Future Emission Scenarios Research Articles

Water scarcity down to earth surface in a Mediterranean climate: The extreme future of soil moisture in Portugal

Climate change constitutes a major threat for all the Mediterranean countries due to the combination of large precipitation reductions and temperature increases and the higher frequency of climate extremes, especially driving water scarcity and all the derived multi-sectoral impacts. Portugal, as most of the Mediterranean countries, already endures larger frequencies of droughts and deficits in soil moisture and water storage. In the current study, the future projections of soil moisture are examined using a multi-model EURO-CORDEX regional climate ensemble, in agreement with three future emission scenarios (RCP2.6, 4.5 and 8.5). The drivers of future soil moisture dynamics are also analysed and its effect on relative humidity and evaporation rates. As expected, the projections show a clear reduction of soil moisture through the entire annual cycle, in response to the large decrease in precipitation and temperature increase, via a massive growth of potential evapotranspiration. The overall total soil moisture decreases ranges from −5% for the RCP2.6 to −20% (-10%) for the RCP8.5 (RCP4.5), w.r.t. the present climate. In the historical period, soil moisture deficits rarely reach values 3x over the standard deviation, but projections reveal that for the RCP4.5 (RCP8.5) for the mid-century deficits up to 5x (6x) are projected to occur, and for the end-of-century even 7x for the RCP8.5. The annual cycle of soil moisture is in present and future climate determined by precipitation and potential evapotranspiration, and deficit is both enhanced and covers a wider monthly window in the future, especially for the RCP8.5. The surface humidity also decreases importantly, up to −4% and −8% in spring and summer in the end-of-the-century, in agreement with RCP4.5 and RCP8.5, respectively. Resulting from the projected changes in precipitation and potential evapotranspiration, the typical semi-arid climate, which in present climate is confined to a small south-eastern region of Portugal, is expected to cover almost 2/3 of the mainland in the case of RCP8.5. Finally, this study was developed in the framework of the National Roadmap for Adaptation XXI - Portuguese Territorial Climate Change Vulnerability Assessment for XXI Century (RNA2100) project and aims at delivering a deeper and different featuring of terrestrial water for adaptation purposes in a Mediterranean country.

Estimating the Burden of Heat-Related Illness Morbidity Attributable to Anthropogenic Climate Change in North Carolina.

Climate change is known to increase the frequency and intensity of hot days (daily maximum temperature ≥30°C), both globally and locally. Exposure to extreme heat is associated with numerous adverse human health outcomes. This study estimated the burden of heat-related illness (HRI) attributable to anthropogenic climate change in North Carolina physiographic divisions (Coastal and Piedmont) during the summer months from 2011 to 2016. Additionally, assuming intermediate and high greenhouse gas emission scenarios, future HRI morbidity burden attributable to climate change was estimated. The association between daily maximum temperature and the rate of HRI was evaluated using the Generalized Additive Model. The rate of HRI assuming natural simulations (i.e., absence of greenhouse gas emissions) and future greenhouse gas emission scenarios were predicted to estimate the HRI attributable to climate change. Over 4years (2011, 2012, 2014, and 2015), we observed a significant decrease in the rate of HRI assuming natural simulations compared to the observed. About 3 out of 20 HRI visits are attributable to anthropogenic climate change in Coastal (13.40% [IQR: -34.90,95.52]) and Piedmont (16.39% [IQR: -35.18,148.26]) regions. During the future periods, the median rate of HRI was significantly higher (78.65%: Coastal and 65.85%: Piedmont), assuming a higher emission scenario than the intermediate emission scenario. We observed significant associations between anthropogenic climate change and adverse human health outcomes. Our findings indicate the need for evidence-based public health interventions to protect human health from climate-related exposures, like extreme heat, while minimizing greenhouse gas emissions.

Rarest rainfall events will see the greatest relative increase in magnitude under future climate change

Future rainfall extremes are projected to increase with global warming according to theory and climate models, but common (annual) and rare (decennial or centennial) extremes could be affected differently. Here, using 25 models from the Coupled Model Intercomparison Project Phase 6 driven by a range of plausible scenarios of future greenhouse gas emissions, we show that the rarer the event, the more likely it is to increase in a future climate. By the end of this century, daily land rainfall extremes could increase in magnitude between 10.5% and 28.2% for annual events, and between 13.5% and 38.3% for centennial events, for low and high emission scenarios respectively. The results are consistent across models though with regional variation, but the underlying mechanisms remain to be determined.

