From funding to financing:perspectives shaping a research agenda for investment in urban climate adaptation

There is growing recognition of the importance of funding and financing arrangements to enable climate change adaptation in cities. However, there has been little critical analysis into the underwriting and governance mechanisms necessary support broader scaled application. Through surveying recent literature, this article offers conceptual clarity for understanding emerging adaptation finance mechanisms that intersect with urban governance, planning, and management functions. The article assesses two key conceptual domains: (i) the distinction between adaptation funding and financing and (ii) the synergies, conflicts, and trade-offs associated with mobilizing adaptation investments in the private sector. The article argues that a clearer delineation of these two domains will clarify the objectives, mechanisms, and larger governance implications of investment in urban adaptation. This article provides a roadmap for future scholarly inquiries that may advance the conceptual and analytical discipline necessary to evaluate the feasibility and desirability of investments from often-conflicting perspectives, interests, and actors. <br/

Snow Accumulation and Firn Properties Across The Antarctic Ice Sheet

As anthropogenic climate change accelerates, the stability of the Antarctic Ice Sheet has come under increased scrutiny. Being Earth's largest body of frozen water, Antarctica sequesters enough ice that if melted entirely, would raise global sea level by more than 58 m. Glaciologists quantify Antarctica's past and present contribution to sea level rise by calculating its mass balance, which is defined as the rate of change of mass per unit time. One of our primary tools for calculating mass balance is repeat satellite altimetry, which entails converting observed changes in ice sheet surface elevation (and thus volume) into changes in mass. Unfortunately, this conversion is confounded by the spatially heterogeneous, vertically evolving, and time-variant density of Antarctica's thick (10s to 100s of meters) firn layer which covers ~99% of the ice sheet. In this dissertation, we develop models designed to simulate Antarctic firn properties with the goal of improving Antarctic mass balance estimates. We first demonstrate improved modeled firn density simulations by adapting an existing one-dimensional snow model to account for drifting snow, which is a ubiquitous feature of the Antarctic surface climate. Next, we improve our understanding of the spatial patterns of Antarctic snow accumulation by incorporating our single-column model's snow redistribution module into a distributed, three-dimensional modeling framework. Finally, we further constrain Antarctic mass balance by leveraging machine learning techniques to scale our improved physics-based firn models across the entire ice sheet. Overall, our improved models and process understanding help to contextualize past and present Antarctic Ice Sheet mass balance, which is essential for informing future projections.</p

Rate My Data: Quantifying the Value of Ecological Data for Models of Terrestrial Carbon Cycle

Primarily driven by concern about rising levels of atmospheric CO2, ecologists and earth system scientists are collecting vast amounts of data related to the carbon cycle. These measurements are generally time-consuming and expensive to make, and, unfortunately, we live in an era where research funding is increasingly hard to come by. Thus, important questions are: ‘Which data streams provide the most valuable information? ’ and, ‘How much data do we need? ’ These questions are relevant not only for model developers, who need observational data to improve, constrain and test their models, but also for experimentalists and those designing ecological observation networks. Here we address these questions using a model-data fusion approach. We constrain a process-oriented, forest ecosystem C cycle model with seventeen different data streams from the Harvard Forest. We iteratively rank each data source according to its contribution to reducing model uncertainty. Results show the importance of some measurements commonly unavailable to carbon cycle modelers, such as estimates of turnover times from different carbon pools. Surprisingly, many data sources are relatively redundant in the presence of others, and do not lead to a significant improvement in model performance. A few select data sources lead to the largest reduction in parameter based model uncertainty. Projections of future carbon cycling were poorly constrained when only hourly net ecosystem exchange measurements were used to inform the model. They were well constrained, however, with only five of the seventeen data streams, even though many individual parameters are not constrained. The approach taken here should stimulate further cooperation between modelers and measurement teams, and may be useful in the context of setting research priorities and allocating research funds.Organismic and Evolutionary Biolog

Cosmogenic-neutron activation of TeO2 and implications for neutrinoless double-beta decay experiments

