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Assessing and managing risks posed by climate variability and change is challenging in the tropics, from both a scientific and a socio-economic perspective. While our understanding of the tropical land rainfall variability and its future projection is highly uncertain, most of the vulnerable countries with a limited adaptation capability are within the tropical band. This dissertation combines a process-based physical understanding with observational analysis to characterize the spatio-temporal changes in the tropical land rainfall during a transient El Niño evolution, with an emphasis on the risk management of the dry and wet extremes. The broad objectives are two-fold: 1) To make better sense of the higher uncertainty in the tropical rainfall response to warming and 2) to improve climate risk management strategies in the tropical developing countries. An ENSO teleconnection mechanism, referred to as the tropical tropospheric temperature or TTT mechanism provides a theoretical framework to study the remote tropical land rainfall behavior during a transient El Niño warming. The TTT mechanism postulates that the tropic-wide free tropospheric warming interacts locally with the deep convection to modulate remote tropical climate. During the growth phase, anomalous free tropospheric temperature causes direct and fast atmospheric adjustments leading to tropospheric stability to deep moist convection and a drier response. Subsequently, during mature phase, a recovery of the initial rainfall deficit follows due to indirect and slower adjustments in surface temperature and humidity fields. In chapter 2 and 3 of this dissertation, the changes in the observed tropical land rainfall characteristics and other climate fields conditional on the growth and mature phase of El Niño warming are investigated and the role of dynamical and thermodynamic mechanisms as hypothesized by the TTT mechanism are elucidated. In chapter 4, an El Niño forecast based early action investment strategy is developed to reduce the socio-economic impacts of rainfall extremes at sub-seasonal to inter-annual lead time scales. In the part I (chapter 2), the analysis is conducted at a regional scale over the tropical Africa. Using the TTT mechanism, a physical explanation is provided for the contrasting rainfall response over the Western Sahel and tropical Eastern Africa during an El Niño. The study finds that the Western Sahel’s main rainy season (July-September) is affected by the growth phase of El Niño through (i) a lack of neighboring North Atlantic sea surface warming, (ii) an absence of an atmospheric column water vapor anomaly over the North Atlantic and Western Sahel, and (iii) higher atmospheric vertical stability over the Western Sahel, resulting in the suppression of mean seasonal rainfall as well as number of wet days. In contrast, the short rainy season (October-December) of tropical Eastern Africa is impacted by the mature phase of El Niño through (i) neighboring Indian Ocean sea surface warming, (ii) positive column water vapor anomalies over the Indian Ocean and tropical Eastern Africa, and (iii) higher atmospheric vertical instability over tropical Eastern Africa, leading to an increase in mean seasonal rainfall as well as in the number of wet days. While the modulation of the frequency of wet days and seasonal mean accumulation is statistically significant, daily rainfall intensity (for days with rainfall >1 mm/day), whether mean, median, or extreme, does not show a significant response in either region. Hence, the variability in seasonal mean rainfall that can be attributed to the El Niño–Southern Oscillation phenomenon in both regions is likely due to changes in the frequency of rainfall. These observed changes agree with the predictions of the TTT mechanism. In the part II (chapter 3), a global scale analysis is performed to more generally characterize the spatio-temporal differences in remote tropical land rainfall response to El Niño warming. The principal conclusions are: 1) during the El Niño growth phase relative to the neutral phase, rainfall decreases. A significant decrease in mean accumulation can be attributed to a significant increase in proportion of dry days and decrease in median and extreme intensity. A significant descent anomaly confirms the vertical stabilization and dominance of dynamical processes. 2) During the mature phase relative to the growth phase, rainfall increases, signifying a recovery from the suppression of deep moist convection. A significant increase in mean accumulation is accompanied by a decrease in proportion of dry days and by an increase in median and extreme intensity characteristics. The significant rise in the moisture field corroborates the dominance of thermodynamic processes. These findings are expected from the TTT mechanism and generalizes the findings of part I to the global scale. In the part III (chapter 4), an El Niño forecast based index insurance policy is developed that can be used as an early action investment instrument. The forecast insurance (FI) design framework is illustrated with an application to El Niño associated flood hazard during the January-February-March-April (JFMA) season over Piura region of Peru. In order to determine the economic utility of the system, a simple cost-loss decision model, incorporating the insurance cost, is developed. The main conclusion is that the proposed El Niño forecast insurance policy with the pre-event Niño1.2 index based trigger has significant reliability and substantial utility for a wide range of policy parameters considered. Relative to a no early action strategy, the advantage of the system generally increases with i) shortening in the lead time from 9 to 1 month, ii) increase in El Niño severity level from 10 to 50 year return period and iii) increase in avoidable loss to cost ratio (LCR) ratio from 1 to 1000. These results and the forecast insurance modeling and utility evaluation frameworks have implications for designing optimal contingent financial instruments for disaster risk reduction and climate change adaptation.

