Climate Change Impacts on Global Agriculture

Based on predicted changes in the magnitude and distribution of global precipitation, temperature and river flow under the IPCC SRES A1B and A2 scenarios, this study assesses the potential impacts of climate change and CO2 fertilization on global agriculture. The analysis uses the new version of the GTAP-W model, which distinguishes between rainfed and irrigated agriculture and implements water as an explicit factor of production for irrigated agriculture. Future climate change is likely to modify regional water endowments and soil moisture. As a consequence, the distribution of harvested land would change, modifying production and international trade patterns. The results suggest that a partial analysis of the main factors through which climate change will affect agricultural productivity lead to different outcomes. Our results show that global food production, welfare and GDP fall in the two time periods and SRES scenarios. Higher food prices are expected. Independently of the SRES scenario, expected losses in welfare are marked in the long term. They are larger under the SRES A2 scenario for the 2020s and under the SRES A1B scenario for the 2050s. The results show that countries are not only influenced by regional climate change, but also by climate-induced changes in competitiveness.Computable General Equilibrium, Climate Change, Agriculture, Water Resources, River Flow

ECOSSE: Estimating Carbon in Organic Soils - Sequestration and Emissions: Final Report

Background Climate change, caused by greenhouse gas ( GHG) emissions, is one of the most serious threats facing our planet, and is of concern at both UK and devolved administration levels. Accurate predictions for the effects of changes in climate and land use on GHG emissions are vital for informing land use policy. Models which are currently used to predict differences in soil carbon (C) and nitrogen (N) caused by these changes, have been derived from those based on mineral soils or deep peat. None of these models is entirely satisfactory for describing what happens to organic soils following land-use change. Reports of Scottish GHG emissions have revealed that approximately 15% of Scotland's total emissions come from land use changes on Scotland's high carbon soils; the figure is much lower for Wales. It is therefore important to reduce the major uncertainty in assessing the carbon store and flux from land use change on organic soils, especially those which are too shallow to be deep peats but still contain a large reserve of C. In order to predict the response of organic soils to external change we need to develop a model that reflects more accurately the conditions of these soils. The development of a model for organic soils will help to provide more accurate values of net change to soil C and N in response to changes in land use and climate and may be used to inform reporting to UKGHG inventories. Whilst a few models have been developed to describe deep peat formation and turnover, none have so far been developed suitable for examining the impacts of land-use and climate change on the types of organic soils often subject to land-use change in Scotland and Wales. Organic soils subject to land-use change are often (but not exclusively) characterised by a shallower organic horizon than deep peats (e.g. organo-mineral soils such as peaty podzols and peaty gleys). The main aim of the model developed in this project was to simulate the impacts of land-use and climate change in these types of soils. The model is, a) be driven by commonly available meteorological data and soil descriptions, b) able to simulate and predict C and N turnover in organic soils, c) able to predict the impacts of land-use change and climate change on C and N stores in organic soils in Scotland and Wales. In addition to developing the model, we have undertaken a number of other modelling exercises, literature searches, desk studies, data base exercises, and experimentation to answer a range of other questions associated with the responses of organic soils in Scotland and Wales to climate and land-use change. Aims of the ECOSSE project The aims of the study were: To develop a new model of C and N dynamics that reflects conditions in organic soils in Scotland and Wales and predicts their likely responses to external factors To identify the extent of soils that can be considered organic in Scotland and Wales and provide an estimate of the carbon contained within them To predict the contribution of CO 2, nitrous oxide and methane emissions from organic soils in Scotland and Wales, and provide advice on how changes in land use and climate will affect the C and N balance In order to fulfil these aims, the project was broken down into modules based on these objectives and the report uses that structure. The first aim is covered by module 2, the second aim by module 1, and the third aim by modules 3 to 8. Many of the modules are inter-linked. Objectives of the ECOSSE project The main objectives of the project were to: Describe the distribution of organic soils in Scotland and Wales and provide an estimate of the C contained in them Develop a model to simulate C and N cycling in organic soils and provide predictions as to how they will respond to land-use, management and climate change using elements of existing peat, mineral and forest soil models Provide predictive statements on the effects of land-use and climate change on organic soils and the relationships to GHG emissions, including CO 2, nitrous oxide and methane. Provide predictions on the effects of land use change and climate change on the release of Dissolved Organic Matter from organic soils Provide estimates of C loss from scenarios of accelerated erosion of organic soils Suggest best options for mitigating C and N loss from organic soils Provide guidelines on the likely effects of changing land-use from grazing or semi-natural vegetation to forestry on C and N in organic soils Use the land-use change data derived from the Countryside Surveys of Scotland and Wales to provide predictive estimates for changes to C and N balance in organic soils over time

Exploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios

Concerns over climate change are motivated in large part because of their impact on human society. Assessing the effect of that uncertainty on specific potential impacts is demanding, since it requires a systematic survey over both climate and impacts models. We provide a comprehensive evaluation of uncertainty in projected crop yields for maize, spring and winter wheat, rice, and soybean, using a suite of nine crop models and up to 45 CMIP5 and 34 CMIP6 climate projections for three different forcing scenarios. To make this task computationally tractable, we use a new set of statistical crop model emulators. We find that climate and crop models contribute about equally to overall uncertainty. While the ranges of yield uncertainties under CMIP5 and CMIP6 projections are similar, median impact in aggregate total caloric production is typically more negative for the CMIP6 projections (+1% to −19%) than for CMIP5 (+5% to −13%). In the first half of the 21st century and for individual crops is the spread across crop models typically wider than that across climate models, but we find distinct differences between crops: globally, wheat and maize uncertainties are dominated by the crop models, but soybean and rice are more sensitive to the climate projections. Climate models with very similar global mean warming can lead to very different aggregate impacts so that climate model uncertainties remain a significant contributor to agricultural impacts uncertainty. These results show the utility of large-ensemble methods that allow comprehensively evaluating factors affecting crop yields or other impacts under climate change. The crop model ensemble used here is unbalanced and pulls the assumption that all projections are equally plausible into question. Better methods for consistent model testing, also at the level of individual processes, will have to be developed and applied by the crop modeling community

Direct soil moisture controls of future global soil carbon changes: An important source of uncertainty

The nature of the climate–carbon cycle feedback depends critically on the response of soil carbon to climate, including changes in moisture. However, soil moisture–carbon feedback responses have not been investigated thoroughly. Uncertainty in the response of soil carbon to soil moisture changes could arise from uncertainty in the relationship between soil moisture and heterotrophic respiration. We used twelve soil moisture–respiration functions (SMRFs) with a soil carbon model (RothC) and data from a coupled climate–carbon cycle general circulation model to investigate the impact of direct heterotrophic respiration dependence on soil moisture on the climate carbon cycle feedback. Global changes in soil moisture acted to oppose temperature‐driven decreases in soil carbon and hence tended to increase soil carbon storage. We found considerable uncertainty in soil carbon changes due to the response of soil respiration to soil moisture. The use of different SMRFs resulted in both large losses and small gains in future global soil carbon stocks, whether considering all climate forcings or only moisture changes. Regionally, the greatest range in soil carbon changes across SMRFs was found where the largest soil carbon changes occurred. Further research is needed to constrain the soil moisture–respiration relationship and thus reduce uncertainty in climate–carbon cycle feedbacks. There may also be considerable uncertainty in the regional responses of soil carbon to soil moisture changes since climate model predictions of regional soil moisture changes are less coherent than temperature changes

Reliability Ensemble Averaging of 21st century projections of terrestrial net primary productivity reduces global and regional uncertainties

Multi-model averaging techniques provide opportunities to extract additional information from large ensembles of simulations. In particular, present-day model skill can be used to evaluate their potential performance in future climate simulations. Multi-model averaging methods have been used extensively in climate and hydrological sciences, but they have not been used to constrain projected plant productivity responses to climate change, which is a major uncertainty in Earth system modelling. Here, we use three global observationally orientated estimates of current net primary productivity (NPP) to perform a reliability ensemble averaging (REA) method using 30 global simulations of the 21st century change in NPP based on the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) <q>business as usual</q> emissions scenario. We find that the three REA methods support an increase in global NPP by the end of the 21st century (2095–2099) compared to 2001–2005, which is 2–3 % stronger than the ensemble ISIMIP mean value of 24.2 Pg C y⁻¹. Using REA also leads to a 45–68 % reduction in the global uncertainty of 21st century NPP projection, which strengthens confidence in the resilience of the CO₂ fertilization effect to climate change. This reduction in uncertainty is especially clear for boreal ecosystems although it may be an artefact due to the lack of representation of nutrient limitations on NPP in most models. Conversely, the large uncertainty that remains on the sign of the response of NPP in semi-arid regions points to the need for better observations and model development in these regions

How can the First ISLSCP Field Experiment contribute to present-day efforts to evaluate water stress in JULESv5.0?

