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Agricultural and environmental policies are being fundamentally reviewed and redesigned in the UK following its exit from the European Union. The UK government and the Devolved Administrations recognise that current land use is not sustainable and that there is now an unprecedented opportunity to define a better land strategy that responds fully to the interconnected challenges of climate change, biodiversity loss and sustainable development. This paper presents evidence from three pathways (current trends, sustainable medium ambition, and sustainable high ambition) to mid-century that were co-created with UK policymakers. The pathways were applied to a national integrated food and land-use model (the FABLE calculator) to explore potential synergies and trade-offs between achieving multiple sustainability targets under limited land availability and constraints to balance food supply and demand at national and global levels. Results show that under the Current Trends pathway all unprotected open natural land would be converted to urban, agriculture and afforested land, with the consequence that from 2030 onwards tree planting targets could not be met. In contrast, the two sustainable pathways illustrate how dietary change, agricultural productivity improvements and waste reduction can free up land for nature recovery and carbon sequestration. This enables a transition to a sustainable food and land-use system that provides a net carbon sink with up to 44% of land able to support biodiversity conservation. We highlight key trade-offs and synergies, which are important to consider for designing and implementing emerging national policies. These include the strong dependence of climate, food and biodiversity targets on dietary shifts, sustainable improvements in agricultural productivity, improved land-use design for protecting and restoring nature, and rapid reductions in food loss and waste.Supplementary informationThe online version contains supplementary material available at 10.1007/s11625-022-01242-8.

Here we explore the use of “counterfactual” weather forecasts, using perturbed initial condition runs of a state-of-the-art high-resolution coupled ocean-atmosphere-sea-ice ensemble NWP system, for the attribution of extreme weather events to anthropogenic climate change. We use the “record-shattering” heatwave experienced by Western North America during summer 2021 as a case study - though our forecast-based approach is applicable to other events.Since we cannot make direct observations of a world without human influence on climate, all approaches to extreme event attribution involve some kind of modelling, either statistical or numerical. Both approaches struggle with the most extreme weather events, which are poorly represented in both observational records and the climate models normally used for attribution studies. Recognising the compromises involved, researchers have traditionally relied on comparing results from several different approaches to assess the robustness of conclusions. We argue that a better approach would be to use initialised numerical models that have demonstrated their ability to simulate the event in question through a successful forecast.This work represents a continuation of a previous EGU talk and published study (https://meetingorganizer.copernicus.org/EGU21/EGU21-5731.html & https://doi.org/10.1073/pnas.2112087118), in which we used demonstrably successful weather forecasts to estimate the direct impact of increased CO2 concentrations (one component, but not the entirety, of human influence) on the 2019 European winter heatwave. In the previous and current work we use the operational ECMWF ensemble prediction system. This state-of-the-art weather forecast system is run at a much higher resolution (Tco639 / 18km) than most climate model simulations - important as even small reductions in resolution often change the representation of extreme events in numerical models. Using a reliable forecast ensemble allows us to quantify the associated uncertainties in our attribution analyses.We have built on this work with the aim of providing a more complete estimate of the human influence on an isolated extreme event. In addition to the reduction of CO2 concentrations back to pre-industrial levels, we now also remove an estimate of the human influence on 3D ocean temperatures since the pre-industrial period from the initial state of the forecast model. These changes allow the model to provide a “counterfactual” picture of what an extreme event might have looked like if it had occurred before human influence on the climate.Using this perturbed initial condition approach, we produce counterfactual forecasts of the Pacific Northwest heatwave at the end of June 2021. This event broke records throughout Western North America, including a new Canadian high temperature record of 49.6°C, shattering the previous record by almost 5°C. The heatwave was driven by a combination of meteorological factors, including an omega block and water vapour transport at the synoptic scale, and high solar irradiation and subsidence at the meso-scale (research into the drivers is ongoing). Crucially, the event was well-predicted by weather forecast models over a week in advance.We estimate the human contribution to this exceptional heatwave by comparing our counterfactual forecasts to the operational forecasts that successfully predicted the event.

