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David A. Portman

Abstract

A detailed study of urban bias in surface temperatures of China's northern plains is described. Temperatures of climatological surface stations were examined using a statistical rank-score procedure that allows screening of the data without knowledge of the station history information. Time series found to exhibit large potential discontinuities (i.e., those introduced as a result of nonclimatic factors such as observation schedule changes, instrument replacements, and station moves) were excluded from further analysis. In addition to the usual total population statistics, census area classifications and population densities were used to distinguish between 21 urban and 8 rural stations. Location-related biases associated with latitude and longitude positions were first removed from all station data, however, using ordinary least-squares regression techniques. Finally, a systematic sampling strategy was employed to estimate magnitudes and trends of urban bias in annual and seasonal mean temperatures.

Results of the study indicate that temperatures for stations located in or near the most highly and densely populated urban centers exhibit the largest biases. For most of these urban stations, magnitudes and trends of the bias are greater during spring or summer than during autumn or winter. Standard errors of the estimated urban biases are large, however. Therefore, only the regionally averaged temperatures were adjusted to remove magnitudes and trends of urban bias. Trends in the original and adjusted temperatures of this study and in gridded temperatures taken from the widely used dataset of Jones et al. were also compared. It is suggested that despite past efforts to remove the effects of the urban beat islands from this and other large-scale, land-surface datasets, large urban warming biases may still remain.

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David A. Portman and David S. Gutzler

Abstract

A study was conducted to identify and separate possible signals of volcanic eruptions and of the El Niño–Southern Oscillation (ENSO) in U.S. surface climate records. Anomalies of monthly mean surface air temperature and total precipitation taken from the U.S. Historical Climatology Network were composited (averaged) over years of major explosive volcanic eruptions. ENSO warm events, and ENSO cold events since the year 1900. It was assumed that volcanic eruptions and ENSO events occur independently of each other. All composite anomalies were assessed for significance with regard to several statistical and physical criteria. The composite ENSO-related anomalies were then subtracted from anomalies of temperature and precipitation associated with the volcanic eruptions.

Removal of large magnitude and highly significant anomalies associated with the ENSO warm and cold events is found to facilitate detection of volcanic signals in monthly records of U.S. temperature and precipitation. Volcanic signals are strongly suggested cast of the Continental Divide, for example, where positive monthly temperature anomalies exceeding 1°C occur during the first fall and winter after eruptions. Negative temperature anomalies occur west of the Continental Divide during the first winter and spring after eruptions and in the southern United States during the summer of the first post-eruption calendar year. Positive monthly precipitation anomalies exceeding 15 mm in magnitude are found in the southeastern United States during the first winter and spring after eruptions. Precipitation anomalies that are smaller in magnitude and yet significant, such as positive anomalies in the northwestern United States and negative anomalies in the central and eastern United States, are found during the summer of the first post-eruption calendar year.

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Clifford F. Mass and David A. Portman

Abstract

No abstract available.

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Clifford F. Mass and David A. Portman

Abstract

This paper examines whether major volcanic eruptions of the past century have had a significant impact on surface land and ocean temperatures surface pressure and precipitation. Both multieruption composites and individual eruption time series are constructed and analyzed. Included in this work is an attempt to remove one source of interannual variability the El Niño/Southern Oscillation (ENSO). These exercises indicate that only the largest eruptions (in terms of producing a stratospheric dust cloud) are suggested in the climatic record. Removing the ENSO signal in the composite and individual eruption series enhances the apparent volcanic effect of the largest eruptions. No volcanic signal is obvious in pressure and precipitation records.

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David A. Portman, Wei-Chyung Wang, and Thomas R. Karl

Abstract

Validation of general circulation model (GCM) current climate simulations is important for further GCM development and application to climate change studies. So far, studies that compare GCM output with observations have focused primarily on large-scale spatial averages of the surface climate variables. Here we discuss two approaches to compare output of individual GCM grid boxes with local station observations near the surface and in the free troposphere. The first approach, proposed by Chervin, involves the application of standard parametric statistical analysis and hypothesis testing procedures. The second approach is nonparametric in the sense that no ideal distributions are postulated a priori to ascertain significance of the difference of mean temperature or the ratio of the temperature variance between model grid boxes and local stations. Instead, station observations are first subjected to a bootstrap technique and then used to define a unique set of distributions and confidence limits for each GCM grid box.

To demonstrate the usefulness of the two approaches, we compare daily and seasonal gridbox temperatures simulated by the National Center for Atmospheric Research (NCAR) Community Climate Model (CCM1) with station temperatures at the surface, 850-mb, 500-mb, and 300-mb levels for three different areas in the United States. We find that although CCM1 gridbox temperatures are mostly cooler than station temperatures, they are equally variable. For all grid boxes, gridbox-to-station differences decrease with height and vary with time of year. We conclude that the techniques presented here can provide useful comparisons of GCM regional and local observed temperatures. Application to other variables and GCMs is also discussed.

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Thomas R. Karl, Wei-Chyung Wang, Michael E. Schlesinger, Richard W. Knight, and David Portman

Abstract

Important surface observations such as the daily maximum and minimum temperature, daily precipitation, and cloud ceilings often have localized characteristics that are difficult to reproduce with the current resolution and the physical parameterizations in state-of-the-art General Circulation climate Models (GCMs). Many of the difficulties can be partially attributed to mismatches in scale, local topography. regional geography and boundary conditions between models and surface-based observations. Here, we present a method, called climatological projection by model statistics (CPMS), to relate GCM grid-point flee-atmosphere statistics, the predictors, to these important local surface observations. The method can be viewed as a generalization of the model output statistics (MOS) and perfect prog (PP) procedures used in numerical weather prediction (NWP) models. It consists of the application of three statistical methods: 1) principle component analysis (FICA), 2) canonical correlation, and 3) inflated regression analysis. The PCA reduces the redundancy of the predictors The canonical correlation is used to develop simultaneous relationships between linear combinations of the predictors, the canonical variables, and the surface-based observations. Finally, inflated regression is used to relate the important canonical variables to each of the surface-based observed variables.

We demonstrate that even an early version of the Oregon State University two-level atmospheric GCM (with prescribed sea surface temperature) produces free-atmosphere statistics than can, when standardized using the model's internal means and variances (the MOS-like version of CPMS), closely approximate the observed local climate. When the model data are standardized by the observed free-atmosphere means and variances (the PP version of CPMS), however, the model does not reproduce the observed surface climate as well. Our results indicate that in the MOS-like version of CPMS the differences between the output of a ten-year GCM control run and the surface-based observations are often smaller than the differences between the observations of two ten-year periods. Such positive results suggest that GCMs may already contain important climatological information that can be used to infer the local climate.

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