Radiative Forcing Scenarios Research Articles

Antarctic extreme seasons under 20th and 21st century climate change

In this study, available large ensemble datasets in the Coupled Model Intercomparison Phase 6 (CMIP6) archive were used to provide the first multi-variate overview of the evolution of extreme seasons over Antarctica and the Southern Ocean during the 20th and 21st centuries following medium-to-high radiative forcing scenarios. The results show significant differences between simulated changes in background mean climate and changes in low (10th percentile) and high (90th percentile) extreme seasons. Regional winter warming is most pronounced for cold extremes. In summer, there are more pronounced increases in high extremes in precipitation and westerly wind during the ozone hole formation period (late 20th century), affecting coastal regions and, in particular, the Antarctic Peninsula. At midlatitudes, there is a reduction in the range of summer season wind extremes. Suggested mechanisms for these differences are provided relating to sea ice retreat and westerly jet position.

Exploring a Data‐Driven Approach to Identify Regions of Change Associated With Future Climate Scenarios

AbstractA key consideration for evaluating climate projections is uncertainty in future radiative forcing scenarios. Although it is straightforward to monitor greenhouse gas concentrations and compare observations with specified climate scenarios, it remains less obvious how to detect and attribute regional pattern changes with plausible future mitigation scenarios. Here we introduce a machine learning approach for linking patterns of climate change with radiative forcing scenarios and use a feature attribution method to understand how these linkages are made. We train a neural network using output from the SPEAR Large Ensemble to classify whether temperature or precipitation maps are most likely to originate from one of several potential radiative forcing scenarios. Despite substantial atmospheric internal variability, the neural network learns to identify “fingerprint” patterns, including significant localized regions of change, that associate specific patterns of climate change with radiative forcing scenarios in each year of the simulations. We illustrate this using output from additional ensembles with sharp reductions in future greenhouse gases and highlight specific regions (in this example, the subpolar North Atlantic and Central Africa) that are critical for associating the new simulations with changes in radiative forcing scenarios. Overall, this framework suggests that explainable machine learning could provide one strategy for detecting a regional climate response to future mitigation efforts.

Wide range of possible trajectories of North Atlantic climate in a warming world.

Decadal variability in the North Atlantic Ocean impacts regional and global climate, yet changes in internal decadal variability under anthropogenic radiative forcing remain largely unexplored. Here we use the Community Earth System Model 2 Large Ensemble under historical and the Shared Socioeconomic Pathway 3-7.0 future radiative forcing scenarios and show that the ensemble spread in northern North Atlantic sea surface temperature (SST) more than doubles during the mid-twenty-first century, highlighting an exceptionally wide range of possible climate states. Furthermore, there are strikingly distinct trajectories in these SSTs, arising from differences in the North Atlantic deep convection among ensemble members starting by 2030. We propose that these are stochastically triggered and subsequently amplified by positive feedbacks involving coupled ocean-atmosphere-sea ice interactions. Freshwater forcing associated with global warming seems necessary for activating these feedbacks, accentuating the impact of external forcing on internal variability. Further investigation on seven additional large ensembles affirms the robustness of our findings. By monitoring these mechanisms in real time and extending dynamical model predictions after positive feedbacks activate, we may achieve skillful long-lead North Atlantic decadal predictions that are effective for multiple decades.

How will permafrost carbon respond to future climate change? A new assessment for future thaw trends of permafrost carbon on the Tibetan Plateau

Permafrost degradation on the Tibetan Plateau (TP) is anticipated to result in the thaw of permafrost carbon. Existing studies have been conducted to assess the future thaw of frozen carbon on the TP, primarily focusing on the deepening of the active layer while neglecting the impact of permafrost area shrinkage. This oversight may lead to a significant underestimation of the potential thaw of frozen carbon. Our research underscores the pivotal role of permafrost area shrinkage in estimating the future thaw of frozen carbon. Our findings reveal that when the combined effects of permafrost area shrinkage and active layer deepening are considered, the thaw rates of frozen carbon in various radiative forcing scenarios are nearly four times those based on active layer deepening alone. Notably, our results demonstrate substantial thaw of frozen organic carbon in the TP permafrost area under all four future scenarios: In the low radiative forcing scenario SSP1-2.6, it is predicted that 55.4 % of the organic carbon in the permafrost area 0–10 m soils will be in a state of thaw by 2100, and more than 90 % in the high radiative forcing scenario SSP5-8.5. This substantial thaw is poised to diminish the TP's current carbon sink function significantly. Our study emphasizes that as global warming persists, frozen carbon in permafrost areas will play a more active role in global carbon cycle processes in the future. Furthermore, we stress the importance of considering permafrost area shrinkage in understanding the thaw of frozen carbon, providing valuable insights for carbon balance studies on the TP.