Recent extreme weather across the globe highlights the need to understand the potential for more extreme events in the present-day, and how such events may change with global warming. We present a methodology for more efficiently sampling extremes in future climate projections. As a proof-of-concept, we examine the UK’s most recent set of national Climate Projections (UKCP18). UKCP18 includes a 15-member perturbed parameter ensemble (PPE) of coupled global simulations, providing a range of climate projections incorporating uncertainty in both internal variability and forced response. However, this ensemble is too small to adequately sample extremes with very high return periods, which are of interest to policy-makers and adaptation planners. To better understand the statistics of these events, we use distributed computing to run three 1000-member initial-condition ensembles with the atmosphere-only HadAM4 model at 60km resolution on volunteers’ computers, taking boundary conditions from three distinct future extreme winters within the UKCP18 ensemble. We find that the magnitude of each winter extreme is captured within our ensembles, and that two of the three ensembles are conditioned towards producing extremes by the boundary conditions. Our ensembles contain several extremes that would only be expected to be sampled by a UKCP18 PPE of over 500 members, which would be prohibitively expensive with current supercomputing resource. The most extreme winters we simulate exceed those within UKCP18 by 0.85 K and 37% of the present-day average for UK winter means of daily maximum temperature and precipitation respectively. As such, our ensembles contain a rich set of multivariate, spatio-temporally and physically coherent samples of extreme winters with wide-ranging potential applications.

Between the 21st and 27th February 2019, climatologically exceptional warm temperature anomalies of 10-15 °C were experienced throughout Northern and Western Europe. In particular, the 25th - 27th February saw record-breaking temperatures measured at many weather stations over wide areas of Iberia, France, the British Isles, the Netherlands, Germany and Southern Sweden. This heatwave was well-predicted by the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system. Their forecasts indicated "extreme" heat was possible at a lead time of around two weeks, and likely at a lead time of around ten days. The performance of these forecasts in predicting the surface heat is also reflected in their ability to predict the synoptic situation. We exploit this successful forecast to perform an attribution analysis of the heatwave that differs from "conventional" analyses in several key regards. Firstly, we are not only confident that the model used is able to simulate the event in question; but that we are unequivocally studying the specific winter heatwave that occurred in Europe during February 2019. This analysis is carried out using a state-of-the-art coupled high-resolution forecast model ensemble, as opposed to the prescribed-SST experiments within climate model ensembles traditionally used for attribution.A crucial distinction between the typical climate model simulations used for attribution and the forecasts used here is that the climate model simulations are usually allowed to spin out for a sufficient length of time such that they have no memory of their initial conditions; an ensemble constructed in this way will therefore be representative of the climatology of the model (possibly conditioned on any prescribed-SST patterns). Unlike these climatological simulations, the successful forecasts used here are clearly heavily dependent on the initial conditions used. Within these simulations, the level of dynamical conditioning can therefore be specified by altering the lead time from initialisation to the event in question. We explore the implications of this aspect of forecast-based attribution, attempting to integrate between the "conventional" climatological and "storyline" frameworks of attribution. To simplify the interpretation of our experiments, here we have decided to only change a single feature between our factual and counterfactual experiments. The analysis presented is therefore limited to attributing the impact of diabatic heating due to increased CO2 concentrations above pre-industrial levels just over the days between the model initialisation date and the event. We carry out simulations at four different lead times from the event, allowing us to investigate the balance between the level of conditioning of the ensemble and the relaxation of the ensemble toward a new equilibrium at the lowered CO2 concentrations.

Here we present an update to the FaIR model for use in probabilistic future climate and scenario exploration, integrated assessment, policy analysis and education. In this update we have focussed on identifying a minimum level of structural complexity in the model. The result is a set of six equations, five of which correspond to the standard Impulse Response model used for greenhouse gas (GHG) metric calculations in the IPCC's fifth assessment report, plus one additional physically-motivated additional equation to represent state-dependent feedbacks on the response timescales of each greenhouse gas cycle. This additional equation is necessary to reproduce non-linearities in the carbon cycle apparent in both Earth System Models and observations. These six equations are transparent and sufficiently simple that the model is able to be written in standard tabular data analysis packages, such as Excel; increasing the potential user base considerably. However, we demonstrate that the equations are flexible enough to be tuned to emulate the behaviour of several key processes within more complex models from CMIP6. The model is exceptionally quick to run, making it ideal for integrating large probabilistic ensembles. We apply a constraint based on the current estimates of the global warming trend to a one million member ensemble, using the constrained ensemble to make scenario dependent projections and infer ranges for properties of the climate system. Through these analyses, we reaffirm that simple climate models (unlike more complex models) are not themselves intrinsically biased hot or cold: it is the choice of parameters and how those are selected that determines the model response, something that appears to have been misunderstood in the past. This updated FaIR model is able to reproduce the global climate system response to GHG and aerosol emissions with sufficient accuracy to be useful in a wide range of applications; and therefore could be used as a lowest common denominator model to provide consistency in different contexts. The fact that FaIR can be written down in just six equations greatly aids transparency in such contexts.

