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Following the recent Global Carbon Project (GCP) synthesis of the decadal methane (CH4) budget over 2000–2012 (Saunois et al., 2016), we analyse here the same dataset with a focus on quasi-decadal and inter-annual variability in CH4 emissions. The GCP dataset integrates results from top-down studies (exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models (including process-based models for estimating land surface emissions and atmospheric chemistry), inventories of anthropogenic emissions, and data-driven approaches. The annual global methane emissions from top-down studies, which by construction match the observed methane growth rate within their uncertainties, all show an increase in total methane emissions over the period 2000–2012, but this increase is not linear over the 13 years. Despite differences between individual studies, the mean emission anomaly of the top-down ensemble shows no significant trend in total methane emissions over the period 2000–2006, during the plateau of atmospheric methane mole fractions, and also over the period 2008–2012, during the renewed atmospheric methane increase. However, the top-down ensemble mean produces an emission shift between 2006 and 2008, leading to 22 [16–32] Tg CH4 yr−1 higher methane emissions over the period 2008–2012 compared to 2002–2006. This emission increase mostly originated from the tropics, with a smaller contribution from mid-latitudes and no significant change from boreal regions. The regional contributions remain uncertain in top-down studies. Tropical South America and South and East Asia seem to contribute the most to the emission increase in the tropics. However, these two regions have only limited atmospheric measurements and remain therefore poorly constrained. The sectorial partitioning of this emission increase between the periods 2002–2006 and 2008–2012 differs from one atmospheric inversion study to another. However, all top-down studies suggest smaller changes in fossil fuel emissions (from oil, gas, and coal industries) compared to the mean of the bottom-up inventories included in this study. This difference is partly driven by a smaller emission change in China from the top-down studies compared to the estimate in the Emission Database for Global Atmospheric Research (EDGARv4.2) inventory, which should be revised to smaller values in a near future. We apply isotopic signatures to the emission changes estimated for individual studies based on five emission sectors and find that for six individual top-down studies (out of eight) the average isotopic signature of the emission changes is not consistent with the observed change in atmospheric 13CH4. However, the partitioning in emission change derived from the ensemble mean is consistent with this isotopic constraint. At the global scale, the top-down ensemble mean suggests that the dominant contribution to the resumed atmospheric CH4 growth after 2006 comes from microbial sources (more from agriculture and waste sectors than from natural wetlands), with an uncertain but smaller contribution from fossil CH4 emissions. In addition, a decrease in biomass burning emissions (in agreement with the biomass burning emission databases) makes the balance of sources consistent with atmospheric 13CH4 observations. In most of the top-down studies included here, OH concentrations are considered constant over the years (seasonal variations but without any inter-annual variability). As a result, the methane loss (in particular through OH oxidation) varies mainly through the change in methane concentrations and not its oxidants. For these reasons, changes in the methane loss could not be properly investigated in this study, although it may play a significant role in the recent atmospheric methane changes as briefly discussed at the end of the paper.

In this work, we have studied the effects of thermal fluctuations on the stability of polaron motion under the influence of an external electric field. Zero temperature calculations have been reported previously showing the existence of critical electric field, E cr , where the system transitions from a stable polaron motion to a Bloch-like oscillation. In this study, we further report that for intermediate polaron sizes the lifetime of such Bloch-like oscillations decay with time due to excessive phonon emission. Our numerical simulations show that the value of E cr is finite for small temperatures. However, E cr rapidly decreases with increasing T and becomes practically zero for T > T cr . In this small but finite temperature window, we report how temperature affects (a) the electric current density, and (b) the Bloch-like frequencies.

We present a stochastic multiscale method for modeling heterogeneous catalysis at the nanoscale. The system is decomposed into the fluid domain and the catalyst-fluid interface. We implemented the fluctuating hydrodynamics framework to model the diffusion of the chemical species in the fluid domain, and the chemical master equation to describe the catalytic activity at the interface. The coupling between the domains occurs simultaneously. Using a simple one-dimensional (1D) linear model, we showed that the predictions of our scheme are in excellent agreement with deterministic simulations. The method was specifically developed to model the spatially asymmetric catalysis on the surface of self-propelled nanoswimmers. Numerical simulations showed that our approach can estimate the uncertainty in the swimming velocity resulting from inherent stochastic nature of the chemical reactions at the catalytic interface. Although the method has been applied to simple 1D and 2D models, it can be generalized to handle different geometries and more sophisticated chemical reactions. Therefore, it can serve as a practical mathematical tool to study how the efficiency of chemically powered nanomachines is affected by the interplay between structural complexity, nonlinear reactivity, and nonequilibrium fluctuations.

