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The effect that climate change and variability will have on waterborne bacteria is a topic of increasing concern for coastal ecosystems, including the Chesapeake Bay. Surface water temperature trends in the Bay indicate a warming pattern of roughly 0.3-0.4°C per decade over the past 30 years. It is unclear what impact future warming will have on pathogens currently found in the Bay, including Vibrio spp. Using historical environmental data, combined with three different statistical models of Vibrio vulnificus probability, we explore the relationship between environmental change and predicted Vibrio vulnificus presence in the upper Chesapeake Bay. We find that the predicted response of V. vulnificus probability to high temperatures in the Bay differs systematically between models of differing structure. As existing publicly available datasets are inadequate to determine which model structure is most appropriate, the impact of climatic change on the probability of V. vulnificus presence in the Chesapeake Bay remains uncertain. This result points to the challenge of characterizing climate sensitivity of ecological systems in which data are sparse and only statistical models of ecological sensitivity exist.

Ethiopia has become warmer over the past century and human induced climate change will bring further warming over the next century at unprecedented rates. On the average, climate models show a tendency for higher mean annual rainfall and for wetter conditions, in particular during October, November and December, but there is much uncertainty about the future amount, distribution, timing and intensity of rainfall. Ethiopia's low level of economic development, combined with its heavy dependence on agriculture and high population growth rate make the country particularly susceptible to the adverse effects of climate change. Nearly 90% of Ethiopia's population lives in the Highlands, which include the critical Blue Nile (Abay) Highlands--a region that holds special importance due to its role in domestic agricultural production and international water resources. A five year study of climate vulnerability and adaptation strategies in communities of Choke Mountain, located in the center of the Abay Highlands, has informed a proposed framework for enhancing climate resilience in communities across the region. The framework is motivated by the critical need to enhance capacity to cope with climate change and, subsequently, to advance a carbon neutral and climate resilient economy in Ethiopia. The implicit hypothesis in applying a research framework for this effort is that science-based information, generated through improved understanding of impacts and vulnerabilities of local communities, can contribute to enhanced resilience strategies. We view adaptation to climate change in a wider context of changes, including, among others, market conditions, the political-institutional framework, and population dynamics. From a livelihood perspective, culture, historical settings, the diversity of income generation strategies, knowledge, and education are important factors that contribute to adaptive capacities. This paper reviews key findings of the Choke Mountain study, describes the principles of the climate resilience framework, and proposes an implementation strategy for climate resilient development to be applied in the Abay Highlands, with potential expansion to agricultural communities across the region and beyond.

Rationale: Avoiding excess health damages attributable to climate change is a primary motivator for policy interventions to reduce greenhouse gas emissions. However, the health benefits of climate mitigation, as included in the policy assessment process, have been estimated without much input from health experts. Objectives: In accordance with recommendations from the National Academies in a 2017 report on approaches to update the social cost of greenhouse gases (SC-GHG), an expert panel of 26 health researchers and climate economists gathered for a virtual technical workshop in May 2021 to conduct a systematic review and meta-analysis and recommend improvements to the estimation of health impacts in economic-climate models. Methods: Regionally resolved effect estimates of unit increases in temperature on net all-cause mortality risk were generated through random-effects pooling of studies identified through a systematic review. Results: Effect estimates and associated uncertainties varied by global region, but net increases in mortality risk associated with increased average annual temperatures (ranging from 0.1% to 1.1% per 1°C) were estimated for all global regions. Key recommendations for the development and utilization of health damage modules were provided by the expert panel and included the following: not relying on individual methodologies in estimating health damages; incorporating a broader range of cause-specific mortality impacts; improving the climate parameters available in economic models; accounting for socioeconomic trajectories and adaptation factors when estimating health damages; and carefully considering how air pollution impacts should be incorporated in economic-climate models. Conclusions: This work provides an example of how subject-matter experts can work alongside climate economists in making continued improvements to SC-GHG estimates.

