The global and regional impacts of climate change under Representative Concentration Pathway forcings and Shared Socioeconomic Pathway socioeconomic scenarios

This paper presents an evaluation of the global and regional consequences of climate change for heat extremes, water resources, river and coastal flooding, droughts, agriculture and energy use. It presents change in hazard and resource base under different rates of climate change (Representative Concentration Pathways: RCP), and socio-economic impacts are estimated for each combination of RCP and Shared Socioeconomic Pathway. Uncertainty in the regional pattern of climate change is characterised by CMIP5 climate model projections. The analysis adopts a novel approach using relationships between level of warming and impact to rapidly estimate impacts under any climate forcing. The projections provided here can be used to inform assessments of the implications of climate change. At the global scale all the consequences of climate change considered here are adverse, with large increases under the highest rates of warming. Under the highest forcing the global average annual chance of a major heatwave increases from 5% now to 97% in 2100, the average proportion of time in drought increases from 7% to 27%, and the average chance of the current 50-year flood increases from 2% to 7%. The socio-economic impacts of these climate changes are determined by socio-economic scenario. There is variability in impact across regions, reflecting variability in projected changes in precipitation and temperature. The range in the estimated impacts can be large, due to uncertainty in future emissions and future socio-economic conditions and scientific uncertainty in how climate changes in response to future emissions. For the temperature-based indicators, the largest source of scientific uncertainty is in the estimated magnitude of equilibrium climate sensitivity, but for the indicators determined by precipitation the largest source is in the estimated spatial and seasonal pattern of changes in precipitation. By 2100 the range across socio-economic scenario is often greater than the range across the forcing levels

Assessing uncertainty and complexity in regional-scale crop model simulations

Crop models are imperfect approximations to real world interactions between biotic and abiotic factors. In some situations, the uncertainties associated with choices in model structure, model inputs and parameters can exceed the spatiotemporal variability of simulated yields, thus limiting predictability. For Indian groundnut, we used the General Large Area Model for annual crops (GLAM) with an existing framework to decompose uncertainty, to first understand how skill changes with added model complexity, and then to determine the relevant uncertainty sources in yield and other prognostic variables (total biomass, leaf area index and harvest index). We developed an ensemble of simulations by perturbing GLAM parameters using two different input meteorology datasets, and two model versions that differ in the complexity with which they account for assimilation. We found that added complexity improved model skill, as measured by changes in the root mean squared error (RMSE), by 5-10% in specific pockets of western, central and southern India, but that 85% of the groundnut growing area either did not show improved skill or showed decreased skill from such added complexity. Thus, adding complexity or using overly complex models at regional or global scales should be exercised with caution. Uncertainty analysis indicated that, in situations where soil and air moisture dynamics are the major determinants of productivity, predictability in yield is high. Where uncertainty for yield is high, the choice of weather input data was found critical for reducing uncertainty. However, for other prognostic variables (including leaf area index, total biomass and the harvest index) parametric uncertainty was generally the most important source, with a contribution of up to 90% in some cases, suggesting that regional-scale data additional to yield to constrain model parameters is needed. Our study provides further evidence that regional-scale studies should explicitly quantify multiple uncertainty sources

Increasing influence of heat stress on French maize yields from the 1960s to the 2030s

Improved crop yield forecasts could enable more effective adaptation to climate variability and change. Here, we explore how to combine historical observations of crop yields and weather with climate model simulations to produce crop yield projections for decision relevant timescales. Firstly, the effects on historical crop yields of improved technology, precipitation and daily maximum temperatures are modelled empirically, accounting for a nonlinear technology trend and interactions between temperature and precipitation, and applied specifically for a case study of maize in France. The relative importance of precipitation variability for maize yields in France has decreased significantly since the 1960s, likely due to increased irrigation. In addition, heat stress is found to be as important for yield as precipitation since around 2000. A significant reduction in maize yield is found for each day with a maximum temperature above 32 °C, in broad agreement with previous estimates. The recent increase in such hot days has likely contributed to the observed yield stagnation. Furthermore, a general method for producing near-term crop yield projections, based on climate model simulations, is developed and utilized. We use projections of future daily maximum temperatures to assess the likely change in yields due to variations in climate. Importantly, we calibrate the climate model projections using observed data to ensure both reliable temperature mean and daily variability characteristics, and demonstrate that these methods work using retrospective predictions. We conclude that, to offset the projected increased daily maximum temperatures over France, improved technology will need to increase base level yields by 12% to be confident about maintaining current levels of yield for the period 2016–2035; the current rate of yield technology increase is not sufficient to meet this target

