Publications
106 results found
Wells C, Kasoar M, Bellouin N, et al., 2023, Local and remote climate impacts of future African aerosol emissions, Atmospheric Chemistry and Physics, Vol: 23, Pages: 3575-3593, ISSN: 1680-7316
The potential future trend in African aerosol emissions is uncertain, with a large range found in future scenarios used to drive climate projections. The future climate impact of these emissions is therefore uncertain. Using the Shared Socioeconomic Pathway (SSP) scenarios, transient future experiments were performed with the UK Earth System Model (UKESM1) to investigate the effect of African emissions following the high emission SSP370 scenario as the rest of the world follows the more sustainable SSP119, relative to a global SSP119 control. This isolates the effect of Africa following a relatively more polluted future emissions pathway. Compared to SSP119, SSP370 projects higher non-biomass-burning (non-BB) aerosol emissions, but lower biomass burning emissions, over Africa. Increased shortwave (SW) absorption by black carbon aerosol leads to a global warming, but the reduction in the local incident surface radiation close to the emissions is larger, causing a local cooling effect. The local cooling persists even when including the higher African CO2 emissions under SSP370 than SSP119. The global warming is significantly higher by 0.07 K when including the non-BB aerosol increases and higher still (0.22 K) when including all aerosols and CO2. Precipitation also exhibits complex changes. Northward shifts in the Inter-tropical Convergence Zone (ITCZ) occur under relatively warm Northern Hemisphere land, and local rainfall is enhanced due to mid-tropospheric instability from black carbon absorption. These results highlight the importance of future African aerosol emissions for regional and global climate and the spatial complexity of this climate influence.
Wells CD, Kasoar M, Bellouin N, et al., 2022, Supplementary material to "Local and remote climate impacts of future African aerosol emissions"
Grillakis M, Voulgarakis A, Rovithakis A, et al., 2022, Climate drivers of global wildfire burned area, Environmental Research Letters, Vol: 17, Pages: 1-10, ISSN: 1748-9326
Wildfire is an integral part of the Earth system, but at the same time it can pose serious threats to human society and to certain types of terrestrial ecosystems. Meteorological conditions are a key driver of wildfire activity and extent, which led to the emergence of the use of fire danger indices that depend solely on weather conditions. The Canadian Fire Weather Index (FWI) is a widely used fire danger index of this kind. Here, we evaluate how well the FWI, its components, and the climate variables from which it is derived, correlate with observation-based burned area (BA) for a variety of world regions. We use a novel technique, according to which monthly BA are grouped by size for each Global Fire Emissions Database (GFED) pyrographic region. We find strong correlations of BA anomalies with the FWI anomalies, as well as with the underlying deviations from their climatologies for the four climate variables from which FWI is estimated, namely, temperature, relative humidity, precipitation, and wind. We quantify the relative sensitivity of the observed BA to each of the four climate variables, finding that this relationship strongly depends on the pyrographic region and land type. Our results indicate that the BA anomalies strongly correlate with FWI anomalies at a GFED region scale, compared to the strength of the correlation with individual climate variables. Additionally, among the individual climate variables that comprise the FWI, relative humidity and temperature are the most influential factors that affect the observed BA. Our results support the use of the composite fire danger index FWI, as well as its sub-indices, the Build-Up Index (BUI) and the Initial Spread Index (ISI), comparing to single climate variables, since they are found to correlate better with the observed forest or non-forest BA, for the most regions across the globe.
Rovithakis A, Grillakis MG, Seiradakis KD, et al., 2022, Future climate change impact on wildfire danger over the Mediterranean: the case of Greece, Environmental Research Letters, Vol: 17, ISSN: 1748-9326
Recent studies have shown that temperature and precipitation in the Mediterranean are expected to change, contributing to longer and more intense summer droughts that even extend out of season. In connection to this, the frequency of forest fire occurrence and intensity will likely increase. In the present study, the changes in future fire danger conditions are assessed for the different regions of Greece using the Canadian fire weather index (FWI). Gridded future climate output as estimated from three regional climate models from the Coordinated Regional Downscaling Experiment are utilized. We use three representative concentration pathways (RCPs) consisting of an optimistic emissions scenario where emissions peak and decline beyond 2020 (RCP2.6), a middle-of-the-road scenario (RCP4.5) and a pessimistic scenario, in terms of mitigation where emissions continue to rise throughout the century (RCP8.5). Based on established critical fire FWI threshold values for Greece, the future change in days with critical fire danger were calculated for different areas of Greece domains. The results show that fire danger is expected to progressively increase in the future especially in the high-end climate change scenario with southern and eastern regions of Greece expected to have up to 40 additional days of high fire danger relative to the late 20th century, on average. Crete, the Aegean Islands, the Attica region, as well as parts of Peloponnese are predicted to experience a stronger increase in fire danger.