In 2023 human populations experienced multiple record‐breaking climate events, with widespread impacts on human health and well‐being. These events include extreme heat domes, drought, severe storms, flooding, and wildfires. Due to inherent lags in the climate system, we can expect such extremes to continue for multiple decades after reaching net zero carbon emissions. Unfortunately, despite these significant current and future impacts, funding for research in climate and health has lagged behind that for other geoscience and biomedical research. While some initial efforts from funding agencies are evident, there is still a significant need to increase the resources available for multidisciplinary research in the face of this issue. As a group of experts at this important intersection, we call for a more concerted effort to encourage interdisciplinary and policy‐relevant investigations into the detrimental health effects of continued climate change. Plain Language Summary: Recent climate events highlight the need for additional funding for interdisciplinary research focused on the impact of climate on human health. Key Points: Climate change is increasingly impacting populations across the globeFunding for interdisciplinary research in climate and health has lagged behind biomedical researchWe call for a more proactive effort to encourage interdisciplinary research into the negative effects of climate change [ABSTRACT FROM AUTHOR]

The Upper Blue Nile river basin (uBN) is vital to three countries' water, food, and energy security (Ethiopia, Sudan, and Egypt). It is also a region with substantial interannual precipitation variability and the potential for significant climate change in coming decades. The El Niño–Southern Oscillation (ENSO) affects both variability and trends in uBN precipitation, with El Niño episodes historically connected with below‐average rainfall. In this context, this study seeks to: (a) examine the representation of ENSO–uBN rainfall teleconnections in simulations from the sixth Coupled Model Intercomparison Project (CMIP6), as compared to the previous generation of models (CMIP5); (b) distinguish between teleconnections during the peak of the rainy season (July–August [JA]) and the agriculturally critical end‐of‐season rains (September–October [SO]); (c) investigate evidence for Tropical Easterly Jet (TEJ) mediation of these teleconnections in CMIP6 versus CMIP5 and for both peak rains and end of the rainy season. Using a subset of high performing models, we find that CMIP6 simulations of uBN precipitation show somewhat less bias than CMIP5 for JA total rainfall, and that correlations with ENSO have become more consistent across models, but that differences between CMIP5 and CMIP6 are modest. CMIP6 simulations, like CMIP5, overestimate SO rainfall and suggest a stronger ENSO association in SO than is indicated by observations. Model representation of the TEJ and its association with both ENSO and uBN precipitation show no systematic change between CMIP5 and CMIP6. The mediating influence of the TEJ appears to be more important in JA than SO. [ABSTRACT FROM AUTHOR]

The Middle East is one of the world's most vulnerable areas to climate change, which has exacerbated environmental, agricultural, water conflict, and public health issues in the region. Here we analyze the latest climate model projections of precipitation and temperature for the very high emissions scenario, SSP5‐8.5, to detect potential future changes in this region. A baseline period (1981–2010) is compared with the middle (2040–2069) and end (2070–2099) of the 21st century. The results, representing the worst‐case scenario, identify the Tigris‐Euphrates headwaters as the hotspot of future compounding effects of climate change in the Middle East. Those effects result from the coincidence of elevated temperature, reduced precipitation, and enhanced interannual variability of precipitation. The hotspot overlays the location of the Southeastern Anatolia Project (in Turkish, Güneydoğu Anadolu Projesi [GAP]) irrigation initiative. In this climate context, risks to GAP viability and downstream water security, and associated potential for water‐related conflicts and migration are considerable and demand a reconsideration of the risk‐benefit assessment of GAP. This need has become more urgent after the recent widespread and deadly climate‐related conflicts and wildfires in summer 2021 across the Middle East that further underlined vulnerability of the region to climate extremes. Plain Language Summary: The Middle East, one of the world's most vulnerable regions to climate change, has experienced a growing number of severe extreme events in recent decades. These events not only have imposed lasting socio‐environmental impacts locally but have contributed to transboundary water issues with implications for regional and global security. Climate model projections show that the adverse effects of such events will likely be exacerbated over downstream countries of the Tigris‐Euphrates basin in coming decades. This conclusion is drawn because concurrent extreme hot and dry conditions and enhanced precipitation variability are projected over the Tigris‐Euphrates headwaters, where the Southeastern Anatolia Project (in Turkish, Güneydoğu Anadolu Projesi [GAP]) is located. As a result, the negative impacts of GAP may outnumber its benefits, and this study attempts to warn decision‐makers of the urgent need to integrate these trends to water resource planning and international negotiations. Key Points: The Tigris‐Euphrates headwaters is identified as the hotspot of future compound effects of climate change in the Middle EastThe adverse impacts of the Southeastern Anatolia Project (in Turkish, Güneydoğu Anadolu Projesi [GAP]) may outnumber its benefits in coming decadesGAP may exacerbate environmental, agricultural, water conflict, and public health issues in the region [ABSTRACT FROM AUTHOR]

