From Many to One: Consensus Inference in a MIP

A Model Intercomparison Project (MIP) consists of teams who each estimate the same underlying quantity (e.g., temperature projections to the year 2070), and the spread of the estimates indicates their uncertainty. It recognizes that a community of scientists will not agree completely but that there is value in looking for a consensus and information in the range of disagreement. A simple average of the teams' outputs gives a consensus estimate, but it does not recognize that some outputs are more variable than others. Statistical analysis of variance (ANOVA) models offer a way to obtain a weighted consensus estimate of outputs with a variance that is the smallest possible and hence the tightest possible 'one-sigma' and 'two-sigma' intervals. Modulo dependence between MIP outputs, the ANOVA approach weights a team's output inversely proportional to its variation. When external verification data are available for evaluating the fidelity of each MIP output, ANOVA weights can also provide a prior distribution for Bayesian Model Averaging to yield a consensus estimate. We use a MIP of carbon dioxide flux inversions to illustrate the ANOVA-based weighting and subsequent consensus inferences

Hierarchical stochastic modelling of large river ecosystems and fish growth across spatio-temporal scales and climate models: the Missouri River endangered pallid sturgeon example

We present a hierarchical series of spatially decreasing and temporally increasing models to evaluate the uncertainty in the atmosphere – ocean global climate model (AOGCM) and the regional climate model (RCM) relative to the uncertainty in the somatic growth of the endangered pallid sturgeon (Scaphirhynchus albus). For effects on fish populations of riverine ecosystems, climate output simulated by coarse-resolution AOGCMs and RCMs must be downscaled to basins to river hydrology to population response. One needs to transfer the information from these climate simulations down to the individual scale in a way that minimizes extrapolation and can account for spatio-temporal variability in the intervening stages. The goal is a framework to determine whether, given uncertainties in the climate models and the biological response, meaningful inference can still be made. The non-linear downscaling of climate information to the river scale requires that one realistically account for spatial and temporal variability across scale. Our downscaling procedure includes the use of fixed/calibrated hydrological flow and temperature models coupled with a stochastically parameterized sturgeon bioenergetics model. We show that, although there is a large amount of uncertainty associated with both the climate model output and the fish growth process, one can establish significant differences in fish growth distributions between models, and between future and current climates for a given model

A hierarchical statistical framework for emergent constraints: application to snow-albedo feedback

Emergent constraints use relationships between future and current climate states to constrain projections of climate response. Here, we introduce a statistical, hierarchical emergent constraint (HEC) framework in order to link future and current climate with observations. Under Gaussian assumptions, the mean and variance of the future state is shown analytically to be a function of the signal-to-noise (SNR) ratio between data-model error and current-climate uncertainty, and the correlation between future and current climate states. We apply the HEC to the climate-change, snow-albedo feedback, which is related to the seasonal cycle in the Northern Hemisphere. We obtain a snow-albedo-feedback prediction interval of $(-1.25, -0.58)$ \%$K^{-1}$. The critical dependence on SNR and correlation shows that neglecting these terms can lead to bias and under-estimated uncertainty in constrained projections. The flexibility of using HEC under general assumptions throughout the Earth System is discussed.Comment: 19 pages, 5 Figure

Extreme value theory: Applications to estimation of stochastic traffic capacity and statistical downscaling of precipitation extremes

