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Summarization: Understanding and estimating regional climate change under different anthropogenic emission scenarios is pivotal for informing societal adaptation and mitigation measures. However, the high computational complexity of state-of-the-art climate models remains a central bottleneck in this endeavour. Here we introduce a machine learning approach, which utilises a unique dataset of existing climate model simulations to learn relationships between short-term and long-term temperature responses to different climate forcing scenarios. This approach not only has the potential to accelerate climate change projections by reducing the costs of scenario computations, but also helps uncover early indicators of modelled long-term climate responses, which is of relevance to climate change detection, predictability, and attribution. Our results highlight challenges and opportunities for data-driven climate modelling, especially concerning the incorporation of even larger model datasets in the future. We therefore encourage extensive data sharing among research institutes to build ever more powerful climate response emulators, and thus to enable faster climate change projections. Presented on: npj Climate and Atmospheric Science

Summarization: Fire constitutes a key process in the Earth system (ES), being driven by climate as well as affecting the climate by changing atmospheric composition and impacting the terrestrial carbon cycle. However, studies on the effects of fires on atmospheric composition, radiative forcing and climate have been limited to date, as the current generation of ES models (ESMs) does not include fully atmosphere–composition–vegetation coupled fires feedbacks. The aim of this work is to develop and evaluate a fully coupled fire–composition–climate ES model. For this, the INteractive Fires and Emissions algoRithm for Natural envirOnments (INFERNO) fire model is coupled to the atmosphere-only configuration of the UK's Earth System Model (UKESM1). This fire–atmosphere interaction through atmospheric chemistry and aerosols allows for fire emissions to influence radiation, clouds and generally weather, which can consequently influence the meteorological drivers of fire. Additionally, INFERNO is updated based on recent developments in the literature to improve the representation of human and/or economic factors in the anthropogenic ignition and suppression of fire. This work presents an assessment of the effects of interactive fire coupling on atmospheric composition and climate compared to the standard UKESM1 configuration that uses prescribed fire emissions. Results show a similar performance when using the fire–atmosphere coupling (the “online” version of the model) when compared to the offline UKESM1 that uses prescribed fire. The model can reproduce observed present-day global fire emissions of carbon monoxide (CO) and aerosols, despite underestimating the global average burnt area. However, at a regional scale, there is an overestimation of fire emissions over Africa due to the misrepresentation of the underlying vegetation types and an underestimation over equatorial Asia due to a lack of representation of peat fires. Despite this, comparing model results with observations of CO column mixing ratio and aerosol optical depth (AOD) show that the fire–atmosphere coupled configuration has a similar performance when compared to UKESM1. In fact, including the interactive biomass burning emissions improves the interannual CO atmospheric column variability and consequently its seasonality over the main biomass burning regions – Africa and South America. Similarly, for aerosols, the AOD results broadly agree with the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Aerosol Robotic Network (AERONET) observations. Presented on: Geoscientific Model Development

Shortwave cloud radiative effects (SWCREs), defined as the difference of the shortwave radiative flux between all-sky and clear-sky conditions at the surface, have been reported to play an important role in influencing the Earth's energy budget and temperature extremes. In this study, we employed a set of global climate models to examine the SWCRE responses to CO2, black carbon (BC) aerosols, and sulfate aerosols in boreal summer over the Northern Hemisphere. We found that CO2 causes positive SWCRE changes over most of the NH, and BC causes similar positive responses over North America, Europe, and eastern China but negative SWCRE over India and tropical Africa. When normalized by effective radiative forcing, the SWCRE from BC is roughly 3–5 times larger than that from CO2. SWCRE change is mainly due to cloud cover changes resulting from changes in relative humidity (RH) and, to a lesser extent, changes in cloud liquid water, circulation, dynamics, and stability. The SWCRE response to sulfate aerosols, however, is negligible compared to that for CO2 and BC because part of the radiation scattered by clouds under all-sky conditions will also be scattered by aerosols under clear-sky conditions. Using a multilinear regression model, it is found that mean daily maximum temperature (Tmax) increases by 0.15 and 0.13 K per watt per square meter (W m−2) increase in local SWCRE under the CO2 and BC experiment, respectively. When domain-averaged, the contribution of SWCRE change to summer mean Tmax changes was 10 %–30 % under CO2 forcing and 30 %–50 % under BC forcing, varying by region, which can have important implications for extreme climatic events and socioeconomic activities.

