed continuous northward propagation over the Indian Ocean due to a cold model bias. The CFSA run removes the cold bias in the Indian Ocean and the simulation of the development and propagation of BSISO anomalies are significantly improved. Enhanced and suppressed convection anomalies exhibit the observed quadrupole-like configuration, and phase relationships between precipitation and surface dynamic and thermodynamic variables for the northward propagation are shown to be coherent and consistent with the observations. It is shown that the surface meridional moisture convergence is an important factor for the northward propagation of the BSISO. On the other hand, both the GFS and CFS runs do not realistically simulate an eastward-propagating equatorial mode. The CFSA run produces a more realistic eastward-propagation mode only over the Indian Ocean and Java Sea due to the improved mean state in SST, low-level winds, and vertical wind shear. Reasons for the failure of farther eastward propagation into the west Pacific in CFSA are discussed. This study reconfirms the significance of air–sea interactions. More importantly, however, the results suggest that in order for the influence of the coupled air–sea interaction to be properly communicated, the mean state SST in the coupled model should be reasonably simulated. This is because the basic-state SST itself acts to sustain BSISO convection and it makes the large-scale dynamical environment (i.e., easterly vertical wind shear or low-level westerly zonal wind) more favorable for the propagation of the moist Rossby–Kelvin wave packet.

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Kyong-Hwan Seo, Wanqiu Wang, Jon Gottschalck, Qin Zhang, Jae-Kyung E. Schemm, Wayne R. Higgins, and Arun Kumar

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This work examines the performance of Madden–Julian oscillation (MJO) forecasts from NCEP’s coupled and uncoupled general circulation models (GCMs) and statistical models. The forecast skill from these methods is evaluated in near–real time. Using a projection of El Niño–Southern Oscillation (ENSO)-removed variables onto the principal patterns of MJO convection and upper- and lower-level circulations, MJO-related signals in the dynamical model forecasts are extracted. The operational NCEP atmosphere–ocean fully coupled Climate Forecast System (CFS) model has useful skill (>0.5 correlation) out to ∼15 days when the initial MJO convection is located over the Indian Ocean. The skill of the CFS hindcast dataset for the period from 1995 to 2004 is nearly comparable to that from a lagged multiple linear regression model, which uses information from the previous five pentads of the leading two principal components (PCs). In contrast, the real-time analysis for the MJO forecast skill for the period from January 2005 to February 2006 using the lagged multiple linear regression model is reduced to ∼10–12 days. However, the operational CFS forecast for this period is skillful out to ∼17 days for the winter season, implying that the coupled dynamical forecast has some usefulness in predicting the MJO compared to the statistical model.

It is shown that the coupled CFS model consistently, but only slightly, outperforms the uncoupled atmospheric model (by one to two days), indicating that only limited improvement is gained from the inclusion of the coupled air–sea interaction in the MJO forecast in this model. This slight improvement may be the result of the existence of a propagation barrier around the Maritime Continent and the far western Pacific in the NCEP Global Forecast System (GFS) and CFS models, as shown in several previous studies. This work also suggests that the higher horizontal resolution and finer initial data might contribute to improving the forecast skill, presumably as a result of an enhanced representation of the Maritime Continent region.

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