School of Geography, Earth and Atmospheric Sciences - Research Publications

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    Dynamic modelling shows substantial contribution of ecosystem restoration to climate change mitigation
    Littleton, EW ; Dooley, K ; Webb, G ; Harper, AB ; Powell, T ; Nicholls, Z ; Meinshausen, M ; Lenton, TM (IOP Publishing Ltd, 2021-12-01)
    Limiting global warming to a 1.5°C temperature rise requires drastic emissions reductions and removal of carbon-dioxide from the atmosphere. Most modelled pathways for 1.5°C assume substantial removals in the form of biomass energy with carbon capture and storage, which brings with it increasing risks to biodiversity and food security via extensive land-use change. Recently, multiple efforts to describe and quantify potential removals via ecosystem-based approaches have gained traction in the climate policy discourse. However, these options have yet to be evaluated in a systematic and scientifically robust way. We provide spatially explicit estimates of ecosystem restoration potential quantified with a Dynamic Global Vegetation Model. Simulations covering forest restoration, reforestation, reduced harvest, agroforestry and silvopasture were combined and found to sequester an additional 93 Gt C by 2100, reducing mean global temperature increase by ~0.12°C (5%–95% range 0.06°C–0.21°C) relative to a baseline mitigation pathway. Ultimately, pathways to achieving the 1.5°C goal garner broader public support when they include land management options that can bring about multiple benefits, including ecosystem restoration, biodiversity protection, and resilient agricultural practices.
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    Historical greenhouse gas concentrations for climate modelling (CMIP6)
    Meinshausen, M ; Vogel, E ; Nauels, A ; Lorbacher, K ; Meinshausen, N ; Etheridge, DM ; Fraser, PJ ; Montzka, SA ; Rayner, PJ ; Trudinger, CM ; Krummel, PB ; Beyerle, U ; Canadell, JG ; Daniel, JS ; Enting, IG ; Law, RM ; Lunder, CR ; O'Doherty, S ; Prinn, RG ; Reimann, S ; Rubino, M ; Velders, GJM ; Vollmer, MK ; Wang, RHJ ; Weiss, R (Copernicus Publications, 2017-05-31)
    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.