In this work, we propose a novel two-level discretization for solving semilinear elliptic equations with random coefficients. Motivated by the two-grid method for deterministic partial differential equations (PDEs) introduced by Xu \cite{xu1994novel}, our two-level stochastic collocation method utilizes a two-grid finite element discretization in the physical space and a two-level collocation method in the random domain. In particular, we solve semilinear equations on a coarse mesh $\mathcal{T}_H$ with a low level stochastic collocation (corresponding to the polynomial space $\mathcal{P}_{\boldsymbol{P}}$) and solve linearized equations on a fine mesh $\mathcal{T}_h$ using high level stochastic collocation (corresponding to the polynomial space $\mathcal{P}_{\boldsymbol{p}}$). We prove that the approximated solution obtained from this method achieves the same order of accuracy as that from solving the original semilinear problem directly by stochastic collocation method with $\mathcal{T}_h$ and $\mathcal{P}_{\boldsymbol{p}}$. The two-level method is computationally more efficient than the standard stochastic collocation method for solving nonlinear problems with random coefficients. Numerical experiments are provided to verify the theoretical results., Comment: 20 pages, 2 figures

We present a mean-field fluctuating hydrodynamics (FHD) method for studying the structural and transport properties of ionic liquids in bulk and near electrified surfaces. The free energy of the system consists of two competing terms: (1) a Landau–Lifshitz functional that models the spontaneous separation of the ionic groups, and (2) the standard mean-field electrostatic interaction between the ions in the liquid. The numerical approach used to solve the resulting FHD-Poisson equations is very efficient and models thermal fluctuations with remarkable accuracy. Such density fluctuations are sufficiently strong to excite the experimentally observed spontaneous formation of liquid nano-domains. Statistical analysis of our simulations provides quantitative information about the properties of ionic liquids, such as the mixing quality, stability, and the size of the nano-domains. Our model, thus, can be adequately parameterized by directly comparing our prediction with experimental measurements and all-atom simulations. Conclusively, this work can serve as a practical mathematical tool for testing various theories and designing more efficient mixtures of ionic liquids.

Agricultural biomass burning (ABB) has been of particular concern due to its influence on air quality and atmospheric radiation, as it produces large amounts of gaseous and aerosol emissions. This paper presents an integrated observation of a significant ABB episode in Nanjing, China, during early June 2011, using combined ground-based and satellite sensors (Moderate Resolution Imaging Spectroradiometer, Atmospheric Infrared Sounder, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), and Ozone Monitoring Instrument products). The time-height distribution, optical properties, sources and transport of smoke, and its impacts on air quality are investigated. Lidar profiles indicate that the smoke aerosols are confined to the planetary boundary layer (PBL) and have a depolarization ratio of less than 0.08. The aerosol optical depths increase from 0.5 to 3.0 at 500 nm, while the extinction-related Angstrom exponent increases from 1.1 to 1.6 at the wavelength pair of 440–870 nm. The single-scattering albedo becomes lower at 670–1020 nm following the ABB intrusion and particularly shows a decreasing tendency between wavelengths of 440 to 1020 nm. The absorption Angstrom exponent (0.7) is smaller than 1.0, which may indicate the aged smoke particles mixed or coated with the urban aerosols. Surface particular matter PM10 and PM2.5 show a dramatic increase, reaching hourly mean of 800 µg/m3 and 485 µg/m3, respectively, which results in a heavy air pollution event. The stagnant and high-moisture weather provides favorable conditions for the aerosols to accumulate near the surface. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) also illustrate that the large-scale aerosols are primarily present in the PBL and transported to the ocean, but some dense smoke plumes are misclassified as cloud or polluted dust. By comparing with the observations, we found that the Weather Research and Forecasting–Chemistry model captured the accumulation and downwind transport of surface PM2.5 from 20:00 on 2 June to 10:00 on 3 June (phase 1) but showed a dramatic underestimate from 20:00 on 3–4 June (phase 2) when dense aerosols are present. Such a discrepancy in the model is associated with the improper vertical apportion of transported smoke and atmospheric diffusion conditions when comparing with the observed aerosol and wind profiles. In addition, the model simulations indicate that the transported smoke can contribute to 50–70% of the ground-level PM2.5 in Nanjing.

