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There are many climate change scenarios that are of interest to explore by climate models, but computational power limits the total number of model runs. Pattern scaling is a useful approach to approximate mean changes in climate model projections, and we extend this methodology to build a climate model emulator that also accounts for variability of temperature projections at the seasonal scale. Using 30 runs from the NCAR/DOE CESM1 large initial condition ensemble for RCP8.5 from 2006 to 2080, we fit a pattern scaling model to grid-specific seasonal average temperature change. We then use this fitted model to emulate seasonal average temperature change for the RCP4.5 scenario based on its global average temperature trend. By using a linear mixed-effects model and carefully resampling the residuals from the RCP8.5 model, we emulate the variability of RCP4.5 and allow the variability to depend on global average temperature. Specifically, we emulate both the internal variability affecting the long-term trends across initial condition ensemble members, and the variability superimposed on the long-term trend within individual ensemble members. The 15 initial condition ensemble members available for RCP4.5 from the same climate model are then used to validate the emulator. We view this approach as a step forward in providing relevant climate information for avoided impacts studies, and more broadly for impact models, since we allow both forced changes and internal variability to play a role in determining future impact risks. [ABSTRACT FROM AUTHOR]

The characteristics associated with an exponential distribution is used as a yard stick to assess distributions for 24-hr precipitation records from 13 771 rain gauges from mainly the USA and Europe. It is shown that the daily accumulated precipitation amount is approximately similar to an exponential distribution, but not identical, and that quantiles of the distributions can to a zeroth order be specified from the mean of the wet-day precipitation. However, the data have a thicker upper tail than the exponential distribution. We propose a simple method for making a crude estimate for quantiles of wet-day 24-hr precipitation distribution, and a refinement of the exponential distribution based on principal component analysis. We also show that the high quantiles are related to the wet-day mean. The associations between the wet-day mean and quantiles from the observations are compared with results from regional climate model simulations, taken from a number of regional climate models. Similar tendencies are seen in the models as in the rain gauge data. [ABSTRACT FROM AUTHOR]

Given a collection of science-based computational models that all estimate states of the same environmental system, we compare the forecast skill of the average of the collection to the skills of the individual members. We illustrate our results through an analysis of regional climate model data and give general criteria for the average to perform more or less skillfully than the most skillful individual model, the “best” model. The average will only be more skillful than the best model if the individual models in the collection produce very different forecasts; if the individual forecasts generally agree, the average will not be as skillful as the best model. [ABSTRACT FROM AUTHOR]

This article presents the authors' comments on the article Calibrated Probabilistic Mesoscale Weather Field Forecasting: The Geostatistical Output Perturbation Period by Yulia Gel, Adrian E. Raftery and Tilmann Gneiting. Forecasting the weather presents a unique context for statistics, blending physical modeling with complicated observational data to produce information that is used at many different levels of sophistication. We are pleased that Gel, Raftery, and Gneiting (GRG) have brought this area to the attention of a statistical audience. In this discussion we give the reader a broader view of the use of ensemble techniques in numerical weather prediction (NWP). We have some comments about the use of ensembles idea presented by GRG and also present some of our recent analysis of the value of ensemble forecasts. The authors have applied elegant statistical methods to the area of ensemble forecasting and, thus, have brought it into the spotlight for the larger statistical community. Although the geostatistical output perturbation (GOP) method could be improved through the use of the richer physical content of real ensembles, we also believe that the information that the GOP ensemble delivers has a complementary value, taking the traditional model output statistics (MOS) approach one large step further by providing a spatially coherent forecast calibration.

This article provides an overview of statistics in the atmospheric sciences. The motion and evolution of the atmosphere can be determined from a system of several nonlinear partial differential equations. These equations, derived from classical results in thermodynamics and fluid mechanics on a rotating sphere, are well accepted as the fundamental description of atmospheric flow and are referred to as the primitive equations. A focus for climate studies is the use of a general circulation model (GCM). A GCM is an adaptation of the primitive equations to a spatial grid and discrete steps in time. The need for accurate forecasts of the weather is undisputed. Equations on the statistical foundations of weather forecasts are provided. Turbulence in the atmosphere and ocean is an area with still many questions. In most cases turbulence can be readily identified, but a simple and rigorous definition of turbulent flow is still elusive. Popular views of statistics in the geophysical sciences often focus on spatial methods and time series.