This work explores two applications of extreme value analysis. First, we apply EV techniques to traffic stream data to develop an accurate distribution of capacity. Data were collected by the NHDOT along Interstate I93, and two adjacent locations in Salem, NH were examined. Daily flow maxima were used to estimate capacity, and data not associated with daily breakdown were deemed censored values. Under this definition, capacity values are approximated by the generalized extreme value (GEV) distribution for block maxima. To address small sample sizes and the presence of censoring, a Bayesian framework using semi-informative priors was implemented. A simple cross validation procedure reveals the GEV model, using both censored and observed capacity data, is suitable for probabilistic prediction. To overcome the uncertainty associated with a high number of censored values at one location, a hierarchical model was developed to share information between locations and generally improve fitted results. Next, we perform a statistical downscaling by applying a CDF transformation function to local-level daily precipitation extremes (from NCDC station data) and corresponding NARCCAP regional climate model (RCM) output to derive local-scale projections. These high-resolution projections are essential in assessing the impacts of projected climate change. The downscaling method is performed on 58 locations throughout New England, and from the projected distribution of extreme precipitation local-level 25-year return levels are calculated. To obtain uncertainty estimates for future return levels, both a parametric bootstrap and Bayesian procedure are implemented. The Bayesian method consists of a semi-parametric mixture model for daily precipitation where extremes are modeled parametrically using generalized Pareto distributions, and non-extremes are modeled non-parametrically using quantiles. We find that these Bayesian credibility intervals are generally larger than those obtained from a previously applied parametric Bootstrap procedure, indicating that projected trends in New England precipitation tend to be less significant than is hinted at in many studies

Climate Change Impacts to Estuarine Hydrodynamics: Tools, Methodologies, and Predictions

Coastal communities in the U.S. Pacific Northwest (PNW) are rapidly engaging with the idea that hazards and environmental pressures are changing and may be characteristically different in the near future. This has led to a need for scientific knowledge and tools that can help coastal communities prepare and build resilience in the face of changing risk. This is especially true for coastal systems that experience multiple and complex hazard drivers. In these systems, extreme events can result from the sum of multiple drivers and it is unclear how the changing climate will impact the overall sum. This dissertation focuses on such a system, specifically estuaries, and provides key steps towards characterizing how the risk of flooding will change through time. This is broadly approached through developing tools, methodologies, and predictions for PNW estuaries, however the developed tools and approaches should be applicable to other regions as well. Estuarine hydrodynamics are changing as a result of the changing climate. Our current best predictions for how the climate is evolving come from global climate models (GCMs). However, climate models often exhibit bias when compared to observations. These biases can propagate through a modeling framework and lead to corresponding biases in predicted flood magnitude. Chapter 2 of this dissertation evaluates a tool, statistical bias correction (BC), for removing bias from model output. This chapter examines a variety of BC procedures as applied to GCM-forced wave model output. BC methodologies are generally applicable but waves are chosen for testing due to large biases (from poorly resolved GCM wind fields) and because waves are significant contributors to coastal flooding in the PNW. BC is shown to be an efficient and effective method for removing bias. Particular emphasis is placed on evaluating the difference between univariate and bivariate analysis with results demonstrating the importance of considering correlation between variables when performing corrections. All methods are evaluated under a nonstationary climate with extensions developed to BC methodologies for this new context. A primary difficulty with quantifying future flooding hazards in estuaries is that extremes are controlled by a variety of forcings (e.g. waves, streamflow, El Nino, winds, etc.), all of which may change in the future. This is investigated in Chapter 3 using a multi-component modeling framework which considers the combined evolution of estuarine forcings under climate change. Forcings are simulated with sub-models and then used as inputs for decadal-scale hydrodynamic simulations. This modeling framework is utilized for two climatological periods (historic and future) and for two study sites (Coos and Tillamook Bays). It is found that extreme water levels (e.g., 100 year return interval events) exhibit spatial variability with magnitudes increasing with distance into the estuaries. For the climate model studied, forcing of extreme events relaxes into the future, but this effect is eventually overcome by sea level rise (SLR). Overall results show significant system complexity, highlighting the importance of dynamically modeling multiple processes relevant to flooding rather than considering a simple SLR adjustment. The process-based modeling in Chapter 3 has the advantage of resolving many physical processes, but has the disadvantage of requiring significant computational resources. Chapter 4 evaluates an alternative hybrid methodology (process-based and statistical) that seeks to calculate water levels with much greater computational efficiency. Specifically, an emulation-based approach is evaluated which replaces the process-based model with a fast statistical representation. Emulation is validated against a variety of observations and is found to be skillful with reasonably low overall error. A hierarchical validation finds that the largest loss of skill in emulator construction is associated with the process-based model attempting to reproduce observations. After emulator construction, simulation time is nearly instantaneous, making emulation a promising strategy for a variety of research questions such as probabilistic future flood hazard assessments

