Abstract. Anthropogenic increases in atmospheric greenhouse gas concentrations are the main driver of current and future climate change. The integrated assessment community has quantified anthropogenic emissions for the shared socio-economic pathway (SSP) scenarios, each of which represents a different future socio-economic projection and political environment. Here, we provide the greenhouse gas concentrations for these SSP scenarios – using the reduced-complexity climate–carbon-cycle model MAGICC7.0. We extend historical, observationally based concentration data with SSP concentration projections from 2015 to 2500 for 43 greenhouse gases with monthly and latitudinal resolution. CO2 concentrations by 2100 range from 393 to 1135 ppm for the lowest (SSP1-1.9) and highest (SSP5-8.5) emission scenarios, respectively. We also provide the concentration extensions beyond 2100 based on assumptions regarding the trajectories of fossil fuels and land use change emissions, net negative emissions, and the fraction of non-CO2 emissions. By 2150, CO2 concentrations in the lowest emission scenario are approximately 350 ppm and approximately plateau at that level until 2500, whereas the highest fossil-fuel-driven scenario projects CO2 concentrations of 1737 ppm and reaches concentrations beyond 2000 ppm by 2250. We estimate that the share of CO2 in the total radiative forcing contribution of all considered 43 long-lived greenhouse gases increases from 66 % for the present day to roughly 68 % to 85 % by the time of maximum forcing in the 21st century. For this estimation, we updated simple radiative forcing parameterizations that reflect the Oslo Line-By-Line model results. In comparison to the representative concentration pathways (RCPs), the five main SSPs (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) are more evenly spaced and extend to lower 2100 radiative forcing and temperatures. Performing two pairs of six-member historical ensembles with CESM1.2.2, we estimate the effect on surface air temperatures of applying latitudinally and seasonally resolved GHG concentrations. We find that the ensemble differences in the March–April–May (MAM) season provide a regional warming in higher northern latitudes of up to 0.4 K over the historical period, latitudinally averaged of about 0.1 K, which we estimate to be comparable to the upper bound (∼5 % level) of natural variability. In comparison to the comparatively straight line of the last 2000 years, the greenhouse gas concentrations since the onset of the industrial period and this studies' projections over the next 100 to 500 years unequivocally depict a “hockey-stick” upwards shape. The SSP concentration time series derived in this study provide a harmonized set of input assumptions for long-term climate science analysis; they also provide an indication of the wide set of futures that societal developments and policy implementations can lead to – ranging from multiple degrees of future warming on the one side to approximately 1.5 ∘C warming on the other.
Abstract. Atmospheric greenhouse gas (GHG) concentrations are at unprecedented, record-high levels compared to the last 800 000 years. Those elevated GHG concentrations warm the planet and – partially offset by net cooling effects by aerosols – are largely responsible for the observed warming over the past 150 years. An accurate representation of GHG concentrations is hence important to understand and model recent climate change. So far, community efforts to create composite datasets of GHG concentrations with seasonal and latitudinal information have focused on marine boundary layer conditions and recent trends since the 1980s. Here, we provide consolidated datasets of historical atmospheric concentrations (mole fractions) of 43 GHGs to be used in the Climate Model Intercomparison Project – Phase 6 (CMIP6) experiments. The presented datasets are based on AGAGE and NOAA networks, firn and ice core data, and archived air data, and a large set of published studies. In contrast to previous intercomparisons, the new datasets are latitudinally resolved and include seasonality. We focus on the period 1850–2014 for historical CMIP6 runs, but data are also provided for the last 2000 years. We provide consolidated datasets in various spatiotemporal resolutions for carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), as well as 40 other GHGs, namely 17 ozone-depleting substances, 11 hydrofluorocarbons (HFCs), 9 perfluorocarbons (PFCs), sulfur hexafluoride (SF6), nitrogen trifluoride (NF3) and sulfuryl fluoride (SO2F2). In addition, we provide three equivalence species that aggregate concentrations of GHGs other than CO2, CH4 and N2O, weighted by their radiative forcing efficiencies. For the year 1850, which is used for pre-industrial control runs, we estimate annual global-mean surface concentrations of CO2 at 284.3 ppm, CH4 at 808.2 ppb and N2O at 273.0 ppb. The data are available at https://esgf-node.llnl.gov/search/input4mips/ and http://www.climatecollege.unimelb.edu.au/cmip6. While the minimum CMIP6 recommendation is to use the global- and annual-mean time series, modelling groups can also choose our monthly and latitudinally resolved concentrations, which imply a stronger radiative forcing in the Northern Hemisphere winter (due to the latitudinal gradient and seasonality).
