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Mineral dust plays an important role in Earth system models and is linked to many components, including atmospheric wind speed, precipitation and radiation, surface vegetation cover and soil properties and oceanic biogeochemical systems. In this paper, the dust scheme in the first configuration of the United Kingdom Earth System Model UKESM1 is described, and simulations of dust and its radiative effects are presented and compared with results from the parallel coupled atmosphere–ocean general circulation model (GCM) HadGEM3-GC3.1. Not only changes in the driving model fields but also changes in the dust size distribution are shown to lead to considerable differences to the present-day dust simulations and to projected future changes. UKESM1 simulations produce a present-day, top-of-the-atmosphere (ToA) dust direct radiative effect (DRE – defined as the change in downward net flux directly due to the presence of dust) of 0.086 W m−2 from a dust load of 19.5 Tg. Under climate change pathways these values decrease considerably. In the 2081–2100 mean of the Shared Socioeconomic Pathway SSP5–8.45 ToA DRE reaches 0.048 W m−2 from a load of 15.1 Tg. In contrast, in HadGEM3-GC3.1 the present-day values of −0.296 W m−2 and 15.0 Tg are almost unchanged at −0.289 W m−2 and 14.5 Tg in the 2081–2100 mean. The primary mechanism causing the differences in future dust projections is shown to be the vegetation response, which dominates over the direct effects of warming in our models. Though there are considerable uncertainties associated with any such estimates, the results presented demonstrate both the importance of the size distribution for dust modelling and also the necessity of including Earth system processes such as interactive vegetation in dust simulations for climate change studies.

Volcanic eruptions provide invaluable natural experiments to evaluate the transport, evolution, and potential impact of sulphate aerosol on clouds in global climate models (GCMs). The 2014 fissure eruption in Holuhraun, Iceland had an emission rate greater than a third of daily global sulphur dioxide emissions at its peak, resulting in significant perturbations to cloud radiative properties across vast swathes on the North Atlantic. Probing the GCM representation of aerosol lifecycle during transport in the volcanic plume, offers a unique insight to improve aerosol source and sink process understanding, which is essential to reducing one of the largest sources of uncertainty in climate modelling - aerosol-cloud-interactions.Using rural aerosol measurement sites with climatologically relevant time series, we first perform a Eulerian evaluation of the UK Met Office Earth System Model (UKESM1) simulation of the volcanic eruption. Both in-situ observations and UKESM1 demonstrate a significant increase in aerosol concentration during the volcanic eruption compared to the climatology (2008-2013). However, the ‘standard’ version of the model fails to replicate the significant growth events associated with new particle formation seen in the observations during the volcanic eruption. A second simulation of UKESM1 with the addition of boundary layer nucleation, which is not included in the standard configuration, accurately reproduces the timing of nucleation events seen during the eruption period. Finally, we utilise a Lagrangian framework, in which HYSPLIT trajectories are calculated for both GCMs and reanalysis data, to analyse the relevant aerosol source and sink processes during transport that drive the differences between modelled and observed aerosol at the measurement sites.

Global studies of climate change impacts that use future climate model projections also require projections of land surface changes. Simulated land surface performance in Earth System models is often affected by the atmospheric models’ climate biases, leading to errors in land surface projections. Here we run the JULES-ES land surface model with ISIMIP2b bias-corrected climate model data from 4 global climate models (GCMs). The bias correction reduces the impact of the climate biases present in individual models. We evaluate JULES-ES performance against present-day observations to demonstrate its usefulness for providing required information for impacts such as fire and river flow. We simulate a historical and two future scenarios; a mitigation scenario RCP2.6 and RCP6.0, which has very little mitigation. We include a standard JULES-ES configuration without fire as a contribution to ISIMIP2b and JULES-ES with fire as a potential future development. Simulations for gross primary productivity (GPP), evapotranspiration (ET) and albedo compare well against observations. Including fire improves the simulations, especially for ET and albedo and vegetation distribution, with some degradation in shrub cover and river flow. This configuration represents some of the most current earth system science for land surface modelling. The suite associated with this configuration provides a basis for past and future phases of ISIMIP, providing a simulation setup, postprocessing and initial evaluation using ILAMB. This suite ensures that it is as straightforward, reproducible and transparent as possible to follow the protocols and participate fully in ISIMIP using JULES.

Here we present the experimental design and results from a new mid-Pliocene simulation using the latest version of the UK’s physical climate model, HadGEM3-GC31-LL, conducted under the auspices of CMIP6/PMIP4/PlioMIP2. Although two other paleoclimate simulations have been recently run using this model, they both focused on more recent periods within the Quaternary and therefore this is the first time this version of the UK model has been run this far back in time. The mid-Pliocene Warm Period, ~3 Ma, is of particular interest because it represents a time period when the Earth was in equilibrium with CO2 concentrations roughly equivalent to those of today, providing a possible analogue for current and future climate change. The implementation of the Pliocene boundary conditions is firstly described in detail, based on the PRISM4 dataset, including CO2, ozone, orography, ice mask, lakes, vegetation fractions and vegetation functional types. These were incrementally added into the model, to change from a preindustrial setup to a Pliocene setup. The results of the simulation are then presented, which are firstly compared with the model’s pre-industrial simulation, secondly with previous versions of the same model and with available proxy data, and thirdly with all other models included in PlioMIP2. Firstly, the comparison with preindustrial suggests that the Pliocene simulation is consistent with current understanding and existing work, showing warmer and wetter conditions, and with the greatest warming occurring over high latitude and polar regionsthe. The global mean surface air temperature anomaly at the end of the Pliocene simulation is 5.1°C, which is the 2nd highest of all models included in PlioMIP2 and is consistent with the fact that HadGEM3-GC31-LL has one of the highest Effective Climate Sensitivities of all CMIP6 models. Secondly, the comparison with previous generation models and with proxy data suggests a clear increase in global sea surface temperatures as the model has undergone development. Up to a certain level of warming, this results in a better agreement with available proxy data, and the “sweet spot” appears to be the previous CMIP5 generation of the model, HadGEM2-AO. The most recent simulation presented here, however, appears to show poorer agreement with the proxy data compared with HadGEM2, and may be overly sensitive to the Pliocene boundary conditions resulting in a climate that is too warm. Thirdly, the comparison with other models from PlioMIP2 further supports this conclusion, with HadGEM3-GC31-LL being one of the warmest and wettest models in all of PlioMIP2 and, if all the models are ordered according to agreement with proxy data, HadGEM3-GC31-LL ranks approximately halfway among them. A caveat to these results is the relatively short run length of the simulation, meaning the model is not in full equilibrium. Given the computational cost of the model it was not possible to run for longer; a Gregory plot analysis indicates that had it been allowed to come to full equilibrium, the final global mean surface temperature could have been approximately 1.5°C higher.