This paper presents a comprehensive description and benchmark evaluation of the tropospheric gas-phase chemistry component of the Multiscale Online Nonhydrostatic AtmospheRe CHemistry model (NMMB-MONARCH), formerly known as NMMB/BSC-CTM, that can be run on both regional and global domains. Here, we provide an extensive evaluation of a global annual cycle simulation using a variety of background surface stations (EMEP, WDCGG and CASTNET), ozonesondes (WOUDC, CMD and SHADOZ), aircraft data (MOZAIC and several campaigns), and satellite observations (SCIAMACHY and MOPITT). We also include an extensive discussion of our results in comparison to other state-of-the-art models. We note that in this study, we omitted aerosol processes and some natural emissions (lightning and volcano emissions). The model shows a realistic oxidative capacity across the globe. The seasonal cycle for CO is fairly well represented at different locations (correlations around 0.3–0.7 in surface concentrations), although concentrations are underestimated in spring and winter in the Northern Hemisphere, and are overestimated throughout the year at 800 and 500 hPa in the Southern Hemisphere. Nitrogen species are well represented in almost all locations, particularly NO2 in Europe (root mean square error – RMSE – below 5 ppb). The modeled vertical distributions of NOx and HNO3 are in excellent agreement with the observed values and the spatial and seasonal trends of tropospheric NO2 columns correspond well to observations from SCIAMACHY, capturing the highly polluted areas and the biomass burning cycle throughout the year. Over Asia, the model underestimates NOx from March to August, probably due to an underestimation of NOx emissions in the region. Overall, the comparison of the modeled CO and NO2 with MOPITT and SCIAMACHY observations emphasizes the need for more accurate emission rates from anthropogenic and biomass burning sources (i.e., specification of temporal variability). The resulting ozone (O3) burden (348 Tg) lies within the range of other state-of-the-art global atmospheric chemistry models. The model generally captures the spatial and seasonal trends of background surface O3 and its vertical distribution. However, the model tends to overestimate O3 throughout the troposphere in several stations. This may be attributed to an overestimation of CO concentration over the Southern Hemisphere leading to an excessive production of O3 or to the lack of specific chemistry (e.g., halogen chemistry, aerosol chemistry). Overall, O3 correlations range between 0.6 and 0.8 for daily mean values. The overall performance of the NMMB-MONARCH is comparable to that of other state-of-the-art global chemistry models. The authors wish to thank WOUDC, GAW, EMEP, WDCGG, CASTNET-EPA, NADP and EANET for the provision of measurement stations. The authors acknowledge for the strong support of the European Commission, Airbus, and the Airlines (Lufthansa, Austrian, Air France) who carry free of charge the MOZAIC equipment and perform the maintenance since 1994. MOZAIC is presently funded by INSU-CNRS (France), Meteo-France, and Forschungszentrum (FZJ, Julich, Germany). The MOZAIC database is supported by ETHER (CNES and INSU-CNRS). Also, thanks go to the free use of the MOPITTCO data obtained from the NASA Langley Research Center Atmospheric Science Data Center. SCIAMACHY radiances have been provided by ESA. We also thank Beatriz Monge-Sanz for providing the COPCAT coefficients. This work is funded by grants CGL2013-46736-R, Supercomputación and e-ciencia Project (CSD2007-0050) from the Consolider-Ingenio 2010 program of the Spanish Ministry of Economy and Competitiveness. Further support was provided by the SEV-2011-00067 grant of the Severo Ochoa Program, awarded by the Spanish Government. Andreas Hilboll received funding from the Earth System Science Research School (ESSReS), an initiative of the Helmholtz Association of German research centres (HGF) at the Alfred Wegener Institute for Polar and Marine Research. Carlos Pérez García-Pando acknowledges long-term support from the AXA Research Fund. The authors thankfully acknowledge the computer resources at MareNostrum and the technical support provided by Barcelona Supercomputing Center (RES-AECT-2015-1-0007). Comments from two anonymous reviewers are gratefully acknowledge.