Here we present a Generalised Impulse Response (GIR) model for use in probabilistic future climate and scenario exploration, integrated assessment, policy analysis and teaching. This model is based on a set of only six equations, which correspond to the standard Impulse Response model used for greenhouse gas metric calculations by the IPCC, plus one physically-motivated additional equation to represent state-dependent feedbacks on the response timescales of each greenhouse gas cycle. These six equations are simple and transparent enough to be easily understood and implemented in other models without reliance on the original source code, but flexible enough to reproduce observed well-mixed greenhouse gas (GHG) concentrations and atmospheric lifetimes, best-estimate effective radiative forcing, and temperature response. We describe the assumptions and methods used in selecting the default parameters, but emphasize that other methods would be equally valid: our focus here is on identifying a minimum level of structural complexity. The tunable nature of the model lends it to use as a fully transparent emulator of complex Earth System Models, such as those participating in CMIP6, while also reproducing the behaviour of other simple climate models. We argue that this GIR model is adequate to reproduce the global temperature response to global emissions and effective radiative forcing, and that it should be used as a lowest-common denominator to provide consistency and continuity between different climate assessments. The model design is such that it can be written in tabular data analysis software, such as Excel, increasing the potential user base considerably.

Here we present results from the first phase of the Reduced Complexity Model Intercomparison Project (RCMIP). RCMIP is a systematic examination of reduced complexity climate models (RCMs), which are used to complement and extend the insights from more complex Earth System Models (ESMs), in particular those participating in the Sixth Coupled Model Intercomparison Project (CMIP6). In Phase 1 of RCMIP, with 14 participating models namely ACC2, AR5IR (2 and 3 box versions), CICERO-SCM, ESCIMO, FaIR, GIR, GREB, Hector, Held et al. two layer model, MAGICC, MCE, OSCAR and WASP, we highlight the structural differences across various RCMs and show that RCMs are capable of reproducing global-mean surface air temperature (GSAT) changes of ESMs and historical observations. We find that some RCMs are capable of emulating the GSAT response of CMIP6 models to within a root-mean square error of 0.2 °C (of the same order of magnitude as ESM internal variability) over a range of scenarios. Running the same model configurations for both RCP and SSP scenarios, we see that the SSPs exhibit higher effective radiative forcing throughout the second half of the 21st Century. Comparing our results to the difference between CMIP5 and CMIP6 output, we find that the change in scenario explains approximately 46 % of the increase in higher end projected warming between CMIP5 and CMIP6. This suggests that changes in ESMs from CMIP5 to CMIP6 explain the rest of the increase, hence the higher climate sensitivities of available CMIP6 models may not be having as large an impact on GSAT projections as first anticipated. A second phase of RCMIP will complement RCMIP Phase 1 by exploring probabilistic results and emulation in more depth to provide results available for the IPCC's Sixth Assessment Report author teams.

Simple climate models can be valuable if they are able to replicate aspects of complex fully coupled earth system models. Larger ensembles can be produced, enabling a probabilistic view of future climate change. A simple emissions-based climate model, FAIR, is presented, which calculates atmospheric concentrations of greenhouse gases and effective radiative forcing (ERF) from greenhouse gases, aerosols, ozone and other agents. Model runs are constrained to observed temperature change from 1880 to 2016 and produce a range of future projections under the Representative Concentration Pathway (RCP) scenarios. The constrained estimates of equilibrium climate sensitivity (ECS), transient climate response (TCR) and transient climate response to cumulative CO2 emissions (TCRE) are 2.86 (2.01 to 4.22) K, 1.53 (1.05 to 2.41) K and 1.40 (0.96 to 2.23) K (1000 GtC)−1 (median and 5–95 % credible intervals). These are in good agreement with the likely Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) range, noting that AR5 estimates were derived from a combination of climate models, observations and expert judgement. The ranges of future projections of temperature and ranges of estimates of ECS, TCR and TCRE are somewhat sensitive to the prior distributions of ECS∕TCR parameters but less sensitive to the ERF from a doubling of CO2 or the observational temperature dataset used to constrain the ensemble. Taking these sensitivities into account, there is no evidence to suggest that the median and credible range of observationally constrained TCR or ECS differ from climate model-derived estimates. The range of temperature projections under RCP8.5 for 2081–2100 in the constrained FAIR model ensemble is lower than the emissions-based estimate reported in AR5 by half a degree, owing to differences in forcing assumptions and ECS∕TCR distributions.