Myhre G, Samset B, Forster PM, et al., 2022, Scientific data from precipitation driver response model intercomparison project, Scientific Data, Vol: 9, Pages: 123-123, ISSN: 2052-4463
This data descriptor reports the main scientific values from General Circulation Models (GCMs) in the Precipitation Driver and Response Model Intercomparison Project (PDRMIP). The purpose of the GCM simulations has been to enhance the scientific understanding of how changes in greenhouse gases, aerosols, and incoming solar radiation perturb the Earth's radiation balance and its climate response in terms of changes in temperature and precipitation. Here we provide global and annual mean results for a large set of coupled atmospheric-ocean GCM simulations and a description of how to easily extract files from the dataset. The simulations consist of single idealized perturbations to the climate system and have been shown to achieve important insight in complex climate simulations. We therefore expect this data set to be valuable and highly used to understand simulations from complex GCMs and Earth System Models for various phases of the Coupled Model Intercomparison Project.
Kasoar M, Corsaro C, Voulgarakis A, 2022, Metrics for Regional Climate Responses to Regional Pollutant Emissions
<jats:p>&lt;p&gt;The Absolute Global Temperature change Potential (AGTP) and Absolute Global Precipitation change Potential (AGPP) are widely used climate change indices.&amp;#160; They can be applied quickly and easily to estimate the global mean temperature and precipitation responses to a pulse emission of a long-lived climate pollutant at a given time horizon, making them invaluable policy-relevant metrics.&amp;#160; They can also be extended to short-lived climate pollutants - where a sustained emission is more useful to consider than a pulse emission - by using their time-integrated forms (iAGTP and iAGPP).&lt;/p&gt;&lt;p&gt;However, these metrics are only useful when taking a global-average perspective, and do not allow us to account for the regional nature of either emissions or their climate response.&amp;#160; Although long-lived greenhouse gases induce a relatively homogeneous radiative forcing (RF) which is not sensitive to emission location, nonetheless due to transport of heat there is not a one-to-one correspondence between the RF in a region and the local temperature response.&amp;#160; Moreover when considering short-lived pollutants such as aerosols, the region of emission is potentially critical because the short lifetime of such pollutants results in an inhomogeneous distribution of RF.&amp;#160; Therefore, for both long-lived and short-lived pollutants the AGTP/AGPP (or iAGTP/iAGPP) are not adequate when looking at climate responses on a regional scale, even though this would be the most relevant when evaluating different policy scenarios or climate change impacts.&lt;/p&gt;&lt;p&gt;Here, we combine the results of simulations from the Precipitation Driver Response Model Intercomparison Project (PDRMIP) where emissions (or concentrations) of multiple long- and short-lived climate pollutants were perturbed globally in nine
Misal H, Kountouris I, Voulgarakis A, et al., 2022, Eliciting public preferences for wildfire management policies in Crete, Greece
<jats:p>&lt;p&gt;Fire regimes form an integral part of terrestrial biomes in the Mediterranean region as they provide essential disturbances which change&amp;#160;the&amp;#160;structure and function of plants that favour Mediterranean type climates. Fire is inextricably linked to such ecosystems and cannot be excluded from them. However, the intensification of human activities in Greece, coupled with increasingly unpredictable wildfires has created huge imbalances and jeopardised the ecological integrity of ecosystems. Expansions into the wildland urban interface, rural abandonment, and the focus on fire suppression are increasing the vulnerability and flammability of the Greek environment. The duality of fire is delicate, both at local and national level, catastrophic wildfires singe deeply on landscapes and economies, social burns can take just as long to heal. In Greece, this is further exacerbated by the burgeoning socio-economic and political complexities that have catalysed the current ineffective and unsustainable fire management strategies. Damages from wildfires affect ecosystem services which can lead to a reduction in human wellbeing. Understanding the interactions between ecosystems and humans through environmental valuation is key to implementing effective policy. This study uses economic valuation methods in the form of a choice experiment to elicit public preference for a wildfire management policy in Crete. A survey was deployed around the island, with respondents asked about their preferences between different management strategies. The policies outlined in the survey are made up of the following attributes: risk of fire, agricultural production, landscape quality and post-wildfire damage mitigation. Results from this study indicate a positive preference by the public for a new proposed policy. The findings from this study can be used for decision making in Crete and other similar southern European environments
Teixeira J, Burton C, Kelley DI, et al., 2022, Representing socio-economic factors in INFERNO using the Human Development Index