The present report explains how the SPHINXproject will disseminate and communicate its developments and how the consortium will achieve visibility of the project and dissemination of its results throughout its duration. It provides a context analysis of SPHINX Dissemination and Communication plan, along with overall and specifiedobjectives of project’s dissemination actions. It defines the branding and promotion tools, the channels to beused, the respective methodology to be followed. Moreover, it identifies key stakeholder groups and elaborates relevant messages and methods for each audience. Also, it discusses the tracking of each activity, their timeline and the monitoring procedures regarding them.

Quantifying the efficacy of different climate forcings is important for understanding the real‐world climate sensitivity. This study presents a systematic multimodel analysis of different climate driver efficacies using simulations from the Precipitation Driver and Response Model Intercomparison Project (PDRMIP). Efficacies calculated from instantaneous radiative forcing deviate considerably from unity across forcing agents and models. Effective radiative forcing (ERF) is a better predictor of global mean near‐surface air temperature (GSAT) change. Efficacies are closest to one when ERF is computed using fixed sea surface temperature experiments and adjusted for land surface temperature changes using radiative kernels. Multimodel mean efficacies based on ERF are close to one for global perturbations of methane, sulfate, black carbon, and insolation, but there is notable intermodel spread. We do not find robust evidence that the geographic location of sulfate aerosol affects its efficacy. GSAT is found to respond more slowly to aerosol forcing than CO2 in the early stages of simulations. Despite these differences, we find that there is no evidence for an efficacy effect on historical GSAT trend estimates based on simulations with an impulse response model, nor on the resulting estimates of climate sensitivity derived from the historical period. However, the considerable intermodel spread in the computed efficacies means that we cannot rule out an efficacy‐induced bias of ±0.4 K in equilibrium climate sensitivity to CO2 doubling when estimated using the historical GSAT trend., Key Points Multimodel mean efficacies computed using effective radiative forcing are close to one for major anthropogenic drivers, but there is notable intermodel spreadSurface temperature‐driven radiative feedbacks are generally not constant through time across forcing experimentsPDRMIP results suggest that the efficacy impact on equilibrium climate sensitivity derived from the historical period is limited to ±0.4 °C or better

Global warming due to greenhouse gases and atmospheric aerosols alter precipitation rates, but the influence on extreme precipitation by aerosols relative to greenhouse gases is still not well known. Here we use the simulations from the Precipitation Driver and Response Model Intercomparison Project that enable us to compare changes in mean and extreme precipitation due to greenhouse gases with those due to black carbon and sulfate aerosols, using indicators for dry extremes as well as for moderate and very extreme precipitation. Generally, we find that the more extreme a precipitation event is, the more pronounced is its response relative to global mean surface temperature change, both for aerosol and greenhouse gas changes. Black carbon (BC) stands out with distinct behavior and large differences between individual models. Dry days become more frequent with BC-induced warming compared to greenhouse gases, but so does the intensity and frequency of extreme precipitation. An increase in sulfate aerosols cools the surface and thereby the atmosphere, and thus induces a reduction in precipitation with a stronger effect on extreme than on mean precipitation. A better understanding and representation of these processes in models will provide knowledge for developing strategies for both climate change and air pollution mitigation. Black carbon and sulfate aerosols have conflicting effects on global extreme dry and precipitation events. An international team of researchers led by Jana Sillmann at the Center for International Climate Research, Oslo use model simulations to examine the effect of sulfate aerosols and black carbon compared to greenhouse gases on global mean precipitation and extreme precipitation events. Sulfate aerosols, which cool the Earth’s surface, decrease mean precipitation and in particular strongly reduce extreme precipitation events. Conversely, black carbon, which warms the Earth’s surface, may increase the intensity and frequency of extreme precipitation events, but also reduces mean precipitation leading to more extreme dry drought-like conditions. This demonstrates the need to incorporate aerosols into future climate modeling effects on global precipitation trends.