Tropical highland environments present substantial challenges for climate projections due to sparse observations, significant local heterogeneity and inconsistent performance of global climate models (GCMs). Moreover, these areas are often densely populated, with agriculture‐based livelihoods sensitive to transient climate extremes not always included in available climate projections. In this context, we present an analysis of observed and projected trends in temperature and precipitation extremes across agroecosystems (AESs) in the northwest Ethiopian Highlands, to provide more relevant information for adaptation. Limited observational networks are supplemented with a satellite‐station hybrid product, and trends are calculated locally and summarized at the adaptation‐relevant unit of the AES. Projections are then presented from GCM realizations with divergent climate projections, and results are interpreted in the context of agricultural climate sensitivities. Trends in temperature extremes (1981–2016) are typically consistent across sites and AES, but with different implications for agricultural activities in the other AES. Trends in temperature extremes from GCM projected data also generally have the same sign as the observed trends. For precipitation extremes, there is greater site‐to‐site variability. Summarized by AES, however, there is a clear tendency towards reduced precipitation, associated with decreases in wet extremes and a tendency towards temporally clustered wet and dry days. Over the retrospective analysis period, neither of the two analysed GCMs captures these trends. Future projections from both GCMs include significant wetting and an increase in precipitation extremes across AES. However, given the lack of agreement between GCMs and observations with respect to trends in recent decades, the reliability of these projections is questionable. The present study is consistent with the "East Africa Paradox" that observations show drying in summer season rainfall while GCMs project wetting. This has an expression in summertime Ethiopian rain that has not received significant attention in previous studies. [ABSTRACT FROM AUTHOR]

Diarrheal disease, still a major cause of childhood illness, is caused by numerous, diverse infectious microorganisms, which are differentially sensitive to environmental conditions. Enteropathogen‐specific impacts of climate remain underexplored. Results from 15 studies that diagnosed enteropathogens in 64,788 stool samples from 20,760 children in 19 countries were combined. Infection status for 10 common enteropathogens—adenovirus, astrovirus, norovirus, rotavirus, sapovirus, Campylobacter, ETEC, Shigella, Cryptosporidium and Giardia—was matched by date with hydrometeorological variables from a global Earth observation dataset—precipitation and runoff volume, humidity, soil moisture, solar radiation, air pressure, temperature, and wind speed. Models were fitted for each pathogen, accounting for lags, nonlinearity, confounders, and threshold effects. Different variables showed complex, non‐linear associations with infection risk varying in magnitude and direction depending on pathogen species. Rotavirus infection decreased markedly following increasing 7‐day average temperatures—a relative risk of 0.76 (95% confidence interval: 0.69–0.85) above 28°C—while ETEC risk increased by almost half, 1.43 (1.36–1.50), in the 20–35°C range. Risk for all pathogens was highest following soil moistures in the upper range. Humidity was associated with increases in bacterial infections and decreases in most viral infections. Several virus species' risk increased following lower‐than‐average rainfall, while rotavirus and ETEC increased with heavier runoff. Temperature, soil moisture, and humidity are particularly influential parameters across all enteropathogens, likely impacting pathogen survival outside the host. Precipitation and runoff have divergent associations with different enteric viruses. These effects may engender shifts in the relative burden of diarrhea‐causing agents as the global climate changes. Plain Language Summary: Diarrheal disease is a big health problem for children. It can be caused by different bugs, which can be caught more easily in certain weather conditions, though not much is understood about this because the climate varies so much from one place to the next. This study combined data from many different countries where diarrhea‐causing bugs were diagnosed in children's stool. Satellites recorded what the weather was like on the day each sample was collected. Rotavirus is easiest to catch in cold weather and when water washes over the ground after rain. Dry weather also makes it and other viruses easy to catch. Bacteria spread best when the air is warm and humid, and the soil moist, though one type of E. coli can also be spread in rainwater. Climate change will make dry places drier, wet places wetter and everywhere warmer. This might lead to more diarrhea caused by bacteria and less by viruses in some places, though places with moist soil might see more of every kind of bug. Key Points: Many pathogens cause diarrhea in children and are affected in different ways by weather conditions like rainfall, temperature, and humidityDiarrhea‐causing bacteria infections increase in warm, humid weather and when soil is moist, though ETEC is sensitive to rainfall extremesHigh humidity decreases prevalence of several viruses, though only rotavirus increases in cold weather and following heavy surface runoff [ABSTRACT FROM AUTHOR]