<jats:p>&lt;p&gt;INFERNO human fire ignitions and fire suppression functions excluded the representation of socio-economic factors (aside population density) that can affect anthropogenic behaviour regarding fire ignitions. To address this, we implement a socio-economic factor in the fire ignition and suppression parametrisation in INFERNO based on an Human Development Index (HDI) term. The HDI is calculated based on three indicators designed to capture the income, health, and education dimensions of human development. Therefore, we assume this leads to a representation where if there is more effort in improving human development, there is also investment on higher fire suppression by the population. Including this representation of socio-economic factors in INFERNO we were able to reduce large positive biases that were found for the regions of Temperate North America, Central America, Europe and Southern Hemisphere South America without significant impact to other regions, improving the model performance at a regional level and better representing processes that drive fire behaviour in the Earth System.&lt;/p&gt;</jats:p>
Myhre G, Stjern C, Samset B, et al., 2022, The timescales of climate responses to carbon dioxide and aerosols
<jats:p>&lt;p&gt;Enhanced emissions of both greenhouse gases and aerosols generate climate responses on a wide range of time scales. An initial radiative response triggers a set of rapid adjustments, which are eventually followed by surface-temperature-driven feedbacks. While a lot happens during the first days and months after a perturbation, the monthly mean data typically used in climate studies are too coarse to show the temporal evolution of responses. In these analyses, we take a closer look at how the climate system responds during the very first hours and days after a sudden increase in carbon dioxide (CO2), in black carbon (BC) or in sulfate (SO4). Five models have performed PDRMIP simulations with hourly output, and we also compare results to monthly PDRMIP and CMIP6 results. We find that the effect of increasing ocean temperatures kicks in after a couple of months. Rapid precipitation reductions are for all three climate perturbations established after just a couple of days, and does for BC not differ much from the full-time response. &amp;#160;For CO2 and SO4, the magnitude of the precipitation response gradually increases with surface warming, and for CO2 the sign of the response changes for negative to positive after two years. Rapid cloud adjustments are typically established within the first 24 hours and while the magnitude of cloud feedbacks for CO2 and SO4 increases over time, the latitude-height pattern of the total cloud changes is clearly present after one year. While previously known that climate responses to BC are dominated by rapid adjustments, this work underlines the swiftness of the processes involved.&lt;/p&gt;</jats:p>
Teixeira JC, Folberth GA, O'Connor FM, et al., 2021, Coupling interactive fire with atmospheric composition and climate in the UK Earth System Model, Geoscientific Model Development, Vol: 14, Pages: 6515-6539, ISSN: 1991-959X
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, co
Tang T, Shindell D, Zhang Y, et al., 2021, Distinct surface response to black carbon aerosols, Atmospheric Chemistry and Physics, Vol: 21, Pages: 13797-13809, ISSN: 1680-7316
For the radiative impact of individual climate forcings, most previous studies focused on the global mean values at the top of the atmosphere (TOA), and less attention has been paid to surface processes, especially for black carbon (BC) aerosols. In this study, the surface radiative responses to five different forcing agents were analyzed by using idealized model simulations. Our analyses reveal that for greenhouse gases, solar irradiance, and scattering aerosols, the surface temperature changes are mainly dictated by the changes of surface radiative heating, but for BC, surface energy redistribution between different components plays a more crucial role. Globally, when a unit BC forcing is imposed at TOA, the net shortwave radiation at the surface decreases by −5.87±0.67 W m−2 (W m−2)−1 (averaged over global land without Antarctica), which is partially offset by increased downward longwave radiation (2.32±0.38 W m−2 (W m−2)−1 from the warmer atmosphere, causing a net decrease in the incoming downward surface radiation of −3.56±0.60 W m−2 (W m−2)−1. Despite a reduction in the downward radiation energy, the surface air temperature still increases by 0.25±0.08 K because of less efficient energy dissipation, manifested by reduced surface sensible (−2.88±0.43 W m−2 (W m−2)−1) and latent heat flux (−1.54±0.27 W m−2 (W m−2)−1), as well as a decrease in Bowen ratio (−0.20±0.07 (W m−2)−1). Such reductions of turbulent fluxes can be largely explained by enhanced air stability (0.07±0.02 K (W m−2)−1), measured as the difference of the potential temperature between 925 hPa and surface, and reduc
Thomas C, Voulgarakis A, Lim G, et al., 2021, An unsupervised learning approach to identifying blocking events:the case of European summer, Weather and Climate Dynamics, Vol: 2, ISSN: 2698-4016