Past changes in global industrial aerosol emissions have played a significant role in historical shifts in African rainfall, and yet assessment of the impact on African rainfall of near-term (10–40 yr) potential aerosol emission pathways remains largely unexplored. While existing literature links future aerosol declines to a northward shift of Sahel rainfall, existing climate projections rely on RCP scenarios that do not explore the range of air quality drivers. Here we present projections from two emission scenarios that better envelop the range of potential aerosol emissions. More aggressive emission cuts result in northward shifts of the tropical rainbands whose signal can emerge from expected internal variability on short, 10–20-yr time horizons. We also show for the first time that this northward shift also impacts East Africa, with evidence of delays to both onset and withdrawal of the short rains. However, comparisons of rainfall impacts across models suggest that only certain aspects of both the West and East African model responses may be robust, given model uncertainties. This work motivates the need for wider exploration of air quality scenarios in the climate science community to assess the robustness of these projected changes and to provide evidence to underpin climate adaptation in Africa. In particular, revised estimates of emission impacts of legislated measures every 5–10 years would have a value in providing near-term climate adaptation information for African stakeholders.

Water vapour in the atmosphere is the source of a major climate feedback mechanism and potential increases in the availability of water vapour could have important consequences for mean and extreme precipitation. Future precipitation changes further depend on how the hydrological cycle responds to different drivers of climate change, such as greenhouse gases and aerosols. Currently, neither the total anthropogenic influence on the hydrological cycle nor that from individual drivers is constrained sufficiently to make solid projections. We investigate how integrated water vapour (IWV) responds to different drivers of climate change. Results from 11 global climate models have been used, based on simulations where CO2, methane, solar irradiance, black carbon (BC), and sulfate have been perturbed separately. While the global-mean IWV is usually assumed to increase by ĝ1/47 per kelvin of surface temperature change, we find that the feedback response of IWV differs somewhat between drivers. Fast responses, which include the initial radiative effect and rapid adjustments to an external forcing, amplify these differences. The resulting net changes in IWV range from 6.4±0.9K-1 for sulfate to 9.8±2K-1 for BC. We further calculate the relationship between global changes in IWV and precipitation, which can be characterized by quantifying changes in atmospheric water vapour lifetime. Global climate models simulate a substantial increase in the lifetime, from 8.2±0.5 to 9.9±0.7d between 1986-2005 and 2081-2100 under a high-emission scenario, and we discuss to what extent the water vapour lifetime provides additional information compared to analysis of IWV and precipitation separately. We conclude that water vapour lifetime changes are an important indicator of changes in precipitation patterns and that BC is particularly efficient in prolonging the mean time, and therefore likely the distance, between evaporation and precipitation. © Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License.

Many climate modeling studies have demon-strated the importance of two-way interactions betweenozone and atmospheric dynamics. However, atmosphericchemistry models needed for calculating changes in ozoneare computationally expensive. Nowack et al. [1] high-lighted the potential of machine learning-based ozoneparameterizations in constant climate forcing simulations,with ozone being predicted as a function of the atmo-spheric temperature state. Here we investigate the roleof additional time-lagged temperature information underpreindustrial forcing conditions. In particular, we testif the use of Long Short-Term Memory (LSTM) neuralnetworks can significantly improve the predictive skill ofthe parameterization. We then introduce a novel workflowto transfer the regression model to the new UK EarthSystem Model (UKESM). For this, we show for the firsttime how machine learning parameterizations could betransferred between climate models, a pivotal step tomaking any such parameterization widely applicable inclimate science. Our results imply that ozone parame-terizations could have much-extended scope as they arenot bound to individual climate models but, once trained,could be used in a number of different models. We hope tostimulate similar transferability tests regarding machinelearning parameterizations developed for other Earthsystem model components such as ocean eddy modeling,convection, clouds, or carbon cycle schemes.