Comparing the effects of calibration and climate errors on a statistical crop model and a process-based crop model

Understanding the relationship between climate and crop productivity is a key component of projections of future food production, and hence assessments of food security. Climate models and crop yield datasets have errors, but the effects of these errors on regional scale crop models is not well categorized and understood. In this study we compare the effect of synthetic errors in temperature and precipitation observations on the hindcast skill of a process-based crop model and a statistical crop model. We find that errors in temperature data have a significantly stronger influence on both models than errors in precipitation. We also identify key differences in the responses of these models to different types of input data error. Statistical and process-based model responses differ depending on whether synthetic errors are overestimates or underestimates. We also investigate the impact of crop yield calibration data on model skill for both models, using datasets of yield at three different spatial scales. Whilst important for both models, the statistical model is more strongly influenced by crop yield scale than the process-based crop model. However, our results question the value of high resolution yield data for improving the skill of crop models; we find a focus on accuracy to be more likely to be valuable. For both crop models, and for all three spatial scales of yield calibration data, we found that model skill is greatest where growing area is above 10-15 %. Thus information on area harvested would appear to be a priority for data collection efforts. These results are important for three reasons. First, understanding how different crop models rely on different characteristics of temperature, precipitation and crop yield data allows us to match the model type to the available data. Second, we can prioritize where improvements in climate and crop yield data should be directed. Third, as better climate and crop yield data becomes available, we can predict how crop model skill should improve

Effects of olive oil and tomato lycopene combination on serum lycopene, lipid profile, and lipid oxidation

Objective: We compared the effect of two diets (a diet high in olive oil and a diet high in carbohydrate and low in olive oil) with high lycopene content and other controlled carotenoids on serum lycopene, lipids, and in vitro oxidation. Methods: This was a randomized crossover dietary intervention study carried out in Launceston, Tasmania, Australia in healthy free-living individuals. Twenty-one healthy subjects who were 22 to 70 y old were recruited by advertisements in newspapers and a university newsletter. A randomized dietary intervention was done with two diets of 10 d each. One diet was high in olive oil and the other was high in carbohydrate and low in olive oil; the two diets contained the same basic foods and a controlled carotenoid content high in lycopene. Results: Significant increases (P < 0.001) in serum lycopene concentration on both diets were to similar final concentrations. Higher serum high-density lipoprotein cholesterol (P < 0.01), lower ratio of total cholesterol to high-density lipoprotein (P < 0.01), and lower triacylglycerols (P < 0.05) occurred after the olive oil diet compared with the high-carbohydrate, low-fat diet. There was no difference in total antioxidant status and susceptibility of serum lipids to oxidation. Conclusions: Serum lycopene level changes with dietary lycopene intake irrespective of the amount of fat intake. However, a diet high in olive oil and rich in lycopene may decrease the risk of coronary heart disease by improving the serum lipid profile compared with a high-carbohydrate, low-fat, lycopene-rich diet

Influences of increasing temperature on Indian wheat: quantifying limits to predictability

As climate changes, temperatures will play an increasing role in determining crop yield. Both climate model error and lack of constrained physiological thresholds limit the predictability of yield. We used a perturbed-parameter climate model ensemble with two methods of bias-correction as input to a regional-scale wheat simulation model over India to examine future yields. This model configuration accounted for uncertainty in climate, planting date, optimization, temperature-induced changes in development rate and reproduction. It also accounts for lethal temperatures, which have been somewhat neglected to date. Using uncertainty decomposition, we found that fractional uncertainty due to temperature-driven processes in the crop model was on average larger than climate model uncertainty (0.56 versus 0.44), and that the crop model uncertainty is dominated by crop development. Simulations with the raw compared to the bias-corrected climate data did not agree on the impact on future wheat yield, nor its geographical distribution. However the method of bias-correction was not an important source of uncertainty. We conclude that bias-correction of climate model data and improved constraints on especially crop development are critical for robust impact predictions