A shared socio-economic pathway based framework for characterising future emissions of chemicals to the natural environment

Chemicals are used in all aspects of our lives and are either intentionally or unintentionally released into the natural environment, leading to chemical pollution which negatively effects biodiversity and ecosystem and human health. The world is going through socio-economic, climate and technological changes that will affect chemical emissions to the natural environment but the extent of these affects is unknown. Scenarios of future chemical emissions are therefore needed to inform research and policy decisions to protect the health of humans and ecosystems into the future. In this article, we present a framework, based on Shared Socio-economics Pathways (SSPs) in combination with Representative concentration pathways (RCPs), to develop future chemical environmental emissions scenarios for single molecules or groups of chemicals sharing similar features. The framework has 4 steps: 1) determination of the characteristics of the scenario; 2) review and prioritisation of socio-economics and climate drivers; 3) development of scenarios; and 4) consistency checks. The framework is demonstrated for antidepressant and insecticide emissions into European freshwater-systems in 2050. Output narratives provide multiple pathways of chemical emissions in the future and can be used by researchers, regulators, politicians, governments, and the private sector to develop mitigation and adaptation strategies to chemical pollution issue.

Circulation and Transport Processes during an Extreme Freshwater Discharge Event at the Tagus Estuary

During the winter of 2013, the Tagus estuary was under the influence of intense winds and extreme freshwater discharge that changed its hydrodynamics and, consequently, the salt and heat transport. Moreover, the dynamics of the estuary may change due to climate change which will increase the frequency of heat waves and increase the mean sea level. Therefore, it is of utmost importance to study the impact of the future increase in air temperature and mean sea level under extreme events, such as that in the winter of 2013, to ascertain the foreseen changes in water properties transport within the estuary and near coastal zone. Several scenarios were developed and explored, using the Delft3D model suite, considering the results of the CMIP6 report as forcing conditions. Before the event, the mixing region of the estuary presented well-mixed conditions and its marine area a slight stratification. During the event, the estuary was filled with freshwater and the mixing region migrated toward the coast, leading to lower water temperature values inside the estuary. SLR has a higher impact on the salinity and stratification patterns than the air temperature increase. The response of water temperature is directly related to the increase in air temperature. The estuary mouth and the shallow regions will be more prone to changes than the upstream region of the estuary. The projected changes are directly linked to the future CO2 emissions scenarios, being intensive with the highest emission scenario.

Globally Increasing Atmospheric Aridity Over the 21st Century

AbstractVapor pressure deficit (VPD) is of great importance to control the land‐atmosphere exchange of water and CO2. Here we use in situ observations to assess the performance of monthly VPD calculated from state‐of‐the‐art data sets including CRU, ERA5, and Modern‐Era Retrospective analysis for Research and Applications, Version 2 (MERRA2). We investigate trends in VPD at global scale and for different climatic zones for 1981–2020 and future trends (2021–2100) from Coupled Model Inter‐comparison Project phase 6 (CMIP6) outputs. The results show that monthly VPD estimated from CRU, ERA5, and MERRA2 correlated well against in situ estimates from 15,531 World Meteorological Organization stations, with R2 ranging between 0.92 and 0.96. Moreover, robust correlations were also found across in situ stations and when analyzing different months separately. During 1981–2020, VPD increased in all climatic zones, with the strongest increase in the arid zone, followed by tropical, temperate, cold and polar zones. CMIP6 simulations show a continuously increasing trend in VPD (0.028 hPa year−1), with the largest increase in the arid zone (0.063 hPa year−1). The magnitudes of trends are found to increase following the magnitude of CO2 increases in the future emission scenarios. We highlight that atmospheric aridification will continue under global warming, which may pose an increasing threat to terrestrial ecosystems and particularly dryland agricultural systems.

Climate change simulation and trend analysis of extreme precipitation and floods in the mesoscale Rur catchment in western Germany until 2099 using Statistical Downscaling Model (SDSM) and the Soil & Water Assessment Tool (SWAT model).