The Last Interglacial (LIG), a warmer period 130,000–116,000 years before present, is a potential analogue for future climate change. Stronger LIG summertime insolation at high northern latitudes drove Arctic land summer temperatures 4–5 °C higher than in the pre-industrial era. Climate model simulations have previously failed to capture these elevated temperatures, possibly because they were unable to correctly capture LIG sea-ice changes. Here, we show that the latest version of the fully coupled UK Hadley Center climate model (HadGEM3) simulates a more accurate Arctic LIG climate, including elevated temperatures. Improved model physics, including a sophisticated sea-ice melt-pond scheme, result in a complete simulated loss of Arctic sea ice in summer during the LIG, which has yet to be simulated in past generations of models. This ice-free Arctic yields a compelling solution to the long-standing puzzle of what drove LIG Arctic warmth and supports a fast retreat of future Arctic summer sea ice. Arctic climate in the Last Interglacial (LIG)—a warm period 130,000–116,000 years ago—is poorly simulated by modern climate models. A model with improved sea-ice melt-pond physics reproduces LIG Arctic temperatures, suggests an ice-free Arctic during this period and predicts the same by 2035.

International audience; Climate forcing, sensitivity, and feedback metrics are evaluated in both the United Kingdom's physical climate model HadGEM3‐GC3.1 at low (‐LL) and medium (‐MM) resolution and the United Kingdom's Earth System Model UKESM1. The effective climate sensitivity (EffCS) to a doubling of CO2 is 5.5 K for HadGEM3.1‐GC3.1‐LL and 5.4 K for UKESM1. The transient climate response is 2.5 and 2.8 K, respectively. While the EffCS is larger than that seen in the previous generation of models, none of the model's forcing or feedback processes are found to be atypical of models, though the cloud feedback is at the high end. The relatively large EffCS results from an unusual combination of a typical CO2 forcing with a relatively small feedback parameter. Compared to the previous U.K. climate model, HadGEM3‐GC2.0, the EffCS has increased from 3.2 to 5.5 K due to an increase in CO2 forcing, surface albedo feedback, and midlatitude cloud feedback. All changes are well understood and due to physical improvements in the model. At higher atmospheric and ocean resolution (HadGEM3‐GC3.1‐MM), there is a compensation between increased marine stratocumulus cloud feedback and reduced Antarctic sea‐ice feedback. In UKESM1, a CO2 fertilization effect induces a land surface vegetation change and albedo radiative effect. Historical aerosol forcing in HadGEM3‐GC3.1‐LL is −1.1 W m−2. In HadGEM3‐GC3.1‐LL historical simulations, cloud feedback is found to be less positive than in abrupt‐4xCO2, in agreement with atmosphere‐only experiments forced with observed historical sea surface temperature and sea‐ice variations. However, variability in the coupled model's historical sea‐ice trends hampers accurate diagnosis of the model's total historical feedback.

Here we present the experimental design and results from a new mid-Pliocene simulation using the latest version of the UK's physical climate model, HadGEM3-GC31-LL, conducted under the auspices of CMIP6/PMIP4/PlioMIP2. Although two other palaeoclimate simulations have been recently run using this model, they both focused on more recent periods within the Quaternary, and therefore this is the first time this version of the UK model has been run this far back in time. The mid-Pliocene Warm Period, ∼3 Ma, is of particular interest because it represents a time period when the Earth was in equilibrium with CO2 concentrations roughly equivalent to those of today, providing a possible analogue for current and future climate change. The implementation of the Pliocene boundary conditions is firstly described in detail, based on the PRISM4 dataset, including CO2, ozone, orography, ice mask, lakes, vegetation fractions and vegetation functional types. These were incrementally added into the model, to change from a pre-industrial setup to a Pliocene setup. The results of the simulation are then presented, which are firstly compared with the model's pre-industrial simulation, secondly with previous versions of the same model and with available proxy data, and thirdly with all other models included in PlioMIP2. Firstly, the comparison with the pre-industrial simulation suggests that the Pliocene simulation is consistent with current understanding and existing work, showing warmer and wetter conditions, and with the greatest warming occurring over high-latitude and polar regions. The global mean surface air temperature anomaly at the end of the Pliocene simulation is 5.1 ∘C, which is the second highest of all models included in PlioMIP2 and is consistent with the fact that HadGEM3-GC31-LL has one of the highest Effective Climate Sensitivities of all CMIP6 models. Secondly, the comparison with previous generation models and with proxy data suggests a clear increase in global sea surface temperatures as the model has undergone development. Up to a certain level of warming, this results in a better agreement with available proxy data, and the “sweet spot” appears to be the previous CMIP5 generation of the model, HadGEM2-AO. The most recent simulation presented here, however, appears to show poorer agreement with the proxy data compared with HadGEM2 and may be overly sensitive to the Pliocene boundary conditions, resulting in a climate that is too warm. Thirdly, the comparison with other models from PlioMIP2 further supports this conclusion, with HadGEM3-GC31-LL being one of the warmest and wettest models in all of PlioMIP2, and if all the models are ordered according to agreement with proxy data, HadGEM3-GC31-LL ranks approximately halfway among them. A caveat to these results is the relatively short run length of the simulation, meaning the model is not in full equilibrium. Given the computational cost of the model it was not possible to run it for a longer period; a Gregory plot analysis indicates that had it been allowed to come to full equilibrium, the final global mean surface temperature could have been approximately 1.5 ∘C higher.