CMIP6 Simulation-Based Daily Surface Air Temperature and Precipitation Projections over the Qinghai-Tibetan Plateau in the 21st Century

The Qinghai-Tibetan Plateau (QTP), the source of many major Asian rivers, is sensitive to climate change, affecting billions of people’s livelihoods across Asia. Here, we developed high-resolution projections of precipitation and daily maximum/minimum temperatures at 0.1° spatial resolution over the QTP. The projections are based on the output from seven global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) for historical (1979–2013) and projected (2015–2100) climates across four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). An updated nonstationary cumulative distribution function matching method (called CNCDFm) was used to remove model systemic bias. We verify the necessity of taking into account altitude in downscaling processes and the validity of nonstationary bias correction. Compared to the historical period, the climate in the QTP in the 21st century is warmer (1.2–5.1 °C, for maximum surface temperature) and wetter (3.9–26.8%) according to the corrected GCM projection. For precipitation, the Indus River (IDR), Tarim River (TMR), Inner of Qiangtang Basin (IQTB), Yarlung Zangbo (YLZBR), and Qaidam Basin (QDB) showed growth well above the global average across high radiative forcing scenarios, which could have a profound impact on the regional hydrological cycle. However, there is great uncertainty in precipitation prediction, which is demonstrated by a very low signal-to-noise ratio (SNR) and a large difference between Bayesian model averaging (BMA) and multi-model averages (MMAs). This bias-corrected dataset is available for climate change impact research in the QTP at the subregion scale.

Prediction of the Climate Change and Radiative Forcing Scenarios on Monthly Runoff Urban Basin in Kerman, Iran

Prediction of the Climate Change and Radiative Forcing Scenarios on Monthly Runoff Urban Basin in Kerman, Iran

Future Joint Probability Characteristics of Extreme Precipitation in the Yellow River Basin

The relationship between climate change and extreme precipitation is extremely complex. From a probabilistic perspective, a proper understanding of the response of extreme precipitation to climate change is of significant importance. This study was based on daily precipitation provided by CMIP6 climate models and employed copula functions to construct joint distributions of precipitation amount and precipitation intensity indices at different quantile levels. A spatial–temporal assessment of the susceptibility areas for extreme precipitation in the Yellow River Basin was conducted while considering bivariate return periods and design values. The results indicate that there were significant spatial differences in the bivariate return periods. Taking the R90P-SDII (90) index for a 20a return period as an example, the difference between the maximum and minimum joint return periods within the Yellow River Basin was 1.4 times, while the co-occurring return period was 7.0 times, and the Kendall return period was 4 times. Moreover, this difference increased with the increase in the return period. The magnitude order of the four return periods is as follows: TAnd > TKendall > TSingle-variable > TOr. Joint return periods (Or) and co-occurring return periods (And) could be considered as the extreme cases under single-variable return periods, serving as an estimation interval for actual return periods. Under the influence of climate change, the bivariate design values for future periods exhibited a variability increase of 6.76–28.8% compared to historical periods, and this increase grew with higher radiative forcing scenarios, ranking as SSP126 < SSP245 < SSP585. The bivariate design values showed a noticeable difference in variability compared to the single-variable design values, ranging from −0.79% to 18.67%. This difference increased with higher quantile values, with R95P-SDII (95) > R90P-SDII (90) > PRCPTOT-SDII.