Some of the differences between recent estimates of the remaining budget of carbon dioxide (CO2) emissions consistent with limiting warming to 1.5 °C arise from different estimates of the level of warming to date relative to pre-industrial conditions, but not all. Here we show that, for simple geometrical reasons, the combination of both the level and rate of human-induced warming provides a remarkably accurate prediction of remaining emission budgets to peak warming across a broad range of scenarios, if budgets are expressed in terms of CO2-forcing-equivalent emissions. These in turn predict CO2 emissions budgets if (but only if) the fractional contribution of non-CO2 drivers to warming remains approximately unchanged, as it does in some ambitious mitigation scenarios, indicating a best-estimate remaining budget for 1.5 °C of about 22 years’ current emissions from mid-2017, with a ‘likely’ (1 standard error) range of 13–32 years. This provides a simple, transparent and model-independent metric of progress towards an ambitious temperature stabilization goal that could be used to inform the Paris Agreement stocktake process. It is less applicable to less ambitious goals. Alternative definitions of current warming and scenarios for non-CO2 drivers give lower 1.5 °C budgets. Lower budgets based on the MAGICC simple modelling system widely used in integrated assessment studies reflect its relatively high simulated current warming rates. A combination of the level and rate of human-induced warming allows estimation of remaining emission budgets to peak warming across a broad range of scenarios, suggests an analysis of emissions budgets expressed in terms of CO2-forcing-equivalent emissions.

The relationship between cumulative CO2 emissions and CO2-induced warming is determined by the Transient Climate Response to Emissions (TCRE), but total anthropogenic warming also depends on non-CO2 forcing, complicating the interpretation of emissions budgets based on CO2 alone. An alternative is to frame emissions budgets in terms of CO2-forcing-equivalent (CO2-fe) emissions – the CO2 emissions that would yield a given total anthropogenic radiative forcing pathway. Unlike conventional ‘CO2-equivalent’ emissions, these are directly related to warming by the TCRE and need to fall to zero to stabilise warming: hence CO2-fe emissions generalise the concept of a cumulative carbon budget to multi-gas scenarios. Cumulative CO2-fe emissions from 1870-2015 inclusive are found to be 2900 ± 600GtCO2-fe, increasing at a rate of 67 ± 9.5GtCO2-fe/year. A TCRE range of 0.8–2.5° Cper 1,000 GtC implies a total budget for 0.6° C of additional warming above the present decade of 880–2,750 GtCO2-fe, with 1,290 GtCO2-fe implied by the CMIP5 median response, corresponding to 19 years' CO2-fe emissions at the current rate.

Simple climate models can be valuable if they are able to replicate aspects of complex fully coupled earth system models. Larger ensembles can be produced, enabling a probabilistic view of future climate change. A simple emissions-based climate model, FAIR, is presented which calculates atmospheric concentrations of greenhouse gases and effective radiative forcing (ERF) from greenhouse gases, aerosols, ozone precursors and other agents. The ERFs are integrated into global mean surface temperature change. Model runs are constrained to observed temperature change from 1880 to 2016 and produce a range of future projections under the Representative Concentration Pathway (RCP) scenarios. For the historical period the ERF time series in FAIR emulates the results in the IPCC Fifth Assessment Report (AR5), whereas for RCP historical and future scenarios, the greenhouse gas concentrations in FAIR closely track the observations and projections in the RCPs. The constrained estimates of equilibrium climate sensitivity (ECS) of 2.79 (1.97 to 4.08) K, transient climate response (TCR) of 1.47 (1.03 to 2.23) K and transient climate response to cumulative CO2 emissions (TCRE) of 1.43 (1.01 to 2.16) K (1000 GtC)−1 (median and 5–95 % credible intervals) are in good agreement, with tighter uncertainty bounds, than AR5 (1.5 to 4.5 K, 1.0 to 2.5 K, and 0.8 to 2.5 K respectively). The ranges of future projections of temperature and ranges of estimates of ECS, TCR and TCRE are moderately sensitive to the historical temperature dataset used to constrain, prior distributions of ECS/TCR parameters, aerosol radiative forcing relationship and ERF from a doubling of CO2. Taking these sensitivities into account, there is no evidence to suggest that the median and credible range of observationally constrained TCR or ECS differ from climate model-derived estimates. However, the range of temperature projections under the RCP scenarios for 2081–2100 in the constrained FAIR model ensemble are lower than the emissions-based estimates reported in AR5.

