The ‘long tail’ of anthropogenic CO₂ decline in the atmosphere and its consequences for post-closure performance assessments for disposal of radioactive wastes

AbstractThe extended timescales involved in the decay of radioactive wastes to safe levels mean that geological disposal facilities must continue to function effectively long into the future. It is therefore essential to consider long-term climate evolution in post-closure performance assessments in order to evaluate a geological disposal system's response and robustness to a variety of potential environmental changes, driven by both natural and anthropogenic forcings. In this paper, we illustrate the multiple decay components that characterize the primary driver of climate change – atmospheric CO2 – in response to fossil fuel carbon emissions. We perform a multi-exponential analysis on a series of atmospheric CO2 decay curves predicted by an Earth system model and create an empirical response function that encapsulates the long-term (&gt;1 kyr) removal of excess CO2 from the atmosphere. We present this response function as a simple tool for rapidly projecting the future atmospheric CO2 concentration resulting from any plausible cumulative release of CO2. We discuss the implications of the long 'tail' to this atmospheric CO2 decay curve, both in terms of future climate evolution as well as potential impacts on radioactive waste repositories.</jats:p

Sensitivity of the Greenland Ice Sheet to interglacial climate forcing:MIS 5e Versus MIS 11

The Greenland Ice Sheet (GrIS) is thought to have contributed substantially to high global sea levels during the interglacials of Marine Isotope Stage (MIS) 5e and 11. Geological evidence suggests that the mass loss of the GrIS was greater during the peak interglacial of MIS 11 than MIS 5e, despite a weaker boreal summer insolation. We address this conundrum by using the three‐dimensional thermomechanical ice sheet model Glimmer forced by Community Climate System Model version 3 output for MIS 5e and MIS 11 interglacial time slices. Our results suggest a stronger sensitivity of the GrIS to MIS 11 climate forcing than to MIS 5e forcing. Besides stronger greenhouse gas radiative forcing, the greater MIS 11 GrIS mass loss relative to MIS 5e is attributed to a larger oceanic heat transport toward high latitudes by a stronger Atlantic meridional overturning circulation. The vigorous MIS 11 ocean overturning, in turn, is related to a stronger wind‐driven salt transport from low to high latitudes promoting North Atlantic Deep Water formation. The orbital insolation forcing, which causes the ocean current anomalies, is discussed

A model-model and data-model comparison for the early Eocene hydrological cycle

A range of proxy observations have recently provided constraints on how Earth's hydrological cycle responded to early Eocene climatic changes. However, comparisons of proxy data to general circulation model (GCM) simulated hydrology are limited and inter-model variability remains poorly characterised. In this work, we undertake an intercomparison of GCM-derived precipitation and <i>P</i> − <i>E</i> distributions within the extended EoMIP ensemble (Eocene Modelling Intercomparison Project; Lunt et al., 2012), which includes previously published early Eocene simulations performed using five GCMs differing in boundary conditions, model structure, and precipitation-relevant parameterisation schemes. <br><br> We show that an intensified hydrological cycle, manifested in enhanced global precipitation and evaporation rates, is simulated for all Eocene simulations relative to the preindustrial conditions. This is primarily due to elevated atmospheric paleo-CO₂, resulting in elevated temperatures, although the effects of differences in paleogeography and ice sheets are also important in some models. For a given CO₂ level, globally averaged precipitation rates vary widely between models, largely arising from different simulated surface air temperatures. Models with a similar global sensitivity of precipitation rate to temperature (d<i>P</i>∕d<i>T</i>) display different regional precipitation responses for a given temperature change. Regions that are particularly sensitive to model choice include the South Pacific, tropical Africa, and the Peri-Tethys, which may represent targets for future proxy acquisition. <br><br> A comparison of early and middle Eocene leaf-fossil-derived precipitation estimates with the GCM output illustrates that GCMs generally underestimate precipitation rates at high latitudes, although a possible seasonal bias of the proxies cannot be excluded. Models which warm these regions, either via elevated CO₂ or by varying poorly constrained model parameter values, are most successful in simulating a match with geologic data. Further data from low-latitude regions and better constraints on early Eocene CO₂ are now required to discriminate between these model simulations given the large error bars on paleoprecipitation estimates. Given the clear differences between simulated precipitation distributions within the ensemble, our results suggest that paleohydrological data offer an independent means by which to evaluate model skill for warm climates