Predictive Study on Extreme Precipitation Trends in Henan and Their Impact on Population Exposure

This study employs precipitation data sets from historical trials on 20 CMIP6 global climate models and four shared socioeconomic pathway scenario trials (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) to predict trends in extreme precipitation changes in Henan Province quantitatively, while ascertaining the risk of population exposure to extreme precipitation in this area. The capacity of the CMIP6 models to simulate extreme precipitation indices from 1985 to 2014 is assessed using CN05.1 daily precipitation observational data. The correlation coefficients of the multi-model ensemble median’s simulation of the extreme precipitation indices are approximately 0.8, with a standard deviation ratio closer to 1 compared with the single models, demonstrating superior modeling ability. Analyses using the multi-model ensemble median demonstrate an overall increase in the total amount, frequency, and intensity of extreme precipitation in Henan throughout this century, particularly in its southern regions; in the mid-century high-emission scenario (SSP5-8.5), the maximum increase in annual total precipitation exceeds 150 mm, and it can be over 250 mm in the late-century period. For the entire province, the maximum five-day precipitation increase relative to the historical period is nearly 25 mm in the late-century SSP5-8.5 scenario. The spatiotemporal concentration of precipitation will significantly increase, heightening the risk of flood disasters. Comparative analysis reveals that, under the same population prediction, the total population exposure will be higher in high radiative forcing scenarios than in low radiative forcing scenarios, especially in Kaifeng City, where the total population exposure in SSP1 and SSP5-8.5 exceeds that in SSP1-2.6 by 2 million person-days. However, in the same radiative forcing scenario, the total population exposure in the development pathway dominated by traditional fossil fuels (SSP5) will not be significantly higher than that in the sustainable development pathway (SSP1), indicating that population activity in this century will not be the main contributor to changes in total exposure. Overall, for Henan, in the same population forecast scenario, population exposure to extreme precipitation will gradually rise with global warming.

Effective climate sensitivity distributions from a 1D model of global ocean and land temperature trends, 1970–2021

Current theoretically based Earth system models (ESMs) produce Effective Climate Sensitivities (EffCS) that range over a factor of three, with 80% of those models producing stronger global warming trends for 1970–2021 than do observations. To make a more observationally based estimate of EffCS, a 1D time-dependent forcing-feedback model of temperature departures from energy equilibrium is used to match measured ranges of global-average surface and sub-surface land and ocean temperature trends during 1970–2021. In response to two different radiative forcing scenarios, a full range of three model free parameters are evaluated to produce fits to a range of observed surface temperature trends (± 2σ) from four different land datasets and three ocean datasets, as well as deep-ocean temperature trends and borehole-based trend retrievals over land. Land-derived EffCS are larger than over the ocean, and EffCS is lower using the newer Shared Socioeconomic Pathways (SSP245, 1.86 °C global EffCS, ± 34% range 1.48–2.15 °C) than the older Representative Concentration Pathway forcing (RCP6, 2.49 °C global average EffCS, ± 34% range 2.04–2.87 °C). The strongest dependence of the EffCS results is on the assumed radiative forcing dataset, underscoring the role of radiative forcing uncertainty in determining the sensitivity of the climate system to increasing greenhouse gas concentrations from observations alone. The results are consistent with previous observation-based studies that concluded EffCS during the observational period is on the low end of the range produced by current ESMs.

Emerging extreme heat conditions as part of the new climate normal

As a result of global warming, extreme heat conditions have become more frequent and severe. This will likely continue or accelerate in the future, particularly under high radiative forcing scenarios. In the present study, based on an ensemble of global climate model simulations, we identify the absolute historical extremes expressed by several temperature indices. Considering projections under two future pathways (SSP1-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-$$\\end{document}2.6, SSP5-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-$$\\end{document}8.5), we investigate to what extent extreme heat conditions will become predominant during the rest of the century. The timing of a transition to prevailing hot weather extremes is critical for the development of mitigation and adaptation strategies; therefore, we also identify the projected first year of such a transition, as well as the persistence in subsequent decades. Different aspects of heat extremes are investigated, including both maximum and minimum temperature. For some climate zones, our results highlight that regardless of mitigation efforts, hot weather conditions will be at least similar but likely more harsh compared to historical extremes within the following decades. By the end of the century, under a business-as-usual pathway, successive years will be much more extreme than the most severe conditions in the recent past virtually everywhere.