Comprehensive analysis of design storm formulation across Newfoundland and under climate change with scarce data

Urban and rural watersheds are becoming increasingly vulnerable to extreme weather events and their consequences. One such consequence is flooding. Stormwater management systems need to be efficiently designed to handle both the quantity and quality of floodwaters. Efficient stormwater systems can be achieved when design parameters are set to their optimum. The design parameters for proper sizing of stormwater infrastructure are obtained from design storms, a combination of Intensity-Duration-Frequency (IDF) curves and a rainfall temporal distribution. IDF curves are developed using rainfall data; as such, changes to the climate will affect these curves. There is a need to re-evaluate the current design storms to determine how they will be affected by the changing climate. Evaluating a design storm from a chaotic variable such as precipitation is complex, and the variation in climate makes it more complicated. Information on IDF curves is challenging to obtain, especially at locations where precipitation data is lacking or for which there is little data. The focus of this study is the use of models for data generation and analysis of data for appropriate temporal distribution identification. The application of the work in this thesis provides information to guide engineering design and other hydrological studies under climate change. This thesis presents a series of studies that: assess the impact of climate variations on temporal distributions used in design storm analysis; analyzes how these temporal distribution patterns - when combined with other hydrologic factors - can impact mapping for risk of floods, especially under climate change projections, and develops a precipitation disaggregation model. The assessment of temporal distribution variation with climate shows that current temporal distributions being used may result in under- or over-design based on the location of interest and climate condition used, either current climate or future climate projections. It highlights the importance of using the appropriate temporal distribution to justify the conservative design. The temporal distributions identified are taken a step further to determine their interplay with hydrologic loss methods and their impact on mapping for the risk of floods. The outcome shows that the extent of a flooded area is highly sensitive to the temporal distribution and loss method used. A precipitation disaggregation model is also developed by coupling a method-of-fragments model with a crossover operator and applied to meteorological stations at Ruby Line, St. John’s and Corner Brook to generate hourly data from daily data. These stations were chosen to mainly draw attention to the lack of precipitation data across most locations in the province. The results show that the model can generate hourly data statistics similar to that observed

Caracterización morfológica, bromatológica y agroclimática del cultivo de café (Coffea arábica L.) en la región Piura.

La presente investigación, tuvo como objetivo determinar la caracterización morfológica, bromatológica y climática de Coffea arábica en 3 provincias, de la región Piura del Perú. Para la evaluación morfológica, se utilizaron 12 descriptores que incluyeron hábitos de crecimiento flexible y compacto, en su mayoría con muy pocas ramas primarias, con la forma del ápice de las hojas puntiagudas y apicular. Todas las plantas fueron semi erectas, con colores verdes, pardosas y bronceados para color de la punta de la hoja joven. Se presentaron formas de la hoja entre aovadas, elípticas y lanceoladas, y formas de la estípula redonda, aovada, triangular y delta. La coloración del fruto maduro varió entre luz roja, roja, roja oscuro y amarillo y con formas de la fruta: redonda, elíptica y redonda. Para la caracterización bromatológica se realizó un análisis proximal donde estaban las variables de Humedad, Proteína cruda, Fibra cruda, Cenizas, Grasa cruda y Carbohidrato. Esta información permitió determinar correlaciones positivas altas. En la caracterización climática se estableció un récord de 30 años, de la precipitación máxima (639.9 mm Huancabamba, 685.1 mm Morropón y 1030.7 mm Ayabaca), mínimas (0 mm para Huancabamba, Morropón y Ayabaca) y la temperatura promedio (Ayabaca 13.6°C, Morropón 25.1°C y Huancabamba 19.8°C), donde se utilizó la estadística de Bonferroni para comparar diferencias estadísticas entre meses. Se espera que la presente investigación sirva para futuras gestiones de mejora en el cultivo relacionado con el clima