Due to climate change and global warming, speed and intensity of the hydrological cycle will accelerate. In order to carry out regional risk assessment, integrated water resources management and flood protection, far reaching predictions and future scenarios of climate change effects on extreme precipitation and flooding are of particular relevance. In this study, trends in frequencies of extreme precipitation and floods until 2099 are analysed for the German Rur catchment, which is half located in highlands and half in lowlands and therefore has a high topographical and climatological contrast. To predict future trends, coupled modeling is performed based on NCEP reanalysis data and a General Circulation Model (GCM). Assuming HadCM3 future emission scenarios A2a and B2a, an empirical Statistical Downscaling Model (SDSM) is developed and daily precipitation amounts are projected until 2099 by a stochastic weather generator. The generated precipitation data are used as an input for the ecohydrological Soil & Water Assessment Tool (SWAT model) to simulate daily water discharge until 2099. Statistical trend analyses are implemented based on three annual extreme precipitation indices (EPIs) and the magnitudes of ten flood return periods derived with GEV and Gumbel extreme value distributions for 109 30-year moving periods using regression analyses and Mann-Kendall tendency tests to check for significant trends in the frequencies until 2099. As a result, it could be demonstrated for all EPIs that the frequency of extreme precipitation in the upper Rur catchment will significantly increase by +33% to +51% until 2099 compared to the base period 1961-1990, whereas mostly non-significant negative trends of extreme precipitation can be projected in the lowlands. For runoff, it was found that the magnitudes of the ten flood return periods will significantly increase by +31% for B2a to +36% for A2a until 2099 compared to the base period.

Within-population variability in coral heat tolerance indicates climate adaptation potential

Coral reefs are facing unprecedented mass bleaching and mortality events due to marine heatwaves and climate change. To avoid extirpation, corals must adapt. Individual variation in heat tolerance and its heritability underpin the potential for coral adaptation. However, the magnitude of heat tolerance variability within coral populations is largely unresolved. We address this knowledge gap by exposing corals from a single reef to an experimental marine heatwave. We found that double the heat stress dosage was required to induce bleaching in the most-tolerant 10%, compared to the least-tolerant 10% of the population. By the end of the heat stress exposure, all of the least-tolerant corals were dead, whereas the most-tolerant remained alive. To contextualize the scale of this result over the coming century, we show that under an ambitious future emissions scenario, such differences in coral heat tolerance thresholds equate to up to 17 years delay until the onset of annual bleaching and mortality conditions. However, this delay is limited to only 10 years under a high emissions scenario. Our results show substantial variability in coral heat tolerance which suggests scope for natural or assisted evolution to limit the impacts of climate change in the short-term. For coral reefs to persist through the coming century, coral adaptation must keep pace with ocean warming, and ambitious emissions reductions must be realized.

Long-term probabilistic temperature projections for all locations

The climate change projections of the Intergovernmental Panel on Climate Change are based on scenarios for future emissions, but these are not statistically-based and do not have a full probabilistic interpretation. Raftery et al. (Nat Clim Change 7:637–641, 2017) and Liu and Raftery (Commun Earth Environ 2:1–10, 2021) developed probabilistic forecasts for global average temperature change to 2100, but these do not give forecasts for specific parts of the globe. Here we develop a method for probabilistic long-term spatial forecasts of local average annual temperature change, combining the probabilistic global method with a pattern scaling approach. This yields a probability distribution for temperature in any year and any part of the globe in the future. Out-of-sample predictive validation experiments show the method to be well calibrated. Consistent with previous studies, we find that for long-term temperature changes, high latitudes warm more than low latitudes, continents more than oceans, and the Northern Hemisphere more than the Southern Hemisphere, except for the North Atlantic. There is a 5% chance that the temperature change for the Arctic would reach 16 ^circ C. With probability 95%, the temperature of North Africa, West Asia and most of Europe will increase by at least 2 ^circ C. We find that natural variability is a large part of the uncertainty in early years, but this declines so that by 2100 most of the overall uncertainty comes from model uncertainty and uncertainty about future emissions.

Climate induced phenological shifts in pears – A crop of economic importance in the UK