Atmospheric blocking events are mid-latitudeweather patterns, which obstruct the usual path of the polar jet streams. They are often associated with heat wavesin summer and cold snaps in winter. Despite being centralfeatures of mid-latitude synoptic-scale weather, there is nowell-defined historical dataset of blocking events. Variousblocking indices (BIs) have thus been suggested for automatically identifying blocking events in observational and inclimate model data. However, BIs show significant regionaland seasonal differences so that several indices are typicallyapplied in combination to ensure scientific robustness. Here,we introduce a new BI using self-organizing maps (SOMs),an unsupervised machine learning approach, and compare itsdetection skill to some of the most widely applied BIs. Toenable this intercomparison, we first create a new groundtruth time series classification of European blocking basedon expert judgement. We then demonstrate that our method(SOM-BI) has several key advantages over previous BIs because it exploits all of the spatial information provided in theinput data and reduces the dependence on arbitrary thresholds. Using ERA5 reanalysis data (1979–2019), we find thatthe SOM-BI identifies blocking events with a higher precision and recall than other BIs. In particular, SOM-BI alreadyperforms well using only around 20 years of training data sothat observational records are long enough to train our newmethod. We present case studies of the 2003 and 2019 European heat waves and highlight that well-defined groups ofSOM nodes can be an effective tool to diagnose such weatherevents, although the domain-based approach can still lead toerrors in the identification of certain events in a fashion similar to the other BIs. We further test the red blocking detectionskill of SOM-BI depending on the meteorological variableused to study blocking, including geopotential height, sealevel pressure and four variables related to potential vorticity,and t
Thomas C, Voulgarakis A, Lim G, et al., 2021, An unsupervised learning approach to identifying blocking events: the case of European summer, Weather and Climate Dynamics, Vol: 2, Pages: 581-608, ISSN: 2698-4016
Atmospheric blocking events are mid-latitude weather patterns, which obstruct the usual path of the polar jet streams. They are often associated with heat waves in summer and cold snaps in winter. Despite being central features of mid-latitude synoptic-scale weather, there is no well-defined historical dataset of blocking events. Various blocking indices (BIs) have thus been suggested for automatically identifying blocking events in observational and in climate model data. However, BIs show significant regional and seasonal differences so that several indices are typically applied in combination to ensure scientific robustness. Here, we introduce a new BI using self-organizing maps (SOMs), an unsupervised machine learning approach, and compare its detection skill to some of the most widely applied BIs. To enable this intercomparison, we first create a new ground truth time series classification of European blocking based on expert judgement. We then demonstrate that our method (SOM-BI) has several key advantages over previous BIs because it exploits all of the spatial information provided in the input data and reduces the dependence on arbitrary thresholds. Using ERA5 reanalysis data (1979–2019), we find that the SOM-BI identifies blocking events with a higher precision and recall than other BIs. In particular, SOM-BI already performs well using only around 20 years of training data so that observational records are long enough to train our new method. We present case studies of the 2003 and 2019 European heat waves and highlight that well-defined groups of SOM nodes can be an effective tool to diagnose such weather events, although the domain-based approach can still lead to errors in the identification of certain events in a fashion similar to the other BIs. We further test the red blocking detection skill of SOM-BI depending on the meteorological variable used to study blocking, including geopotential height, sea level pressure and four variables related to
Kuhn- Regnier A, Voulgarakis A, Nowack P, et al., 2021, The importance of antecedent vegetation and drought conditions as global drivers of burnt areas, Biogeosciences, Vol: 18, Pages: 3861-3879, ISSN: 1726-4170
The seasonal and longer-term dynamics of fuel accumulation affect fire seasonality and the occurrence of extreme wildfires. Failure to account for their influence may help to explain why state-of-the-art fire models do not simulate the length and timing of the fire season or interannual variability in burnt area well. We investigated the impact of accounting for different timescales of fuel production and accumulation on burnt area using a suite of random forest regression models that included the immediate impact of climate, vegetation, and human influences in a given month and tested the impact of various combinations of antecedent conditions in four productivity-related vegetation indices and in antecedent moisture conditions. Analyses were conducted for the period from 2010 to 2015 inclusive. Inclusion of antecedent vegetation conditions representing fuel build-up led to an improvement of the global, climatological out-of-sample R2 from 0.579 to 0.701, but the inclusion of antecedent vegetation conditions on timescales ≥ 1 year had no impact on simulated burnt area. Current moisture levels were the dominant influence on fuel drying. Additionally, antecedent moisture levels were important for fuel build-up. The models also enabled the visualisation of interactions between variables, such as the importance of antecedent productivity coupled with instantaneous drying. The length of the period which needs to be considered varies across biomes; fuel-limited regions are sensitive to antecedent conditions that determine fuel build-up over longer time periods (∼ 4 months), while moisture-limited regions are more sensitive to current conditions that regulate fuel drying.
