The low-resolution version of HadGEM3 GC3.1: development and evaluation for global climate

A new climate model, HadGEM3 N96ORCA1, is presented that is part of the GC3.1 configuration of HadGEM3. N96ORCA1 has a horizontal resolution of ~135 km in the atmosphere and 1° in the ocean and requires an order of magnitude less computing power than its medium-resolution counterpart, N216ORCA025, while retaining a high degree of performance traceability. Scientific performance is compared both to observations and the N216ORCA025 model. N96ORCA1 reproduces observed climate mean and variability almost as well as N216ORCA025. Patterns of biases are similar across the two models. In the north-west Atlantic, N96ORCA1 shows a cold surface bias of up to 6K, typical of ocean models of this resolution. The strength of the Atlantic meridional overturning circulation (16 to 17 Sv) matches observations. In the Southern Ocean, a warm surface bias (up to 2K) is smaller than in N216ORCA025 and linked to improved ocean circulation. Model El Niño/Southern Oscillation and Atlantic Multidecadal Variability are close to observations. Both the cold bias in the Northern hemisphere (N96ORCA1) and the warm bias in the Southern hemisphere (N216ORCA025) develop in the first few decades of the simulations. As in many comparable climate models, simulated interhemispheric gradients of top-of-atmosphere radiation are larger than observations suggest, with contributions from both hemispheres. HadGEM3 GC3.1 N96ORCA1 constitutes the physical core of the UK Earth System Model (UKESM1) and will be used extensively in the Coupled Model Intercomparison Project 6 (CMIP6), both as part of UKESM1 and as a stand-alone coupled climate model

Description of the resolution hierarchy of the global coupled HadGEM3-GC3.1 model as used in CMIP6 HighResMIP experiments

CMIP6 HighResMIP is a new experimental design for global climate model simulations that aims to assess the impact of model horizontal resolution on climate simulation fidelity. We describe a hierarchy of global coupled model resolutions based on the HadGEM3-GC3.1 model that range from an atmosphere-ocean resolution of 130 km-1° to 25 km-1/12°, all using the same forcings and initial conditions. In order to make such high resolution simulations possible, the experiments have a short 30 year spinup, followed by at least century-long simulations with both constant forcing (to assess drift and the focus of this work), and historic forcing. We assess the change in model biases as a function of both atmosphere and ocean resolution, together with the effectiveness and robustness of this new experimental design. We find reductions in the biases in top of atmosphere radiation components and cloud forcing. There are significant reductions in some common surface climate model biases as resolution is increased, particularly in the Atlantic for sea surface temperature and precipitation, primarily driven by increased ocean resolution. There is also a reduction in drift from the initial conditions both at the surface and in the deeper ocean at higher resolution. Using an eddy-present and eddy-rich ocean resolution enhances the strength of the North Atlantic ocean circulation (boundary currents, overturning circulation and heat transports), while an eddy-present ocean resolution has a considerably reduced Antarctic Circumpolar Current strength. All models have a reasonable representation of El Nino – Southern Oscillation. In general the biases present after 30 years of simulations do not change character markedly over longer timescales, justifying the experimental design

Rapid Summertime Sea Ice Melt in a Coupled Numerical Weather Prediction System

Coupled Numerical Weather Prediction (NWP) models have only recently been implemented for short-term environmental prediction and both challenges and benefits are evident in polar regions. Their simulation of surface exchange over sea ice depends on the model's sea-ice characteristics, however these are hard to constrain due to a lack of in situ and accurate remotely sensed observations. We focus on the Fram Strait region during peak melt conditions and during the passage of an Arctic cyclone: very challenging conditions for coupled NWP. We use in situ aircraft observations from the Arctic Summertime Cyclones field campaign in July-August 2022, plus satellite products, to evaluate a set of 5-day forecasts from the Met Office Unified Model. Our model set ups are based on operational GC4 (Global Coupled 4) and developmental GC5 (Global Coupled 5) configurations, which use the CICE5.1 and SI3 sea-ice models respectively. We find a combination of deficiencies in the simulated sea-ice field, due to initialization and modeling problems. An initially low concentration of sea ice results in excessive absorption of shortwave radiation by the ocean, leading to excessive basal melting of the sea ice, and further sea-ice loss; leading to relatively poorly simulated sea-ice fields in general. In contrast, the passage of an Arctic cyclone and its impact on sea-ice velocities are captured well. Although we demonstrate several deficiencies in the short-term forecasts of two state-of-the-art coupled NWP models, we also find promising aspects of model performance and some clear benefits from a fully coupled atmosphere-ice-ocean system