In the biomedical products industry, measures of the quality of individual clinical specimens or manufacturing production units are often available in the form of high-dimensional data such as continuous recordings obtained from an analytical instrument. These recordings are then examined by experts in the field who extract certain features and use these to classify individuals. To formalize and quantify this procedure, an approach for extracting features from recordings based on nonparametric regression is described. These features are then used to build a classification model that incorporates the knowledge of the expert. The procedure is illustrated with the problem of grading of syringes from associated friction profile data. Features of the syringe friction profiles used in the classification are extracted via smoothing splines, and grades of the syringes are assigned by an expert tribologist. A nonlinear classification model is constructed to predict syringe grades based on the extracted features. The classification model males it possible to grade syringes automatically without expert inspection. Using leave-one-out cross-validation, the prediction accuracy of the classification model is found to be about the same as the accuracy obtained from the expert. [ABSTRACT FROM AUTHOR]

Comments on the article on calculations for regional climate changes using the reliability ensemble averaging method. Statistical interpretation of method; Measure of natural variability.

Climate change is expected to have many impacts on the environment, including changes in ozone concentrations at the surface level. A key public health concern is the potential increase in ozone-related summertime mortality if surface ozone concentrations rise in response to climate change. Although ozone formation depends partly on summertime weather, which exhibits considerable inter-annual variability, previous health impact studies have not incorporated the variability of ozone into their prediction models. A major source of uncertainty in the health impacts is the variability of the modeled ozone concentrations. We propose a Bayesian model and Monte Carlo estimation method for quantifying health effects of future ozone. An advantage of this approach is that we include the uncertainty in both the health effect association and the modeled ozone concentrations. Using our proposed approach, we quantify the expected change in ozone-related summertime mortality in the contiguous United States between 2000 and 2050 under a changing climate. The mortality estimates show regional patterns in the expected degree of impact. We also illustrate the results when using a common technique in previous work that averages ozone to reduce the size of the data, and contrast these findings with our own. Our analysis yields more realistic inferences, providing clearer interpretation for decision making regarding the impacts of climate change. [ABSTRACT FROM AUTHOR]

Despite a growing body of evidence demonstrating the importance of cities as sources of many local, regional, and global impacts on the atmosphere, ecosystems, and human populations, most theories on the relationship between society and the environment have focused on the global or national level. A variety of theories exist on human-environment interactions; for example, ecological modernization, urban transitions, and human ecology. However, with the exception of urban transitions, these theories have been mainly concerned with nation states and have ignored the subnational and local (city) levels. This article aims at filling this gap by employing ordinary least squares regression to examine these theories at the city level using the STIRPAT formula. It finds that with the exception of population (which shows an unstable relationship with the impacts indicators applied in the analysis) a remarkable level of variation exists in the importance of drivers across the three exercises. This led us to conclude that urban atmospheric pollutants result from diverse activities (e.g., transportation, industrial), are formed through different processes (vehicle combustion, biomass burning), have a residence time ranging from hours to years, and are the outcome of diverse sets of societal and environmental drivers. [ABSTRACT FROM AUTHOR]

Projections of future climate change caused by increasing greenhouse gases depend critically on numerical climate models coupling the ocean and atmosphere (global climate models [GCMs]). However, different models differ substantially in their projections, which raises the question of how the different models can best be combined into a probability distribution of future climate change. For this analysis, we have collected both current and future projected mean temperatures produced by nine climate models for 22 regions of the earth. We also have estimates of current mean temperatures from actual observations, together with standard errors, that can be used to calibrate the climate models. We propose a Bayesian analysis that allows us to combine the different climate models into a posterior distribution of future temperature increase, for each of the 22 regions, while allowing for the different climate models to have different variances. Two versions of the analysis are proposed: a univariate analysis in which each region is analyzed separately, and a multivariate analysis in which the 22 regions are combined into an overall statistical model. A cross-validation approach is proposed to confirm the reasonableness of our Bayesian predictive distributions. The results of this analysis allow for a quantification of the uncertainty of climate model projections as a Bayesian posterior distribution, substantially extending previous approaches to uncertainty in climate models. [ABSTRACT FROM AUTHOR]

