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Seasonally predicted precipitation at a resolution of 2.5° was statistically downscaled to a fine spatial scale of ~20 km over the southeastern United States. The downscaling was conducted for spring and summer, when the fine-scale prediction of precipitation is typically very challenging in this region. We obtained the global model precipitation for downscaling from the National Center for Environmental Prediction/Climate Forecast System (NCEP/CFS) retrospective forecasts. Ten member integration data with time-lagged initial conditions centered on mid- or late February each year were used for downscaling, covering the period from 1987 to 2005. The primary techniques involved in downscaling are Cyclostationary Empirical Orthogonal Function (CSEOF) analysis, multiple regression, and stochastic time series generation. Trained with observations and CFS data, CSEOF and multiple regression facilitated the identification of the statistical relationship between coarse-scale and fine-scale climate variability, leading to improved prediction of climate at a fine resolution. Downscaled precipitation produced seasonal and annual patterns that closely resemble the fine resolution observations. Prediction of long-term variation within two decades was improved by the downscaling in terms of variance, root mean square error, and correlation. Relative to the coarsely resolved unskillful CFS forecasts, the proposed downscaling drove a significant reduction in wet biases, and correlation increased by 0.1–0.5. Categorical predictability of seasonal precipitation and extremes (frequency of heavy rainfall days), measured with the Heidke skill score (HSS), was also improved by the downscaling. For instance, domain averaged HSS for two category predictability by the downscaling are at least 0.20, while the scores by the CFS are near zero and never exceed 0.1. On the other hand, prediction of the frequency of subseasonal dry spells showed limited improvement over half of the Georgia and Alabama region. [ABSTRACT FROM AUTHOR]

An advanced land model [the National Center for Atmospheric Research (NCAR) Community Land Model, version 2 (CLM2)] is coupled to the Florida State University (FSU) regional spectral model to improve seasonal surface climate outlooks at very high spatial and temporal resolution and to examine its potential for crop yield estimation. The regional model domain is over the southeast United States and is run at 20-km resolution, roughly resolving the county level. Warm-season (March–September) simulations from the regional model coupled to the CLM2 are compared with those from the model with a simple land surface scheme (i.e., the original FSU model). In this comparison, two convective schemes are also used to evaluate their roles in simulating seasonal climate, primarily for rainfall. It is shown that the inclusion of the CLM2 produces consistently better seasonal climate scenarios of surface maximum and minimum temperatures, precipitation, and shortwave radiation, and hence provides superior inputs to a site-based crop model to simulate crop yields. The FSU regional model with the CLM2 exhibits some capability in the simulation of peanut (Arachis hypogaea L.) yields, depending upon the convective scheme employed and the site selected. [ABSTRACT FROM AUTHOR]

The current Florida State University (FSU) climate model is upgraded by coupling the National Center for Atmospheric Research (NCAR) Community Land Model Version 2 (CLM2) as its land component in order to make a better simulation of surface air temperature and precipitation on the seasonal time scale, which is important for crop model application. Climatological and seasonal simulations with the FSU climate model coupled to the CLM2 (hereafter FSUCLM) are compared to those of the control (the FSU model with the original simple land surface treatment). The current version of the FSU model is known to have a cold bias in the temperature field and a wet bias in precipitation. The implementation of FSUCLM has reduced or eliminated this bias due to reduced latent heat flux and increased sensible heat flux. The role of the land model in seasonal simulations is shown to be more important during summertime than wintertime. An additional experiment that assimilates atmospheric forcings produces improved land-model initial conditions, which in turn reduces the biases further. The impact of various deep convective parameterizations is examined as well to further assess model performance. The land scheme plays a more important role than the convective scheme in simulations of surface air temperature. However, each convective scheme shows its own advantage over different geophysical locations in precipitation simulations. [ABSTRACT FROM AUTHOR]

