Food security outcomes under a changing climate: impacts of mitigation and adaptation on vulnerability to food insecurity

Climate change is a potential threat to achieving food security, particularly in the most food insecure regions. However, interpreting climate change projections to better understand the potential impacts of a changing climate on food security outcomes is challenging. This paper addresses this challenge through presenting a framework that enables rapid country-level assessment of vulnerability to food insecurity under a range of climate change and adaptation investment scenarios. The results show that vulnerability to food insecurity is projected to increase under all emissions scenarios, and the geographic distribution of vulnerability is similar to that of the present-day; parts of sub-Saharan Africa and South Asia are most severely affected. High levels of adaptation act to off-set these increases; however, only the scenario with the highest level of mitigation combined with high levels of adaptation shows improvements in vulnerability compared to the present-day. The results highlight the dual requirement for mitigation and adaptation to avoid the worst impacts of climate change and to make gains in tackling food insecurity. The approach is an update to the existing Hunger and Climate Vulnerability Index methodology to enable future projections, and the framework presented allows rapid updates to the results as and when new information becomes available, such as updated country-level yield data or climate model output. This approach provides a framework for assessing policy-relevant human food security outcomes for use in long-term climate change and food security planning; the results have been made available on an interactive website for policymakers ( www.metoffice.gov.uk/food-insecurity-index )

A rapid application emissions-to-impacts tool for scenario assessment: Probabilistic Regional Impacts from Model patterns and Emissions (PRIME)

Climate policies evolve quickly, and new scenarios designed around these policies are used to illustrate how they impact global mean temperatures using simple climate models (or climate emulators). Simple climate models are extremely efficient although limited to showing only the global picture. Within the Intergovernmental Panel on Climate Change (IPCC) framework, there is a need to understand the regional impacts of scenarios that include the most recent science and policy decisions quickly to support government in negotiations. To address this, we present PRIME (Probabilistic Regional Impacts from Model patterns and Emissions), a new flexible probabilistic framework which aims to provide an efficient means to run new scenarios without the significant overheads of larger more complex Earth system models (ESMs). PRIME provides the capability to include the most recent models, science and scenarios to run ensemble simulations on multi-centennial timescales and include analysis of many variables that are relevant and important for impacts assessments. We use a simple climate model to provide the global temperatures to scale the patterns from a large number of the CMIP6 ESMs. These provide the inputs to a weather generator and a land-surface model, which generates an estimate of the land-surface impacts from the emissions scenarios. Here we test PRIME using known scenarios in the form of the Shared Socioeconomic Pathways (SSPs) to demonstrate that PRIME reproduces the climate response to a range of emissions scenarios, as shown in the IPCC reports. We show results for a range of scenarios including the SSP5-8.5 high emissions scenario, which was used to define the patterns; SSP1-2.6, a mitigation scenario with low emissions and SSP5-3.4-OS, an overshoot scenario. PRIME correctly represents the climate response for these known scenarios, which gives us confidence that PRIME will be useful for rapidly providing probabilistic spatially resolved information for novel climate scenarios; substantially reducing the time between the scenarios being released and being used in impacts assessments

Improved representation of plant functional types and physiology in the Joint UK Land Environment Simulator (JULES v4.2) using plant trait information