Kuhn-Régnier A, Voulgarakis A, Nowack P, et al., 2021, Quantifying the Importance of antecedent fuel-related vegetationproperties for burnt area using random forests, Biogeosciences, Vol: 8, ISSN: 1726-4170
The seasonal and longer-term dynamics of fuel accumulation affect fire seasonality and the occurrence of extreme wildfires. Failure to account for their influence mayhelp to explain why state-of-the-art fire models do not simulate the length and timing of the fire season or interannual variability in burnt area well. We investigated the impact of accounting for different timescales of fuel production and accumulation on burnt area using a suite of random forest regression models that included the immediateimpact of climate, vegetation, and human influences in agiven month and tested the impact of various combinationsof antecedent conditions in four productivity-related vegetation indices and in antecedent moisture conditions. Analyses were conducted for the period from 2010 to 2015 inclusive. Inclusion of antecedent vegetation conditions representing fuel build-up led to an improvement of the global,climatological out-of-sample R2from 0.579 to 0.701, but theinclusion of antecedent vegetation conditions on timescales≥ 1 year had no impact on simulated burnt area. Currentmoisture levels were the dominant influence on fuel drying. Additionally, antecedent moisture levels were importantfor fuel build-up. The models also enabled the visualisationof interactions between variables, such as the importanceof antecedent productivity coupled with instantaneous drying. The length of the period which needs to be consideredvaries across biomes; fuel-limited regions are sensitive to antecedent conditions that determine fuel build-up over longertime periods (∼ 4 months), while moisture-limited regionsare more sensitive to current conditions that regulate fuel drying.
Kuhn-Régnier A, Voulgarakis A, Nowack P, et al., 2021, Supplementary material to "Quantifying the Importance of Antecedent Fuel-Related VegetationProperties for Burnt Area using Random Forests", Biogeosciences, ISSN: 1726-4170
Qu Y, Voulgarakis A, Wang T, et al., 2021, A study of the effect of aerosols on surface ozone through meteorology feedbacks over China, Atmospheric Chemistry and Physics, Vol: 21, Pages: 5705-5718, ISSN: 1680-7316
Interactions between aerosols and gases in the atmosphere have been the focus of an increasing number of studies in recent years. Here, we focus on aerosol effects on tropospheric ozone that involve meteorological feedbacks induced by aerosol–radiation interactions. Specifically, we study the effects that involve aerosol influences on the transport of gaseous pollutants and on atmospheric moisture, both of which can impact ozone chemistry. For this purpose, we use the UK Earth System Model (UKESM1), with which we performed sensitivity simulations including and excluding the aerosol direct radiative effect (ADE) on atmospheric chemistry, and focused our analysis on an area with a high aerosol presence, namely China. By comparing the simulations, we found that ADE reduced shortwave radiation by 11 % in China and consequently led to lower turbulent kinetic energy, weaker horizontal winds and a shallower boundary layer (with a maximum of 102.28 m reduction in north China). On the one hand, the suppressed boundary layer limited the export and diffusion of pollutants and increased the concentration of CO, SO2, NO, NO2, PM2.5 and PM10 in the aerosol-rich regions. The NO/NO2 ratio generally increased and led to more ozone depletion. On the other hand, the boundary layer top acted as a barrier that trapped moisture at lower altitudes and reduced the moisture at higher altitudes (the specific humidity was reduced by 1.69 % at 1493 m on average in China). Due to reduced water vapour, fewer clouds were formed and more sunlight reached the surface, so the photolytical production of ozone increased. Under the combined effect of the two meteorology feedback methods, the annual average ozone concentration in China declined by 2.01 ppb (6.2 %), which was found to bring the model into closer agreement with surface ozone measurements from different parts of China.
