Risk Analysis

Scientific challenges exist on how to extract information from the wide range of projected impacts simulated by crop models driven by climate ensembles. A stronger focus is required to understand and identify the mechanisms and drivers of projected changes in crop yield. In this study, we investigate the robustness of future projections of five metrics relevant to agriculture stakeholders (accumulated frost days, dry days, growing season length, plant heat stress and start of field operations). We use a large ensemble of climate simulations by the MIT IGSM-CAM integrated assessment model that accounts for the uncertainty associated with different emissions scenarios, climate sensitivity, and natural variability. By end of century, the US is projected to experience fewer frosts, a longer growing season, more heat stress and an earlier start of field operations—although the magnitude and even the sign of these changes vary greatly by regions. Projected changes in dry days are shown not to be robust. We highlight the important role of natural variability, in particular for changes in dry days (a precipitation-related index) and heat stress (a threshold index). The wide range of our projections compares well the CMIP5 multi-model ensemble, especially for temperature-related indices. This suggests that using a single climate model that accounts for key sources of uncertainty can provide an efficient and complementary framework to the more common approach of multi-model ensembles. We also show that greenhouse gas mitigation has the potential to significantly reduce adverse effects (heat stress, risks of pest and disease) of climate change on agriculture, while also curtailing potentially beneficial impacts (earlier planting, possibility for multiple cropping). A major benefit of climate mitigation is potentially preventing changes in several indices to emerge from the noise of natural variability, even by 2100. This has major implications considering that any significant climate change impacts on crop yield would result in nation-wide changes in the agriculture sector. Finally, we argue that the analysis of agro-climate indices should more often complement crop model projections, as they can provide valuable information to better understand the drivers of changes in crop yield and production and thus better inform adaptation decisions.

The sustainability of future water resources is of paramount importance and is affected by many factors, including population, wealth and climate. Inherent in current methods to estimate these factors in the future is the uncertainty of their prediction. In this study, we integrate a large ensemble of scenarios—internally consistent across economics, emissions, climate, and population—to develop a risk portfolio of water stress over a large portion of Asia that includes China, India, and Mainland Southeast Asia in a future with unconstrained emissions. We isolate the effects of socioeconomic growth from the effects of climate change in order to identify the primary drivers of stress on water resources. We find that water needs related to socioeconomic changes, which are currently small, are likely to increase considerably in the future, often overshadowing the effect of climate change on levels of water stress. As a result, there is a high risk of severe water stress in densely populated watersheds by 2050, compared to recent history. There is strong evidence to suggest that, in the absence of autonomous adaptation or societal response, a much larger portion of the region’s population will live in water-stressed regions in the near future. Tools and studies such as these can effectively investigate large-scale system sensitivities and can be useful in engaging and informing decision makers.

© 2016 the authors

Drought is one of the most destructive natural disasters causing serious damages to human society, and studies have projected more severe and widespread droughts in the coming decades associated with the warming climate. Although several drought indices have been developed for drought monitoring, most of them were based on large scale environmental conditions rather than ecosystem transitional patterns to drought. Here, we propose using the ecosystem function oriented Normalized Ecosystem Drought Index (NEDI) to quantify drought severity, loosely related to Sprengel’s and Liebig’s Law of the Minimum for plant nutrition. Extensive eddy covariance measurements from 60 AmeriFlux sites across 8 IGBP vegetation types were used to validate the use of NEDI. The results show that NEDI can reasonably capture ecosystem transitional responses to limited water availability, suggesting that drought conditions detected by NEDI are ecosystem function oriented. The wildly used Palmer Drought Severity Index (PDSI), on the other hand, does not have a clear relationship with ecosystem responses to drought conditions because ecosystem adaptation ability is not considered in PDSI calculation.

In this thesis, I study polycyclic aromatic hydrocarbons (PAHs) and perfluorocarboxylic acids (PFCAs). PAHs are by-products of burning and therefore have important anthropogenic sources in the combustion of fuels, biomass, etc. PFCAs and their atmospheric precursors are used in making firefighting foams, non-stick coatings, and other surfactant applications.

I quantitatively examine the relative importance of uncertainty in emissions and physicochemical properties (including reaction rate constants) to Northern Hemisphere (NH) and Arctic PAH concentrations. NH average concentrations are more sensitive to uncertainty in the atmospheric lifetime than to emissions rate. The largest uncertainty reductions would come from precise experimental determination of PHE, PYR and BaP rate constants for the reaction with OH.

I calculate long-chain PFCA formation theoretical maximum yields for the degradation of precursor species at a representative sample of atmospheric conditions from a three dimensional chemical transport model, finding that atmospheric conditions farther from pollution sources have both higher capacities to form long chain PFCAs and higher uncertainties in those capacities.

