Professor Myles Allen CBE FRS

Abstract:

This study aims to improve our understanding of the response of precipitation to forcings by proposing a physically-based equation that resolves simulated precipitation based on the atmospheric energy budget. The equation considers the balance between latent heat release by precipitation and the sum of the slow response by tropospheric temperature changes and the fast response by abrupt radiative forcing (RF) changes. The equation is tuned with three parameters for each climate model and then adequately reproduces time-varying precipitation. By decomposing the equation, we highlight the slow response as the largest contributor to forcing-driven responses and uncertainty sizes in simulations. The second largest one to uncertainty is the fast-RF response from aerosols or greenhouse gases (GHG), depending on the low or highest Coupled Model Intercomparison Projection 6 future scenarios. The likely range of precipitation change at specific warming levels under GHG removal (GGR) and solar radiation management (SRM) mitigation plans is evaluated by a simple model optimizing the relationship between temperature and decomposed contributions from multi-simulations under three scenarios. The results indicate that GGR has more severe effects from aerosols than GHG for a 1.5 K warming, resulting in 0.91%–1.62% increases in precipitation. In contrast, SRM pathways project much drier conditions than GGR results due to the tropospheric cooling and remaining anthropogenic radiative heating. Overall, the proposed physically-based equation, the decomposition analysis, and our simple model provide valuable insights into the uncertainties under different forcings and mitigation pathways, highlighting the importance of slow and fast responses to human-induced forcings in shaping future precipitation changes.

Abstract:

The vast majority of agri-food climate-based sustainability analyses use global warming potential (GWP₁₀₀) as an impact assessment, usually in isolation; however, in recent years, discussions have criticised the 'across-the-board' application of GWP₁₀₀ in Life Cycle Assessments (LCAs), particularly of food systems which generate large amounts of methane (CH₄) and considered whether reporting additional and/or alternative metrics may be more applicable to certain circumstances or research questions (e.g. Global Temperature Change Potential (GTP)). This paper reports a largescale sensitivity analysis using a pasture-based beef production system (a high producer of CH₄ emissions) as an exemplar to compare various climatatic impact assessments: CO₂-equivalents using GWP₁₀₀ and GTP₁₀₀, and 'CO₂-warming-equivalents' using 'GWP Star', or GWP*. The inventory for this system was compiled using data from the UK Research and Innovation National Capability, the North Wyke Farm Platform, in Devon, SW England. LCAs can have an important bearing on: (i) policymakers' decisions; (ii) farmer management decisions; (iii) consumers' purchasing habits; and (iv) wider perceptions of whether certain activities can be considered 'sustainable' or not; it is, therefore, the responsibility of LCA practitioners and scientists to ensure that subjective decisions are tested as robustly as possible through appropriate sensitivity and uncertainty analyses. We demonstrate herein that the choice of climate impact assessment has dramatic effects on interpretation, with GWP₁₀₀ and GTP₁₀₀ producing substantially different results due to their different treatments of CH₄ in the context of carbon dioxide (CO₂) equivalents. Given its dynamic nature and previously proven strong correspondence with climate models, out of the three assessments covered, GWP* provides the most complete coverage of the temporal evolution of temperature change for different greenhouse gas emissions. We extend previous discussions on the limitations of static emission metrics and encourage LCA practitioners to consider due care and attention where additional information or dynamic approaches may prove superior, scientifically speaking, particularly in cases of decision support.