We present an evaluation of Aerosol Optical Depth (AOD) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) over Australia covering the period 2001–2020. We focus on retrievals from the Deep Blue (DB) and Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithms, showing how these compare to one another in time and space. We further employ speciated AOD estimates from Copernicus Atmospheric Monitoring Service (CAMS) reanalyses to help diagnose aerosol types and hence sources. Considering Australia as a whole, monthly mean AODs show similar temporal behaviour, with a well-defined seasonal peak in the Austral summer. However, excepting periods of intense biomass burning activity, MAIAC values are systematically higher than their DB counterparts by, on average, 50%. Decomposing into seasonal maps, the patterns of behaviour show distinct differences, with DB showing a larger dynamic range in AOD, with markedly higher AODs (ΔAOD∼0.1) in northern and southeastern regions during Austral winter and summer. This is counter-balanced by typically smaller DB values across the Australian interior. Site level comparisons with all available level 2 AOD data from Australian Aerosol Robotic Network (AERONET) sites operational during the study period show that MAIAC tends to marginally outperform DB in terms of correlation (RMAIAC = 0.71, RDB = 0.65) and root-mean-square error (RMSEMAIAC = 0.065, RMSEDB = 0.072). To probe this behaviour further, we classify the sites according to the predominant surface type within a 25 km radius. This analysis shows that MAIAC’s advantage is retained across all surface types for R and all but one for RMSE. For this surface type (Bare, comprising just 1.2% of Australia) the performance of both algorithms is relatively poor, (RMAIAC = 0.403, RDB = 0.332).

To better understand the processes contributing to future climate change, palaeoclimate model simulations are an important tool because they allow testing of the models’ ability to simulate very different climates than that of today. As part of CMIP6/PMIP4, the latest version of the UK’s physical climate model, HadGEM3-GC31-LL (hereafter, for brevity, HadGEM3), was recently used to simulate the mid-Holocene (~6 ka) and Last Interglacial (~127 ka) simulations and the results were compared to the preindustrial era, previous versions of the same model and proxy data (see Williams et al. 2020, Climate of the Past). Here, we use the same model to go further back in time, presenting the results from the mid-Pliocene Warm Period (~3.3 to 3 ma, hereafter the “Pliocene” for brevity). This period is of particular interest when it comes to projections of future climate change under various scenarios of CO2 emissions, because it is the most recent time in Earth’s history when CO2 levels were roughly equivalent to today. In response, albeit due to slower mechanisms than today’s anthropogenic fossil fuel driven-change, during the Pliocene global mean temperatures were 2-3°C higher than today, more so at the poles. Here, we present results from the HadGEM3 Pliocene simulation. The model is responding to the Pliocene boundary conditions in a manner consistent with current understanding and existing literature. When compared to the preindustrial era, global mean temperatures are currently ~5°C higher, with the majority of warming coming from high latitudes due to polar amplification from a lack of sea ice. Relative to other models within the Pliocene Modelling Intercomparison Project (PlioMIP), this is the 2nd warmest model, with the majority of others only showing up to a 4.5°C increase and many a lot less. This is consistent with the relatively high sensitivity of HadGEM3, relative to other CMIP6-class models. When compared to a previous generation of the same UK model, HadCM3, similar patterns of both surface temperature and precipitation changes are shown (relative to preindustrial). Moreover, when the simulations are compared to proxy data, the results suggest that the HadGEM3 Pliocene simulation is closer to the reconstructions than its predecessor.

Earth system models enable a broad range of climate interactions that physical climate models are unable to simulate. However, the extent to which adding Earth system components changes or improves the simulation of the physical climate is not well understood. Here we present a broad multivariate evaluation of the North Atlantic climate system in historical simulations of the UK Earth System Model (UKESM1) performed for CMIP6. In particular, we focus on the mean state and the decadal time scale evolution of important variables that span the North Atlantic climate system. In general, UKESM1 performs well and realistically simulates many aspects of the North Atlantic climate system. Like the physical version of the model, we find that changes in external forcing, and particularly aerosol forcing, are an important driver of multidecadal change in UKESM1, especially for Atlantic Multidecadal Variability and the Atlantic Meridional Overturning Circulation. However, many of the shortcomings identified are similar to common biases found in physical climate models, including the physical climate model that underpins UKESM1. For example, the summer jet is too weak and too far poleward; decadal variability in the winter jet is underestimated; intraseasonal stratospheric polar vortex variability is poorly represented; and Arctic sea ice is too thick. Forced shortwave changes may be also too strong in UKESM1, which, given the important role of historical aerosol forcing in shaping the evolution of the North Atlantic in UKESM1, motivates further investigation. Therefore, physical model development, alongside Earth system development, remains crucial in order to improve climate simulations.

We describe the approach taken to develop the United Kingdom's first community Earth system model, UKESM1. This is a joint effort involving the Met Office and the Natural Environment Research Council (NERC), representing the U.K. academic community. We document our model development procedure and the subsequent U.K. submission to CMIP6, based on a traceable hierarchy of coupled physical and Earth system models. UKESM1 builds on the well‐established, world‐leading HadGEM models of the physical climate system and incorporates cutting‐edge new representations of aerosols, atmospheric chemistry, terrestrial carbon, and nitrogen cycles and an advanced model of ocean biogeochemistry. A high‐level metric of overall performance shows that both models, HadGEM3‐GC3.1 and UKESM1, perform better than most other CMIP6 models so far submitted for a broad range of variables. We point to much more extensive evaluation performed in other papers in this special issue. The merits of not using any forced climate change simulations within our model development process are discussed. First results from HadGEM3‐GC3.1 and UKESM1 include the emergent climate sensitivity (5.5 and 5.4 K, respectively) which is high relative to the current range of CMIP5 models. The role of cloud microphysics and cloud‐aerosol interactions in driving the climate sensitivity, and the systematic approach taken to understand this role, is highlighted in other papers in this special issue. We place our findings within the broader modeling landscape indicating how our understanding of key processes driving higher sensitivity in the two U.K. models seems to align with results from a number of other CMIP6 models., Key Points The United Kingdom has developed its first community Earth system model and delivered a traceable hierarchy of models to CMIP6We applied a process‐based evaluation strategy in model development but chose not to use historic trends or measures of climate responseThe U.K. models exhibit higher climate sensitivity than seen in CMIP5 arising in part from more positive cloud feedbacks