Millington J, Perkins O, Kasoar M, et al., 2021, Advancing representation of anthropogenic fire in dynamic global vegetation models
<jats:p>&lt;div&gt;&lt;p&gt;It is now commonly-understood that improved understanding of global fire regimes demands better representation of anthropogenic fire in dynamic global vegetation models (DGVMs). However, currently there is no clear agreement on how human activity should be incorporated into fire-enabled DGVMs and existing models exhibit large differences in the sensitivities of socio-economic variables. Furthermore, existing approaches are limited to empirical statistical relations between fire regime variables and globally available socio-economic indicators such as population density or GDP. Although there has been some limited representation in global models of the contrasting ways in which different classes of actors use or manage fires, we argue that fruitful progress in advancing representation of anthropogenic fire in DGVMs will come by building on agent-based modelling approaches. Here, we report on our progress developing a global agent-based representation of anthropogenic fire and its coupling with the JULES-INFERNO fire-enabled DGVM.&amp;#160;&amp;#160;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;p&gt;Our modelling of anthropogenic fire adopts an approach that classifies &amp;#8216;agent functional types&amp;#8217; (AFTs) to represent human fire activity based on land use/cover and Stephen Pyne&amp;#8217;s fire development stages. For example, the &amp;#8216;swidden&amp;#8217; AFT represents shifting cultivation farmers managing cropland and secondary vegetation in a pre-industrial development setting. This approach is based on the assumption that anthropogenic fire use and management is primarily a function of land use but influenced by socio-economic context, leading different AFTs to produce qualitatively distinct fire regimes. The literature empirically supports this assumption, however data
Wells C, Voulgarakis A, 2021, The local and remote atmospheric impacts of Africa&#8217;s 21st century aerosol emission trajectory
<jats:p>&lt;p&gt;Aerosols are a major climate forcer, but their historical effect has the largest uncertainty of any forcing; their mechanisms and impacts are not well 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. Using the Shared Socioeconomic Pathway scenarios (SSPs), transient future experiments were performed in UKESM1, testing the effect of African emissions following the SSP3-RCP7.0 scenario as the rest of the world follows SSP1-RCP1.9, relative to a global SSP1-RCP1.9 control. SSP3 sees higher direct anthropogenic aerosol emissions, but lower biomass burning emissions, over Africa. Experiments were performed changing each of these sets of emissions, and both. A further set of experiments additionally accounted for changing future CO&lt;sub&gt;2&lt;/sub&gt; concentrations, to investigate the impact of CO&lt;sub&gt;2&lt;/sub&gt; on the responses to aerosol perturbations. Impacts on radiation fluxes, temperature, circulation and precipitation are investigated, both over the emission region (Africa), where microphysical effects dominate, and remotely, where dynamical influences become more relevant.&amp;#160;&lt;/p&gt;</jats:p>
Kasoar M, Hamilton D, Dalmonech D, et al., 2021, Improved estimates of future fire emissions under CMIP6 scenarios and implications for aerosol radiative forcing
<jats:p>&lt;p&gt;The CMIP6 Shared Socioeconomic Pathway (SSP) scenarios include projections of future changes in anthropogenic biomass-burning.&amp;#160; Globally, they assume a decrease in total fire emissions over the next century under all scenarios.&amp;#160; However, fire regimes and emissions are expected to additionally change with future climate, and the methodology used to project fire emissions in the SSP scenarios is opaque.&lt;/p&gt;&lt;p&gt;We aim to provide a more traceable estimate of future fire emissions under CMIP6 scenarios and evaluate the impacts for aerosol radiative forcing. &amp;#160;We utilise interactive wildfire emissions from four independent land-surface models (CLM5, JSBACH3.2, LPJ-GUESS, and ISBA-CTRIP) used within CMIP6 ESMs, and two different machine-learning methods (a random forest, and a generalised additive model) trained on historical data, to predict year 2100 biomass-burning aerosol emissions consistent with the CMIP6-modelled climate for three different scenarios: SSP126, SSP370, and SSP585.&amp;#160; This multi-method approach provides future fire emissions integrating information from observations, projections of climate, socioeconomic parameters and changes in vegetation distribution and fuel loads.&lt;/p&gt;&lt;p&gt;Our analysis shows a robust increase in fire emissions for large areas of the extra-tropics until the end of this century for all methods.&amp;#160; Although this pattern was present to an extent in the original SSP projections, both the interactive fire models and machine-learning methods predict substantially higher increases in extra-tropical emissions in 2100 than the corresponding SSP datasets.&amp;#160; Within the tropics the signal is mixed. Increases in emissions are largely driven by the temperature changes, while in some tropical areas reductions in fire emissions are