There is increasing concern that avoiding climate change impacts will require proactive adaptation, particularly for infrastructure systems with long lifespans. However, one challenge in adaptation is the uncertainty surrounding climate change projections generated by general circulation models (GCMs). This uncertainty has been addressed in different ways. For example, some researchers use ensembles of GCMs to generate probabilistic climate change projections, but these projections can be highly sensitive to assumptions about model independence and weighting schemes. Because of these issues, others argue that robustness-based approaches to climate adaptation are more appropriate, since they do not rely on a precise probabilistic representation of uncertainty. In this research, we present a new approach for characterizing climate change risks that leverages robust decision frameworks and probabilistic GCM ensembles. The scenario discovery process is used to search across a multi-dimensional space and identify climate scenarios most associated with system failure, and a Bayesian statistical model informed by GCM projections is then developed to estimate the probability of those scenarios. This provides an important advancement in that it can incorporate decision-relevant climate variables beyond mean temperature and precipitation and account for uncertainty in probabilistic estimates in a straightforward way. We also suggest several advancements building on prior approaches to Bayesian modeling of climate change projections to make them more broadly applicable. We demonstrate the methodology using proposed water resources infrastructure in Lake Tana, Ethiopia, where GCM disagreement on changes in future rainfall presents a major challenge for infrastructure planning. [ABSTRACT FROM AUTHOR]

Background A better understanding of how heat waves affect fatal traffic crashes will be useful to promote awareness of drivers’ vulnerability during an extreme heat event. Objective and Methods We applied a time-stratified case-crossover design to examine associations between heat waves and fatal traffic crashes during May-September of 2001–2011 in the continental United States (US). Heat waves, defined as the daily mean temperature >95% threshold for ≥2 consecutive days, were derived using gridded 12.5 km 2 air temperatures from Phase 2 of the North American Land Data Assimilation System (NLDAS-2). Dates and locations of fatal traffic crash records were acquired from the National Highway Traffic Safety Administration (NHTSA). Results Results show a significant positive association between fatal traffic crashes and heat waves with a 3.4% (95% CI: 0.9, 5.9%) increase in fatal traffic crashes on heat wave days versus non-heat wave days. The association was more positive for 56–65 years old drivers [8.2% (0.3, 16.7%)] and driving on rural roadways [6.1% (2.8, 9.6%)]. Moreover, a positive association was only present when the heat wave days were characterized by no precipitation [10.9% (7.3%, 14.6%)] and medium or high solar radiation [24.6% (19.9%, 29.5%) and 19.9% (15.6%, 24.4%), respectively]. Conclusions These findings are relevant for developing targeted interventions for these driver groups and driving situations to efficiently reduce the negative effects of heat waves on fatal traffic crashes. [ABSTRACT FROM AUTHOR]