For simulations intended to study the influence of anthropogenic forcing on climate, temporal stability of the Earth's natural heat, freshwater, and biogeochemical budgets is critical. Achieving such coupled model equilibration is scientifically and computationally challenging. We describe the protocol used to spin‐up the UK Earth system model (UKESM1) with respect to preindustrial forcing for use in the sixth Coupled Model Intercomparison Project (CMIP6). Due to the high computational cost of UKESM1's atmospheric model, especially when running with interactive full chemistry and aerosols, spin‐up primarily used parallel configurations using only ocean/land components. For the ocean, the resulting spin‐up permitted the carbon and heat contents of the ocean's full volume to approach equilibrium over 5,000 years. On land, a spin‐up of 1,000 years brought UKESM1's dynamic vegetation and soil carbon reservoirs toward near‐equilibrium. The end‐states of these parallel ocean‐ and land‐only phases then initialized a multicentennial period of spin‐up with the full Earth system model, prior to this simulation continuing as the UKESM1 CMIP6 preindustrial control (piControl). The realism of the fully coupled spin‐up was assessed for a range of ocean and land properties, as was the degree of equilibration for key variables. Lessons drawn include the importance of consistent interface physics across ocean‐ and land‐only models and the coupled (parent) model, the extreme simulation duration required to approach equilibration targets, and the occurrence of significant regional land carbon drifts despite global‐scale equilibration. Overall, the UKESM1 spin‐up underscores the expense involved and argues in favor of future development of more efficient spin‐up techniques.

We describe the scientific and technical implementation of two models for a core set of\ud experiments contributing to the sixth phase of the Coupled Model Intercomparison Project (CMIP6).\ud The models used are the physical atmosphere-land-ocean-sea ice model HadGEM3-GC3.1 and the\ud Earth system model UKESM1 which adds a carbon-nitrogen cycle and atmospheric chemistry to\ud HadGEM3-GC3.1. The model results are constrained by the external boundary conditions (forcing data)\ud and initial conditions.We outline the scientific rationale and assumptions made in specifying these.\ud Notable details of the implementation include an ozone redistribution scheme for prescribed ozone\ud simulations (HadGEM3-GC3.1) to avoid inconsistencies with the model's thermal tropopause, and land use\ud change in dynamic vegetation simulations (UKESM1) whose influence will be subject to potential biases in\ud the simulation of background natural vegetation.We discuss the implications of these decisions for\ud interpretation of the simulation results. These simulations are expensive in terms of human and CPU\ud resources and will underpin many further experiments; we describe some of the technical steps taken to\ud ensure their scientific robustness and reproducibility.

Increasing the current understanding of how the Earth will respond to a warming climate requires a more accurate representation of aerosols by Earth system models (ESMs). Reducing current uncertainties associated with model estimates of climate change sensitivity to greenhouse gas emissions is hampered by our understanding of the impact aerosol particles have on the radiative budget via their interactions with clouds. The complexity of such interactions leads to their imperfect representation in models. Emissions of marine organic aerosols play a relevant role on cloud formation in regions where there is a high concentration of phytoplankton, for example in the Southern Ocean (SO). Comparisons between GCMs and satellite observations over the SO show that the models simulate too little reflection of shortwave radiation and this is strongly linked with modelled cloud properties. A potential cause of this issue is a source of missing aerosols in the ESM.In this study we evaluate the ability of a state-of-the-art ESM, UKESM1, in reproducing aerosol particles originating from organic marine sources that reach a measurement station in pristine air through long-range transport. UKESM1 is developed by the Met. Office and our simulation is nudged by reanalysis datasets for a fair comparison with observations. This ESM is unique in using its ocean biogeochemistry module to interactively simulate the emission of marine organic aerosols. To this end, a novel Lagrangian trajectory framework for evaluating GCMs has been developed. This method makes use of satellite measurements of chlorophyll concentration (a proxy of phytoplankton abundance in the sea surface) at the sea surface, together with the cloud condensation nuclei (CCN) concentration at 0.5% of supersaturation measured at Cape Grim (Southern Ocean, Tasmania) station. Chlorophyll and wind speed data are collocated along air mass trajectories, which are calculated through the HYSPLIT model. A source-receptor analysis is then performed to look for potential spatial correlation between the collocated chlorophyll concentration experienced by air parcel paths coming from a defined clean air sector of the boundary layer (to avoid anthropogenic influences) and CCN measurements. Additionally, a temporal correlation analysis is performed in this framework. This method is applied to both UKESM1 output data and observations to evaluate aerosol processes in climate models. Preliminary results show a positive correlation in model data between marine organic activity and CCN production that is found also in the observations. Despite the model well representing the seasonal variability of CCN at the station, the model struggles to reproduce the positive relationship obtained from the observations between wind speed and CCN concentration during the winter season. This can be attributed to a potential missing source in the model.