The Arctic is experiencing rapid climate change in response to changes in greenhouse gases, aerosols, and other climate drivers. Emission changes in general, as well as geographical shifts in emissions and transport pathways of short-lived climate forcers, make it necessary to understand the influence of each climate driver on the Arctic. In the Precipitation Driver Response Model Intercomparison Project, 10 global climate models perturbed five different climate drivers separately (CO2, CH4, the solar constant, black carbon, and SO4). We show that the annual mean Arctic amplification (defined as the ratio between Arctic and the global mean temperature change) at the surface is similar between climate drivers, ranging from 1.9 (± an intermodel standard deviation of 0.4) for the solar to 2.3 (±0.6) for the SO4 perturbations, with minimum amplification in the summer for all drivers. The vertical and seasonal temperature response patterns indicate that the Arctic is warmed through similar mechanisms for all climate drivers except black carbon. For all drivers, the precipitation change per degree global temperature change is positive in the Arctic, with a seasonality following that of the Arctic amplification. We find indications that SO4 perturbations produce a slightly stronger precipitation response than the other drivers, particularly compared to CO2. ©2019. The Authors.

American Geophysical Union. All Rights Reserved. We compare six methods of estimating effective radiative forcing (ERF) using a set of atmosphere-ocean general circulation models. This is the first multiforcing agent, multimodel evaluation of ERF values calculated using different methods. We demonstrate that previously reported apparent consistency between the ERF values derived from fixed sea surface temperature simulations and linear regression holds for most climate forcings, excluding black carbon (BC). When land adjustment is accounted for, however, the fixed sea surface temperature ERF values are generally 10–30% larger than ERFs derived using linear regression across all forcing agents, with a much larger (~70–100%) discrepancy for BC. Except for BC, this difference can be largely reduced by either using radiative kernel techniques or by exponential regression. Responses of clouds and their effects on shortwave radiation show the strongest variability in all experiments, limiting the application of regression-based ERF in small forcing simulations.

Fresh fruits and vegetables are a special group of products in need of special treatment, due to their sensitivity to external factors such as microorganisms, moisture, etc., as well as, their movement and storage across the supply chain. As consumer needs and requirements are constantly growing, a number of new practices in packaging have been introduced to cater for the proper transport and storage of fruit and vegetables. An important role in this field is represented by reusable boxes/containers which offer, in addition to safe transportation and storage, space saving, less damage, more stable stacking, etc. while facilitating and supporting the deployment of green and sustainable supply chain practices and technologies. The purpose of this paper is to present and analyze the movement of fresh fruit and vegetables using reusable containers in a Greek retail chain. The methodology included the collection of information from electronic databases, from publications on the subject under consideration and from direct contact with executives of large retail chains. The results demonstrate both the importance of packaging to maintain the freshness of fresh fruits and vegetables and the importance of recycling.

We examine the effectiveness of GARCH models and the SV model in foreign exchange rates and the stock indices of the BRICS. The empirical analysis is conducted using the daily log returns. The data cover the period 13/05/1999-22/11/2018. The theoretical results are confirmed by running the models with the use of R programming language. The conditional variance is modelled with a GARCH (1,1), IGARCH, EGARCH,GJR-GARCH and the Standard Stochastic Volatility model (SV) model. The results suggest that the GJR model outperforms the other GARCH-family models and provides a clear direction on how to critically estimate volatility and infer conclusions. The SV model provides better forecasting results from the standardized GARCH(1,1). The forecasting power of EGARCH performs better, as it indicates lower values in the loss functions After the forecasting tools for evaluation the forecasting results are used. These are the Root Mean Squared Error (RMSE),the Mean Squared Error (MSE) the Mean Absolute Error (MAE). Our findings strongly suggest that more research should be conducted by using different distributions to get more accurate results. Therefore we suggest that the asset returns of sectors and their independent relations, as it would be very helpful for policy practitioners.