Climate extremes, such as droughts or heat waves, can lead to harvest failures and threaten the livelihoods of agricultural producers and the food security of communities worldwide. Improving our understanding of their impacts on crop yields is crucial to enhance the resilience of the global food system. This study analyses, to our knowledge for the first time, the impacts of climate extremes on yield anomalies of maize, soybeans, rice and spring wheat at the global scale using sub-national yield data and applying a machine-learning algorithm. We find that growing season climate factorsincluding mean climate as well as climate extremes-explain 20%-49% of the variance of yield anomalies (the range describes the differences between crop types), with 18%-43% of the explained variance attributable to climate extremes, depending on crop type. Temperature-related extremes show a stronger association with yield anomalies than precipitation-related factors, while irrigation partly mitigates negative effects of high temperature extremes. We developed a composite indicator to identify hotspot regions that are critical for global production and particularly susceptible to the effects of climate extremes. These regions include North America for maize, spring wheat and soy production, Asia in the case of maize and rice production as well as Europe for spring wheat production. Our study highlights the importance of considering climate extremes for agricultural predictions and adaptation planning and provides an overview of critical regions that are most susceptible to variations in growing season climate and climate extremes.
General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.
Abstract. Atmospheric greenhouse gas concentrations are at unprecedented, record-high levels compared to pre-industrial reconstructions over the last 800,000 years. Those elevated greenhouse gas concentrations warm the planet and together with net cooling effects by aerosols, they are the reason of observed climate change over the past 150 years. An accurate representation of those concentrations is hence important to understand and model recent and future climate change. So far, community efforts to create composite datasets with seasonal and latitudinal information have focused on marine boundary layer conditions and recent trends since 1980s. Here, we provide consolidated data sets of historical atmospheric (volume) mixing ratios of 43 greenhouse gases specifically for the purpose of climate model runs. The presented datasets are based on AGAGE and NOAA networks and a large set of literature studies. In contrast to previous intercomparisons, the new datasets are latitudinally resolved, and include seasonality over the period between year 0 to 2014. We assimilate data for CO2, methane (CH4) and nitrous oxide (N2O), 5 chlorofluorocarbons (CFCs), 3 hydrochlorofluorocarbons (HCFCs), 16 hydrofluorocarbons (HFCs), 3 halons, methyl bromide (CH3Br), 3 perfluorocarbons (PFCs), sulfur hexafluoride (SF6), nitrogen triflouride (NF3) and sulfuryl fluoride (SO2F2). We estimate 1850 annual and global mean surface mixing ratios of CO2 at 284.3 ppmv, CH4 at 808.2 ppbv and N2O at 273.0 ppbv and quantify the seasonal and hemispheric gradients of surface mixing ratios. Compared to earlier intercomparisons, the stronger implied radiative forcing in the northern hemisphere winter (due to the latitudinal gradient and seasonality) may help to improve the skill of climate models to reproduce past climate and thereby reduce uncertainty in future projections.
Background To date, the effects of extreme weather events on nutrient supply within the population have not been quantified. In this study, we investigated micronutrient, macronutrient, and fibre supply changes during 175 extreme weather events within 87 countries in the year that a major extreme weather event occurred, with a targeted focus on low-income settings.Methods We collected data from the International Disasters Database and the Global Expanded Nutrient Supply model for the period 1961-2010, and applied superposed epoch analysis to calculate the percentage change in nutrient supply during the year of an extreme weather event relative to its historical context. We composited globally and by subgroup (EU, landlocked developing countries, least developed countries, low-income food deficit countries, and net food-importing developing countries). Lastly, we reported nutrient supply changes in terms of recommended dietary allowance for children aged 1-3 years.Findings Globally, all micronutrient supplies had a modest negative percentage change during the year of an extreme weather event; of these effects, those that reached an α=0·05 significance level included calcium, folate, thiamin, vitamin B6, and vitamin C, with nutrient supply changes ranging from -0·40 to -1·73% of the average supply. The effect of an extreme weather event was especially magnified among landlocked developing countries and low-income food deficit countries, with significant nutrient supply changes ranging from -1·61 to -7·57% of the average supply. Furthermore, the observed nutrient supply deficits in landlocked developing countries constituted a large percentage (ranging from 1·95 to 39·19%) of what a healthy child's sufficient average dietary intake should be.Interpretation The global effects of extreme weather events on nutrient supply found in this study are modest in isolation; however, in the context of nutrient needs for healthy child development in low-income settings, the effects observed are substantial.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.