The BRIDGE HadCM3 family of climate models: HadCM3@Bristol v1.0

Understanding natural and anthropogenic climate change processes involves using computational models that represent the main components of the Earth system: the atmosphere, ocean, sea ice, and land surface. These models have become increasingly computationally expensive as resolution is increased and more complex process representations are included. However, to gain robust insight into how climate may respond to a given forcing, and to meaningfully quantify the associated uncertainty, it is often required to use either or both ensemble approaches and very long integrations. For this reason, more computationally efficient models can be very valuable tools. Here we provide a comprehensive overview of the suite of climate models based around the HadCM3 coupled general circulation model. This model was developed at the UK Met Office and has been heavily used during the last 15 years for a range of future (and past) climate change studies, but has now been largely superseded for many scientific studies by more recently developed models. However, it continues to be extensively used by various institutions, including the BRIDGE (Bristol Research Initiative for the Dynamic Global Environment) research group at the University of Bristol, who have made modest adaptations to the base HadCM3 model over time. These adaptations mean that the original documentation is not entirely representative, and several other relatively undocumented configurations are in use. We therefore describe the key features of a number of configurations of the HadCM3 climate model family, which together make up HadCM3@Bristol version 1.0. In order to differentiate variants that have undergone development at BRIDGE, we have introduced the letter B into the model nomenclature. We include descriptions of the atmosphere-only model (HadAM3B), the coupled model with a low-resolution ocean (HadCM3BL), the high-resolution atmosphere-only model (HadAM3BH), and the regional model (HadRM3B). These also include three versions of the land surface scheme. By comparing with observational datasets, we show that these models produce a good representation of many aspects of the climate system, including the land and sea surface temperatures, precipitation, ocean circulation, and vegetation. This evaluation, combined with the relatively fast computational speed (up to 1000 times faster than some CMIP6 models), motivates continued development and scientific use of the HadCM3B family of coupled climate models, predominantly for quantifying uncertainty and for long multi-millennial-scale simulations

PMIP4-CMIP6: the contribution of the Paleoclimate Modelling Intercomparison Project to CMIP6

The goal of the Palaeoclimate Modelling Intercomparison Project (PMIP) is to understand the response of the climate system to changes in different climate forcings and to feedbacks. Through comparison with observations of the environmental impacts of these climate changes, or with climate reconstructions based on physical, chemical or biological records, PMIP also addresses the issue of how well state-of-the-art models simulate climate changes. Palaeoclimate states are radically different from those of the recent past documented by the instrumental record and thus provide an out-of-sample test of the models used for future climate projections and a way to assess whether they have the correct sensitivity to forcings and feedbacks. Five distinctly different periods have been selected as focus for the core palaeoclimate experiments that are designed to contribute to the objectives of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). This manuscript describes the motivation for the choice of these periods and the design of the numerical experiments, with a focus upon their novel features compared to the experiments performed in previous phases of PMIP and CMIP as well as the benefits of common analyses of the models across multiple climate states. It also describes the information needed to document each experiment and the model outputs required for analysis and benchmarking

Sea Surface Temperature of the mid-Piacenzian Ocean:A Data-Model Comparison

The mid-Piacenzian climate represents the most geologically recent interval of long-term average warmth relative to the last million years, and shares similarities with the climate projected for the end of the 21st century. As such, it represents a natural experiment from which we can gain insight into potential climate change impacts, enabling more informed policy decisions for mitigation and adaptation. Here, we present the first systematic comparison of Pliocene sea surface temperature (SST) between an ensemble of eight climate model simulations produced as part of PlioMIP (Pliocene Model Intercomparison Project) with the PRISM (Pliocene Research, Interpretation and Synoptic Mapping) Project mean annual SST field. Our results highlight key regional and dynamic situations where there is discord between the palaeoenvironmental reconstruction and the climate model simulations. These differences have led to improved strategies for both experimental design and temporal refinement of the palaeoenvironmental reconstruction