Flux-averaged cross sections for cosmogenic-neutron activation of natural tellurium were measured using a neutron beam containing neutrons of kinetic energies up to $\sim$800 MeV, and having an energy spectrum similar to that of cosmic-ray neutrons at sea-level. Analysis of the radioisotopes produced reveals that 110mAg will be a dominant contributor to the cosmogenic-activation background in experiments searching for neutrinoless double-beta decay of 130Te, such as CUORE and SNO+. An estimate of the cosmogenic-activation background in the CUORE experiment has been obtained using the results of this measurement and cross-section measurements of proton activation of tellurium. Additionally, the measured cross sections in this work are also compared with results from semi-empirical cross-section calculations.Comment: 11 pages, 5 figure

Using Model-Data Fusion to Interpret Past Trends, and Quantify Uncertainties in Future Projections, of Terrestrial Ecosystem Carbon Cycling

Uncertainties in model projections of carbon cycling in terrestrial ecosystems stem from inaccurate parameterization of incorporated processes (endogenous uncertainties) and processes or drivers that are not accounted for by the model (exogenous uncertainties). Here, we assess endogenous and exogenous uncertainties using a model-data fusion framework benchmarked with an artificial neural network (ANN). We used 18 years of eddy-covariance carbon flux data from the Harvard forest, where ecosystem carbon uptake has doubled over the measurement period, along with 15 ancillary ecological data sets relative to the carbon cycle. We test the ability of combinations of diverse data to constrain projections of a process-based carbon cycle model, both against the measured decadal trend and under future long-term climate change. The use of high-frequency eddy-covariance data alone is shown to be insufficient to constrain model projections at the annual or longer time step. Future projections of carbon cycling under climate change in particular are shown to be highly dependent on the data used to constrain the model. Endogenous uncertainties in long-term model projections of future carbon stocks and fluxes were greatly reduced by the use of aggregated flux budgets in conjunction with ancillary data sets. The data-informed model, however, poorly reproduced interannual variability in net ecosystem carbon exchange and biomass increments and did not reproduce the long-term trend. Furthermore, we use the model-data fusion framework, and the ANN, to show that the long-term doubling of the rate of carbon uptake at Harvard forest cannot be explained by meteorological drivers, and is driven by changes during the growing season. By integrating all available data with the model-data fusion framework, we show that the observed trend can only be reproduced with temporal changes in model parameters. Together, the results show that exogenous uncertainty dominates uncertainty in future projections from a data-informed process-based model.Organismic and Evolutionary Biolog

Impact of Different Fecal Processing Methods on Assessments of Bacterial Diversity in the Human Intestine.

The intestinal microbiota are integral to understanding the relationships between nutrition and health. Therefore, fecal sampling and processing protocols for metagenomic surveys should be sufficiently robust, accurate, and reliable to identify the microorganisms present. We investigated the use of different fecal preparation methods on the bacterial community structures identified in human stools. Complete stools were collected from six healthy individuals and processed according to the following methods: (i) randomly sampled fresh stool, (ii) fresh stool homogenized in a blender for 2 min, (iii) randomly sampled frozen stool, and (iv) frozen stool homogenized in a blender for 2 min, or (v) homogenized in a pneumatic mixer for either 10, 20, or 30 min. High-throughput DNA sequencing of the 16S rRNA V4 regions of bacterial community DNA extracted from the stools showed that the fecal microbiota remained distinct between individuals, independent of processing method. Moreover, the different stool preparation approaches did not alter intra-individual bacterial diversity. Distinctions were found at the level of individual taxa, however. Stools that were frozen and then homogenized tended to have higher proportions of Faecalibacterium, Streptococcus, and Bifidobacterium and decreased quantities of Oscillospira, Bacteroides, and Parabacteroides compared to stools that were collected in small quantities and not mixed prior to DNA extraction. These findings indicate that certain taxa are at particular risk for under or over sampling due to protocol differences. Importantly, homogenization by any method significantly reduced the intra-individual variation in bacteria detected per stool. Our results confirm the robustness of fecal homogenization for microbial analyses and underscore the value of collecting and mixing large stool sample quantities in human nutrition intervention studies