Food insecurity is a complex phenomenon with biophysical, climatic, economic, and infrastructure facets. Despite this understanding, there are few stakeholder-based modelling tools that can capture these dynamics and thereby evaluate the direct and indirect impacts that climatic change, economic change, and policy interventions can have on food security. To address this need, we have developed the Food Distributed Extendable COmplementarity (Food-DECO) model. The Food-DECO model represents individual aggregated stakeholders as decision-makers within the agricultural, transportation, and economic systems. In this paper, we demonstrate the model's capabilities by applying it to a food system based on characteristics of Ethiopia, a frequently food-insecure country. Food-DECO produces results that show the effects of seasonality and regional distribution networks on human nutrition while disaggregating those effects by age, gender, and per capita income. We explore the impacts of a regional crop failure and evaluate the possible effectiveness of several commonly proposed food security interventions. The economic integration of agriculture and transportation in Food-DECO enables us to see, counterintuitively, that improving the capacity of the existing food distribution network between regions can negatively impact the nutritional outcomes in the region experiencing crop failure; the increased ability to meet high demand elsewhere leads to an increase in regional exports – even during a food shortage. [ABSTRACT FROM AUTHOR]

The Madden-Julian Oscillation (MJO) is the dominant mode of sub-seasonal climate variability in the global tropics. As such it represents an opportunity for intra-seasonal rainfall prediction and, perhaps, for explaining dynamics that underlie longer term variability and trends. This opportunity is of substantial interest for tropical Africa, where rainfall variability has significant impacts on agriculture, energy, and natural ecosystems. The objective of this review paper is to inventory and assess the state of knowledge of MJO influence on African rainfall. A number of studies have identified statistical links between MJO and sub-seasonal rainfall variability in West, East, and Southern Africa. The proposed mechanisms to explain this influence differ by region and by season, and they often involve multiscale interactions between local precipitation processes and MJO-associated atmospheric dynamics. Dynamically-based forecast systems have some skill in predicting MJO evolution to time horizons of 3–4 weeks, and some can capture teleconnections to Africa. On longer time scales, there is evidence that MJO activity both modulates and is modulated by the El Niño Southern Oscillation and the Indian Ocean Dipole. The implications of these interactions for MJO connections to Africa require further research, as does the potential for trends in MJO behavior and impacts on Africa under global climate change. [ABSTRACT FROM AUTHOR]

Many studies have documented dramatic climatic and environmental changes that have affected Africa over different time scales. These studies often raise questions regarding the spatial extent and regional connectivity of changes inferred from observations and proxies and/or derived from climate models. Objective regionalization offers a tool for addressing these questions. To demonstrate this potential, applications of hierarchical climate regionalizations of Africa using observations and GCM historical simulations and future projections are presented. First, Africa is regionalized based on interannual precipitation variability using Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data for the period 1981-2014. A number of data processing techniques and clustering algorithms are tested to ensure a robust definition of climate regions. These regionalization results highlight the seasonal and even month-to-month specificity of regional climate associations across the continent, emphasizing the need to consider time of year as well as research question when defining a coherent region for climate analysis. CHIRPS regions are then compared to those of five GCMs for the historic period, with a focus on boreal summer. Results show that some GCMs capture the climatic coherence of the Sahel and associated teleconnections in a manner that is similar to observations, while other models break the Sahel into uncorrelated subregions or produce a Sahel-like region of variability that is spatially displaced from observations. Finally, shifts in climate regions under projected twenty-first-century climate change for different GCMs and emissions pathways are examined. A projected change is found in the coherence of the Sahel, in which the western and eastern Sahel become distinct regions with different teleconnections. This pattern is most pronounced in high-emissions scenarios. [ABSTRACT FROM AUTHOR]

