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Open Access Publications from the University of California

Scholarly Works (54 results)

  • Article
LBL Publications (2012)
    Cover page: Parallel Statistical Extreme Value Analysis of Climate Data
    • Article
    • Peer Reviewed
    Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 35 (2013)
      Cover page: Semantic Implicit Learning in Language
      • Article
      • Peer Reviewed
      UC Berkeley Previously Published Works (2024)
      Changes in precipitation patterns with climate change could have important impacts on human and natural systems. Zhang et al. (2021, https://doi.org/10.1029/2020gl092293) report trends in daily precipitation patterns over the last five decades in the western United States, focusing on meteorological drought. They report that dry intervals (calculated at the annual or seasonal level) have increased across much of the southwestern U.S., with statistical assessment suggesting the results are statistically robust. However, Zhang et al. (2021, https://doi.org/10.1029/2020gl092293) preprocess their annual (or seasonal) averages to compute 5-year moving window averages before using established statistical techniques for trend analysis that assume independence about some fixed trend. Here we show that the moving window preprocessing violates that independence assumption and inflates the statistical significance of their trend estimates. This raises questions about the robustness of their results. We conclude by discussing the difficulty of adjusting for spatial structure when assessing time trends in a regional context.
        Cover page: Comment on “Five Decades of Observed Daily Precipitation Reveal Longer and More Variable Drought Events Across Much of the Western United States”Creative Commons 'BY' version 4.0 license
        • Thesis
        • Peer Reviewed
        UC Berkeley Electronic Theses and Dissertations (2024)

        Accurate high-resolution spatiotemporal data on environmental variables is critical for informing natural resource management and guiding climate change mitigation and adaptation practices. From a hydrological perspective, mountain snowpack is a primary source of freshwater for meeting societal drinking and agricultural needs in various regions. As a result of climate change, these water resources are becoming increasingly vulnerable and declining at a steady pace. To support informed decision-making around water resource planning, accurate data on regular fine-scale spatial and temporal intervals is crucial. Several such gridded data products on key snowpack properties such as snow water equivalent (SWE) and snow depth (SD) have been introduced and provide great utility to the scientific community and practitioners in the environmental space. However, errors and uncertainties in these gridded snow products are not well understood and assumptions underlying the generation of these products are inadequately investigated. In this dissertation, questions around uncertainty and representativeness of gridded snow data are examined and quantified, and a novel statistical approach to estimating SWE is introduced.

        The first chapter introduces key terms, concepts, and datasets used throughout the dissertation. The second chapter quantifies error and uncertainty underlying a widely-used gridded SWE product. The third chapter discusses limitations in this gridded product and proposes an alternative framework for empirical SWE prediction that is intuitive, scalable, and statistically sound. Finally, the fourth chapter explores questions around representativeness of point measurements and gridded estimates of true SD and SWE, addressing concerns associated with standard point-to-grid comparisons commonly used in evaluation methodologies.

          • Article
          • Peer Reviewed
          LBL Publications (2016)
          Extreme event attribution characterizes how anthropogenic climate change may have influenced the probability and magnitude of selected individual extreme weather and climate events. Attribution statements often involve quantification of the fraction of attributable risk (FAR) or the risk ratio (RR) and associated confidence intervals. Many such analyses use climate model output to characterize extreme event behavior with and without anthropogenic influence. However, such climate models may have biases in their representation of extreme events. To account for discrepancies in the probabilities of extreme events between observational datasets and model datasets, we demonstrate an appropriate rescaling of the model output based on the quantiles of the datasets to estimate an adjusted risk ratio. Our methodology accounts for various components of uncertainty in estimation of the risk ratio. In particular, we present an approach to construct a one-sided confidence interval on the lower bound of the risk ratio when the estimated risk ratio is infinity. We demonstrate the methodology using the summer 2011 central US heatwave and output from the Community Earth System Model. In this example, we find that the lower bound of the risk ratio is relatively insensitive to the magnitude and probability of the actual event.
            Cover page: Quantile-based bias correction and uncertainty quantification of extreme event attribution statements
            • Article
            • Peer Reviewed
            UC San Francisco Previously Published Works (2012)

            Background

            The aim of this study was to determine the optimal treatment for a patient with newly diagnosed prostate cancer weighing the individual's risk of disease progression against his risk of non-cancer death.

            Methods

            We developed a predictive model incorporating clinicopathological tumor variables, patient age, comorbidity status, and primary treatment modality. We identified 6091 patients with clinically-localized prostate cancer managed with radical prostatectomy (n=4117) or radiation therapy (n=1974) from the Cancer of the Prostate Strategic Urologic Research Endeavor database. Fine and Gray competing-risks proportional hazards regression models were used to calculate the risks of prostate cancer-specific mortality (PCSM) and non-prostate cancer death and to generate a nomogram.