Aerosols are a major climate forcer, but their historical effect has the largest uncertainty of any forcing, and so their mechanisms and impacts must be better understood. Due to their short lifetime, aerosols have large impacts near their emission region, but they also have effects on the climate in remote locations. In recent years, studies have investigated the influences of regional aerosols on global and regional climate, and the mechanisms that lead to remote responses to their inhomogeneous forcing. However, there has been little work on the influence of emissions from the tropics, as the aforementioned studies typically focused only on northern mid-latitude pollution effects. This work uses the new UK Earth System Model (UKESM1) to investigate the atmospheric composition and climate effects of tropical aerosols and aerosol precursor emissions. We performed three idealised perturbation experiments in which a) tropical SO2 emissions were multiplied by a factor of 10; b) tropical biomass burning carbonaceous aerosol emissions were multiplied by 10; and c) tropical biomass burning carbonaceous aerosol emissions were entirely removed. Impacts on radiation fluxes, temperature, circulation and precipitation are investigated, both over the emission regions, where microphysical effects dominate, and remotely, where dynamical influences become more relevant. Increasing tropical SO2 emissions causes a global cooling, and the asymmetric forcing (stronger negative forcing in the Northern Hemisphere Tropics) drives a southward shift of the intertropical convergence zone (ITCZ). The experiment with the large increase in tropical biomass burning organic carbon (OC) and black carbon (BC) features a net warming globally, and a local cooling in locations where the aerosol load increases the most, since OC and BC reduce radiation at the surface locally, causing cooling. However, whereas OC scatters radiation with a negative forcing, BC has a warming effect since it reduces the planetary albedo, and this warming wins out on the global scale. The forcing is asymmetric, but changes sign between seasons as biomass burning in Africa shifts across the Equator, driving a more complex response of the ITCZ. The removal of biomass burning OC and BC leads to opposite effects to the 10x increase, but with a smaller magnitude, and with dynamical changes playing a more important role than microphysical ones, relative to the larger perturbations. Using the Shared Socioeconomic Pathway scenarios (SSPs), transient future experiments have also been performed, testing the effect of Africa following a relatively more polluting route (SSP3-RCP7.0) to the rest of the world (SSP1-RCP1.9), relative to a global SSP1-RCP1.9 control. Preliminary results from this analysis will also be presented.

Vegetation build up is a major controlling factor for wildfires globally. The exact nature of the dependency of wildfire activity on past vegetation productivity is still under debate, however. Given the potential future rise in conditions conducive to extremely damaging fires in many regions of the world, controlling factors like this need to be investigated urgently to better understand and manage especially extreme wildfire events.To improve our understanding of wildfires and the advice given to policy makers, a comprehensive understanding of all contributing factors is required. Changes to land management can be controversial and thus concrete evidence is required to assess and modify longstanding management practices and regulations if needed.We therefore used global satellite datasets extending from 2005 to 2011 to assess the relationship between burnt area and various biophysical variables. Vegetation proxy data included vegetation optical depth and the fraction of absorbed photosynthetically activate radiation. Different regions and time periods were analysed separately to isolate regional and temporal effects respectively. The relationship between pre-season vegetation productivity and burnt area was modelled as a regionally and temporally varying weighted sum of past monthly productivity proxies.As expected, significant differences in fire regimes were found across biomes, signified for example by significant shifts in the seasonality of burnt area. Understanding these shifts in the seasonality of both burnt area and the accompanying temporal dependence on past vegetation growth is key to reproducing observed wildfire regimes in fire models. As these relationships were found to vary both temporally and regionally, judicious inclusion of biophysical variables in fire models coupled with algorithms able to capture these relationships is necessary. However, remotely sensed observations were of different quality in different areas due to inhomogeneous cloud cover patterns, making assessments for much-affected regions like South America and South East Asia especially difficult. Likewise, the found correlation between decreasing cloud cover and increasing burnt area biased our results. Due also to the short time span of the data available in this investigation, these factors warrant further investigation to more fully quantify the temporal and regional relationships at work.

Projections of future atmospheric compositionchange and its impacts on air quality and climate dependheavily on chemistry–climate models that allow us to investigatethe effects of changing emissions and meteorology.These models are imperfect as they rely on our understandingof the chemical, physical and dynamical processes governingatmospheric composition, on the approximations neededto represent these numerically, and on the limitations of theobservations required to constrain them. Model intercomparisonstudies show substantial diversity in results that reflectunderlying uncertainties, but little progress has been madein explaining the causes of this or in identifying the weaknessesin process understanding or representation that couldlead to improved models and to better scientific understanding.Global sensitivity analysis provides a valuable methodof identifying and quantifying the main causes of diversity incurrent models. For the first time, we apply Gaussian processemulation with three independent global chemistry-transportmodels to quantify the sensitivity of ozone and hydroxyl radicals(OH) to important climate-relevant variables, poorlycharacterised processes and uncertain emissions. We show aclear sensitivity of tropospheric ozone to atmospheric humidityand precursor emissions which is similar for the models,but find large differences between models for methane lifetime,highlighting substantial differences in the sensitivity ofOH to primary and secondary production. This approach allowsus to identify key areas where model improvements arerequired while providing valuable new insight into the processesdriving tropospheric composition change.