With the ever-growing interest from the general public towards understanding climate science, it is becoming increasingly important that we present this information in ways accessible to non-experts. In this pilot study, we use time series data from the first United Kingdom Earth System model (UKESM1) to create six procedurally generated musical pieces and use them to explain the process of modelling the earth system and to engage with the wider community. Scientific data is almost always represented graphically either in figures or in videos. By adding audio to the visualisation of model data, the combination of music and imagery provides additional contextual clues to aid in the interpretation. Furthermore, the audiolisation of model data can be employed to generate interesting and captivating music, which can not only reach a wider audience, but also hold the attention of the listeners for extended periods of time.Each of the six pieces presented in this work was themed around either a scientific principle or a practical aspect of earth system modelling. These pieces demonstrate the concepts of a spin up, a pre-industrial control run, multiple historical experiments, and the use of several future climate scenarios to a wider audience. They also show the ocean acidification over the historical period, the changes in circulation, the natural variability of the pre-industrial simulations, and the expected rise in sea surface temperature over the 20th century. Each of these pieces were arranged using different musical progression, style and tempo. All six pieces were performed by the digital piano synthesizer, TiMidity++, and were published on the lead author's YouTube channel. The videos all show the progression of the data in time with the music and a brief description of the methodology is posted alongside the video. To disseminate these works, links to each piece were published on the lead author's personal and professional social media accounts. The reach of these works was also analysed using YouTube's channel monitoring toolkit for content creators, YouTube studio.

The Last Interglacial (LIG) is a period of great importance as an analog for future climate change. Global sea level was 6-9 m higher than present. Stronger LIG summertime insolation at high northern latitudes drove Arctic land summer temperatures around 4-5 K higher than during the preindustrial era. Climate-model simulations have previously failed to capture these elevated temperatures. This may be because these models failed to correctly capture LIG sea ice changes.Here, we show that the latest version of the UK Hadley Center coupled ocean-atmosphere climate model (HadGEM3) simulates a much improved Arctic LIG climate, including the observed high temperatures. Improved model physics in HadGEM3, including a sophisticated sea ice melt-pond scheme, results in the first-ever simulation of the complete loss of Arctic sea ice in summer during the LIG.Our ice-free Arctic yields a compelling solution to the long-standing puzzle of what drove LIG Arctic warmth. The LIG simulation result is a new independent constraint on the strength of Arctic sea ice decline in climate-model projections, and provides support for a fast retreat of Arctic summer sea ice in the future.

Methane (CH4) is the second most important anthropogenic greenhouse gas. Its Global Warming Potential over 100 years (GWP100) exceeds 28 times that of CO2. Methane surface concentrations have steadily increased since the pre-industrial due to industrialisation combined with fossil fuel use. In 1850, around the onset of heavy industrialisation in Europe and North America, the CH4 mole fraction was approximately 700 ppbv, and since then it has increased 2.5-fold to slightly more than 1830 ppbv in 2015. Fossil fuel use, raising of livestock, and cultivation of rice are the dominant contributions to the atmospheric methane burden at present with significant emissions from natural wetlands also playing a central role.Here we present first results from the UK Earth System Model (UKESM1.0). The default release version of UKESM1.0 has been extended to represent the methane cycle fully interactively, including dynamic wetlands with global CH4, full stratospheric-tropospheric CH4 chemistry, and CH4 surface deposition. The extended configuration is capable of simulating the climate feedbacks on methane wetland emissions which are typically neglected in current Earth system models. Our simulation is driven with anthropogenic CH4 emissions from CMIP6. We conducted fully-coupled transient simulations of the atmospheric CH4 burden from 1850 to 2100 based on the historic and two future scenarios (SSP3-7.0 and SSP1-2.6) scenarios.We compare the time series of global CH4 surface concentrations between the default CH4 concentration-driven configuration of UKESM1.0 with the fully-interactive emissions-driven configuration. Surface concentrations for the emissions-driven simulation show reasonable agreement with the concentration-driven simulation, but a low bias in the fully interactive simulation gradually emerges after about 1920 which reaches approximately -250 ppbv in the 2000s. We then present a full-cycle CH4 budget analysis based on decadal means for every 50 years between 1850 and 2100. We demonstrate that methane burden and surface mole fractions are expected to return to their 1930s values under SSP1-2.6, albeit with the natural methane sources still heavily disturbed from their original state. We also produce a detailed analysis of the contribution of wetland CH4 emissions for the 250 years of simulation.

Poor air quality is currently responsible for large impacts on human health across the world. In addition, the air pollutants, ozone (O3) and particulate matter less than 2.5 microns in diameter (PM2.5), are also radiatively active in the atmosphere and can influence Earth’s climate. It is important to understand the effect of air quality and climate mitigation measures over the historical period and in different future scenarios to ascertain any impacts from air pollutants on both climate and human health. The 6th Coupled Model Intercomparison Project (CMIP6) presents an opportunity to analyse the change in air pollutants simulated by the current generation of climate and Earth system models that include a representation of chemistry and aerosols (particulate matter). The shared socio-economic pathways (SSPs) used within CMIP6 encompass a wide range of trajectories in precursor emissions and climate change, allowing for an improved analysis of future changes to air pollutants. Firstly, we conduct an evaluation of the available CMIP6 models against surface observations of O3 and PM2.5. CMIP6 models show a consistent overestimation of observed surface O3 concentrations across most regions and in most seasons, with a large diversity in simulated values over northern hemisphere continental regions. Conversely, observed surface PM2.5 concentrations are consistently underestimated by CMIP6 models, particularly for the northern hemisphere winter months, with the largest model diversity near natural emission source regions. Over the historical period (1850–2014) large increases in both surface O3 and PM2.5 are simulated by the CMIP6 models across all regions, particularly over the mid to late 20th Century when anthropogenic emissions increase markedly. Large regional historical changes are simulated for both pollutants, across East and South Asia, with an increase of up to 40 ppb for O3 and 12 µg m-3 for PM2.5. In future scenarios containing strong air quality and climate mitigation measures (ssp126), air pollutants are substantially reduced across all regions by up to 15 ppb for O3 and 12 µg m-3 for PM2.5. However, for scenarios that encompass weak action on mitigating climate and reducing air pollutant emissions (ssp370), increases of both surface O3 (up 10 ppb) and PM2.5 (up to 8 µg m-3) are simulated across most regions. Although, for regions like North America and Europe small reductions in PM2.5 are simulated in this scenario. A comparison of simulated regional changes in both surface O3 and PM2.5 from individual CMIP6 models highlights important differences due to the interaction of aerosols, chemistry, climate and natural emission sources within models. The prediction of regional air pollutant concentrations from the latest climate and Earth system models used within CMIP6 shows that the particular future trajectory of climate and air quality mitigation measures could have important consequences for regional air quality, human health and near-term climate. Differences between individual models emphasises the importance of understanding how future Earth system feedbacks influence natural emission sources.