            Results

            The median follow-up after treatment was 53 months (interquartile range 30, 80 months). In total, 983 men died during follow-up, including 167 who died of prostate cancer and 816 who died of non-prostate cancer causes. On multivariate analysis, higher Cancer of the Prostate Risk Assessment score and primary treatment with radiation were associated with an increased risk of PCSM, whereas older age, African-American race, and treatment with radiation predicted non-prostate cancer death. The number of comorbidities and receipt of androgen deprivation therapy correlated with an increased risk of non-prostate cancer death, but not PCSM. The resulting nomogram allows quantification and comparison of the 10-year risk of PCSM and non-prostate cancer death.

            Conclusions

            Integrating clinicopathological variables with comorbid conditions in a competing-risks model affords quantification and comparison of relative probabilities of PCSM and non-prostate cancer death following treatment. Our model thereby facilitates an individualized approach for counseling patients regarding prostate cancer management.
              Cover page: Evaluating prostate cancer mortality and competing risks of death in patients with localized prostate cancer using a comprehensive nomogram
              • Article
              • Peer Reviewed
              LBL Publications (2019)
              The Weather Risk Attribution Forecast (WRAF) is a forecasting tool that uses output from global climate models to make simultaneous attribution statements about whether and how greenhouse gas emissions have contributed to extreme weather across the globe. However, in conducting a large number of simultaneous hypothesis tests, the WRAF is prone to identifying false “discoveries.” A common technique for addressing this multiple testing problem is to adjust the procedure in a way that controls the proportion of true null hypotheses that are incorrectly rejected, or the false discovery rate (FDR). Unfortunately, generic FDR procedures suffer from low power when the hypotheses are dependent, and techniques designed to account for dependence are sensitive to misspecification of the underlying statistical model. In this article, we develop a Bayesian decision-theoretical approach for dependent multiple testing and a nonparametric hierarchical statistical model that flexibly controls false discovery and is robust to model misspecification. We illustrate the robustness of our procedure to model error with a simulation study, using a framework that accounts for generic spatial dependence and allows the practitioner to flexibly specify the decision criteria. Finally, we apply our procedure to several seasonal forecasts and discuss implementation for the WRAF workflow. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
                Cover page: Spatially Dependent Multiple Testing Under Model Misspecification, With Application to Detection of Anthropogenic Influence on Extreme Climate Events
                • Article
                • Peer Reviewed
                LBL Publications (2018)
                Event attribution in the context of climate change seeks to understand the role of anthropogenic greenhouse gas emissions on extreme weather events, either specific events or classes of events. A common approach to event attribution uses climate model output under factual (real-world) and counterfactual (world that might have been without anthropogenic greenhouse gas emissions) scenarios to estimate the probabilities of the event of interest under the two scenarios. Event attribution is then quantified by the ratio of the two probabilities. While this approach has been applied many times in the last 15 years, the statistical techniques used to estimate the risk ratio based on climate model ensembles have not drawn on the full set of methods available in the statistical literature and have in some cases used and interpreted the bootstrap method in non-standard ways. We present a precise frequentist statistical framework for quantifying the effect of sampling uncertainty on estimation of the risk ratio, propose the use of statistical methods that are new to event attribution, and evaluate a variety of methods using statistical simulations. We conclude that existing statistical methods not yet in use for event attribution have several advantages over the widely-used bootstrap, including better statistical performance in repeated samples and robustness to small estimated probabilities. Software for using the methods is available through the climextRemes package available for R or Python. While we focus on frequentist statistical methods, Bayesian methods are likely to be particularly useful when considering sources of uncertainty beyond sampling uncertainty.
                  Cover page: Quantifying statistical uncertainty in the attribution of human influence on severe weather
                  • Article
                  • Peer Reviewed
                  LBL Publications (2013)
                    Cover page: Inferring the anthropogenic contribution to local temperature extremes
                    • Article
                    • Peer Reviewed
                    UC Berkeley Previously Published Works (2019)
                    The gridding of daily accumulated precipitation—especially extremes—from ground-based station observations is problematic due to the fractal nature of precipitation, and therefore estimates of long period return values and their changes based on such gridded daily datasets are generally underestimated. In this paper, we characterize high-resolution changes in observed extreme precipitation from 1950 to 2017 for the contiguous United States (CONUS) based on in situ measurements only. Our analysis utilizes spatial statistical methods that allow us to derive gridded estimates that do not smooth extreme daily measurements and are consistent with statistics from the original station data while increasing the resulting signal-to-noise ratio. Furthermore, we use a robust statistical technique to identify significant pointwise changes in the climatology of extreme precipitation while carefully controlling the rate of false positives. We present and discuss seasonal changes in the statistics of extreme precipitation: the largest and most spatially coherent pointwise changes are in fall (SON), with approximately 33% of CONUS exhibiting significant changes (in an absolute sense). Other seasons display very few meaningful pointwise changes (in either a relative or absolute sense), illustrating the difficulty in detecting pointwise changes in extreme precipitation based on in situ measurements. While our main result involves seasonal changes, we also present and discuss annual changes in the statistics of extreme precipitation. In this paper we only seek to detect changes over time and leave attribution of the underlying causes of these changes for future work.
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