A Bayesian statistical model is proposed that combines information from a multimodel ensemble of atmosphere–ocean general circulation models (AOGCMs) and observations to determine probability distributions of future temperature change on a regional scale. The posterior distributions derived from the statistical assumptions incorporate the criteria of bias and convergence in the relative weights implicitly assigned to the ensemble members. This approach can be considered an extension and elaboration of the reliability ensemble averaging method. For illustration, the authors consider the output of mean surface temperature from nine AOGCMs, run under the A2 emission scenario from the Synthesis Report on Emission Scenarios (SRES), for boreal winter and summer, aggregated over 22 land regions and into two 30-yr averages representative of current and future climate conditions. The shapes of the final probability density functions of temperature change vary widely, from unimodal curves for regions where model results agree (or outlying projections are discounted) to multimodal curves where models that cannot be discounted on the basis of bias give diverging projections. Besides the basic statistical model, the authors consider including correlation between present and future temperature responses, and test alternative forms of probability distributions for the model error terms. It is suggested that a probabilistic approach, particularly in the form of a Bayesian model, is a useful platform from which to synthesize the information from an ensemble of simulations. The probability distributions of temperature change reveal features such as multimodality and long tails that could not otherwise be easily discerned. Furthermore, the Bayesian model can serve as an interdisciplinary tool through which climate modelers, climatologists, and statisticians can work more closely. For example, climate modelers, through their expert judgment, could contribute to the formulations of prior distributions in the statistical model. [ABSTRACT FROM AUTHOR]

Spatiotemporal processes are ubiquitous in the environmental and physical sciences. This is certainly true of atmospheric and oceanic processes, which typically exhibit many different scales of spatial and temporal variability. The complexity of these processes and the large number of observation/prediction locations preclude the use of traditional covariance-based spatiotemporal statistical methods. Alternatively, we focus on conditionally specified (i.e., hierarchical) spatiotemporal models. These methods offer several advantages over traditional approaches. Primarily, physical and dynamical constraints can be easily incorporated into the conditional formulation, so that the series of relatively simple yet physically realistic conditional models leads lo a much more complicated spatiotemporal covariance structure than can be specified directly. Furthermore, by making use of the sparse structure inherent in the hierarchical approach, as well as multiresolution (wavelet) bases, the models can be computed with very large datasets. This modeling approach was necessitated by a scientifically meaningful problem in the geosciences. Satellite-derived wind estimates have high spatial resolution but limited global coverage. In contrast, wind fields provided by the major weather centers provide complete coverage but have low spatial resolution. The goal is to combine these data in a manner that incorporates the space-time dynamics inherent in the surface wind field. This is an essential task to enable meteorological research, because no complete high-resolution surface wind datasets exist over the world oceans. High-resolution datasets of this type are crucial for improving our understanding of global air-sea interactions affecting climate and tropical disturbances, and for driving large-scale ocean circulation models. [ABSTRACT FROM AUTHOR]

ABSTRACT A statistical method for establishing the cause-effect relationship between a land surface modification and some component of observed climatic change is presented. This method aids attribution in two ways. First, the climatic changes that are unique to the area influenced by some land surface modification are identified. This isolates changes caused by the spatially restricted forcing from changes caused by other factors. Second, most of the short-term climatic variability in the records from the affected area is removed based on information from the surrounding region. This makes it possible to identify smaller climatic changes. This method is used to identify the changes in surface air temperature that have resulted from desiccation of the Aral Sea (1960-97). Desiccation has weakened the "lake effect" of the Aral Sea, so regional climatic changes are expected. Substantial temperature trends, unrelated to desiccation, are observed across a broad region of central Asia (Is similar to 2000 km) between 1960 and 1997. These trends are similar in magnitude to the changes from desiccation. These trends are removed from the records from the Aral region because they would enhance or offset the local temperature changes caused by desiccation. There is also substantial year-to-year temperature variability that is spatially coherent across central Asia... [ABSTRACT FROM AUTHOR]

Presents a corrected reprint of the article "Quantifying uncertainty in projections of regional climate change: A Bayesian approach," published in a 2005 issue of the "Journal of Climate".

A better understanding of natural climate variability is crucial for global climate change studies and the evaluation of the sensitivity of the climate system to imposed perturbations. External forcing factors might contribute substantially to both high and low frequency variations in climate but a clear separation of their impact from internally generated fluctuations is difficult. We employ wavelet decomposition to identify common characteristics in forcing and climatic time series of the last four centuries. Here, we focus on solar irradiance variations by applying this statistical method to a selection of widely used proxy-based reconstructions. Major variability components are isolated through time-scale decomposition. Two classical solar modes (85 and 11 years) are not only identified within the limited time period covered by the solar datasets, but their relative influences on climate as represented by hemispheric surface temperature reconstructions are also estimated. While the low-frequency component shows close ties between solar variations and surface climate, a relationship between the 11-year sunspot cycle and temperature reconstructions is more difficult to attribute. However, the statistical multi-resolution analysis appears to be an ideal tool to uncover relationships and their changes at different temporal scales normally hidden by the strong background noise in the climate system. [Copyright &y& Elsevier]