CMIP6/PMIP4 simulations of the mid-Holocene and Last Interglacial using HadGEM3: comparison to the pre-industrial era, previous model versions and proxy data

Abstract

Abstract. Palaeoclimate model simulations are an important tool to improve our understanding of the mechanisms of climate change. These simulations also provide tests of the ability of models to simulate climates very different to today. Here we present the results from two brand-new simulations using the latest version of the UK's physical climate model, HadGEM3-GC3.1; they are the mid-Holocene (∼6 ka) and Last Interglacial (∼127 ka) simulations, both conducted under the auspices of CMIP6/PMIP4. This is the first time this version of the UK model has been used to conduct palaeoclimate simulations. These periods are of particular interest to PMIP4 because they represent the two most recent warm periods in Earth history, where atmospheric concentration of greenhouse gases and continental configuration are similar to the pre-industrial period but where there were significant changes to the Earth's orbital configuration, resulting in a very different seasonal cycle of radiative forcing. Results for these simulations are assessed firstly against the same model's pre-industrial control simulation (a simulation comparison, to describe and understand the differences between the pre-industrial – PI – and the two palaeo simulations) and secondly against previous versions of the same model relative to newly available proxy data (a model–data comparison, to compare all available simulations from the same model with proxy data to assess any improvements due to model advances). The introduction of this newly available proxy data adds further novelty to this study. Globally, for metrics such as 1.5 m temperature and surface rainfall, whilst both the recent palaeoclimate simulations are mostly capturing the expected sign and, in some places, magnitude of change relative to the pre-industrial, this is geographically and seasonally dependent. Compared to newly available proxy data (including sea surface temperature – SST – and rainfall) and also incorporating data from previous versions of the model shows that the relative accuracy of the simulations appears to vary according to metric, proxy reconstruction used for comparison and geographical location. In some instances, such as mean rainfall in the mid-Holocene, there is a clear and linear improvement, relative to proxy data, from the oldest to the newest generation of the model. When zooming into northern Africa, a region known to be problematic for models in terms of rainfall enhancement, the behaviour of the West African monsoon in both recent palaeoclimate simulations is consistent with current understanding, suggesting a wetter monsoon during the mid-Holocene and (more so) the Last Interglacial, relative to the pre-industrial era. However, regarding the well-documented “Saharan greening” during the mid-Holocene, results here suggest that the most recent version of the UK's physical model is still unable to reproduce the increases suggested by proxy data, consistent with all other previous models to date.

Simulating the mid-Holocene, last interglacial and mid-Pliocene climate with EC-Earth3-LR

“…The reconstructed vegetation is a nine-type megabiome map classified after Harrison and Prentice (2003). In the EC-Earth3 HTESSEL land model, the vegetation type classification is based on the Biosphere-Atmosphere Transfer Scheme (BATS) model (Yang and Dickinson, 1996), which contains 20 biome classifications. Each grid point is characterized by a maximum of two dominant vegetation types, high vegetation and low vegetation, and their area fractions.…”

Geological Society of London Scientific Statement: what the geological record tells us about our present and future climate

“…Regional seasonal weather patterns can also be imprinted in the geological record, including the West African and Indian Monsoons, which are crucial for the socio-economic stability and food and water security of billions of people (Dilley et al 2005;Turner and Annamalai 2012). The geological record shows a dynamic history of these monsoon systems, caused by changes in orbital parameters and feedbacks in the climate system that are the subject of ongoing research (Gebregiorgis et al 2018;Pausata et al 2020;Williams et al 2020). Palaeoclimate studies also indicate that past changes in the Indian and West Africa Monsoons may have triggered abrupt events in other regions ('induced tipping') and may have had a domino effect impacting climates in areas as far away as the Arctic (Nilsson-Kerr et al 2019;Pausata et al 2020).…”

“…Current state-of-the-art climate models generally underestimate the extent of these observed changes (e.g. Williams et al 2020), which were paced by orbital parameters, but involved other feedbacks including atmosphereocean, atmosphere-land and dust interactions (Hopcroft and Valdes 2019;Pausata et al 2020). Identifying and simulating all relevant feedbacks is an ongoing target in climate-change research and it is hoped that future improvements in models, such as their representation of atmospheric convection, will reconcile these differences.…”

“…In the meantime, in the majority of instances, models have underestimated the changes seen in the geological record (e.g. Valdes 2011; Gasson and Kiesling 2020;Pausata et al 2020;Williams et al 2020).…”

Response of Convective Systems to the Orbital Forcing of the Last Interglacial in a Global Nonhydrostatic Atmospheric Model With and Without a Convective Parameterization

“…Many modelling studies on the last interglacial using the atmosphere-ocean coupled general circulation model (AOGCM) have been made with the changes of the orbital parameters and greenhouse gases (Fischer and Jungclaus, 2010;Guarino et al, 2020;Kubatzki et al, 2000;Langebroek and Nisancioglu, 2014;Montoya et al, 1998;Nikolova et al, 2013;Pedersen et al, 2017;Williams et al, 2020;Zheng et al, 2020), some of which add fresh water input (Clark et al, 2020;Govin et al, 2012) and an interactive vegetation model (O'ishi et al, 2021). These simulations were made with the grid-spacing of the atmosphere ranging from 5.6° at the lowest to 1° at the highest.…”