This dissertation includes three papers related to climate, resource use, and market applications for sustainable development in developed/developing countries. The first paper describes the development of a game-theoretic economic model investigating the linkages between credit and insurance markets for smallholder farmers, and how a new market tool (weather index insurance) may help to overcome credit constraints in rural financial markets of developing countries. The second paper extends consideration of agricultural climate-risk management to potential skill in predictions of late-season rainfall over monsoonal Indonesia, with the aim of better understanding: (i) forecast skill and risk in this region, and (ii) how forecast information may be better tied with financial market products, such as index insurance, to improve smallholder farmer incentives, decision-making, and livelihoods under climate risk. The third paper, in turn, looks at forest resource management in Canada, and what the application of new carbon constraints and a market for carbon would mean for investment, production decisions, and indicators of sustainable forest management in the Canadian boreal, both in respects to the management of the landscape (i.e. the forest resource), and the development of the forestry sector and forest-based communities.

One out of every three workers on the planet is employed in agriculture. Consequently, major changes to the way that agriculture is practiced will have outsized effects on society. This dissertation focuses on technology and climate change, two key variables that will exert increasing influence on the rural sector and broader patterns of economic development. While the implementation of new technologies to increase crop productivity will be essential in satisfying rising global food demand, shifts in global climate may undermine those productivity gains in terms of both agronomic and labor market output. Chapter 2 exploits a quasi-experimental research design to assess how crop productivity gains resulting from a new technology affect gender wage disparities in agricultural labor markets. Using high-frequency temperature data merged with nationally representative time use data from Indian workers, Chapter 3 estimates a labor supply response function to temperature shocks that informs projected labor market effects under climate change. Chapter 4 demonstrates that a very parsimonious statistical model offers accurate out-of-sample predictions and provides a discussion on modeling weather's role in agriculture and the current state of adaptation research.

To borrow from Van Jones's book on the green economy, we have a “dual problem” on our hands. First, that we continue to damage the environment by our destructive practices; and second, that we have a large group of people that were laid off during the last economic regime, because of the supposed insufficiency of their skillsets. There exists a solution to both these problems, and it comes in the form of green jobs. This thesis was undertaken, in part, because of a lack of clarity in the scholarly literature about the nature of green jobs: which jobs are they? and what do they require in the way of skills? The aim of this paper is to restart the conversation surrounding green jobs by showing evidence of a broad-based economic transformation that is primed for the previously maligned, and their likely level of skills. To accomplish this, I had to adopt an understanding of green jobs that allowed me to see the broad-based transformation, and that understanding was to defer to the firm as the main determinant for what makes a job green. From there, I picked up where the Bureau of Labor Statistics left off, and associated their findings with the economy of the state of New York. And, using a mix of files from the federal and state governments, I pieced together a picture of the green economy—one that would be relevant to those with less formal education. I found, among other things, that the green economy does lean toward occupations that are typically romanticized in the literature (those in production and construction); but, also, that office work is an important entry point into the green economy for those that would not–or could not–seek employment in the aforementioned. With regard to skills, I found that more than any other skill type, jobs suited to this population depend heavily on the proficiency of basic skills. This finding runs contrary to a pervasive idea in the literature that propounds the need for technical skills and training as related to green jobs. This thesis offers a look, through a broad lens, at the occupations that are being affected by greener industry practices, and the skills that are needed to be included in this oncoming economic paradigm.

Climate change is a major environmental threat and likely one of the most important challenges of our time. In particular, climate extremes ���such as heat waves��� can have a significant negative effect on society. Yet, many impacts of climate change are poorly understood and binding international climate change agreements are notoriously hard to reach. This work deals with the economics of climate change in three separate essays. The first one introduces a new methodology to estimate the impacts of climate extremes on public health. The second utilizes this methodology to assess the impacts of several climate change scenarios on Europe. The third explores a way to increase cooperation on climate change mitigation policies through explicit communication of the uncertainty of future climate change impacts. In general, human mortality shows an oscillatory pattern on top of a nonlinear trend. It tends to be highest in winter and lowest in summer. The nonlinear trend follows changes in health policies, economic growth rates, and other institutional factors. The first essays shows that singular spectrum analysis can be used for the estimation of this base rate mortality and thus allows to isolate the impacts of climate extremes on human mortality. This methodology is an improvement over approaches based on fixed effects or classic spectral analysis. It makes it possible to extend climate impact analysis to regions and countries for which there are no detailed data from hospital records as only coarse monthly data on mortality are needed. The danger of climate change lies not necessarily in the shift in average temperatures, but more so the increase in frequency of extreme heat events. Yet, while heat waves become more common, cold spells become less frequent. As both types of extreme temperature events increase human morbidity and mortality, the net effect of this shift is unknown. The second essay finds that a scenario of moderate warming can have a positive net effect on some European countries, creating winners and losers. In contrast ���severe warming as a result of failed climate change mitigation policies��� affects all examined European countries in a negative way. There would be no winners, just losers. As a result of the uncertainty associated with it, climate change poses a different challenge than other social dilemma situations: The negative effects of climate change do not necessarily take place incrementally. While this should be a focal point for policy makers, the costs of climate change tend to be presented within an expected utility framework. Yet, the potential behavioral reactions to this uncertainty are ���so far��� neither explored nor accounted for in game-theoretic models of climate coalition building. The third essay finds that cooperation in a public goods game can be increased when the uncertainty is communicated explicitly. This means that uncertainty should not be hidden behind expected costs and benefits, but rather be acknowledged when the goal is to form a climate change mitigation agreement.