Thomas C, Voulgarakis A, Lim G, et al., 2021, An unsupervised learning approach to identifying blocking events: the case of European summer
<jats:p>&lt;p&gt;Atmospheric blocking events are mid-latitude weather patterns, which obstruct the usual path of the polar jet stream. Several blocking indices (BIs) have been developed to study blocking patterns and their associated trends, but these show significant seasonal and regional differences. Despite being central features of mid-latitude synoptic-scale weather, there is no well-defined historical dataset of blocking events. Here, we introduce a new blocking index using self-organizing maps (SOMs), an unsupervised machine learning approach, and compare its detection skill to some of the most widely applied BIs. To enable this intercomparison, we first create a new ground truth time series classification of European blocking based on expert judgement. We then demonstrate that our method (SOM-BI) has several key advantages over previous BIs because it exploits all the spatial information provided in the input data and avoids the need for arbitrary thresholds. Using ERA5 reanalysis data (1979-2019), we find that the SOM-BI identifies blocking events with a higher precision and recall than other BIs. We present a case study of the 2003 European heat wave and highlight that well-defined groups of SOM nodes can be an effective tool to reliably and accurately diagnose such weather events. This contrasts with the way SOMs are commonly used, where an individual SOM node can be wrongly assumed to represent a weather pattern. We also evaluate the SOM-BI performance on about 100 years of climate model data from a preindustrial simulation with the new UK Earth System Model (UK-ESM1). For the model data, all blocking detection methods have lower skill than for the ERA5 reanalysis, but SOM-BI performs significantly better than the conventional indices. This shows that our method can be effectively applied to climate models to develop our understanding of how climate change will affect regional blocking characteristics. Overall, our results demonstra
Mansfield L, Nowack P, Voulgarakis A, 2021, Predicting climate model response to changing emissions
<jats:p>&lt;p&gt;In order to make predictions on how the climate would respond to changes in global and regional emissions, we typically run simulations on Global Climate Models (GCMs) with perturbed emissions or concentration fields. These simulations are highly expensive and often require the availability of high-performance computers. Machine Learning (ML) can provide an alternative approach to estimating climate response to various emissions quickly and cheaply.&amp;#160;&lt;/p&gt;&lt;p&gt;We will present a Gaussian process emulator capable of predicting the global map of temperature response to different types of emissions (both greenhouse gases and aerosol pollutants), trained on a carefully designed set of simulations from a GCM. This particular work involves making short-term predictions on 5 year timescales but can be linked to an emulator from previous work that predicts on decadal timescales. We can also examine uncertainties associated with predictions to find out where where the method could benefit from increased training data. This is a particularly useful asset when constructing emulators for complex models, such as GCMs, where obtaining training runs is costly.&amp;#160;&lt;/p&gt;</jats:p>
Hodnebrog Ø, Myhre G, Kramer RJ, et al., 2020, The effect of rapid adjustments to halocarbons and N2O on radiative forcing, npj Climate and Atmospheric Science, Vol: 3, Pages: 1-7, ISSN: 2397-3722
Rapid adjustments occur after initial perturbation of an external climate driver (e.g., CO2) and involve changes in, e.g. atmospheric temperature, water vapour and clouds, independent of sea surface temperature changes. Knowledge of such adjustments is necessary to estimate effective radiative forcing (ERF), a useful indicator of surface temperature change, and to understand global precipitation changes due to different drivers. Yet, rapid adjustments have not previously been analysed in any detail for certain compounds, including halocarbons and N2O. Here we use several global climate models combined with radiative kernel calculations to show that individual rapid adjustment terms due to CFC-11, CFC-12 and N2O are substantial, but that the resulting flux changes approximately cancel at the top-of-atmosphere due to compensating effects. Our results further indicate that radiative forcing (which includes stratospheric temperature adjustment) is a reasonable approximation for ERF. These CFCs lead to a larger increase in precipitation per kelvin surface temperature change (2.2 ± 0.3% K−1) compared to other well-mixed greenhouse gases (1.4 ± 0.3% K−1 for CO2). This is largely due to rapid upper tropospheric warming and cloud adjustments, which lead to enhanced atmospheric radiative cooling (and hence a precipitation increase) and partly compensate increased atmospheric radiative heating (i.e. which is associated with a precipitation decrease) from the instantaneous perturbation.