Phenological advancements in flowering have been well documented in many food crop, ornamental, and native plant species. However, there is lack of information on how flowering times in crop species, especially fruit trees will react to future climate scenarios. This is important as changes in phenology could have significant implications for ecosystem services and function, biological interactions and agronomic outputs. Using 60 years of data from pear (Pyrus communis L.) orchards in two research organisations in Kent, UK we explored temporal changes in flowering phenology, identified the weather variables driving this change, and predicted how flowering times may be altered by 2080 with respect to future emissions scenarios. We show pear flowering (1990–2020) in the last 30 years has advanced 11.44 days compared to historical data (1960–1989). Furthermore, we highlight this advancement is apparent in all twelve pear cultivars and the four phenological stages analysed, including Conference, the most common UK pear cultivar. Our results indicate that this advancement in flowering began after 1982; that air temperature and frost days significantly impact pear flowering; and this change in flowering phenology is likely to continue under future climate scenarios. Four Representative Concentration Pathways from the UK Climate Projections 2018 report were used to model the impact of future climate, including low, medium, medium-high and high emission scenarios. Under all scenarios a phenological advancement in flowering time was predicted by 2080 with the greatest advancement in flowering time observed under the high emission scenario. Earlier flowering and budburst could result in phenological mismatches between plant and pollinators, alter agricultural spraying regimes, increase risk of frost exposure and exacerbate impacts of pest populations within an agroecosystem, thus it is vital to monitor advancements in flowering phenology.

Maximum entropy modelling for predicting the potential distribution of Invasive Rugose Spiralling Whitefly, Aleurodicus rugioperculatus in India

The potential distribution of invasive rugose spiralling whitefly, Aleurodicus rugioperculatus Martin (Hemiptera: Aleyrodidae) was predicted under present (2020) and future climate change emission scenarios in 2050 and 2070 under four representative concentration pathways (RCP 2.6, RCP 4.5, RCP 6.0, RCP 8.5) using maximum entropy (MaxEnt) niche modelling. The model performance was assessed using area under the curve of the receiver operating characteristic values. Spatial mapping of habitat suitability for rugose spiralling whitefly suitability maps was prepared for each selected climate scenarios. The study revealed that the most dominant climatic factors i.e annual mean temperature, mean diurnal range, precipitation seasonality and iso-thermality with 23.2, 21.4,17.5 and 16.4% respectively were significantly influenced the potential distribution and establishment of rugose spiralling whitefly. Eastern coastal parts of Tamil Nadu, North-Eastern parts of Andhra Pradesh, Eastern coastal belts of Odisha, North-Western coastal belts of Kerala, South-Western coastal parts of Karnataka and Western coastal belts of Maharashtra and Gujarat were predicted the highest habitat suitability places/ regions. The present study data would help to formulate control measures for monitoring, surveillance and early pest warning of rugose spiralling whitefly and combating outbreaks well in advance in newer geographical locations.

Connecting the dots: An interdisciplinary perspective on climate change effects on whales and whale watching in Skjálfandi Bay, Iceland

The paper presents a synthesis of some of the interdisciplinary work from the ARCPATH project that focuses on the effects of climate change on Arctic social-ecological systems. It does so through the prism of whales and their recreational ecosystem services (ES). Whales present a group of species that are vulnerable to climate change and, at the same time, are central to the economies, cultures, and identities of many Arctic coastal communities. One such community is the town of Húsavík in Skjálfandi Bay, Iceland. The paper conducts an initial literature review to examine the effects of climate change on whales, globally, before using these findings and site-specific data from climate change modelling, whale observations from whale watching boats and whale watching trip records to investigate possible future impacts on whale watching in Skjálfandi Bay. The literature review identifies three categories of impacts on whales due to climate change, which concern changing distributions and migration, prey availability, and sea-ice and ocean temperature. Linear regression models identify statistically significant relationships between sea-surface temperatures (SST) and cetacean sightings for minke whales, blue whales and white-beaked-dolphins over the period 1995 to 2017. These species appear to have changed their usual feeding areas, and the results imply that further increases in SST are likely to further affect whale distributions. Future climate scenarios indicate that at least 2 °C of SST warming in Skjálfandi Bay up to 2050 might be inevitable regardless of the future emissions scenario, which implies nearly certain change that would require adaptation. The reliance of the local tourism sector on whale watching makes Húsavík vulnerable to the effects of climate change on whales. The results of this interdisciplinary inquiry emphasize the interconnectedness of different components of social-ecological systems and calls for adaptation planning that would enhance the resilience of local community to climate change and conservation measures that could enhance the protection of whales beyond the scope of the current whale sanctuary in Skjálfandi Bay.