The First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE), Kansas, US, 1987–1989, made important contributions to the understanding of energy and CO2 exchanges between the land surface and the atmosphere, which heavily influenced the development of numerical land-surface modelling. Now, 30 years on, we demonstrate how the wealth of data collected during FIFE and its subsequent in-depth analysis in the literature continue to be a valuable resource for the current generation of land-surface models. To illustrate, we use the FIFE dataset to evaluate the representation of water stress on tallgrass prairie vegetation in the Joint UK Land Environment Simulator (JULES) and highlight areas for future development. We show that, while JULES is able to simulate a decrease in net carbon assimilation and evapotranspiration during a dry spell, the shape of the diurnal cycle is not well captured. Evaluating the model parameters and results against this dataset provides a case study on the assumptions in calibrating “unstressed” vegetation parameters and thresholds for water stress. In particular, the responses to low water availability and high temperatures are calibrated separately. We also illustrate the effect of inherent uncertainties in key observables, such as leaf area index, soil moisture and soil properties. Given these valuable lessons, simulations for this site will be a key addition to a compilation of simulations covering a wide range of vegetation types and climate regimes, which will be used to improve the way that water stress is represented within JULES

Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2.

Future climate change and increasing atmospheric CO2 are expected to cause major changes in vegetation structure and function over large fractions of the global land surface. Seven global vegetation models are used to analyze possible responses to future climate simulated by a range of general circulation models run under all four representative concentration pathway scenarios of changing concentrations of greenhouse gases. All 110 simulations predict an increase in global vegetation carbon to 2100, but with substantial variation between vegetation models. For example, at 4 °C of global land surface warming (510-758 ppm of CO2), vegetation carbon increases by 52-477 Pg C (224 Pg C mean), mainly due to CO2 fertilization of photosynthesis. Simulations agree on large regional increases across much of the boreal forest, western Amazonia, central Africa, western China, and southeast Asia, with reductions across southwestern North America, central South America, southern Mediterranean areas, southwestern Africa, and southwestern Australia. Four vegetation models display discontinuities across 4 °C of warming, indicating global thresholds in the balance of positive and negative influences on productivity and biomass. In contrast to previous global vegetation model studies, we emphasize the importance of uncertainties in projected changes in carbon residence times. We find, when all seven models are considered for one representative concentration pathway × general circulation model combination, such uncertainties explain 30% more variation in modeled vegetation carbon change than responses of net primary productivity alone, increasing to 151% for non-HYBRID4 models. A change in research priorities away from production and toward structural dynamics and demographic processes is recommended.The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7 2007-2013) under Grant 238366. R.B., R.K., R.D., A.W., and P.D.F. were supported by the Joint Department of Energy and Climate Change/Department for Environment, Food and Rural Affairs Met Office Hadley Centre Climate Programme (GA01101). A.I. and K.N. were supported by the Environment Research and Technology Development Fund (S-10) of the Ministry of the Environment, Japan. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for the Coupled Model Intercomparison Project (CMIP), and we thank the climate modeling groups responsible for the GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M models for producing and making available their model output. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. This work has been conducted under the framework of the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP). The ISI-MIP Fast Track project was funded by the German Federal Ministry of Education and Research (BMBF) with project funding Reference 01LS1201A.This is the author accepted manuscript. The final version is available from PNAS via http://dx.doi.org/10.1073/pnas.122247711

Scoping potential routes to UK civil unrest via the food system: Results of a structured expert elicitation

We report the results of a structured expert elicitation to identify the most likely typesof potential food system disruption scenarios for the UK, focusing on routes to civil unrest. Wetake a backcasting approach by defining as an end-point a societal event in which 1 in 2000 peoplehave been injured in the UK, which 40% of experts rated as “Possible (20–50%)”, “More likely thannot (50–80%)” or “Very likely (>80%)” over the coming decade. Over a timeframe of 50 years, thisincreased to 80% of experts. The experts considered two food system scenarios and ranked theirplausibility of contributing to the given societal scenario. For a timescale of 10 years, the majorityidentified a food distribution problem as the most likely. Over a timescale of 50 years, the expertswere more evenly split between the two scenarios, but over half thought the most likely route tocivil unrest would be a lack of total food in the UK. However, the experts stressed that the variouscauses of food system disruption are interconnected and can create cascading risks, highlighting theimportance of a systems approach. We encourage food system stakeholders to use these results intheir risk planning and recommend future work to support prevention, preparedness, response andrecovery planning