Scientific data is almost always represented graphically either in figures or in videos. With the ever-growing interest from the general public towards understanding climate science, it is becoming increasingly important that we present this information in ways accessible to non-experts. In this pilot study, we use time series data from the first United Kingdom Earth System model (UKESM1) to create six procedurally generated musical pieces and use them to test whether we can use music to engage with the wider community. Each of these pieces is based around a unique part of UKESM1's ocean component model, either in terms of a scientific principle or a practical aspect of modelling. In addition, each piece is arranged using a different musical progression, style and tempo. These pieces were performed by the digital piano synthesizer, TiMidity++, and were published on the lead author's YouTube channel. The videos all show the time progression of the data in time with the music and a brief description of the methodology is posted below the video. To disseminate these works, a link to each piece was published on the lead authors personal and professional social media accounts. The reach of these works was analysed using YouTube's channel monitoring toolkit for content creators, YouTube studio. In the first ninety days after the first video was published, the six pieces reached at least 251 unique viewers, and have 553 total views. We found that most of the views occurred in the fourteen days immediately after each video was published. In effect, once the concept had been demonstrated to an audience, there was reduced enthusiasm from that audience to return to it immediately. This suggests that to use music effectively as an science outreach tool, the works needs to reach new audiences or new and unique content needs to be delivered to a returning audience.

Poor air quality is currently responsible for large impacts on human health across the world. In addition, the air pollutants ozone (O3) and particulate matter less than 2.5 µm in diameter (PM2.5) are also radiatively active in the atmosphere and can influence Earth's climate. It is important to understand the effect of air quality and climate mitigation measures over the historical period and in different future scenarios to ascertain any impacts from air pollutants on both climate and human health. The Coupled Model Intercomparison Project Phase 6 (CMIP6) presents an opportunity to analyse the change in air pollutants simulated by the current generation of climate and Earth system models that include a representation of chemistry and aerosols (particulate matter). The shared socio-economic pathways (SSPs) used within CMIP6 encompass a wide range of trajectories in precursor emissions and climate change, allowing for an improved analysis of future changes to air pollutants. Firstly, we conduct an evaluation of the available CMIP6 models against surface observations of O3 and PM2.5. CMIP6 models consistently overestimate observed surface O3 concentrations across most regions and in most seasons by up to 16 ppb, with a large diversity in simulated values over Northern Hemisphere continental regions. Conversely, observed surface PM2.5 concentrations are consistently underestimated in CMIP6 models by up to 10 µg m−3, particularly for the Northern Hemisphere winter months, with the largest model diversity near natural emission source regions. The biases in CMIP6 models when compared to observations of O3 and PM2.5 are similar to those found in previous studies. Over the historical period (1850–2014) large increases in both surface O3 and PM2.5 are simulated by the CMIP6 models across all regions, particularly over the mid to late 20th century, when anthropogenic emissions increase markedly. Large regional historical changes are simulated for both pollutants across East and South Asia with an annual mean increase of up to 40 ppb for O3 and 12 µg m−3 for PM2.5. In future scenarios containing strong air quality and climate mitigation measures (ssp126), annual mean concentrations of air pollutants are substantially reduced across all regions by up to 15 ppb for O3 and 12 µg m−3 for PM2.5. However, for scenarios that encompass weak action on mitigating climate and reducing air pollutant emissions (ssp370), annual mean increases in both surface O3 (up 10 ppb) and PM2.5 (up to 8 µg m−3) are simulated across most regions, although, for regions like North America and Europe small reductions in PM2.5 are simulated due to the regional reduction in precursor emissions in this scenario. A comparison of simulated regional changes in both surface O3 and PM2.5 from individual CMIP6 models highlights important regional differences due to the simulated interaction of aerosols, chemistry, climate and natural emission sources within models. The projection of regional air pollutant concentrations from the latest climate and Earth system models used within CMIP6 shows that the particular future trajectory of climate and air quality mitigation measures could have important consequences for regional air quality, human health and near-term climate. Differences between individual models emphasise the importance of understanding how future Earth system feedbacks influence natural emission sources, e.g. response of biogenic emissions under climate change.

Radiative forcing (RF) time series for total ozone from 1850 up to the present day are calculated based on historical simulations of ozone from 10 climate models contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6). In addition, RF is calculated for ozone fields prepared as an input for CMIP6 models without chemistry schemes and from a chemical transport model simulation. A radiative kernel for ozone is constructed and used to derive the RF. The ozone RF in 2010 (2005–2014) relative to 1850 is 0.35 W m−2 [0.08–0.61] (5–95% uncertainty range) based on models with both tropospheric and stratospheric chemistry. One of these models has a negative present-day total ozone RF. Excluding this model, the present-day ozone RF increases to 0.39 W m−2 [0.27–0.51] (5–95% uncertainty range). The rest of the models have RF close to or stronger than the RF time series assessed by the Intergovernmental Panel on Climate Change in the fifth assessment report with the primary driver likely being the new precursor emissions used in CMIP6. The rapid adjustments beyond stratospheric temperature are estimated to be weak and thus the RF is a good measure of effective radiative forcing.

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.

