The semiarid region between the North African Savanna and Sahara Desert, known as the Sahel, experienced dramatic multidecadal precipitation variability in the 20th century that was unparalleled in the rest of the world, including devastating droughts and famine in the early 1970s and 80s. Accurate predictions of this region’s hydroclimate future are essential to avoid future disasters of this kind, yet simulations from state of the art general circulation models (GCMs) do a poor job of simulating past Sahel rainfall variability, and don’t even agree on whether future precipitation will increase or decrease under global warming. Furthermore, climate scientists are still not in agreement about whether anthropogenic emissions played an important role relative to natural variability in dictating past Sahel rainfall change. Because the climate system is complex and coupled, it is difficult to determine which processes should be considered causal drivers of circulation changes and which should be considered part of the climate response, and therefore many theories for monsoon rainfall variability coexist in the literature. It is difficult to evaluate these competing theories because observational studies generally cannot be interpreted causally, but simulated experiments may not represent the dynamics of the real world. The Coupled Model Intercomparison Project (CMIP) provides a wealth of data in which GCMs maintained at research institutions worldwide perform similar experiments, allowing the researcher to reach conclusions that are robust to differences in parameterization between GCMs. The scientific community has been using a wide range of statistical techniques to analyze this data, and each has notable limitations. This dissertation explores two statistical techniques for leveraging CMIP to explore the drivers and mechanisms of historical Sahel rainfall variability: analysis of ensemble-mean responses to prescribed variables, and causal inference. In ‎Chapter 1, we give an overview of the climatology and variability of Sahel rainfall and present relevant physical theory. In ‎Chapter 2, we examine the roles of various types of anthropogenic forcing in observations and coupled simulations, using a 3-tiered multi-model mean (MMM) to extract robust climate signals from CMIP phase 5 (CMIP5). We examine “20th century” historical and single-forcing simulations—which separate the influence of anthropogenic aerosols, greenhouse gases (GHG), and natural radiative forcing on global coupled ocean-atmosphere system, and were specifically designed for attribution studies—as well as pre-Industrial control simulations, which only contain unforced internal climate variability, to investigate the drivers of simulated Sahel precipitation variability. The comparison of single-forcing and historical simulations highlights the importance of anthropogenic and volcanic aerosols over GHG in generating forced Sahel rainfall variability that reinforces the observed pattern, with anthropogenic aerosols alone responsible for the low-frequency component of simulated variability. However, the forced MMM only accounts for a small fraction of observed variance. A residual consistency test shows that simulated internal variability cannot explain the residual observed multidecadal variability, and points to model deficiency in simulating multidecadal variability in the forced response, internal variability, or both. In ‎Chapter 3, we investigate the causes for discrepancies in low-frequency Sahel precipitation variability between these ensembles and for model deficiency in reproducing observations. In the most recent version of CMIP – phase 6 of the Coupled Model Intercomparison Project (CMIP6) – the differences between observed and simulated variability are amplified rather than reduced: CMIP6 still grossly underestimates the magnitude of low-frequency variability in Sahel rainfall, but unlike CMIP5, historical mean precipitation in CMIP6 does not even correlate with observed multi-decadal variability. We continue to use a MMM to extract robust climate signals from simulations, but now additionally include sea surface temperature (SST) as a mediating variable in order to test the proposed physical processes. This partitions all influences on Sahel precipitation variability into five components: (1) teleconnections to SST; (2) atmospheric and (3) oceanic variability internal to the climate system; (4) the SST response to external radiative forcing; and (5) the “fast” (not mediated by SST) precipitation response to forcing. Though the coupled simulations perform quite poorly, in a vast improvement from previous ensembles, the CMIP6 atmosphere-only ensemble is able to reproduce the full magnitude of observed low-frequency Sahel precipitation variance when observed SST is prescribed. The high performance is due entirely to the atmospheric response to observed global SST – the fast response to forcing has a relatively small impact on Sahel rainfall, and only lowers the performance of the ensemble when it is included. Using the previously-established North Atlantic Relative Index (NARI) to approximate the role of global SST, we estimate that the strength of simulated teleconnections is consistent with observations. Applying the lessons of the atmosphere-only ensemble to coupled settings, we infer that both coupled CMIP ensembles fail to explain low-frequency historical Sahel rainfall variability mostly because they cannot explain the observed combination of forced and internal variability in SST. Though the fast response is small relative to the simulated response to observed SST variability, it is influential relative to simulated SST variability, and differences between CMIP5 and CMIP6 in the simulation of Sahel precipitation and its correlation with observations can be traced to differences in the simulated fast response to forcing or the role of other unexamined SST patterns. In this chapter, we use NARI to approximate the role of global SST because it is considered by some to be the best single index for estimating teleconnections to the Sahel. However, we show that NARI is only able to explain half of the high-performing simulated low-frequency Sahel precipitation variability in the atmospheric simulations with prescribed global SST. Statistical techniques commonly applied in the literature cannot distinguish between correlation and causality, so we cannot analyze the response of Sahel rainfall to global SST in more depth without atmospheric CMIP simulations targeted at every ocean basin of interest or a new method. In ‎Chapter 4, we turn to a novel technique called causal inference to qualify the notion that NARI can adequately represent the role of global SST in determining Sahel rainfall. We apply a causal discovery algorithm to CMIP6 pre-Industrial control simulations to determine which ocean basins influence Sahel rainfall in individual GCMs. Though we find that state of the art causal discovery algorithms for time series still struggle with data that isn’t generated specifically for algorithm evaluation, we robustly find that NARI does not mediate the full effect of global SST variability on Sahel rainfall in any of the climate simulations. This chapter lays the foundation for future work to fully-characterize the dependence of Sahel precipitation on individual ocean basins using the non-targeted simulations already available in CMIP – an approach which can be validated by comparing the composite results to the interventional historical simulations that are available. Furthermore, we hope this chapter will guide algorithm improvement efforts that are needed to increase the performance and usefulness of time series causal discovery algorithms on climate data.