A methodology for evaluating the effects of climate change on climatic design conditions for buildings and application to a case study in Madison, Wisconsin

Abstract Climatic design conditions are widely used by the building community as environmental parameters informing the size and energy requirements for heating, ventilation and air conditioning systems, along with other building design characteristics. Climatic design conditions are calculated by the American Society of Heating, Refrigerating and Air-conditioning Engineers using historical climate data. Our work advances methods for projecting future climate design conditions based on data from global climate models. These models do not typically archive the hourly data required for climate design condition calculations, and they often exhibit large biases in extreme conditions, daily minimum temperatures and daily maximum temperatures needed for climatic design conditions. We present a method for rescaling historical hourly data under future climatic states to estimate the impact of climate change on future building climatic design conditions. This rescaling method is then used to calculate future climatic design conditions in Madison, Wisconsin, throughout the 21st century for two future greenhouse gas emissions scenarios. The results are consistent with a warming climate and show increases in heating, cooling, humidification and dehumidification design conditions, suggesting less extreme cold conditions and more extreme hot and humid conditions in Madison. The design conditions used for estimating energy demand, degree days, show that under a business-as-usual scenario, by the mid-century, building heating and cooling in Madison (climate zone 5A) will be similar to the current heating demand in Chicago, IL (climate zone 5A) and cooling demand in Baltimore, MD (climate zone 4A); by the late-century, building heating and cooling in Madison will resemble the current heating demand in St Louis, MO (climate zone 4A) and cooling demand in Augusta, GA (climate zone 3A). Given the rapid pace of climate change in the 21st century, our work suggests that historical design conditions may become obsolete during even the initial stages of a building’s expected life span. Changes in climatic design conditions in Madison highlight the importance of considering future climatic changes in building design to ensure that buildings built today meet the performance needs of the future.

The contribution of emission sources to the future air pollution disease burden in China

Air pollution exposure is a leading public health problem in China. Despite recent air quality improvements, fine particulate matter (PM2.5) exposure remains large, the associated disease burden is substantial, and population ageing is projected to increase the susceptibility to disease. Here, we used emulators of a regional chemical transport model to quantify the impacts of future emission scenarios on air pollution exposure in China. We estimated how key emission sectors contribute to these future health impacts from air pollution exposure. We found that PM2.5 exposure declines in all scenarios across China over 2020–2050, with reductions of 15% under current air quality legislation, 36% when exploiting the full potential of air pollutant emission reduction technologies, and 39% when that technical mitigation potential is combined with emission controls for climate mitigation. However, population ageing means that the PM2.5 disease burden under current legislation (CLE) increases by 17% in 2050 relative to 2020. In comparison to CLE in 2050, the application of the best air pollution technologies provides substantial health benefits, reducing the PM2.5 disease burden by 16%, avoiding 536 600 (95% uncertainty interval, 95UI: 497 800–573 300) premature deaths per year. These public health benefits are mainly due to reductions in industrial (43%) and residential (30%) emissions. Climate mitigation efforts combined with the best air pollution technologies leads to an additional 2% reduction in the PM2.5 disease burden, avoiding 57 000 (95UI: 52 800–61 100) premature deaths per year. Up to 90% of the 2020–2050 reductions in PM2.5 exposure are already achieved by 2030, assuming efficient implementation and enforcement of currently committed air quality policies in key sectors. Achieving reductions in PM2.5 exposure and the associated disease burden after 2030 will require further tightening of emission limits for regulated sectors, addressing other sources including agriculture and waste management, and international coordinated action to mitigate air pollution across Asia.

A Flood Risk Management Model to Identify Optimal Defence Policies in Coastal Areas Considering Uncertainties in Climate Projections

Coastal areas are particularly vulnerable to flooding from heavy rainfall, sea storm surge, or a combination of the two. Recent studies project higher intensity and frequency of heavy rains, and progressive sea level rise continuing over the next decades. Pre-emptive and optimal flood defense policies that adaptively address climate change are needed. However, future climate projections have significant uncertainty due to multiple factors: (a) future CO2 emission scenarios; (b) uncertainties in climate modelling; (c) discount factor changes due to market fluctuations; (d) uncertain migration and population growth dynamics. Here, a methodology is proposed to identify the optimal design and timing of flood defense structures in which uncertainties in 21st century climate projections are explicitly considered probabilistically. A multi-objective optimization model is developed to minimize both the cost of the flood defence infrastructure system and the flooding hydraulic risk expressed by Expected Annual Damage (EAD). The decision variables of the multi-objective optimization problem are the size of defence system and the timing of implementation. The model accounts for the joint probability density functions of extreme rainfall, storm surge and sea level rise, as well as the damages, which are determined dynamically by the defence system state considering the probability and consequences of system failure, using a water depth–damage curve related to the land use (Corine Land Cover); water depth due to flooding are calculated by hydraulic model. A new dominant sorting genetic algorithm (NSGAII) is used to solve the multi-objective problem optimization. A case study is presented for the Pontina Plain (Lazio Italy), a coastal region, originally a swamp reclaimed about a hundred years ago, that is rich in urban centers and farms. A set of optimal adaptation policies, quantifying size and timing of flood defence constructions for different climate scenarios and belonging to the Pareto curve obtained by the NSGAII are identified for such a case study to mitigate the risk of flooding and to aid decision makers.

Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: a multi-species, multi-model study

Abstract. While carbon dioxide is the main cause for global warming, modeling short-lived climate forcers (SLCFs) such as methane, ozone, and particles in the Arctic allows us to simulate near-term climate and health impacts for a sensitive, pristine region that is warming at 3 times the global rate. Atmospheric modeling is critical for understanding the long-range transport of pollutants to the Arctic, as well as the abundance and distribution of SLCFs throughout the Arctic atmosphere. Modeling is also used as a tool to determine SLCF impacts on climate and health in the present and in future emissions scenarios. In this study, we evaluate 18 state-of-the-art atmospheric and Earth system models by assessing their representation of Arctic and Northern Hemisphere atmospheric SLCF distributions, considering a wide range of different chemical species (methane, tropospheric ozone and its precursors, black carbon, sulfate, organic aerosol, and particulate matter) and multiple observational datasets. Model simulations over 4 years (2008–2009 and 2014–2015) conducted for the 2022 Arctic Monitoring and Assessment Programme (AMAP) SLCF assessment report are thoroughly evaluated against satellite, ground, ship, and aircraft-based observations. The annual means, seasonal cycles, and 3-D distributions of SLCFs were evaluated using several metrics, such as absolute and percent model biases and correlation coefficients. The results show a large range in model performance, with no one particular model or model type performing well for all regions and all SLCF species. The multi-model mean (mmm) was able to represent the general features of SLCFs in the Arctic and had the best overall performance. For the SLCFs with the greatest radiative impact (CH4, O3, BC, and SO42-), the mmm was within ±25 % of the measurements across the Northern Hemisphere. Therefore, we recommend a multi-model ensemble be used for simulating climate and health impacts of SLCFs. Of the SLCFs in our study, model biases were smallest for CH4 and greatest for OA. For most SLCFs, model biases skewed from positive to negative with increasing latitude. Our analysis suggests that vertical mixing, long-range transport, deposition, and wildfires remain highly uncertain processes. These processes need better representation within atmospheric models to improve their simulation of SLCFs in the Arctic environment. As model development proceeds in these areas, we highly recommend that the vertical and 3-D distribution of SLCFs be evaluated, as that information is critical to improving the uncertain processes in models.

Conservation laws in a neural network architecture: enforcing the atom balance of a Julia-based photochemical model (v0.2.0)

Abstract. Models of atmospheric phenomena provide insight into climate, air quality, and meteorology and provide a mechanism for understanding the effect of future emissions scenarios. To accurately represent atmospheric phenomena, these models consume vast quantities of computational resources. Machine learning (ML) techniques such as neural networks have the potential to emulate computationally intensive components of these models to reduce their computational burden. However, such ML surrogate models may lead to nonphysical predictions that are difficult to uncover. Here we present a neural network architecture that enforces conservation laws to numerical precision. Instead of simply predicting properties of interest, a physically interpretable hidden layer within the network predicts fluxes between properties which are subsequently related to the properties of interest. This approach is readily generalizable to physical processes where flux continuity is an essential governing equation. As an example application, we demonstrate our approach on a neural network surrogate model of photochemistry, trained to emulate a reference model that simulates formation and reaction of ozone. We design a physics-constrained neural network surrogate model of photochemistry using this approach and find that it conserves atoms as they flow between molecules while outperforming two other neural network architectures in terms of accuracy, physical consistency, and non-negativity of concentrations.

What are the likely changes in mercury concentration in the Arctic atmosphere and ocean under future emissions scenarios?