Dynamic global vegetation models are used to predict the response of vegetation to climate change. They are essential for planning ecosystem management, understanding carbon cycle–climate feedbacks, and evaluating the potential impacts of climate change on global ecosystems. JULES (the Joint UK Land Environment Simulator) represents terrestrial processes in the UK Hadley Centre family of models and in the first generation UK Earth System Model. Previously, JULES represented five plant functional types (PFTs): broadleaf trees, needle-leaf trees, C3 and C4 grasses, and shrubs. This study addresses three developments in JULES. First, trees and shrubs were split into deciduous and evergreen PFTs to better represent the range of leaf life spans and metabolic capacities that exists in nature. Second, we distinguished between temperate and tropical broadleaf evergreen trees. These first two changes result in a new set of nine PFTs: tropical and temperate broadleaf evergreen trees, broadleaf deciduous trees, needle-leaf evergreen and deciduous trees, C3 and C4 grasses, and evergreen and deciduous shrubs. Third, using data from the TRY database, we updated the relationship between leaf nitrogen and the maximum rate of carboxylation of Rubisco (Vcmax), and updated the leaf turnover and growth rates to include a trade-off between leaf life span and leaf mass per unit area. Overall, the simulation of gross and net primary productivity (GPP and NPP, respectively) is improved with the nine PFTs when compared to FLUXNET sites, a global GPP data set based on FLUXNET, and MODIS NPP. Compared to the standard five PFTs, the new nine PFTs simulate a higher GPP and NPP, with the exception of C3 grasses in cold environments and C4 grasses that were previously over-productive. On a biome scale, GPP is improved for all eight biomes evaluated and NPP is improved for most biomes – the exceptions being the tropical forests, savannahs, and extratropical mixed forests where simulated NPP is too high. With the new PFTs, the global present-day GPP and NPP are 128 and 62 Pg C year−1, respectively. We conclude that the inclusion of trait-based data and the evergreen/deciduous distinction has substantially improved productivity fluxes in JULES, in particular the representation of GPP. These developments increase the realism of JULES, enabling higher confidence in simulations of vegetation dynamics and carbon storage

Global wetland contribution to 2000-2012 atmospheric methane growth rate dynamics

Increasing atmospheric methane (CH4) concentrations have contributed to approximately 20% of anthropogenic climate change. Despite the importance of CH4 as a greenhouse gas, its atmospheric growth rate and dynamics over the past two decades, which include a stabilization period (1999–2006), followed by renewed growth starting in 2007, remain poorly understood. We provide an updated estimate of CH4 emissions from wetlands, the largest natural global CH4 source, for 2000–2012 using an ensemble of biogeochemical models constrained with remote sensing surface inundation and inventory-based wetland area data. Between 2000–2012, boreal wetland CH4 emissions increased by 1.2 Tg yr−1 (−0.2–3.5 Tg yr−1), tropical emissions decreased by 0.9 Tg yr−1 (−3.2−1.1 Tg yr−1), yet globally, emissions remained unchanged at 184 ± 22 Tg yr−1. Changing air temperature was responsible for increasing high-latitude emissions whereas declines in low-latitude wetland area decreased tropical emissions; both dynamics are consistent with features of predicted centennial-scale climate change impacts on wetland CH4 emissions. Despite uncertainties in wetland area mapping, our study shows that global wetland CH4 emissions have not contributed significantly to the period of renewed atmospheric CH4 growth, and is consistent with findings from studies that indicate some combination of increasing fossil fuel and agriculture-related CH4 emissions, and a decrease in the atmospheric oxidative sink

Representation of phosphorus cycle in Joint UK Land Environment Simulator (vn5.5_JULES-CNP)

Most land surface models (LSMs), the land components of Earth system models (ESMs), include representation of N limitation on ecosystem productivity. However only few of these models have incorporated phosphorus (P) cycling. In tropical ecosystems, this is likely to be particularly important as N tends to be abundant but the availability of rock-derived elements, such as P, can be very low. Thus, without a representation of P cycling, tropical forest response in areas such as Amazonia to rising atmospheric CO2 conditions remains highly uncertain. In this study, we introduced P dynamics and its interactions with the N and carbon (C) cycles into the Joint UK Land Environment Simulator (JULES). The new model (JULES-CNP) includes the representation of P stocks in vegetation and soil pools, as well as key processes controlling fluxes between these pools. We evaluate JULES-CNP at the Amazon nutrient fertilization experiment (AFEX), a low fertility site, representative of about 60 % of Amazon soils. We apply the model under ambient CO2 and elevated CO2. The model is able to reproduce the observed plant and soil P pools and fluxes under ambient CO2. We estimate P to limit net primary productivity (NPP) by 24 % under current CO2 and by 46 % under elevated CO2. Under elevated CO2, biomass in simulations accounting for CNP increase by 10 % relative to at contemporary CO2, although it is 5 % lower compared with CN and C-only simulations. Our results highlight the potential for high P limitation and therefore lower CO2 fertilization capacity in the Amazon forest with low fertility soils

Variability and quasi-decadal changes in the methane budget overthe period 2000–2012