I present results from newly developed simulations of atmospheric PFCA formation and fate using the chemical transport model GEOS-Chem, simulating the degradation of fluorotelomer precursors, as well as deposition and transport of the precursors, intermediates and end-products of the PFCA formation chemistry. I compare the model results to remote deposition measurements and find that it reproduces Arctic deposition of PFOA effectively. Given the most recent precursor emission inventory, the atmospheric indirect source of PFOA and PFNA is 10-45 t/yr globally and 0.2-0.7 t/yr to the Arctic.

The economics of climate change involves a vast array of uncertainties, complicating both the analysis and development of climate policy. This study presents the results of the first comprehensive study of uncertainty in climate change using multiple integrated assessment models. The study looks at model and parametric uncertainties for population, total factor productivity, and climate sensitivity. It estimates the pdfs of key output variables, including CO2 concentrations, temperature, damages, and the social cost of carbon (SCC). One key finding is that parametric uncertainty is more important than uncertainty in model structure. Our resulting pdfs also provide insights on tail events.

Infrastructure located along the U.S. Atlantic and Gulf coasts is exposed to rising risk of flooding from sea level rise, increasing storm surge, and subsidence. In these circumstances coastal management commonly based on 100-year flood maps assuming current climatology is no longer adequate. A dynamic programming cost–benefit analysis is applied to the adaptation decision, illustrated by application to an energy facility in Galveston Bay. Projections of several global climate models provide inputs to estimates of the change in hurricane and storm surge activity as well as the increase in sea level. The projected rise in physical flood risk is combined with estimates of flood damage and protection costs in an analysis of the multi-period nature of adaptation choice. The result is a planning method, using dynamic programming, which is appropriate for investment and abandonment decisions under rising coastal risk.

© the authors 2015

Malawi confronts a growth and development imperative that it must meet in a context characterised by rising temperatures and deep uncertainty about trends in precipitation. This article evaluates the potential implications of climate change for overall growth and development prospects in Malawi. We combine climate, biophysical and economic models to develop a structural analysis focused on three primary impact channels: agriculture, road infrastructure and hydropower generation. We account explicitly for the uncertainty in climate forecasts by exploiting the best available information on the likely distribution of climate outcomes. We find that climate change is unlikely to substantially slow overall economic growth over the next couple of decades. However, assuming that global emissions remain effectively unconstrained, climate change implications become more pronounced over time. Reduced agricultural yields and increased damage to road infrastructure due to increased frequency and intensity of extreme events are the principal impact channels. Owing to the potential for positive impacts in the near term, the net present value of climate impacts from 2007 to 2050 (using a 5% discount rate) can be positive or negative with an average loss of about USD 610 million. The main implication of our findings is that Malawian policy makers should look to exploit the coming decade or two as these represent a window of opportunity to develop smart and forward looking adaptation policies. As many of these policies take time to develop, implement, and then execute, there is little cause for complacency.

© 2014 the authors.

In this study, we present a new modeling framework and a large ensemble of climate projections to investigate the uncertainty in regional climate change over the United States (US) associated with four dimensions of uncertainty. The sources of uncertainty considered in this framework are the emissions projections, global climate system parameters, natural variability and model structural uncertainty. The modeling framework revolves around the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model with an Earth System Model of Intermediate Complexity (EMIC) (with a two-dimensional zonal-mean atmosphere). Regional climate change over the US is obtained through a two-pronged approach. First, we use the IGSMCAM framework, which links the IGSM to the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM). Second, we use a pattern-scaling method that extends the IGSM zonal mean based on climate change patterns from various climate models. Results show that the range of annual mean temperature changes are mainly driven by policy choices and the range of climate sensitivity considered. Meanwhile, the four sources of uncertainty contribute more equally to end-of-century precipitation changes, with natural variability dominating until 2050. For the set of scenarios used in this study, the choice of policy is the largest driver of uncertainty, defined as the range of warming and changes in precipitation, in future projections of climate change over the US.

© 2014 Springer Science+Business Media

The sustainability of future water resources is of paramount importance and is affected by many factors, including population, wealth and climate. Inherent in how these factors change in the future is the uncertainty of their prediction. In this study, we integrate a large ensemble of scenarios—internally consistent across economics, emissions, climate, and population—to develop a risk portfolio of water stress over a large portion of Asia that includes China, India, and Mainland Southeast Asia. We isolate the effects of socioeconomic growth from the effects of climate change in order to identify the primary drivers of stress on water resources. We find that water needs related to socioeconomic changes, which are currently small, are likely to increase considerably in the future, often overshadowing the effect of climate change on levels of water stress. As a result, there is a high risk of severe water stress in densely populated watersheds by 2050, compared to recent history. If socio-economic growth is unconstrained by global actions to limit greenhouse gas concentrations, water-stressed populations may increase from about 800 million to 1.7 billion in this region.

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