c-type cytochromes are characterized by covalent attachment of haem to the protein by two thioether bonds formed between the haem vinyl groups and the cysteine sulphurs in a CXXCH peptide motif. In Escherichia coli and many other Gram-negative bacteria, this post-translational haem attachment is catalysed by the Ccm (cytochrome c maturation) system. The features of the apocytochrome substrate required and recognized by the Ccm apparatus are uncertain. In the present study, we report investigations of maturation of cytochrome b562 variants containing CXXCR, CXXCK or CXXCM haem-binding motifs. None of them showed any evidence for correct maturation by the Ccm system. However, we have determined, for each variant, that the proteins (i) were expressed in large amounts, (ii) could bind haem in vivo and/or in vitro and (iii) were not degraded in the cell. Together with previous observations, these results strongly suggest that the apocytochrome substrate feature recognized by the Ccm system is simply the two cysteine residues and the histidine of the CXXCH haem-binding motif. Using the same experimental approach, we have also investigated a cytochrome b562 variant containing the special CWSCK motif that binds the active-site haem of E. coli nitrite reductase NrfA. Whereas a CWSCH analogue was matured by the Ccm apparatus in large amounts, the CWSCK form was not detectably matured either by the Ccm system or by the dedicated Nrf biogenesis proteins, implying that the substrate recognition features for haem attachment in NrfA may be more extensive than the CWSCK motif.

We demonstrate that, in attribution studies, events defined over longer time scales generally produce higher probability ratios due to lower interannual variability, reconciling seemingly inconsistent attribution results of Europe’s 2018 summer heatwaves in reported studies.

Over the last decades, climate science has evolved rapidly across multiple expert domains. Our best tools to capture state‐of‐the‐art knowledge in an internally self‐consistent modeling framework are the increasingly complex fully coupled Earth System Models (ESMs). However, computational limitations and the structural rigidity of ESMs mean that the full range of uncertainties across multiple domains are difficult to capture with ESMs alone. The tools of choice are instead more computationally efficient reduced complexity models (RCMs), which are structurally flexible and can span the response dynamics across a range of domain‐specific models and ESM experiments. Here we present Phase 2 of the Reduced Complexity Model Intercomparison Project (RCMIP Phase 2), the first comprehensive intercomparison of RCMs that are probabilistically calibrated with key benchmark ranges from specialized research communities. Unsurprisingly, but crucially, we find that models which have been constrained to reflect the key benchmarks better reflect the key benchmarks. Under the low‐emissions SSP1‐1.9 scenario, across the RCMs, median peak warming projections range from 1.3 to 1.7°C (relative to 1850–1900, using an observationally based historical warming estimate of 0.8°C between 1850–1900 and 1995–2014). Further developing methodologies to constrain these projection uncertainties seems paramount given the international community's goal to contain warming to below 1.5°C above preindustrial in the long‐term. Our findings suggest that users of RCMs should carefully evaluate their RCM, specifically its skill against key benchmarks and consider the need to include projections benchmarks either from ESM results or other assessments to reduce divergence in future projections., Key Points Probabilistic global‐mean temperature projections often use reduced complexity climate models (RCMs) because of their low computational costWe evaluate how well RCMs’ probabilistic setups can synthesize knowledge from multiple research domains for policy relevant projectionsNo RCM is able to capture all forcing, warming, heat uptake and carbon cycle metrics we evaluate, however some come close across a range