The work presented in this dissertation is driven by three fundamental questions in climate science: (1) What is the natural variability of our climate system? (2) What components of this variability are predictable? (3) How does climate change affect variability and predictability? Determining the variability and predictability of the chaotic and nonlinear climate system is an inherently challenging problem. Climate scientists face the additional complications from limited and error-filled observational data of the true climate system and imperfect dynamical climate models used to simulate the climate system. This dissertation contains four chapters, each of which explores at least one of the three fundamental questions by providing novel approaches to address the complications. Chapter 1 examines the uncertainty in the observational record. As surface temperature data is among the highest quality historical records of the Earth’s climate, it is a critical source of information about the natural variability and forced response of the climate system. However, there is still uncertainty in global and regional mean temperature series due to limited and inaccurate measurements. This chapter provides an assessment of the global and regional uncertainty in temperature from 1880-present in the NASA Goddard Institute for Space Studies (GISS) Surface Temperature Analysis (GISTEMP). Chapter 2 extends the work of Chapter 1 to the regional spatial scale and monthly time scale. An observational uncertainty ensemble of historical global surface temperature is provided for easy use in future studies. Two applications of this uncertainty ensemble are discussed. First, an analysis of recent global and Arctic warming shows that the Arctic is warming four times faster than the rest of the global, updating the oft-provided statistic that Arctic warming is double that of the global rate. Second, the regional uncertainty product is used to provide uncertainty on country-level temperature change estimates from 1950-present. Chapter 3 investigates the impacts of the El Niño-Southern Oscillation (ENSO) on seasonal precipitation globally. In this study, novel methodology is developed to detect ENSO-precipitation teleconnections while accounting for missing data in the CRU TS historical precipitation dataset. In addition, the predictability of seasonal precipitation is assessed through simple empirical forecasts derived from the historical impacts. These simple forecasts provide significant skill over climatological forecasts for much of the globe, suggesting accurate predictions of ENSO immediately provide skillful forecasts of precipitation for many regions. Chapter 4 explores the role of initialization shock in long-lead ENSO forecasts. Initialized predictions from the CMIP6 decadal prediction project and uninitialized predictions using an analogue prediction method are compared to assess the role of model biases in climatology and variability on long-lead ENSO predictability. Comparable probabilistic skill is found in the first year between the model-analogs and the initialized dynamical forecasts, but the initialized dynamical forecasts generally show higher skill. The presence of skill in the initialized dynamical forecasts in spite of large initialization shocks suggest that initialization of the subsurface ocean may be a key component of multi-year ENSO skill. Chapter 5 brings together ideas from the previous chapters through an attribution of historical temperature variability to various anthropogenic and natural sources of variability. The radiative forcing due to greenhouse gas emissions is necessary to explain the observed variability in temperature nearly everywhere on the land surface. Regional fingerprints of anthropogenic aerosols are detected as well as the impact of major sources of natural variability such as ENSO and Atlantic Multidecadal Variability (AMV).

No description available.

No description available.

Global Circulation Models (GCMs) are used to create projections of possible future climate characteristics under global climate change scenarios. Future local and regional precipitation scenarios can be developed by downscaling synoptic CGM data. Daily 500-mb geopotential heights from the Canadian Centre for Climate Modeling and Analysis's CGCM2 are used to represent future (2020-2050) synoptics and are compared to daily historical (1960-1990) 500-mb geopotential height reanalysis data. The comparisons are made based on manually classified synoptic patterns identified by Changnon et al. (1993.Mon. Weather Rev. 121:633-647). Multiple linear regression models are used to link the historical synoptic pattern frequencies and precipitation amounts for 372 weather stations across western North America,. The station-specific models are then used to forecast future precipitation amounts per weather station based on synoptic pattern frequencies forecast by the CGCM2 climate change forcing scenario. Spatial and temporal variations in precipitation are explored to determine monthly, seasonal and annual trends in climate change impacts on precipitation in western North America. The resulting precipitation scenarios demonstrate a decrease in precipitation from 10 to 30% on an annual basis for much of the south and western regions of the study area. Seasonal forecasts show variations of the same regions with decreases in precipitation and select regions with increases in future precipitation. A major advancement of this analysis was the application of synoptic pattern downscaling to summer precipitation scenarios for western North America. / ix, 209 leaves : col. maps ; 29 cm.