Following the recent Global Carbon Project (GCP) synthesis of the decadal methane (CH4/ budget over 2000– 2012 (Saunois et al., 2016), we analyse here the same dataset with a focus on quasi-decadal and inter-annual variability in CH4 emissions. The GCP dataset integrates results from topdown studies (exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models (including process-based models for estimating land surface emissions and atmospheric chemistry), inventories of anthropogenic emissions, and data-driven approaches.The annual global methane emissions from top-down studies, which by construction match the observed methane growth rate within their uncertainties, all show an increase in total methane emissions over the period 2000–2012, but this increase is not linear over the 13 years. Despite differences between individual studies, the mean emission anomaly of the top-down ensemble shows no significant trend in total methane emissions over the period 2000–2006, during the plateau of atmospheric methane mole fractions, and also over the period 2008–2012, during the renewed atmospheric methane increase. However, the top-down ensemble mean produces an emission shift between 2006 and 2008, leading to 22 [16–32] Tg CH4 yr1 higher methane emissions over the period 2008–2012 compared to 2002–2006. This emission increase mostly originated from the tropics, with a smaller contribution from mid-latitudes and no significant change from boreal regions. The regional contributions remain uncertain in top-down studies. Tropical South America and South and East Asia seem to contribute the most to the emission increase in the tropics. However, these two regions have only limited atmospheric measurements and remain therefore poorly constrained. The sectorial partitioning of this emission increase between the periods 2002–2006 and 2008–2012 differs from one atmospheric inversion study to another. However, all topdown studies suggest smaller changes in fossil fuel emissions (from oil, gas, and coal industries) compared to the mean of the bottom-up inventories included in this study. This difference is partly driven by a smaller emission change in China from the top-down studies compared to the estimate in the Emission Database for Global Atmospheric Research (EDGARv4.2) inventory, which should be revised to smaller values in a near future. We apply isotopic signatures to the emission changes estimated for individual studies based on five emission sectors and find that for six individual top-down studies (out of eight) the average isotopic signature of the emission changes is not consistent with the observed change in atmospheric 13CH4. However, the partitioning in emission change derived from the ensemble mean is consistent with this isotopic constraint. At the global scale, the top-down ensemble mean suggests that the dominant contribution to the resumed atmospheric CH4 growth after 2006 comes from microbial sources (more from agriculture and waste sectors than from natural wetlands), with an uncertain but smaller contribution from fossil CH4 emissions. In addition, a decrease in biomass burning emissions (in agreement with the biomass burning emission databases) makes the balance of sources consistent with atmospheric 13CH4 observations. In most of the top-down studies included here, OH concentrations are considered constant over the years (seasonal variations but without any inter-annual variability). As a result, the methane loss (in particular through OH oxidation) varies mainly through the change in methane concentrations and not its oxidants. For these reasons, changes in the methane loss could not be properly investigated in this study, although it may play a significant role in the recent atmospheric methane changes as briefly discussed at the end of the paper.Published11135–111616A. Geochimica per l'ambienteJCR Journa

Effective radiative forcing and adjustments in CMIP6 models

The effective radiative forcing, which includes the instantaneous forcing plus adjustments from the atmosphere and surface, has emerged as the key metric of evaluating human and natural influence on the climate. We evaluate effective radiative forcing and adjustments in 17 contemporary climate models that are participating in the Coupled Model Intercomparison Project (CMIP6) and have contributed to the Radiative Forcing Model Intercomparison Project (RFMIP). Present-day (2014) global-mean anthropogenic forcing relative to pre-industrial (1850) levels from climate models stands at 2.00 (&plusmn;0.23) W&thinsp;m&minus;2, comprised of 1.81 (&plusmn;0.09) W&thinsp;m&minus;2 from CO2, 1.08 (&plusmn; 0.21) W&thinsp;m&minus;2 from other well-mixed greenhouse gases, &minus;1.01 (&plusmn; 0.23) W&thinsp;m&minus;2 from aerosols and &minus;0.09 (&plusmn;0.13) W&thinsp;m&minus;2 from land use change. Quoted uncertainties are 1&nbsp;standard deviation across model best estimates, and 90&thinsp;% confidence in the reported forcings, due to internal variability, is typically within 0.1 W&thinsp;m&minus;2. The majority of the remaining 0.21&thinsp;W&thinsp;m&minus;2 is likely to be from ozone. In most cases, the largest contributors to the spread in effective radiative forcing (ERF) is from the instantaneous radiative forcing (IRF) and from cloud responses, particularly aerosol&ndash;cloud interactions to aerosol forcing. As determined in previous studies, cancellation of tropospheric and surface adjustments means that the stratospherically adjusted radiative forcing is approximately equal to ERF for greenhouse gas forcing but not for aerosols, and consequentially, not for the anthropogenic total. The spread of aerosol forcing ranges from &minus;0.63 to &minus;1.37&thinsp;W&thinsp;m&minus;2, exhibiting a less negative mean and narrower range compared to 10 CMIP5 models. The spread in 4&times;CO2 forcing has also narrowed in CMIP6 compared to 13 CMIP5 models. Aerosol forcing is uncorrelated with climate sensitivity. Therefore, there is no evidence to suggest that the increasing spread in climate sensitivity in CMIP6 models, particularly related to high-sensitivity models, is a consequence of a stronger negative present-day aerosol forcing and little evidence that modelling groups are systematically tuning climate sensitivity or aerosol forcing to recreate observed historical warming. </div

