BGC-val: a model- and grid-independent Python toolkit to evaluate marine biogeochemical models

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)

The simulation of mineral dust in the United Kingdom Earth System Model UKESM1

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

Using Machine Learning to Make Computationally Inexpensive Projections of 21st Century Stratospheric Column Ozone Changes in the Tropics

Stratospheric ozone projections in the tropics, modeled using the UKESM1 Earth system model, are explored under different Shared Socioeconomic Pathways (SSPs). Consistent with other studies, it is found that tropical stratospheric column ozone does not return to 1980s values by the end of the 21st century under any SSP scenario as increased ozone mixing ratios in the tropical upper stratosphere are offset by continued ozone decreases in the tropical lower stratosphere. Stratospheric column ozone is projected to be largest under SSP scenarios with the smallest change in radiative forcing, and smallest for SSP scenarios with larger radiative forcing, consistent with a faster Brewer-Dobson circulation at high greenhouse gas loadings. This study explores the use of machine learning (ML) techniques to make accurate, computationally inexpensive projections of tropical stratospheric column ozone. Four ML techniques are investigated: Ridge regression, Lasso regression, Random Forests and Extra Trees. All four techniques investigated here are able to make projections of future tropical stratospheric column ozone which agree well with those made by the UKESM1 Earth system model, often falling within the ensemble spread of UKESM1 simulations for a broad range of SSPs. However, all techniques struggle to make accurate projects for the final decades of the SSP5-8.5 scenario. Accurate projections can only be achieved when the ML methods are trained on sufficient data, including both historical and future simulations. When trained only on historical data, the projections made using models based on ML techniques fail to accurately predict tropical stratospheric ozone changes. Results presented here indicate that, when sufficiently trained, ML models have the potential to make accurate, computationally inexpensive projections of tropical stratospheric column ozone. Further development of these models may reduce the computational burden placed on fully coupled chemistry-climate and Earth system models and enable the exploration of tropical stratospheric column ozone recovery under a much broader range of future emissions scenarios

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

UK community Earth system modelling for CMIP6

We describe the approach taken to develop the UK’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 UK academic community. We document our model development procedure and the subsequent UK 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 the physical model, 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 (5.5K and 5.4K 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 modelling landscape indicating how our understanding of key processes driving higher sensitivity in the two UK models seems to align with results from a number of other CMIP6 models

Implementation of U.K. Earth system models for CMIP6

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

Forcings, feedbacks and climate sensitivity in HadGEM3‐GC3.1 and UKESM1

Climate forcing, sensitivity and feedback metrics are evaluated in both the UK’s physical climate model HadGEM3-GC3.1at low (-LL) and medium(-MM) resolution and the UK’s Earth System Model UKESM1. The Effective Climate Sensitivity (EffCS)to a doubling of CO2 is 5.5K for HadGEM3.1-GC3.1-LL and 5.4 K for UKESM1. The transient climate response is 2.5K and 2.8K respectively. Whilst 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 UK climate model, HadGEM3-GC2.0, the EffCS has increased from 3.2K to 5.5K due to an increase in CO2 forcing, surface albedo feedback and mid-latitude 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 stratocumulous 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 Wm-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

Evaluating the physical and biogeochemical state of the global ocean component of UKESM1 in CMIP6 historical simulations

The ocean plays a key role in modulating the climate of the Earth system (ES). At the present time it is also a major sink both for the carbon dioxide (CO2) released by human activities and for the excess heat driven by the resulting atmospheric greenhouse effect. Understanding the ocean's role in these processes is critical for model projections of future change and its potential impacts on human societies. A necessary first step in assessing the credibility of such future projections is an evaluation of their performance against the present state of the ocean. Here we use a range of observational fields to validate the physical and biogeochemical performance of the ocean component of UKESM1, a new Earth system model (ESM) for CMIP6 built upon the HadGEM3-GC3.1 physical climate model. Analysis focuses on the realism of the ocean's physical state and circulation, its key elemental cycles, and its marine productivity. UKESM1 generally performs well across a broad spectrum of properties, but it exhibits a number of notable biases. Physically, these include a global warm bias inherited from model spin-up, excess northern sea ice but insufficient southern sea ice and sluggish interior circulation. Biogeochemical biases found include shallow remineralization of sinking organic matter, excessive iron stress in regions such as the equatorial Pacific, and generally lower surface alkalinity that results in decreased surface and interior dissolved inorganic carbon (DIC) concentrations. The mechanisms driving these biases are explored to identify consequences for the behaviour of UKESM1 under future climate change scenarios and avenues for model improvement. Finally, across key biogeochemical properties, UKESM1 improves in performance relative to its CMIP5 precursor and performs well alongside its fellow members of the CMIP6 ensemble

The evaluation of the North Atlantic climate system in UKESM1 historical simulations for CMIP6

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 multi-variate 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 timescale 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 multi-decadal change in UKESM1, especially for Atlantic Multi-decadal 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; intra-seasonal 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

Description and evaluation of aerosol in UKESM1 and HadGEM3-GC3.1 CMIP6 historical simulations

We document and evaluate the aerosol schemes as implemented in the physical and Earth system models, HadGEM3-GC3.1 (GC3.1) and UKESM1, which are contributing to the 6th Coupled Model Intercomparison Project (CMIP6). The simulation of aerosols in the present-day period of the historical ensemble of these models is evaluated against a range of observations. Updates to the aerosol microphysics scheme are documented as well as differences in the aerosol representation between the physical and Earth system configurations. The additional Earth-system interactions included in UKESM1 leads to differences in the emissions of natural aerosol sources such as dimethyl sulfide, mineral dust and organic aerosol and subsequent evolution of these species in the model. UKESM1 also includes a stratospheric-tropospheric chemistry scheme which is fully coupled to the aerosol scheme, while GC3.1 employs a simplified aerosol chemistry mechanism driven by prescribed monthly climatologies of the relevant oxidants. Overall, the simulated speciated aerosol mass concentrations compare reasonably well with observations. Both models capture the negative trend in sulfate aerosol concentrations over Europe and the eastern United States of America (US) although the models tend to underestimate the sulfate concentrations in both regions. Interactive emissions of biogenic volatile organic compounds in UKESM1 lead to an improved agreement of organic aerosol over the US. Simulated dust burdens are similar in both models despite a 2-fold difference in dust emissions. Aerosol optical depth is biased low in dust source and outflow regions but performs well in other regions compared to a number of satellite and ground-based retrievals of aerosol optical depth. Simulated aerosol number concentrations are generally within a factor of 2 of the observations with both models tending to overestimate number concentrations over remote ocean regions, apart from at high latitudes, and underestimate over Northern Hemisphere continents. Finally, a new primary marine organic aerosol source is implemented in UKESM1 for the first time. The impact of this new aerosol source is evaluated. Over the pristine Southern Ocean, it is found to improve the seasonal cycle of organic aerosol mass and cloud droplet number concentrations relative to GC3.1 although underestimations in cloud droplet number concentrations remain. This paper provides a useful characterization of the aerosol climatology in both models facilitating the understanding of the numerous aerosol-climate interaction studies that will be conducted as part of CMIP6 and beyond