Doctorat en philosophie et lettres / info:eu-repo/semantics/nonPublished

The lagged responses of tree-ring indices to annual climatic or hydrologic series are examined in this study. The objectives are to develop methods to analyze the lagged responses of individual tree-ring indices, and to improve upon conventional methods of adjusting for the lag in response in regression models to reconstruct annual climatic or hydrologic series. The proposed methods are described and applied to test data from Oregon and Southern California. Transfer-function modeling is used to estimate the dependence of the current ring on past years' climate and to select negative lags for reconstruction models. A linear system is assumed; the input is an annual climatic variable, and the output is a tree-ring index. The estimated impulse response function weights the importance of past and current years' climate on the current year's ring. The identified transfer function model indicates how many past years' rings are necessary to account for the effects of past years' climate. Autoregressive-moving-average (ARMA) modeling is used to screen out climatically insensitive tree-ring indices, and to estimate the lag in response to climate unmasked from the effects of autocorrelation in the tree-ring and climatic series. The climatic and tree-ring series are each prewhitened by ARMA models, and crosscorrelation between the ARMA residuals are estimated. The absence of significant crosscorrelations Implies low sensitivity. Significant crosscorrelations at lags other than zero indicate lag in response. This analysis can also aid in selecting positive lags for reconstruction models. An alternative reconstruction method that makes use of the ARMA residuals is also proposed. The basic concept is that random (uncorrelated in time) shocks of climate induce annual random shocks of tree growth, with autocorrelation in the tree-ring index resulting from inertia in the system. The steps in the method are (1) fit ARMA models to the tree-ring index and the climatic variable, (2) regress the ARMA residuals of the climatic variable on the ARMA residuals of the treering index, (3) substitute the long-term prewhitened tree-ring index into the regression equation to reconstruct the prewhitened climatic variable, and (4) build autocorrelation back into the reconstruction with the ARMA model originally fit to the climatic variable. The trial applications on test data from Oregon and Southern California showed that the lagged response of tree rings to climate varies greatly from site to site. Sensitive tree-ring series commonly depend significantly only on one past year's climate (regional rainfall index). Other series depend on three or more past years' climate. Comparison of reconstructions by conventional lagging of predictors with reconstructions by the random-shock method indicate that while the lagged models may reconstruct the amplitude of severe, long-lasting droughts better than the random-shock model, the random-shock model generally has a flatter frequency response. The random-shock model may therefore be more appropriate where the persistence structure is of prime interest. For the most sensitive series with small lag in response, the choice of reconstruction method makes little difference in properties of the reconstruction. The greatest divergence is for series whose impulse response weights from the transfer function analysis do not die off rapidly with time.

A dissertation submitted to the Faculty of Science, University of the Witwatersrand Johannesburg for the degree of Master of Science. Johannesburg, 1992. / Acidified rain is thought to have the potential to affect the ability of plants to acquire nutrients. The effects of artificially acidified rain on calcium (Ca) and magnesium (Mg) uptake by Pinus patula were investigated in terms of changes in the Ca and Mg-levels in the soil and changes in root growth and mycorrhizal coloniZation that might result from exposure to acidified precipitation. The uptake of these ions was also investigated in order to determine the possible effects of acid rain on the uptake process [Abbreviated Abstract. Open document to view full version] / AC2017

Spatial and temporal interpolation methods are commonly used methods for estimating missing precipitation rain gauge data based on values recorded at neighboring gauges. However, these interpolation methods have not been comprehensively checked for their ability to preserve time series characteristics. Assessing the preservation of time series characteristics helps achieving a threshold criteria of length of gaps in a data set that is acceptable to be filled. This study evaluates the efficacy of optimal weighting interpolation for estimation of missing data in preserving time series characteristics. Rain gauges in the state of Kentucky are used as a case study. Several model performance measures are also evaluated to validate the filling model; followed by time series characteristics to evaluate the accuracy of estimation and preservation of precipitation data characteristics. This study resulted in a definition of region-specific threshold of the maximum length of gaps allowed in a data set at five percent. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

The effect of air pollutant emissions in the Indianapolis "Metropolitan area on the acidification of snow was studied. In the winter of 1979-80, several snowfall events were analyzed to determine the levels of acidity in precipitation. This study revealed that the Indianapolis Metropolitan area is contributing to the acidity of its snow. Samples collected near the city were more acidic than those in the outlying rural areas.The impact of meteorological elements, size specific atmospheric particle concentrations and total suspended particulates on prevailing visibility in the greater Indianapolis Metropolitan area was also studied. The most important factors which statistically contributed to decreased visibility were relative humidity, wind speed and total suspended particulates as measured by high volume sampling.Ball State UniversityMuncie, IN 47306