The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool designed to improve comprehensive and routine evaluation of Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP). It has undergone rapid development since the first release in 2016 and is now a well-tested tool that provides end-to-end provenance tracking to ensure reproducibility. It consists of an easy-to-install, well documented Python package providing the core functionalities (ESMValCore) that performs common pre-processing operations and a diagnostic part that includes tailored diagnostics and performance metrics for specific scientific applications. Here we describe large-scale diagnostics of the second major release of the tool that supports the evaluation of ESMs participating in CMIP Phase 6 (CMIP6). ESMValTool v2.0 includes a large collection of diagnostics and performance metrics for atmospheric, oceanic, and terrestrial variables for the mean state, trends, and variability. ESMValTool v2.0 also successfully reproduces figures from the evaluation and projections chapters of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) and incorporates updates from targeted analysis packages, such as the NCAR Climate Variability Diagnostics Package for the evaluation of modes of variability the Thermodynamic Diagnostic Tool (TheDiaTo) to evaluate the energetics of the climate system, as well as parts of AutoAssess that contains a mix of top-down performance metrics. The tool has been fully integrated into the Earth System Grid Federation (ESGF) infrastructure at the Deutsches Klima Rechenzentrum (DKRZ) to provide evaluation results from CMIP6 model simulations shortly after the output is published to the CMIP archive. A result browser has been implemented that enables advanced monitoring of the evaluation results by a broad user community at much faster timescales than what was possible in CMIP5.

Tropospheric Ozone (O3) is both an air pollutant and a greenhouse gas. Predicting changes to O3 is therefore important for both air quality and near-term climate forcing. It is computationally expensive to predict changes in tropospheric O3 from every possible future scenario in composition climate models like those used in the 6th Coupled Model Intercomparison Project (CMIP6). Here we apply the different emission pathways used in CMIP6 with a model based on source-receptor relationships for tropospheric O3 to predict historical and future changes in O3 and its radiative forcing over a 300 year period (1750–2050). Changes in regional precursor emissions (nitrogen oxides, carbon monoxide and volatile organic compounds) and global methane abundance are used to quantify the impact on tropospheric O3 globally and across 16 regions, neglecting any impact from changes in climate. We predict large increases in global surface O3 (+8 ppbv) and O3 radiative forcing (+0.3 W m−2) over the industrial period. Nine different Shared Socio-economic Pathways are used to assess future changes in O3. Scenarios involving weak air pollutant controls and climate mitigation are inadequate in limiting the future degradation of surface O3 air quality and enhancement of near-term climate warming over all regions. Middle-of-the-road and strong mitigation scenarios reduce both surface O3 concentrations and O3 radiative forcing by up to 5 ppbv and 0.17 W m−2 globally, providing benefits to future air quality and near-term climate forcing. Sensitivity experiments show that targeting mitigation measures towards reducing global methane abundances could yield additional benefits for both surface O3 air quality and near-term climate forcing. The parameterisation provides a valuable tool for rapidly assessing a large range of future emission pathways that involve differing degrees of air pollutant and climate mitigation. The calculated range of possible responses in tropospheric O3 from these scenarios can be used to inform other modelling studies in CMIP6.

The biogeochemical evaluation toolkit, BGC-val, is a model- and grid-independent Python toolkit that has been built to evaluate marine biogeochemical models using a simple interface. Here, we present the ideas that motivated the development of the BGC-val software framework, introduce the code structure, and show some applications of the toolkit using model results from the Fifth Climate Model Intercomparison Project (CMIP5). A brief outline of how to access and install the repository is presented in Appendix A, but the specific details on how to use the toolkit are kept in the code repository. The key ideas that directed the toolkit design were model and grid independence, front-loading analysis functions and regional masking, interruptibility, and ease of use. We present each of these goals, why they were important, and what we did to address them. We also present an outline of the code structure of the toolkit illustrated with example plots produced by the toolkit. After describing BGC-val, we use the toolkit to investigate the performance of the marine physical and biogeochemical quantities of the CMIP5 models and highlight some predictions about the future state of the marine ecosystem under a business-as-usual CO2 concentration scenario (RCP8.5).

Aerosol processes and, in particular, aerosol-cloud interactions cut across the traditional physical-Earth system boundary of coupled Earth system models and remain one of the key uncertainties in estimating anthropogenic radiative forcing of climate. Here we calculate the historical aerosol effective radiative forcing (ERF) in the HadGEM3-GA7 climate model in order to assess the suitability of this model for inclusion in the UK Earth system model, UKESM1. The aerosol ERF, calculated for the year 2000 relative to 1850, is large and negative in the standard GA7 model leading to an unrealistic negative total anthropogenic forcing over the twentieth century. We show how underlying assumptions and missing processes in both the physical model and aerosol parameterizations lead to this large aerosol ERF. A number of model improvements are investigated to assess their impact on the aerosol ERF. These include an improved representation of cloud droplet spectral dispersion, updates to the aerosol activation scheme, and black carbon optical properties. One of the largest contributors to the aerosol forcing uncertainty is insufficient knowledge of the preindustrial aerosol climate. We evaluate the contribution of uncertainties in the natural marine emissions of dimethyl sulfide and organic aerosol to the ERF. The combination of model improvements derived from these studies weakens the aerosol ERF by up to 50% of the original value and leads to a total anthropogenic historical forcing more in line with assessed values.

Multi-model ensembles for sea surface temperature (SST), sea surface salinity (SSS), sea surface currents (SSC), and water transports have been developed for the North Sea and the Baltic Sea using outputs from several operational ocean forecasting models provided by different institutes. The individual models differ in model code, resolution, boundary conditions, atmospheric forcing, and data assimilation. The ensembles are produced on a daily basis. Daily statistics are calculated for each parameter giving information about the spread of the forecasts with standard deviation, ensemble mean and median, and coefficient of variation. High forecast uncertainty, i.e., for SSS and SSC, was found in the Skagerrak, Kattegat (Transition Area between North Sea and Baltic Sea), and the Norwegian Channel. Based on the data collected, longer-term statistical analyses have been done, such as a comparison with satellite data for SST and evaluation of the deviation between forecasts in temporal and spatial scale. Regions of high forecast uncertainty for SSS and SSC have been detected in the Transition Area and the Norwegian Channel where a large spread between the models might evolve due to differences in simulating the frontal structures and their movements. A distinct seasonal pattern could be distinguished for SST with high uncertainty between the forecasts during summer. Forecasts with relatively high deviation from the multi-model ensemble (MME) products or the other individual forecasts were detected for each region and each parameter. The comparison with satellite data showed that the error of the MME products is lowest compared to those of the ensemble members.