Ranking the performance of global climate models (GCMs) is a notoriously difficult exercise. Multi-model comparison studies nearly always show that each model has strengths and weaknesses relative to others, and for many purposes the multi-model ensemble mean delivers better estimates than any individual model. Nevertheless, in regions like East Africa, where there is little consensus between models on the magnitude or sign of 21st century precipitation change, the multi-model ensemble mean approach to climate projection provides little value for adaptation planning. Here, we consider several possible frameworks for model evaluation and ranking, and assess the differences in performance of a subset of models participating in the 5th Coupled Model Intercomparison Project (CMIP5) according to each framework. Our test case is precipitation in the Nile River headwaters regions. We find that there is little consistency in the relative performance of models across frameworks based on amount and seasonality of precipitation, interannual precipitation variability, precipitation teleconnections, and continental scale climate patterns. These analyses offer some guidance on which GCMs are most likely to provide meaningful results for specific applications, but they caution that any effort to select 'best performing' GCMs for the Nile River basin must carefully consider the purposes for which GCMs are being selected. [ABSTRACT FROM AUTHOR]

An estimated 1 billion people suffer from hunger worldwide, and climate change, urbanization, and globalization have the potential to exacerbate this situation. Improved models for predicting food security are needed to understand these impacts and design interventions. However, food insecurity is the result of complex interactions between physical and socio-economic factors that can overwhelm linear regression models. More sophisticated data-mining approaches could provide an effective way to model these relationships and accurately predict food insecure situations. In this paper, we compare multiple regression and data-mining methods in their ability to predict the percent of a country's population that suffers from undernourishment using widely available predictor variables related to socio-economic settings, agricultural production and trade, and climate conditions. Averaging predictions from multiple models results in the lowest predictive error and provides an accurate method to predict undernourishment levels. Partial dependence plots are used to evaluate covariate influence and demonstrate the relationship between food insecurity and climatic and socio-economic variables. By providing insights into these relationships and a mechanism for predicting undernourishment using readily available data, statistical models like those developed here could be a useful tool for those tasked with understanding and addressing food insecurity. [ABSTRACT FROM AUTHOR]

Background: The escalating negative impact of climate change on our environment has the potential to result in significant morbidity of rhinologic diseases. Methods: Evidence based review of examples of rhinologic diseases including allergic and nonallergic rhinitis, chronic rhinosinusitis, and allergic fungal rhinosinusitis was performed. Results: The lower socioeconomic population, including historically oppressed groups, will be disproportionately affected. Conclusions: We need a systematic approach to improve healthcare database infrastructure and funding to promote diverse scientific collaboration to address these healthcare needs. [ABSTRACT FROM AUTHOR]

The effect that climate change and variability will have on waterborne bacteria is a topic of increasing concern for coastal ecosystems, including the Chesapeake Bay. Surface water temperature trends in the Bay indicate a warming pattern of roughly 0.3–0.4°C per decade over the past 30 years. It is unclear what impact future warming will have on pathogens currently found in the Bay, including Vibrio spp. Using historical environmental data, combined with three different statistical models of Vibrio vulnificus probability, we explore the relationship between environmental change and predicted Vibrio vulnificus presence in the upper Chesapeake Bay. We find that the predicted response of V. vulnificus probability to high temperatures in the Bay differs systematically between models of differing structure. As existing publicly available datasets are inadequate to determine which model structure is most appropriate, the impact of climatic change on the probability of V. vulnificus presence in the Chesapeake Bay remains uncertain. This result points to the challenge of characterizing climate sensitivity of ecological systems in which data are sparse and only statistical models of ecological sensitivity exist. [ABSTRACT FROM AUTHOR]