A short outcome document from CCSI’s Conference on “Climate Change and Sustainable Investment in Natural Resources: From Consensus to Action” is available here. This document, which was disseminated at COP22, provides a concise set of takeaways from the eleventh annual Columbia International Investment Conference, which took place on November 2-3, 2016, at Columbia University. The Conference offered a high-level opportunity to discuss how countries can reduce their greenhouse gas emissions in accordance with the Paris Agreement, while also advancing the Sustainable Development Goals, and in particular the important implications for the world’s approach to natural resource investments. It featured leaders from government, the private sector, civil society, and academia, and brought together nearly 400 participants. In the lead-up to the Conference, CCSI also published a Blog Series on the Earth Institute’s State of the Planet Blog.

Variability is inherent to any living system, and adaptation, or changing one's behavior in response to variability, is an important way to reduce or eliminate possible adverse consequences of change. Adaptation is particularly important to consider in the face of contemporary climate change, as individuals and communities may be able to adapt their behavior in response to weather variability and reduce or possibly eliminate predicted adverse impacts. To gain a more mechanistic understanding of which factors may lead to enhanced adaptive capacity of individuals and communities to future change, this dissertation uses a multi-disciplinary and multi-scale approach to broadly examine which social, economic, biophysical, and perceptional factors are associated with agricultural adaptation to current weather variability. The results from this dissertation generally show how adapting agricultural practices, like changing cropping patterns or increasing irrigation, can reduce the vulnerability of farmers to weather variability. Importantly, however, we show that adaptation is not simply about adopting appropriate technical solutions like sowing weather-appropriate crops or irrigating optimally, it is also about the complex set of economic, social, and perceptional factors that influence farmer decision-making and adaptive capacity. A global literature review highlights important biases and gaps in our current knowledge about climate change adaptation research in the agricultural sector. Based on these findings, we offer recommendations for future research that may result in a more process-based understanding of adaptation, including conducting multi-disciplinary studies that simultaneously consider the social, economic, biophysical, and perceptional factors that are associated with adaptation, and understanding how weather variability and change influence well-being to more accurately identify which individuals, households, or communities are best able to adapt. Using these recommendations, we design a case study that examines how farmers alter their cropping strategies in response to monsoon variability in Gujarat, India. Much of our research is focused on India given that over 50% of the nation practices smallholder agriculture and is particularly sensitive to climate variability and change. Through this work, we find that farmers altered their cropping decisions in response to a delayed monsoon onset, by increasing irrigation, switching crop type, and/or delaying crop sowing, and these strategies, particularly increasing irrigation, were adaptive considering yield and profit in the year of our study. These results highlight the importance of considering farmer behavior and decision-making in models that estimate future weather and climate impacts on agricultural production. While household-level surveys allow one to assess individual-level decision-making, they are difficult to implement over large spatial and temporal scales. Thus we develop a remote sensing algorithm that quantifies cropped area of smallholder farms over large spatial and temporal scales using readily-available MODIS imagery. Given the importance of irrigation as an adaptation strategy, we link these cropped area maps with rainfall and irrigation data at the village scale across all of India to assess the relative impact of different types of irrigation (e.g. groundwater versus canal) on winter cropped area and its sensitivity to rainfall variability. Overall, we find that deep well irrigation is both associated with the greatest amount of winter cropped area, and is also the least sensitive to monsoon and winter rainfall variability. However, the relative benefit of deep well irrigation varies across India, with the largest benefits seen in the regions that are facing the greatest levels of groundwater depletion. This work highlights the critical importance of groundwater for agriculture in India, and suggests that future work should identify ways to use groundwater more efficiently, increase the recharge rate of groundwater, or improve the performance of canal irrigation in order to maintain similar levels of production in the face of climate variability and change over the upcoming decades. While this dissertation focuses on agricultural adaptation to weather variability, the methods and implications derived from this dissertation are applicable more broadly to the study of resilience and adaptive capacity of social-ecological systems to global environmental change. In a rapidly changing global system, using a multi-disciplinary, multi-scale, and coupled systems approach similar to the one employed in this dissertation will help better understand and identify possible ways to enhance the ability of societies to adapt to global environmental change.