The biogeochemical evaluation toolkit, BGC-val, is a model and grid independent python toolkit that has been built to evaluate marine biogeochemical models using a simple interface. Here, we present the ideas that motivated the development of the BGC-val software framework, introduce the code structure, and show some applications of the toolkit using model results from the Fifth Climate Model Intercomparison Project (CMIP5). The key ideas that directed the toolkit design were model and grid independence, front loading analysis functions and regional masking, interuptabililty, and ease of use. We present each of these goals, why they were important and what we did to address them. We also present an outline of the code structure of the toolkit, illustrated with example plots produced by the toolkit. After describing BGC-val, we use the toolkit to investigate the performance of the marine circulate and biogeochemical parts of the CMIP5 models, and highlight some predictions about the future state of the marine ecosystem under a business as usual CO2 concentration scenario (RCP 8.5).

We describe Global Atmosphere 7.0 and Global Land 7.0 (GA7.0/GL7.0): the latest science configurations of the Met Office Unified Model and JULES land surface model developed for use across weather and climate timescales. GA7.0 and GL7.0 include incremental developments and targeted improvements that between them address four critical errors identified in previous configurations: excessive precipitation biases over India, warm/moist biases in the tropical tropopause layer, a source of energy non-conservation in the advection scheme and excessive surface radiation biases over the Southern Ocean. They also include two new parametrisations, namely the UKCA Glomap-mode aerosol scheme and the JULES multi-layer snow scheme, which improve the fidelity of the simulation and were required for the coupled climate models and Earth system models that will use these configurations in submissions to CMIP6. In addition, we describe the GA7.1 branch configuration, which reduces an overly negative anthropogenic aerosol effective radiative forcing in GA7.0, whilst maintaining the quality of simulations of the present-day climate. GA7.1/GL7.0 will form the physical atmosphere/land component in the HadGEM3-GC3.1 and UKESM1 climate models.

Temperature and salinity profiles are being obtained from instrumented marine mammals in near real-time. The mammals, mostly elephant seals, sample to depths of up to 2000 m in high-latitude regions where there are very few other in situ observations. This study investigates the impact of mammal temperature and salinity observations on the UK Met Office's global ocean forecasting model, focussing on impacts in the Southern Ocean region. Three reanalyses were performed, each assimilating a varying amount of seal data whilst holding all other conditions constant. Assimilation of both temperature and salinity profiles from mammals has a detrimental effect on the model's temperature and salinity fields due to a high bias in the salinity values. Restricting assimilation to temperature profiles only has benefits for both temperature and salinity fields in regions of the ocean where seals are sampling, improving root-mean-square error statistics by between 1 and 6% compared to the case where no mammal data were assimilated. In addition, the assimilation of seal temperature profiles alters the mixed-layer depth and repositions and increases gradients of some fronts compared to the no-seals case.

Under GODAE OceanView the operational ocean modelling community has developed a suite of global ocean forecast, reanalysis and analysis systems. Each system has a critical dependence on ocean obser...

The Forecast Ocean Assimilation Model (FOAM) is an operational ocean analysis and forecast system run daily at the Met Office. FOAM provides modelling capability in both deep ocean and coastal shelf sea regimes using the NEMO (Nucleus for European Modelling of the Ocean) ocean model as its dynamical core. The FOAM Deep Ocean suite produces analyses and 7-day forecasts of ocean tracers, currents and sea ice for the global ocean at 1/4° resolution. Satellite and in situ observations of temperature, salinity, sea level anomaly and sea ice concentration are assimilated by FOAM each day over a 48 h observation window. The FOAM Deep Ocean configurations have recently undergone a major upgrade which has involved the implementation of a new variational, first guess at appropriate time (FGAT) 3D-Var, assimilation scheme (NEMOVAR); coupling to a different, multi-thickness-category, sea ice model (CICE); the use of coordinated ocean-ice reference experiment (CORE) bulk formulae to specify the surface boundary condition; and an increased vertical resolution for the global model. In this paper the new FOAM Deep Ocean system is introduced and details of the recent changes are provided. Results are presented from 2-year reanalysis integrations of the Global FOAM configuration including an assessment of short-range ocean forecast accuracy. Comparisons are made with both the previous FOAM system and a non-assimilative FOAM system. Assessments reveal considerable improvements in the new system to the near-surface ocean and sea ice fields. However there is some degradation to sub-surface tracer fields and in equatorial regions which highlights specific areas upon which to focus future improvements.

The scientific understanding of the Earth's climate system, including the central question of how the climate system is likely to respond to human-induced perturbations, is comprehensively captured in GCMs and Earth System Models(ESM). Diagnosing the simulated climate response, and comparing responses across different models, is crucially dependent on transparent assumptions of how the GCM/ESM has been driven – especially because the implementation can involve subjective decisions and may differ between modelling groups performing the same experiment. This paper outlines the climate forcings and setup of the Met Office Hadley Centre ESM, HadGEM2-ES for the CMIP5 set of centennial experiments. We document the prescribed greenhouse gas concentrations, aerosol precursors, stratospheric and tropospheric ozone assumptions, as well as implementation of land-use change and natural forcings for the HadGEM2-ES historical and future experiments following the Representative Concentration Pathways. In addition, we provide details of how HadGEM2-ES ensemble members were initialised from the control run and how the palaeoclimate and AMIP experiments, as well as the "emission-driven" RCP experiments were performed.

Operational ocean forecast systems provide routine marine products to an ever-widening community of users and stakeholders. The majority of users need information about the quality and reliability of the products to exploit them fully. Hence, forecast centres have been developing improved methods for evaluating and communicating the quality of their products. Global Ocean Data Assimilation Experiment (GODAE) OceanView, along with the Copernicus European Marine Core Service and other national and international programmes, has facilitated the development of coordinated validation activities among these centres. New metrics, assessing a wider range of ocean parameters, have been defined and implemented in real-time. An overview of recent progress and emerging international standards is presented here.