Rainfall in the Sahel region of Africa is prone to large interannual variability, and it has exhibited a recent multidecadal drying trend. The well-documented social impacts of this variability have motivated numerous efforts at seasonal precipitation prediction, many of which employ statistical techniques that forecast Sahelian precipitation as a function of large-scale indices of surface air temperature (SAT) anomalies, sea surface temperature (SST), surface pressure, and other variables. These statistical models have demonstrated some skill, but nearly all have adopted conventional statistical modeling techniques-most commonly generalized linear models-to associate predictor fields with precipitation anomalies. Here, the results of an artificial neural network (ANN) machine-learning algorithm applied to predict summertime (July-September) Sahel rainfall anomalies using indices of springtime (April-June) SST and SAT anomalies for the period 1900-2011 are presented. Principal component analysis was used to remove multicollinearity between predictor variables. Predictive accuracy was assessed using repeated k-fold random holdout and leave-one-out cross-validation methods. It was found that the ANN achieved predictive accuracy superior to that of eight alternative statistical methods tested in this study, and it was also superior to that of previously published predictive models of summertime Sahel precipitation. Analysis of partial dependence plots indicates that ANN skill is derived primarily from the ability to capture nonlinear influences that multiple major modes of large-scale variability have on Sahelian precipitation. These results point to the value of ANN techniques for seasonal precipitation prediction in the Sahel. [ABSTRACT FROM AUTHOR]

Tropical highland regions are experiencing rapid climate change. In these regions the adaptation challenge is complicated by the fact that elevation contrasts and dissected topography produce diverse climatic conditions that are often accompanied by significant ecological and agricultural diversity within a relatively small region. Such is the case for the Choke Mountain watersheds, in the Blue Nile Highlands of Ethiopia. These watersheds extend from tropical alpine environments at over 4000 m elevation to the hot and dry Blue Nile gorge that includes areas below 1000 m elevation, and contain a diversity of slope forms and soil types. This physical diversity and accompanying socio-economic contrasts demand diverse strategies for enhanced climate resilience and adaptation to climate change. To support development of locally appropriate climate resilience strategies across the Blue Nile Highlands, we present here an agroecosystem analysis of Choke Mountain, under the premise that the agroecosystem-the intersection of climatic and physiographic conditions with agricultural practices-is the most appropriate unit for defining adaptation strategies in these primarily subsistence agriculture communities. To this end, we present two approaches to agroecosystem analysis that can be applied to climate resilience studies in the Choke Mountain watersheds and, as appropriate, to other agroecologically diverse regions attempting to design climate adaptation strategies. First, a full agroecoystem analysis was implemented in collaboration with local communities. It identified six distinct agroecosystems that differ systematically in constraints and adaptation potential. This analysis was then paired with an objective landscape classification trained to identify agroecosystems based on climate and physiographic setting alone. It was found that the distribution of Choke Mountain watershed agroecosystems can, to first order, be explained as a function of prevailing climate. This suggests that the conditions that define current agroecosystems are likely to migrate under a changing climate, requiring adaptive management strategies. These agroecosystems show a remarkable degree of differentiation in terms of production orientation and socio-economic characteristics of the farming communities suggesting different options and interventions towards building resilience to climate change. [ABSTRACT FROM AUTHOR]

A simulation experiment was conducted to evaluate the effects of planting time, nitrogen rate, and crop variety choice on potato productivity and to assess the adaptation role of these practices in a changing climate in the northwestern tropical highlands of Ethiopia. The study used agroecosystem (AES) as the lens for spatial analysis. Thirty years of simulations were performed for the baseline (1981–2010), near-term (2011–2040), and mid-century (2041–2070) climate periods using a calibrated and validated SUBSTOR-Potato (DSSAT) model. The results showed that shifting planting time forward and changing the nitrogen application rate had greater productivity benefits than switching varieties in all climate periods and AESs. Late planting and higher nitrogen rates increased tuber yield during the near-term climate period. However, in the mid-century climate period, early planting of both medium and long maturity varieties with higher nitrogen rates showed potential adaptation benefits despite its negative impact on yield in the current climate. This result highlights the distinction between optimal management under current conditions and practices that are truly adaptive to climate change. [Display omitted] • Potato growing tropical highlands are experiencing the effects of climate change. • The adaptive potential of agronomic practices for potato production was assessed. • Combinations of agronomic practices can contribute to adaptation. • Current optimal practices may not be adaptive under future climate conditions. [ABSTRACT FROM AUTHOR]