GOSI9: UK Global Ocean and Sea Ice configurations

The UK Global Ocean and Sea Ice configuration version 9 (GOSI9) is a new traceable hierarchy of three model configurations at 1, and based on version 4.0.4 of the NEMO code. GOSI9 has been developed as part of the UK's Joint Marine Modelling Programme (JMMP), a partnership between the Met Office, the National Oceanography Centre, the British Antarctic Survey, and the Centre for Polar Observation and Modelling. Following a seamless approach, it will be used for a variety of applications across a wide range of spatial and temporal resolutions: short-range coupled numerical weather prediction (NWP) forecasts, ocean forecasts, seasonal and decadal forecasts, and climate and Earth system modelling. The GOSI9 configurations are described in detail with a special focus on the updates since the previous version (GO6-GSI8). Results from 30-year ocean–ice integrations forced by CORE2 fluxes are presented for the three resolutions, and the impacts of the updates are assessed using the integrations. The upgrade to NEMO 4.0.4 includes a new sea ice model SI3 (Sea Ice modelling Integrated Initiative) and faster integration achieved through the use of partially implicit schemes that allow a significant increase in the length of the time step. The quality of the simulations is generally improved compared to GO6-GSI8. The temperature and salinity drifts are largely reduced thanks to the upgrade to NEMO 4.0.4 and the adoption of fourth-order horizontal and vertical advections helping to reduce the numerical mixing. To improve the representation of the Southern Ocean, a scale-aware form of the Gent–McWilliams parameterization and the application of a partial-slip lateral boundary condition on momentum in the Southern Ocean have been added, resulting in a stronger and more realistic Antarctic Circumpolar Current (ACC) transport and a reduction in the temperature and salinity biases along the shelf of Antarctica. In the Arctic, the representation of sea ice is improved, leading to a reduction in surface temperature and salinity biases. In particular, the excessive and unrealistic Arctic summer sea ice melt in GO6-GSI8 is significantly improved in GOSI9 and can be attributed to the change in the sea ice model and to the higher albedos that increased sea ice thickness

Resolving and parameterising the ocean mesoscale in earth system models

Purpose of Review. Assessment of the impact of ocean resolution in Earth System models on the mean state, variability, and future projections and discussion of prospects for improved parameterisations to represent the ocean mesoscale. Recent Findings. The majority of centres participating in CMIP6 employ ocean components with resolutions of about 1 degree in their full Earth Systemmodels (eddy-parameterising models). In contrast, there are alsomodels submitted toCMIP6 (both DECK and HighResMIP) that employ ocean components of approximately 1/4 degree and 1/10 degree (eddy-present and eddy-rich models). Evidence to date suggests that whether the ocean mesoscale is explicitly represented or parameterised affects not only the mean state of the ocean but also the climate variability and the future climate response, particularly in terms of the Atlantic meridional overturning circulation (AMOC) and the Southern Ocean. Recent developments in scale-aware parameterisations of the mesoscale are being developed and will be included in future Earth System models. Summary. Although the choice of ocean resolution in Earth System models will always be limited by computational considerations, for the foreseeable future, this choice is likely to affect projections of climate variability and change as well as other aspects of the Earth System. Future Earth System models will be able to choose increased ocean resolution and/or improved parameterisation of processes to capture physical processes with greater fidelity

Predicting September Arctic Sea Ice: A Multi-Model Seasonal Skill Comparison

Abstract This study quantifies the state-of-the-art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multi-model dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting of community contributions from 17 statistical models and 17 dynamical models. Prediction skill is compared over the period 2001–2020 for predictions of Pan-Arctic sea ice extent (SIE), regional SIE, and local sea ice concentration (SIC) initialized on June 1, July 1, August 1, and September 1. This diverse set of statistical and dynamical models can individually predict linearly detrended Pan-Arctic SIE anomalies with skill, and a multi-model median prediction has correlation coefficients of 0.79, 0.86, 0.92, and 0.99 at these respective initialization times. Regional SIE predictions have similar skill to Pan-Arctic predictions in the Alaskan and Siberian regions, whereas regional skill is lower in the Canadian, Atlantic, and Central Arctic sectors. The skill of dynamical and statistical models is generally comparable for Pan-Arctic SIE, whereas dynamical models outperform their statistical counterparts for regional and local predictions. The prediction systems are found to provide the most value added relative to basic reference forecasts in the extreme SIE years of 1996, 2007, and 2012. SIE prediction errors do not show clear trends over time, suggesting that there has been minimal change in inherent sea ice predictability over the satellite era. Overall, this study demonstrates that there are bright prospects for skillful operational predictions of September sea ice at least three months in advance.</jats:p