Linearization of homogeneous, nearly-isotropic cosmological models

Homogeneous, nearly-isotropic Bianchi cosmological models are considered. Their time evolution is expressed as a complete set of non-interacting linear modes on top of a Friedmann-Robertson-Walker background model. This connects the extensive literature on Bianchi models with the more commonly-adopted perturbation approach to general relativistic cosmological evolution. Expressions for the relevant metric perturbations in familiar coordinate systems can be extracted straightforwardly. Amongst other possibilities, this allows for future analysis of anisotropic matter sources in a more general geometry than usually attempted. We discuss the geometric mechanisms by which maximal symmetry is broken in the context of these models, shedding light on the origin of different Bianchi types. When all relevant length-scales are super-horizon, the simplest Bianchi I models emerge (in which anisotropic quantities appear parallel transported). Finally we highlight the existence of arbitrarily long near-isotropic epochs in models of general Bianchi type (including those without an exact isotropic limit).Comment: 31 pages, 2 figures. Submitted to CQ

Emergence of robust precipitation changes across crop production areas in the 21st century

A warming climate will affect regional precipitation and hence food supply. However, only a few regions around the world are currently undergoing precipitation changes that can be attributed to climate change. Knowing when such changes are projected to emerge outside natural variability—the time of emergence (TOE)—is critical for taking effective adaptation measures. Using ensemble climate projections, we determine the TOE of regional precipitation changes globally and in particular for the growing areas of four major crops. We find relatively early (<2040) emergence of precipitation trends for all four crops. Reduced (increased) precipitation trends encompass 1–14% (3–31%) of global production of maize, wheat, rice, and soybean. Comparing results for RCP8.5 and RCP2.6 clearly shows that emissions compatible with the Paris Agreement result in far less cropped land experiencing novel climates. However, the existence of a TOE, even under the lowest emission scenario, and a small probability for early emergence emphasize the urgent need for adaptation measures. We also show how both the urgency of adaptation and the extent of mitigation vary geographically

Climate-driven spatial mismatches between British orchards and their pollinators: increased risks of pollination deficits

Understanding how climate change can affect crop-pollinator systems helps predict potential geographical mismatches between a crop and its pollinators, and therefore identify areas vulnerable to loss of pollination services. We examined the distribution of orchard species (apples, pears, plums and other top fruits) and their pollinators in Great Britain, for present and future climatic conditions projected for 2050 under the SRES A1B Emissions Scenario. We used a relative index of pollinator availability as a proxy for pollination service. At present there is a large spatial overlap between orchards and their pollinators, but predictions for 2050 revealed that the most suitable areas for orchards corresponded to low pollinator availability. However, we found that pollinator availability may persist in areas currently used for fruit production, but which are predicted to provide sub-optimal environmental suitability for orchard species in the future. Our results may be used to identify mitigation options to safeguard orchard production against the risk of pollination failure in Great Britain over the next 50 years; for instance choosing fruit tree varieties that are adapted to future climatic conditions, or boosting wild pollinators through improving landscape resources. Our approach can be readily applied to other regions and crop systems, and expanded to include different climatic scenarios

Improving the use of crop models for risk assessment and climate change adaptation

Crop models are used for an increasingly broad range of applications, with a commensurate proliferation of methods. Careful framing of research questions and development of targeted and appropriate methods are therefore increasingly important. In conjunction with the other authors in this special issue, we have developed a set of criteria for use of crop models in assessments of impacts, adaptation and risk. Our analysis drew on the other papers in this special issue, and on our experience in the UK Climate Change Risk Assessment 2017 and the MACSUR, AgMIP and ISIMIP projects. The criteria were used to assess how improvements could be made to the framing of climate change risks, and to outline the good practice and new developments that are needed to improve risk assessment. Key areas of good practice include: i. the development, running and documentation of crop models, with attention given to issues of spatial scale and complexity; ii. the methods used to form crop-climate ensembles, which can be based on model skill and/or spread; iii. the methods used to assess adaptation, which need broadening to account for technological development and to reflect the full range options available. The analysis highlights the limitations of focussing only on projections of future impacts and adaptation options using pre-determined time slices. Whilst this long-standing approach may remain an essential component of risk assessments, we identify three further key components: 1. Working with stakeholders to identify the timing of risks. What are the key vulnerabilities of food systems and what does crop-climate modelling tell us about when those systems are at risk? 2. Use of multiple methods that critically assess the use of climate model output and avoid any presumption that analyses should begin and end with gridded output. 3. Increasing transparency and inter-comparability in risk assessments. Whilst studies frequently produce ranges that quantify uncertainty, the assumptions underlying these ranges are not always clear. We suggest that the contingency of results upon assumptions is made explicit via a common uncertainty reporting format; and/or that studies are assessed against a set of criteria, such as those presented in this paper