Transmission of climate risks across sectors and borders

Systemic climate risks, which result from the potential for cascading impacts through inter-related systems, pose particular challenges to risk assessment, especially when risks are transmitted across sectors and international boundaries. Most impacts of climate variability and change affect regions and jurisdictions in complex ways, and techniques for assessing this transmission of risk are still somewhat limited. Here, we begin to define new approaches to risk assessment that can account for transboundary and trans-sector risk transmission, by presenting: (i) a typology of risk transmission that distinguishes clearly the role of climate versus the role of the social and economic systems that distribute resources; (ii) a review of existing modelling, qualitative and systems-based methods of assessing risk and risk transmission; and (iii) case studies that examine risk transmission in human displacement, food, water and energy security. The case studies show that policies and institutions can attenuate risks significantly through cooperation that can be mutually beneficial to all parties. We conclude with some suggestions for assessment of complex risk transmission mechanisms: use of expert judgement; interactive scenario building; global systems science and big data; innovative use of climate and integrated assessment models; and methods to understand societal responses to climate risk. These approaches aim to inform both research and national-level risk assessment

DynamicSDM: an R package for species geographical distribution and abundance modelling at high spatiotemporal resolution

1. Species distribution models (SDM) are widely applied to understand changing species geographical distribution and abundance patterns. However, existing SDM tools are inherently static and inadequate for modelling species distributions that are driven by dynamic environmental conditions. 2. dynamicSDM provides novel tools that explicitly consider the temporal dimension at key SDM stages, including functions for: (a) Cleaning and filtering species occurrence records by spatial and temporal qualities; (b) Generating pseudo-absence records through space and time; (c) Extracting spatiotemporally buffered explanatory variables; (d) Fitting SDMs whilst accounting for temporal biases and autocorrelation and (e) Projecting intra-and inter-annual geographical distributions and abundances at high spatiotemporal resolution. 3. Package functions have been designed to be: flexible for targeting specific study species; compatible with other SDM tools; and, by utilising Google Earth Engine and Google Drive, to have low computing power and storage needs. We illustrate dynamicSDM functions with an example of a nomadic bird in southern Africa, the red-billed quelea Quelea quelea. 4. As dynamicSDM functions are flexible and easily applied, we suggest that these tools could be readily applied to other taxa and systems globally

Setting the agenda: Climate change adaptation and mitigation for food systems in the developing world

New agricultural development pathways are required to meet climate change adaptation and mitigation needs in the food systems of low-income countries. A research and policy agenda is provided to indicate where innovation and new knowledge are needed. Adaptation requires identifying suitable crop varieties and livestock breeds, as well as building resilient farming and natural resources systems, institutions for famine and crop failure relief, and mechanisms for rapid learning by farmers. Mitigation requires transitioning to ‘low climate impact’ agriculture that reduces emissions while achieving food security, economic well-being and sustainability. Efficient interventions, incentives for large-scale shifts in practices, and monitoring systems are required. Integrated assessments of adaptation and mitigation are needed to better understand the synergies and trade-offs among outcomes

Agriculture, food security and climate change: Outlook for knowledge, tools and action

Agriculture and food security are key sectors for intervention under climate change. Agricultural production is highly vulnerable even to 2C (lowend) predictions for global mean temperatures in 2100, with major implications for rural poverty and for both rural and urban food security. Agriculture also presents untapped opportunities for mitigation, given the large land area under crops and rangeland, and the additional mitigation potential of aquaculture. This paper presents a summary of current scientific knowledge on the impacts of climate change on farming and food systems, and on the implications for adaptation and mitigation. Many of the trends and impacts are highly uncertain at a range of spatial and temporal scales; we need significant advances in predicting how climate variability and change will affect future food security. Despite these uncertainties, it is clear that the magnitude and rate of projected changes will require adaptation. Actions towards adaptation fall into two broad overlapping areas: (1) better management of agricultural risks associated with increasing climate variability and extreme events, for example improved climate information services and safety nets, and (2) accelerated adaptation to progressive climate change over decadal time scales, for example integrated packages of technology, agronomy and policy options for farmers and food systems. Maximization of agriculture’s mitigation potential will require, among others, investments in technological innovation and agricultural intensification linked to increased efficiency of inputs, and creation of incentives and monitoring systems that are inclusive of smallholder farmers. The challenges posed by climate change to agriculture and food security require a holistic and strategic approach to linking knowledge with action. Key elements of this are greater interactions between decision-makers and researchers in all sectors, greater collaboration among climate, agriculture and food security communities, and consideration of interdependencies across whole food systems and landscapes. Food systems faced with climate change need urgent action in spite of uncertainties