There are growing risks to economic activity in flood-plain regions due to the increasing severity of climate change-driven storms. In May 2011, heavy rains led to surging flood levels along the entirety of the Mississippi River basin. This eventually required the US Army Corps of Engineering to blast open floodgates in order to save major cities like Baton Rouge and New Orleans from catastrophic damage. However, this was at the cost of sacrificing millions of acres of farmland and sparsely populated parishes all over the southern basin region. Beyond the direct flood damage, business was disrupted while the Mississippi River was unable to be used as a trade route. But these disruptions, known as ‘indirect damage,’ can sometimes have a net positive result for firms that do not sustain much physical damage and can benefit from the ensuing price increases of their products. In the long term, however, increasing defense and recovery measures from flood incidents will be required in such regions in order to maintain economic activity. Overall, sustainable development would require either an avoidance of those risky zones, which is socially and economically impractical due to the large existing populations and infrastructure, or a focus on preventative measures.

The sustainable management of the environment has emerged as a major conceptual and policy challenge. In this thesis, I discuss both general theoretical considerations and aspects of a striking and important case study. In chapters 1-4, I study the dynamics of collective decisions on long-term investments in public goods, in situations in which different stake-holders disagree on the relative valuation of short-term costs and future benefits. The discussion is inspired by climate change economics but is general. Chapters 5-8 discuss aspects of the depletion of groundwater in India, one of the most dramatic resource sustainability crisis in the world, and analyze empirically the implications for irrigation, in terms of sustainability, efficiency and reliability.

Societal needs for climate information on decadal timescales is confirmed in terms of its potential value and relevance as a driver in sector decision-making, but such information is currently lacking. Predictions and observationally based analyses for decadal climate variability and change are needed. In addition, the following issues have been identified as those that must be addressed in order to facilitate effective use of climate information on decadal timescales in the decision-making processes of different socio-economic sectors: building effective partnership systems linking stakeholders, users and decision-making sectors and climate information providers; more research and investment is to translate information of large-scale decadal variations into the regional and local scales required for decisions; maintaining and sustaining the Global Climate Observing System (GCOS), in particular, enhancement of the global ocean observing system; and, ways to assemble, check quality, reprocess and reanalyse datasets relevant to decadal prediction. Ways of securing climate observing systems particularly in least developed regions are urgently needed. Fil: Vera, Carolina Susana. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Barange, M.. Plymouth Marine Laboratory; Reino Unido Fil: Dube, O. P.. University of Botswana; Botsuana Fil: Goddard, L.. Columbia University; Estados Unidos Fil: Griggs, D.. Monash University; Australia Fil: Kobysheva, N.. Voeikov Main Geophysical Observatory; Rusia Fil: Odada, E.. University of Nairobi; Kenia Fil: Parey, S.. Electricite de France; Francia Fil: Polovina, J.. National Oceanic and Atmospheric Administration; Estados Unidos Fil: Poveda, G.. Universidad Nacional de Colombia; Colombia Fil: Seguin, B.. Centre de Recherche de Nantes. Institut National de la Recherche Agronomique; Francia Fil: Trenberth, K.. National Center for Atmospheric Research; Estados Unidos

El Niño–Southern Oscillation (ENSO) phenomenon, a periodic warming of sea surface temperatures in the eastern and central equatorial Pacific, generates a significant proportion of short-term climate variations globally, second only to the seasonal cycle. Global economic losses of tens of billions of dollars are attributed to extremes of ENSO (i.e., El Niño and La Niña), suggesting that these events disproportionately trigger socioeconomic disasters on the global scale. Since global El Niño/La Niña–associated climate impacts were first documented in the 1980s, the prevailing assumption has been that more severe and widespread climate anomalies, and, therefore, greater climate-related socioeconomic losses, should be expected during ENSO extremes. Contrary to expectations, climate anomalies associated with such losses are not greater overall during ENSO extremes than during neutral periods. However, during El Niño and La Niña events climate forecasts are shown to be more accurate. Stronger ENSO events lead to greater predictability of the climate and, potentially, the socioeconomic outcomes. Thus, the prudent use of climate forecasts could mitigate adverse impacts and lead instead to increased beneficial impacts, which could transform years of ENSO extremes into the least costly to life and property.