Arctic mercury (Hg) concentrations respond to changes in anthropogenic Hg emissions and environmental change. This manuscript, prepared for the 2021 Arctic Monitoring and Assessment Programme Mercury Assessment, explores the response of Arctic Ocean Hg concentrations to changing primary Hg emissions and to changing sea-ice cover, river inputs, and net primary production. To do this, we conduct a model analysis using a 2015 Hg inventory and future anthropogenic Hg emission scenarios. We model future atmospheric Hg deposition to the surface ocean as a flux to the surface water or sea ice using three scenarios: No Action, New Policy (NP), and Maximum Feasible Reduction (MFR). We then force a five-compartment box model of Hg cycling in the Arctic Ocean with these scenarios and literature-derived climate variables to simulate environmental change. No Action results in a 51% higher Hg deposition rate by 2050 while increasing Hg concentrations in the surface water by 22% and <9% at depth. Both “action” scenarios (NP and MFR), implemented in 2020 or 2035, result in lower Hg deposition ranging from 7% (NP delayed to 2035) to 30% (MFR implemented in 2020) by 2050. Under this last scenario, ocean Hg concentrations decline by 14% in the surface and 4% at depth. We find that the sea-ice cover decline exerts the strongest Hg reducing forcing on the Arctic Ocean while increasing river discharge increases Hg concentrations. When modified together the climate scenarios result in a ≤5% Hg decline by 2050 in the Arctic Ocean. Thus, we show that the magnitude of emissions-induced future changes in the Arctic Ocean is likely to be substantial compared to climate-induced effects. Furthermore, this study underscores the need for prompt and ambitious action for changing Hg concentrations in the Arctic, since delaying less ambitious reduction measures–like NP–until 2035 may become offset by Hg accumulated from pre-2035 emissions.

Projected impacts of climate change on tourism in the Canary Islands

The Canary Islands are a leading tourist destination. Their strong economic dependence on this sector makes them vulnerable to climate change. The steep orography of the islands causes impact of climate change and their potential influence on tourism to be spatially heterogenous. To account for this variability, regional climate simulations were computed using the Weather Research and Forecast (WRF) numerical weather prediction model driven by the results of three CMIP5 global climate models as boundary conditions, using two different future greenhouse gas emission scenarios (RCP4.5 and RCP8.5) for the projections. The simulations were performed at a spatial resolution of 3 km for three 30-year periods, recent past (1980–2009), mid-century (2030–2059), and end-century (2070–2099). For two widely used indices of tourist attractiveness (the Tourism Climate Index TCI and the Holiday Climate Index HCI), the coastal region of most islands currently has between 20 and 30 “excellent” and “ideal” days per month for tourism, with a decrease at higher elevations. Future leisure conditions are expected to improve at higher locations and during the autumn, winter and spring. In the RCP8.5 scenario, “excellent” days are projected to increase in winter at the end of the century. Nevertheless, in the southern areas, where most of the hotel infrastructure is located, the indices indicate significantly worsened conditions in summer, with only a few “excellent” days expected in some locations. Thermal comfort was identified as the most important factor determining the expected changes.

Genetic assessment and climate modelling of the Iberian specialist butterfly Euchloe bazae (Lepidoptera: Pieridae)

Abstract Although both positive (expansion of thermophilous species) and negative effects (retraction of cold‐adapted species) have been attributed to global warming, range shifts may be constrained by the ecological traits of species. This can be especially true for highly specialised organisms like the Spanish Greenish Black‐tip (Euchloe bazae), an Iberian endemic specialist of semiarid steppes with a debated taxonomic and conservation status. Here, we first seek to clarify the taxonomic status of this butterfly and its populations by using multilocus phylogenetic inference. Then, we update its distribution range and employ ecological niche modelling, combined with other sources of data, to re‐evaluate its conservation status. Our results confirm E. bazae as a well‐differentiated species with one of the most restricted distribution ranges among the European butterflies. We demonstrate that its two disjunct populations, regarded as subspecies, are genetically differentiated and should be treated as independent management units. Climate models under two future emission scenarios suggest an increase of the area climatically suitable. However, the suitability of the areas currently occupied is estimated to decrease, meaning that rapid range shifts could be required for the survival of the species. Given the presumably low dispersal capabilities of E. bazae, its current restricted distribution, high degree of population fragmentation (divided in two distant populations), low intrapopulation genetic variability, decline in extension and abundance, low densities, high yearly fluctuations, and numerous threats to its habitat, we consider that their populations are extremely fragile and we propose to consider it ‘Endangered’ [EN B1ab(i,ii,iii,iv); B2ab(i,ii,iii,iv)].