Objectives: Interpretation of MRI/MRCP in primary sclerosing cholangitis (PSC) at a single time point has low inter-reader agreement. Agreement of interpretation of the dynamic course of du...

Shortwave cloud radiative effects (SWCRE), defined as the difference of shortwave radiative flux between all-sky and clear-sky conditions, have been reported to play an important role in influencing the Earth’s energy budget and temperature extremes. In this study, we employed a set of global climate models to examine the SWCRE responses to CO2, black carbon (BC) aerosols and sulfate aerosols in boreal summer over the Northern Hemisphere. We found that CO2 causes positive SWCRE changes over most of the NH, and BC causes similar positive responses over North America, Europe and East China but negative SWCRE over India and tropical Africa. When normalized by effective radiative forcing, the SWCRE from BC is roughly 3–5 times larger than that from CO2. SWCRE change is mainly due to cloud cover changes resulting from the changes in relative humidity (RH) and, to a lesser extent, changes in circulation and stability. The SWCRE response to sulfate aerosols, however, is negligible compared to that for CO2 and BC. Using a multilinear regression model, it is found that mean daily maximum temperature (Tmax) increases by 0.15 K and 0.13 K per W m−2 increase in local SWCRE under the CO2 and BC experiment, respectively. When domain-averaged, the SWCRE change contribution to summer mean Tmax changes was 10–30 % under CO2 forcing and 30–50 % under BC forcing, varying by region, which can have important implications for extreme climatic events and socio-economic activities.

Global fire-vegetation models are widely used to assess impacts of environmental change on fire regimes and the carbon cycle, and to infer relationships between climate, land use, and fire. However, differences in model structure and parameterizations, in both the vegetation and fire components of these models, could influence overall model performance, and to date there has been limited evaluation of how well different models represent various aspects of fire regimes. The Fire Model Intercomparison Project (FireMIP) is coordinating the evaluation of state-of-the-art global fire models, with the aim of improving projections of fire regime characteristic and fire impacts on ecosystems and human societies under the context of global environmental change. Here we perform a systematic evaluation of historical simulations made by nine FireMIP models in order to quantify their ability to reproduce a range of fire and vegetation benchmarks. The FireMIP models simulate a wide range in global annual total burnt area (39–536 Mha), and global annual fire carbon emission (0.91–4.75 Pg C a−1) for modern conditions (2002–2012), but most of the range in burnt area is within observational uncertainty (345–468 Mha). Benchmarking scores indicate that seven out of nine FireMIP models are able to represent the spatial pattern in burnt area. The models also reproduce the seasonality in burnt area reasonably well but struggle to simulate fire season length and are largely unable to represent inter-annual variations in burnt area. However, models that represent cropland fires see improved simulation of fire seasonality in the northern hemisphere. The three FireMIP models which explicitly simulate individual fires are able to reproduce the spatial pattern in number of fires, but fire sizes are too small in key regions and this results in an underestimation of burnt area. The correct representation of spatial and seasonal patterns in vegetation appears to correlate with a better representation of burnt area. While some FireMIP models are better at representing certain aspects of the fire regime, no model clearly outperforms all other models across the full range of variables assessed.

Different climate drivers influence precipitation in different ways. Here we use radiative kernels to understand the influence of rapid adjustment processes on precipitation in climate models. Rapid adjustments are generally triggered by the initial heating or cooling of the atmosphere from an external climate driver. For precipitation changes, rapid adjustments due to changes in temperature, water vapor, and clouds are most important. In this study we have investigated five climate drivers (CO2, CH4, solar irradiance, black carbon, and sulfate aerosols). The fast precipitation responses to a doubling of CO2 and a 10‐fold increase in black carbon are found to be similar, despite very different instantaneous changes in the radiative cooling, individual rapid adjustments, and sensible heating. The model diversity in rapid adjustments is smaller for the experiment involving an increase in the solar irradiance compared to the other climate driver perturbations, and this is also seen in the precipitation changes., Key Points Separation of instantaneous and rapid adjustment contributions to precipitation changesContributions of rapid adjustments to precipitation changes differ substantially between climate driversRadiative kernels are applied to understand individual rapid adjustment terms