Crop modelling: towards locally relevant and climate-informed adaptation

A gap between the potential and practical realisation of adaptation exists: adaptation strategies need to be both climate-informed and locally relevant to be viable. Place-based approaches study local and contemporary dynamics of the agricultural system, whereas climate impact modelling simulates climate-crop interactions across temporal and spatial scales. Crop-climate modelling and place-based research on adaptation were strategically reviewed and analysed to identify areas of commonality, differences, and potential learning opportunities to enhance the relevance of both disciplines through interdisciplinary approaches. Crop-modelling studies have projected a 7–15% mean yield change with adaptation compared to a non-adaptation baseline (Nature Climate Change 4:1–5, 2014). Of the 17 types of adaptation strategy identified in this study as place-based adaptations occurring within Central America, only five were represented in crop-climate modelling literature, and these were as follows: fertiliser, irrigation, change in planting date, change in cultivar and area cultivated. The breath and agency of real-life adaptation compared to its representation in modelling studies is a source of error in climate impact simulations. Conversely, adaptation research that omits assessment of future climate variability and impact does not enable to provide sustainable adaptation strategies to local communities so risk maladaptation. Integrated and participatory methods can identify and reduce these sources of uncertainty, for example, stakeholder’s engagement can identify locally relevant adaptation pathways. We propose a research agenda that uses methodological approaches from both the modelling and place-based approaches to work towards climate-informed locally relevant adaptation

Global wheat production with 1.5 and 2.0°C above pre‐industrial warming

Efforts to limit global warming to below 2°C in relation to the pre‐industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre‐industrial period) on global wheat production and local yield variability. A multi‐crop and multi‐climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by −2.3% to 7.0% under the 1.5°C scenario and −2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980–2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter‐annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impact of warming <2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade

A Gap Analysis Methodology for Collecting Crop Genepools: A Case Study with Phaseolus Beans

Background The wild relatives of crops represent a major source of valuable traits for crop improvement. These resources are threatened by habitat destruction, land use changes, and other factors, requiring their urgent collection and long-term availability for research and breeding from ex situ collections. We propose a method to identify gaps in ex situ collections (i.e. gap analysis) of crop wild relatives as a means to guide efficient and effective collecting activities. Methodology/Principal Findings The methodology prioritizes among taxa based on a combination of sampling, geographic, and environmental gaps. We apply the gap analysis methodology to wild taxa of the Phaseolus genepool. Of 85 taxa, 48 (56.5%) are assigned high priority for collecting due to lack of, or under-representation, in genebanks, 17 taxa are given medium priority for collecting, 15 low priority, and 5 species are assessed as adequately represented in ex situ collections. Gap “hotspots”, representing priority target areas for collecting, are concentrated in central Mexico, although the narrow endemic nature of a suite of priority species adds a number of specific additional regions to spatial collecting priorities. Conclusions/Significance Results of the gap analysis method mostly align very well with expert opinion of gaps in ex situ collections, with only a few exceptions. A more detailed prioritization of taxa and geographic areas for collection can be achieved by including in the analysis predictive threat factors, such as climate change or habitat destruction, or by adding additional prioritization filters, such as the degree of relatedness to cultivated species (i.e. ease of use in crop breeding). Furthermore, results for multiple crop genepools may be overlaid, which would allow a global analysis of gaps in ex situ collections of the world's plant genetic resource

Changes in meltwater chemistry over a 20-year period following a thermal regime switch from polythermal to cold-based glaciation at Austre Broggerbreen, Svalbard

Our long-term study gives a rare insight into meltwater hydrochemistry following the transition of Austre Brøggerbreen from polythermal to cold-based glaciation and its continued retreat. We find that the processes responsible for ion acquisition did not change throughout the period of records but became more productive. Two regimes before and after July/August 2000 were identified from changes in solute concentrations and pH. They resulted from increased chemical weathering occurring in ice-marginal and proglacial environments that have become progressively exposed by glacier retreat. Carbonate carbonation nearly doubled between 2000 and 2010, whilst increases in the weathering of silicate minerals were also marked. In addition, the end of ablation season chemistry was characterized by reactions in long residence time flow paths like those in subglacial environments, in spite of their absence in the watershed. Furthermore, the retreat of the glacier caused the sudden re-routing of meltwaters through its immediate forefield during 2009, which more than doubled crustal ion yields in this particular year and influenced chemical weathering in 2010 regardless of a low water flux. Such a “flush” of crustally derived ions can be meaningful for downstream terrestrial and marine ecosystems. We therefore find that, during glacier retreat, the recently exposed forefield is the most chemically active part of the watershed, making high rates of weathering possible, even when ice losses have caused a switch to cold-based conditions with no delayed subglacial drainage flowpaths. In addition, the drainage system reorganization events result in significant pCO2 depletion in an otherwise high pCO2 system