Mansfield L, Nowack P, Kasoar M, et al., 2020, Predicting global patterns of long-term climate change from short-term simulations using machine learning, npj Climate and Atmospheric Science, Vol: 3, ISSN: 2397-3722
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-te¬rm 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.
Qu Y, Voulgarakis A, Wang T, et al., 2020, A study of the effect of aerosols on surface ozone through meteorologyfeedbacks over China
<jats:p>Abstract. Interactions between aerosols and gases in the atmosphere have been the focus of an increasing number of studies in recent years. Here, we focus on aerosol effects on tropospheric ozone that involve meteorological feedbacks induced by aerosol-radiation interactions. Specifically, we study the effects that involve aerosol influences on the transport of gaseous pollutants and on atmospheric moisture, both of which can impact ozone chemistry. For this purpose, we use the UK Earth System Model (UKESM1) with which we performed sensitivity simulations including and excluding the aerosol direct radiative effect (ADE) on atmospheric chemistry, and focused our analysis on an area with high aerosol presence, namely China. By comparing the simulations, we found that ADE reduced the shortwave radiation by 11 % in China, and consequently led to lower turbulent kinetic energy, weaker horizontal winds and a shallower boundary layer (with a maximum of 102.28 m reduction in north China). On the one hand, the suppressed boundary layer limited the export and diffusion of pollutants, and increased the concentration of CO, SO2, NO, NO2, PM2.5 and PM10 in the aerosol rich regions. The NO / NO2 ratio generally increased and led to more ozone depletion. On the other hand, the boundary layer top acted as a barrier that trapped moisture at lower altitudes and reduced the moisture at higher altitudes (the specific humidity was reduced by 1.69 % at 1493 m averaged in China). Due to reduced water vapor, fewer clouds were formed, and more sunlight reached the surface, so the photolytical production of ozone increased. Under the combined effect of the two meteorology feedback methods, the annual average ozone concentration in China declined by 2.01 ppb (6.2 %), which was found to bring the model in closer agreement with surface ozone measurements from different parts of China. </jats:p>
Qu Y, Voulgarakis A, Wang T, et al., 2020, Supplementary material to &quot;A study of the effect of aerosols on surface ozone through meteorologyfeedbacks over China&quot;
Teixeira JC, Folberth G, O'Connor FM, et al., 2020, Coupling interactive fire with atmospheric composition and climatein the UK Earth System Model
<jats:p>Abstract. 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) do not include fully coupled fires. The aim of this work is the development and evaluation of 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/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 rati
Hantson S, Kelley DI, Arneth A, et al., 2020, Quantitative assessment of fire and vegetation properties in simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project, Geoscientific Model Development, Vol: 13, Pages: 3299-3318, ISSN: 1991-959X
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, in order to improve projections of fire characteristics and fire impacts on ecosystems and human societies in the context of global environmental change. Here we perform a systematic evaluation of historical simulations made by nine FireMIP models 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 yr−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 interannual 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
Tang T, Shindell D, Zhang Y, et al., 2020, Response of surface shortwave cloud radiative effect to greenhouse gases and aerosols and its impact on summer maximum temperature, Atmospheric Chemistry and Physics, Vol: 20, Pages: 8251-8266, ISSN: 1680-7316
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.
Saunois M, Stavert AR, Poulter B, et al., 2020, The global methane budget 2000-2017, Earth System Science Data, Vol: 12, Pages: 1561-1623, ISSN: 1866-3508
Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. Atmospheric emissions and concentrations of CH4 continue to increase, making CH4 the second most important human-influenced greenhouse gas in terms of climate forcing, after carbon dioxide (CO2). The relative importance of CH4 compared to CO2 depends on its shorter atmospheric lifetime, stronger warming potential, and variations in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in reducing uncertainties in the atmospheric growth rate arise from the variety of geographically overlapping CH4 sources and from the destruction of CH4 by short-lived hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate new research aimed at improving and regularly updating the global methane budget. Following Saunois et al. (2016), we present here the second version of the living review paper dedicated to the decadal methane budget, integrating results of top-down studies (atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up estimates (including process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations).For the 2008–2017 decade, global methane emissions are estimated by atmospheric inversions (a top-down approach) to be 576 Tg CH4 yr−1 (range 550–594, corresponding to the minimum and maximum estimates of the model ensemble). Of this total, 359 Tg CH4 yr−1 or ∼ 60 % is attributed to anthropogenic sources, that is emissions caused by direct human activity (i.e. anthropogenic emissions; range 336–376 Tg CH4 yr−1 or
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