UKESM1: description and evaluation of the UK Earth System Model

We document the development of the first version of the United Kingdom Earth System Model UKESM1. The model represents a major advance on its predecessor HadGEM2‐ES, with enhancements to all component models and new feedback mechanisms. These include: a new core physical model with a well‐resolved stratosphere; terrestrial biogeochemistry with coupled carbon and nitrogen cycles and enhanced land management; tropospheric‐stratospheric chemistry allowing the holistic simulation of radiative forcing from ozone, methane and nitrous oxide; two‐moment, five‐species, modal aerosol; and ocean biogeochemistry with two‐way coupling to the carbon cycle and atmospheric aerosols. The complexity of coupling between the ocean, land and atmosphere physical climate and biogeochemical cycles in UKESM1 is unprecedented for an Earth system model. We describe in detail the process by which the coupled model was developed and tuned to achieve acceptable performance in key physical and Earth system quantities, and discuss the challenges involved in mitigating biases in a model with complex connections between its components. Overall the model performs well, with a stable pre‐industrial state, and good agreement with observations in the latter period of its historical simulations. However, global mean surface temperature exhibits stronger‐than‐observed cooling from 1950 to 1970, followed by rapid warming from 1980 to 2014. Metrics from idealised simulations show a high climate sensitivity relative to previous generations of models: equilibrium climate sensitivity (ECS) is 5.4 K, transient climate response (TCR) ranges from 2.68 K to 2.85 K, and transient climate response to cumulative emissions (TCRE) is 2.49 K/TtC to 2.66 K/TtC

Coral bleaching under unconventional scenarios of climate warming and ocean acidification

Elevated sea surface temperatures have been shown to cause mass coral bleaching. Widespread bleaching, affecting >90% of global coral reefs and causing coral degradation, has been projected to occur by 2050 under all climate forcing pathways adopted by the IPCC for use within the Fifth Assessment Report. These pathways include an extremely ambitious pathway aimed to limit global mean temperature rise to 2 °C (ref.; Representative Concentration Pathway 2.6 - RCP2.6), which assumes full participation in emissions reductions by all countries, and even the possibility of negative emissions. The conclusions drawn from this body of work, which applied widely used algorithms to estimate coral bleaching, are that we must either accept that the loss of a large percentage of the world' s coral reefs is inevitable, or consider technological solutions to buy those reefs time until atmospheric CO 2 concentrations can be reduced. Here we analyse the potential for geoengineering, through stratospheric aerosol-based solar radiation management (SRM), to reduce the extent of global coral bleaching relative to ambitious climate mitigation. Exploring the common criticism of geoengineering - that ocean acidification and its impacts will continue unabated - we focus on the sensitivity of results to the aragonite saturation state dependence of bleaching. We do not, however, address the additional detrimental impacts of ocean acidification on processes such as coral calcification that will further determine the benefit to corals of any SRM-based scenario. Despite the sensitivity of thermal bleaching thresholds to ocean acidification being uncertain, stabilizing radiative forcing at 2020 levels through SRM reduces the risk of global bleaching relative to RCP2.6 under all acidification-bleaching relationships analysed