Rapid evaluation framework for the CMIP7 assessment fast track

As Earth system models (ESMs) grow in complexity and in volumes of output data, there is an increasing need for rapid, comprehensive evaluation of their scientific performance. The upcoming Assessment Fast Track for the Seventh Phase of the Coupled Model Intercomparison Project (CMIP7) will require expeditious response for model analyses designed to inform and drive integrated Earth system assessments. To meet this challenge, the Rapid Evaluation Framework (REF), a community-driven platform for benchmarking and performance assessment of ESMs, was designed and developed. The initial implementation of the REF, constructed to meet the near-term needs of the CMIP7 Assessment Fast Track, builds upon community evaluation and benchmarking tools. The REF runs within a containerized workflow for portability and reproducibility and is aimed at generating and organizing diagnostics covering a variety of model variables. The REF leverages best-available observational datasets to provide assessments of model fidelity across a collection of diagnostics. All diagnostics were identified and finally selected with community involvement and consultation. Operational integration with the Earth System Grid Federation (ESGF) will permit automated execution of the REF for specific diagnostics as soon as model data are published on ESGF by the originating modelling centres. The REF is designed to be portable across a range of current computational platforms to facilitate use by modelling centres for assessing the evolution of model versions or gauging the relative performance of CMIP simulations before being published on ESGF. When integrated into production simulation workflows, results from the REF provide immediate quantitative feedback that allows model developers and scientists to quickly identify model biases and performance issues. After the REF is released to the community, its subsequent development and support will be prioritized by an international consortium of scientists and engineers, enabling a broader impact across Earth science disciplines. For instance, the REF will facilitate improvements to models and reductions in uncertainties for projections since ESMs are the main tool for studying the global Earth system. Production of reproducible diagnostics and community-based assessments are the key features of the REF that help to inform mitigation and adaptation policies

UKESM1: description and evaluation of the UK Earth System Model

We document the development of the first version of the United Kingdom Earth System Model UKESM1. The model represents a major advance on its predecessor HadGEM2‐ES, with enhancements to all component models and new feedback mechanisms. These include: a new core physical model with a well‐resolved stratosphere; terrestrial biogeochemistry with coupled carbon and nitrogen cycles and enhanced land management; tropospheric‐stratospheric chemistry allowing the holistic simulation of radiative forcing from ozone, methane and nitrous oxide; two‐moment, five‐species, modal aerosol; and ocean biogeochemistry with two‐way coupling to the carbon cycle and atmospheric aerosols. The complexity of coupling between the ocean, land and atmosphere physical climate and biogeochemical cycles in UKESM1 is unprecedented for an Earth system model. We describe in detail the process by which the coupled model was developed and tuned to achieve acceptable performance in key physical and Earth system quantities, and discuss the challenges involved in mitigating biases in a model with complex connections between its components. Overall the model performs well, with a stable pre‐industrial state, and good agreement with observations in the latter period of its historical simulations. However, global mean surface temperature exhibits stronger‐than‐observed cooling from 1950 to 1970, followed by rapid warming from 1980 to 2014. Metrics from idealised simulations show a high climate sensitivity relative to previous generations of models: equilibrium climate sensitivity (ECS) is 5.4 K, transient climate response (TCR) ranges from 2.68 K to 2.85 K, and transient climate response to cumulative emissions (TCRE) is 2.49 K/TtC to 2.66 K/TtC

Predicting September Arctic Sea Ice: A Multi-Model Seasonal Skill Comparison

This study quantifies the state-of-the-art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multi-model dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting of community contributions from 17 statistical models and 17 dynamical models. Prediction skill is compared over the period 2001–2020 for predictions of Pan-Arctic sea ice extent (SIE), regional SIE, and local sea ice concentration (SIC) initialized on June 1, July 1, August 1, and September 1. This diverse set of statistical and dynamical models can individually predict linearly detrended Pan-Arctic SIE anomalies with skill, and a multi-model median prediction has correlation coefficients of 0.79, 0.86, 0.92, and 0.99 at these respective initialization times. Regional SIE predictions have similar skill to Pan-Arctic predictions in the Alaskan and Siberian regions, whereas regional skill is lower in the Canadian, Atlantic, and Central Arctic sectors. The skill of dynamical and statistical models is generally comparable for Pan-Arctic SIE, whereas dynamical models outperform their statistical counterparts for regional and local predictions. The prediction systems are found to provide the most value added relative to basic reference forecasts in the extreme SIE years of 1996, 2007, and 2012. SIE prediction errors do not show clear trends over time, suggesting that there has been minimal change in inherent sea ice predictability over the satellite era. Overall, this study demonstrates that there are bright prospects for skillful operational predictions of September sea ice at least three months in advance

Bringing it all together: science priorities for improved understanding of Earth system change and to support international climate policy

We review how the international modelling community, encompassing integrated assessment models, global and regional Earth system and climate models, and impact models, has worked together over the past few decades to advance understanding of Earth system change and its impacts on society and the environment and thereby support international climate policy. We go on to recommend a number of priority research areas for the coming decade, a timescale that encompasses a number of newly starting international modelling activities, as well as the IPCC Seventh Assessment Report (AR7) and the second UNFCCC Global Stocktake. Progress in these priority areas will significantly advance our understanding of Earth system change and its impacts, increasing the quality and utility of science support to climate policy. [...