Monthly and seasonal predictions of mean atmospheric states have traditionally been viewed as a boundary forcing problem, with little regard for the role of atmospheric initial conditions (IC). The potential predictability of these mean states is investigated using hindcasted monthly mean January (JAN) and seasonal mean January– February–March (JFM) 200-hPa geopotential heights from the National Centers for Environmental Prediction Climate Prediction Center (NCEP CPC) Dynamical Seasonal Prediction System along with the corresponding data from the NCEP–National Center for Atmospheric Research (NCAR) reanalysis for the period 1980–2000. With lead times ranging from 1 to 4 months, analyses of variance tests are employed to separate the total variability into an unpredictable internal component, due to atmospheric dynamics, and a potentially predictable external component, due to the boundary forcing. These components represent the noise and signal, respectively, and areas where the signal exceeds the noise designate where time averages could be potentially predicted with some degree of skill. Temporal anomaly correlations (ACs) between ensemble-averaged model height anomalies and reanalysis height anomalies also provide a measure of the model skill. Comparisons between the results of these tests for the different initialization times confirm that, for this model, the atmospheric initial conditions have little effect on the monthly and seasonal means for lead times of one month or more. The model proves to be highly skillful in the Tropics, as expected. Signal-to-noise ratios (SNRs) and ACs also show four areas in the extratropics displaying significant skill: the South Pacific Ocean, Southern Ocean, Southeast Asia, and the Pacific–North America (PNA) region. The skill found in the extratropics outside of the PNA region is highly encouraging. SNRs for JFM are approximately twice those for JAN, suggesting that seasonal forecasts are more reliable than monthly forecasts. Anomaly correlations for El Niño–Southern Oscillation (ENSO) warm and cold events are markedly higher than correlations for both the period 1980–2000 and the subset of ENSO neutral events. The model's ability to accurately capture changes in the atmosphere in response to changes in sea surface temperatures (SSTs) suggests that accurate forecasting of SSTs in the ocean could lead to more accurate forecasts of atmospheric conditions associated with ENSO warm and cold events. [ABSTRACT FROM AUTHOR]

The statistical characteristics of the winds at the Isthmus of Tehuantepec and their seasonal and interannual variability are studied through the analysis of several datasets and a reconstruction of the winds for a 31-yr period. Observations show that the long-term monthly mean wind speeds and frequency of occurrence of northerly winds have a strong seasonal signal, with maximum values during December-January, minimum during May-June, and a relative maximum in July. The frequency distribution of wind speed is bimodal, a feature that is closely related to the wind direction, with northerly winds being stronger. Based on these results and the close relationship between the across-Isthmus pressure differences and the local winds, a statistical model is developed to get a reconstruction of 12-hourly winds through the Isthmus of Tehuantepec for 1964-95. The model reproduces fairly well the main characteristics of the observed winds: the bimodal distribution of the wind speed and the seasonal signal in the wind speed and frequency of occurrence of northerly winds. Reconstructed winds show that the high frequency of northerly winds in July is associated with weaker winds than those observed in winter. The summer maximum seems to be related with the westward displacement and strengthening of the Bermuda high during this time of the year. Based on the model results, the long-term monthly mean wind speeds show larger values during El Niño years compared with La Niña years. During La Niña years winds are significantly weaker than in neutral years for February-March, June-September, and November, and the percentage of occurrence of northerly winds is significantly lower than in neutral years from June to November. The larger occurrence of northerly winds during El Niño years compared with neutral years is statistically significant only for May and September. [ABSTRACT FROM AUTHOR]

Abstract: Global/Regional Circulation Models (GCM/RCM) predict the interannual climate variability better than the absolute values of meteorological variables. Statistical bias-correction methods increase the quality of daily model predictions of incoming solar radiation, maximum and minimum temperatures and rainfall frequency and amount. However, when bias-corrected forecasts/hindcasts are used by dynamic crop models, timing of dry-spell occurrences generate the largest uncertainty during the linking process. In this study, we used 20 ensemble members of an 18-year period provided by the Florida State University/Center for Ocean-Atmospheric Prediction Studies (FSU/COAPS) regional spectral model coupled to the National Center for Atmospheric Research Community Land Model (CLM2). The daily seasonal-climate hindcast was bias-corrected and used as input to the CERES-Maize model, thus producing 20 crop yield ensemble members. Using observed weather data for the same period, a time series of simulated crop yields was produced. Finally, principal component (PC) regression analysis was used to predict this time series using the crop yield ensemble members as predictors. Between 13.7 and 28.8% of the simulated corn yield interannual variability was explained using only one principal component (p <0.05), and estimated yields were in the correct tercile by margins of 16.7 to 38.2% beyond chance. Predictability of simulated corn yields using principal components was improved relative to the use of bias-corrected daily hindcasts. Bias-correcting all meteorological variables used by the crop model increased predictability skills compared with use of raw hindcasts, individual bias-correction of rainfall, and climatological values. [Copyright &y& Elsevier]