To investigate the detectability of renal stones in corticomedullary and nephrographic phases on contrast-enhanced computed tomography (CT). All consecutive patients between January 2012 and February 2016 undergoing CT of the kidneys according to our department’s standard four-phase protocol and having at least one stone in the NC-phase (NCP) were included. Fifty patients with altogether 136 stones were eligible. Two radiologists in consensus evaluated the NCP from each examination and documented the number, location, and size of stones. Three abdominal radiologists blinded to the findings of the NCP reviewed independently the corticomedullary and nephrographic phases on two different occasions. They reported the number and location of stones in each kidney. For the inter-observer agreement the intra-class correlation coefficient (ICC) was estimated. The detection rate of renal stones was calculated for the three radiologists and compared between the two contrast-enhanced phases and the results were analyzed with concern to the size of the stones. The ICC was 0.86. There was no statistically significant difference between corticomedullary and nephrographic phases (p = 0.94). The detection rate for stones measuring 3–5 mm was 82–88% and 98% for stones ≥ 6 mm. The detectability of renal stones ≥ 6 mm on contrast-enhanced CT is extremely high. This means that stones with a higher risk of not passing spontaneously can be safely diagnosed.

Global sensitivity analysis (GSA) is a powerful approach in identifying which inputs or parameters most affect a model's output. This determines which inputs to include when performing model calibration or uncertainty analysis. GSA allows quantification of the sensitivity index (SI) of a particular input – the percentage of the total variability in the output attributed to the changes in that input – by averaging over the other inputs rather than fixing them at specific values. Traditional methods of computing the SIs using the Sobol and extended Fourier Amplitude Sensitivity Test (eFAST) methods involve running a model thousands of times, but this may not be feasible for computationally expensive Earth system models. GSA methods that use a statistical emulator in place of the expensive model are popular, as they require far fewer model runs. We performed an eight-input GSA, using the Sobol and eFAST methods, on two computationally expensive atmospheric chemical transport models using emulators that were trained with 80 runs of the models. We considered two methods to further reduce the computational cost of GSA: (1) a dimension reduction approach and (2) an emulator-free approach. When the output of a model is multi-dimensional, it is common practice to build a separate emulator for each dimension of the output space. Here, we used principal component analysis (PCA) to reduce the output dimension, built an emulator for each of the transformed outputs, and then computed SIs of the reconstructed output using the Sobol method. We considered the global distribution of the annual column mean lifetime of atmospheric methane, which requires ∼ 2000 emulators without PCA but only 5–40 emulators with PCA. We also applied an emulator-free method using a generalised additive model (GAM) to estimate the SIs using only the training runs. Compared to the emulator-only methods, the emulator–PCA and GAM methods accurately estimated the SIs of the ∼ 2000 methane lifetime outputs but were on average 24 and 37 times faster, respectively.

Atmospheric aerosols and greenhouse gases affect cloud properties, radiative balance and, thus, the hydrological cycle. Observations show that precipitation has decreased in the Mediterranean since the beginning of the 20th century, and many studies have investigated possible mechanisms. So far, however, the effects of aerosol forcing on Mediterranean precipitation remain largely unknown. Here we compare the modeled dynamical response of Mediterranean precipitation to individual forcing agents in a set of global climate models (GCMs). Our analyses show that both greenhouse gases and aerosols can cause drying in the Mediterranean and that precipitation is more sensitive to black carbon (BC) forcing than to well-mixed greenhouse gases (WMGHGs) or sulfate aerosol. In addition to local heating, BC appears to reduce precipitation by causing an enhanced positive sea level pressure (SLP) pattern similar to the North Atlantic Oscillation–Arctic Oscillation, characterized by higher SLP at midlatitudes and lower SLP at high latitudes. WMGHGs cause a similar SLP change, and both are associated with a northward diversion of the jet stream and storm tracks, reducing precipitation in the Mediterranean while increasing precipitation in northern Europe. Though the applied forcings were much larger, if forcings are scaled to those of the historical period of 1901–2010, roughly one-third (31±17 %) of the precipitation decrease would be attributable to global BC forcing with the remainder largely attributable to WMGHGs, whereas global scattering sulfate aerosols would have negligible impacts. Aerosol–cloud interactions appear to have minimal impacts on Mediterranean precipitation in these models, at least in part because many simulations did not fully include such processes; these merit further study. The findings from this study suggest that future BC and WMGHG emissions may significantly affect regional water resources, agricultural practices, ecosystems and the economy in the Mediterranean region.