Downscaling Temperature and Precipitation: A Comparison of Regression-Based Methods and Artificial Neural Networks

A comparison of two statistical downscaling methods for daily maximum and minimum surface air temperature, total daily precipitation and total monthly precipitation at Indianapolis, IN, USA, is presented. The analysis is conducted for two seasons, the growing season and the non-growing season, defined based on variability of surface air temperature. The predictors used in the downscaling are indices of the synoptic scale circulation derived from rotated principal components analysis (PCA) and cluster analysis of variables extracted from an 18-year record from seven rawinsonde stations in the Midwest region of the United States. PCA yielded seven significant components for the growing season and five significant components for the non-growing season. These PCs explained 86% and 83% of the original rawinsonde data for the growing and non-growing seasons, respectively. Cluster analysis of the PC scores using the average linkage method resulted in eight growing season synoptic types and twelve non-growing synoptic types. The downscaling of temperature and precipitation is conducted using PC scores and cluster frequencies in regression models and artificial neural networks (ANNs). Regression models and ANNs yielded similar results, but the data for each regression model violated at least one of the assumptions of regression analysis. As expected, the accuracy of the downscaling models for temperature was superior to that for precipitation. The accuracy of all temperature models was improved by adding an autoregressive term, which also changed the relative importance of the dominant anomaly patterns as manifest in the PC scores. Application of the transfer functions to model daily maximum and minimum temperature data from an independent time series resulted in correlation coefficients of 0.34–0.89. In accord with previous studies, the precipitation models exhibited lesser predictive capabilities. The correlation coefficient for predicted versus observed daily precipitation totals was less than 0.5 for both seasons, while that for monthly total precipitation was below 0.65. The downscaling techniques are discussed in terms of model performance, comparison of techniques and possible model improvements

Evaluation of the NCEP/NCAR Reanalysis in Terms of Synoptic Scale Phenomea: A Case Study from the Midwestern USA

We evaluate the ability of the National Centers for Environmental Prediction (NCEP)–National Center for Atmosphere Research (NCAR) reanalysis to represent the synoptic-scale climate of the Midwestern USA relative to radiosonde data. Independent, automated synoptic classifications, based on rotated principal component analysis (PCA) of 500 hPa geopotential heights, 850 hPa air temperatures, and 200 hPa wind speeds and a two-step clustering algorithm, result in a 15-type NCEP–NCAR synoptic classification and a 14-type radiosonde classification. The classifications are examined in terms of similarities and differences in the modes of variance manifest in the PCA solutions, the spatial patterns and variability of input variables within each weather type, and the temporal variability of the occurrence of each weather type. The classifications are then compared in terms of these characteristics and the degree of mutual class occupancy. Although the classifications identify a number of the same weather types (in terms of the input data, PCA solution, and mutual occupancy), the correspondence is imperfect. To assess whether the differences in the classifications are due to errant assignment of data to clusters or to differences in the fundamental modes present in the data sets as represented by the PC loadings and scores, a third targeted classification is undertaken that categorizes the NCEP–NCAR reanalysis data according to the radiosonde PCA solution. This classification exhibits a higher degree of similarity to that derived using the radiosonde data (in terms of both interpretability and mutual class occupancy), but the solutions still exhibit considerable differences. It is probable that the discrepancies are partly a function of the differing data structures and densities, but they may also reflect differences in the intensity of synoptic-scale phenomena as manifest in the data sets

An Evaluation of Two GCMs: Simulation of North American Teleconnection Indices and Synoptic Phenomena

We evaluate the ability of two coupled atmospheric–oceanic GCMs – the Hadley Center’s third generation coupled climate model (HadCM3) and the Canadian Center for Climate Modeling and Analysis second-generation coupled model (CGCM2) – to simulate the North Atlantic Oscillation (NAO), the Pacific North American teleconnection pattern (PNA), and map patterns in the Midwest region of the United States, relative to NCEP/NCAR reanalysis (NNR) data. The observed (NNR-derived) and GCM-derived probability distributions and temporal behavior of the daily teleconnection indices exhibit agreement over the 1990–2001 reference period, and both GCMs successfully reproduce the range of 500-hPa map patterns over the study region. During the reference period, observed and modeled map patterns are similar in terms of frequency, coherence, persistence, and progression, although the most common map pattern occurs too often in HadCM3 relative to NNR and CGCM2-derived map patterns generally exhibit closer agreement with those derived from NNR data. Despite the relatively high degree of correspondence between the observed and simulated teleconnection indices and map patterns in the study area, differences between the GCM and NNR-derived map-pattern frequencies in the reference period are greater than either (1) recent historical changes in map-pattern frequencies or (2) changes in the mappattern frequencies as derived from twenty-first century GCM simulations, indicating that changes in these phenomena over recent and approaching decades are of insufficient magnitude relative to model uncertainty to be definitively identified

Downscaling Daily Maximum and Minimum Air Temperature in the Midwestern USA: A Hybrid Empirical Approach

A new hybrid empirical downscaling technique is presented and applied to assess 21st century projections of maximum and minimum daily surface air temperatures (Tmax, Tmin) over the Midwestern USA. Our approach uses multiple linear regression to downscale the seasonal variations of the mean and standard deviation of daily Tmax and Tmin and the lag-0 and lag-1 correlations between daily Tmax and Tmin based on GCM simulation of the large-scale climate. These downscaled parameters are then used as inputs to a stochastic weather generator to produce time series of the daily Tmax and Tmin at 26 surface stations, in three time periods (1990–2001, 2020–2029, and 2050–2059) based on output from two coupled GCMs (HadCM3 and CGCM2). The new technique is demonstrated to exhibit better agreement with surface observations than a transfer-function approach, particularly with respect to temperature variability. Relative to 1990–2001 values, downscaled temperature projections for 2020–2029 indicate increases that range (across stations) from 0.0 K to 1.7 K (Tmax) and 0.0 K to 1.5 K (Tmin), while increases for 2050–2059 relative to 1990–2001 range from 1.4 K to 2.4 K (Tmax) and 0.8 to 2.2K (Tmin). Although the differences between GCMs demonstrate the continuing uncertainty of GCM-based regional climate downscaling, the inclusion of weather-generator parameters represents an advancement in downscaling methodology

Dynamically and Statistically Downscaled Seasonal Temperature and Precipitation Hindcast Ensembles for the Southeastern USA

We present results from a 15-year 10-member warm season (March–September) hindcast ensemble of maximum and minimum surface air temperatures and precipitation in southeast USA. The hindcasts are derived from the Florida State University/Center for Ocean-Atmospheric Prediction Studies Global Spectral Model (FSU/COAPS GSM) and downscaled using both the FSU/COAPS Nested Regional Spectral Model (NRSM) and a statistical downscaling method based on stochastic weather generator techniques. We additionally consider statistical bias correction of the dynamical model output. Basic descriptive statistics indicate that the bias-corrected and statistically downscaled data reduce the FSU/COAPS GSM bias considerably in terms of basic climatology. Statistics describing the daily precipitation process are improved by both downscaling techniques relative to the bias-corrected GSM. Improvement in monthly and seasonal hindcasts relative to FSU/COAPS GSM is spatially and temporally varying. Precipitation hindcasts are generally less skillful than those for temperature, although useful precipitation predictability exists at many locations. Hindcast improvements due to downscaling are greatest over peninsular Florida. The smallest root mean square errors (RMSE) for temperature hindcasts are found in the southern part of the study region during the spring months of March, April and May (MAM) for maximum surface air temperature, and in the summer, June, July and August (JJA), for minimum surface air temperature. Overall, there is no indication that either downscaling method has a direct advantage over the other