Water vapour in the atmosphere is the source of a major climate feedback mechanism and potential increases in the availability of water vapour could have important consequences for mean and extreme precipitation. Future precipitation changes further depend on how the hydrological cycle responds to drivers of climate change, such as greenhouse gases and aerosols. Currently, neither the total anthropogenic influence on the hydrological cycle, nor those from individual drivers, are constrained sufficiently to make solid projections. We investigate how integrated water vapour (IWV) responds to different drivers of climate change. Results from 11 global climate models have been used, based on simulations where CO2, methane, solar irradiance, black carbon (BC), and sulphate have been perturbed separately. While the global-mean IWV is usually assumed to increase by ~7% per degree K surface temperature change, we find that the feedback response of IWV differs somewhat between drivers. Fast responses, which include the initial radiative effect and rapid adjustments to an external forcing, amplify these differences. The resulting net changes in IWV range from 6.4±0.9%/K for sulphate to 9.8±2%/K for BC. We further calculate the relationship between global changes in IWV and precipitation, which can be characterized by quantifying changes in atmospheric water vapour lifetime. Global climate models simulate a substantial increase in the lifetime, from 8.2±0.5 to 9.9±0.7 days between 1986-2005 and 2081-2100 under a high emission scenario, and we discuss to what extent the water vapour lifetime provides additional information compared to analysis of IWV and precipitation separately. We conclude that water vapour lifetime changes are an important indicator of changes in precipitation patterns and that BC is particularly efficient in prolonging the distance between evaporation and precipitation.

When designing large-scale blasting operations, great consideration should be given to tailor the fragment size distribution in order to fulfil production requirements, while ensuring at the same time efficient and economical downstream operations such as loading, hauling, crushing and grinding. The selection of an optimum inter-hole delay time has been found to have beneficial effects in controlling fragmentation and face movement. The minimum response time Tmin is reported in the literature as a key parameter for the selection of the appropriate inter-hole delay time. In this paper, data collected from 10 monitored full-scale blasts are used to investigate bivariate correlations between various design parameters. Further processing of the data using dimensional analysis resulted in the derivation of a mathematical relation that can estimate Tmin as a function of blast design parameters and the desired size reduction ratio of blasting.

Warm and dry climatological conditions favour the occurrence of forest fires. These fires then become a significant emission source to the atmosphere. Despite this global importance, fires are a local phenomenon and are difficult to represent in large-scale Earth system models (ESMs). To address this, the INteractive Fire and Emission algoRithm for Natural envirOnments (INFERNO) was developed. INFERNO follows a reduced complexity approach and is intended for decadal- to centennial-scale climate simulations and assessment models for policy making. Fuel flammability is simulated using temperature, relative humidity (RH) and fuel load as well as precipitation and soil moisture. Combining flammability with ignitions and vegetation, the burnt area is diagnosed. Emissions of carbon and key species are estimated using the carbon scheme in the Joint UK Land Environment Simulator (JULES) land surface model. JULES also possesses fire index diagnostics, which we document and compare with our fire scheme. We found INFERNO captured global burnt area variability better than individual indices, and these performed best for their native regions. Two meteorology data sets and three ignition modes are used to validate the model. INFERNO is shown to effectively diagnose global fire occurrence (R = 0.66) and emissions (R = 0.59) through an approach appropriate to the complexity of an ESM, although regional biases remain.

We use the HadGEM3-GA4, CESM1, and GISS ModelE2 climate models to investigate the global and regional aerosol burden, radiative flux, and surface temperature responses to removing anthropogenic sulfur dioxide (SO2) emissions from China. We find that the models differ by up to a factor of 6 in the simulated change in aerosol optical depth (AOD) and shortwave radiative flux over China that results from reduced sulfate aerosol, leading to a large range of magnitudes in the regional and global temperature responses. Two of the three models simulate a near-ubiquitous hemispheric warming due to the regional SO2 removal, with similarities in the local and remote pattern of response, but overall with a substantially different magnitude. The third model simulates almost no significant temperature response. We attribute the discrepancies in the response to a combination of substantial differences in the chemical conversion of SO2 to sulfate, translation of sulfate mass into AOD, cloud radiative interactions, and differences in the radiative forcing efficiency of sulfate aerosol in the models. The model with the strongest response (HadGEM3-GA4) compares best with observations of AOD regionally, however the other two models compare similarly (albeit poorly) and still disagree substantially in their simulated climate response, indicating that total AOD observations are far from sufficient to determine which model response is more plausible. Our results highlight that there remains a large uncertainty in the representation of both aerosol chemistry as well as direct and indirect aerosol radiative effects in current climate models, and reinforces that caution must be applied when interpreting the results of modelling studies of aerosol influences on climate. Model studies that implicate aerosols in climate responses should ideally explore a range of radiative forcing strengths representative of this uncertainty, in addition to thoroughly evaluating the models used against observations.