Persistently Southward of the South Asian High During the Radiative Forcing Stabilization

AbstractThe South Asian high (SAH), a large‐scale anticyclone at 100 and 200 hPa on the Asian continent, is driven by South Asian summer monsoon rainfall and heating over the Tibetan Plateau. As one member of the Asian summer monsoon system, the changes of its location and intensity may cause other climate responses. Rainfall‐induced latent heating can affect the SAH change. Previous study revealed that rainfall responses in Indian Ocean are opposite in periods of increased and stabilizing radiative forcing (RF) due to the deep ocean warming over the Southern Ocean. SAH responses during RF increase and stabilization are studied with 13 models from the Extended Representative Concentration Pathway scenario 4.5 (ECP4.5) experiment. At 100 hPa, the SAH intensifies and moves equatorward in increased RF scenario; when RF stabilizing, SAH still shifts southward but with little change in intensity. At 200 hPa, the SAH changes little in both RF increase and stabilization. These opposite responses at different altitudes may be due to the maximum potential temperature at 150–200 hPa, leading to the opposite changes in vertical motion. Results of the linear baroclinic model indicate that (a) during RF increase, diabatic heating () contributes to SAH strengthening, both and the mean advection of stratification change (MASC) lead to SAH southward movement; (b) during RF stabilization, and MASC, contribute to the SAH equatorward displacement. Two components of , the latent heating and residual heating, are canceled out and lead to little change in SAH intensity.

Evolution of the Internal Climate Modes under Future Warming

Abstract Climate modes play an important role in weather and climate variability over multiple spatial and temporal scales. This research assesses Earth system model (ESM) projections of the spatiotemporal characteristics of key internal climate modes (NAM, SAM, PNA, ENSO, PDO, and AMO) under high (SSP585) and low (SSP126) radiative forcing scenarios and contextualizes those projections using historical fidelity. Time series analyses are used to assess trends and mode phase characteristics are summarized for the historical period and for the end of the twenty-first century. Spatial patterns are compared to infer morphological changes. Shifts in the power spectra are used to examine changes in variability at subannual, interannual, and interdecadal scales. Changes in time-lagged correlations are used to capture the evolution of first-order interactions. While differences in historical skill are predominantly ESM dependent, changing mode characteristics in a warmer climate also exhibit variability between individual ensemble realizations. NAM, SAM, and ENSO tend to evolve toward increased prevalence of the positive phase up to 2100 across the multimodel ensemble while the PNA and PDO exhibit little trend but increasing phase intensity. AMO characteristics are shown to depend on the method used to remove the external signal. ESMs that show higher historical fidelity tend to show more modest changes in those modes under global nonstationarity. Changes in mode interactions are found to be highly ESM dependent but exhibit broadly similar behavior to historical relationships. These findings have implications for our understanding of internal variability and make clear that the choice of ESM, and even the ESM realization, matters for applications of climate projections. Significance Statement Internal modes of variability are important to understand due to their impact on local, regional, and global weather and climate patterns. Future climate changes will not only be affected by the variability arising from these modes, but the modes will themselves change in response to the changing climate. Spatial and temporal aspects of the modes are assessed from projections of future climate and related to how well they are captured in the historical climate. This yields some measure of confidence in the changes exhibited by the models. In most cases, when historically skillful models exhibit changes that are different from those produced by less skillful models, they tend to produce more modest changes. These results, as well as the variability between model outcomes, mean decisions on which ESM to use for projections of the future climate matter significantly.

The Impact of Climate Change on Jiroft Spring Wheat Yield Using Radiative Forcing Scenarios

The Impact of Climate Change on Jiroft Spring Wheat Yield Using Radiative Forcing Scenarios

Projecting Future Energy Production from Operating Wind Farms in North America. Part II: Statistical Downscaling

Abstract Capacity factors (CFs) derived from daily expected power at 22 operating wind farms in different regions of North America are used as predictands to train statistical downscaling algorithms using output from ERA5. The statistical downscaling models are then used to make CF projections for a suite of CMIP6 Earth System Models (ESMs). Downscaling is performed using a hybrid statistical approach that employs synoptic types derived using k-means clustering applied to sea level pressure fields with variance corrections applied as a function of the pressure gradient intensity. ESMs exhibit marked variability in terms of the skill with which the frequency of synoptic types and pressure gradients are reproduced relative to ERA5, and that differential skill is used to infer differential credibility in the associated CF projections. Projections of median annual mean CF [P50(CF)] in each 20-yr period from 1980 to 2099 show evidence of declines at most wind farms except in parts of the southern Great Plains, although the magnitude of the changes is strongly dependent on the ESM. For example, P50(CF) in 2080–99 deviate from those in 1980–99 by from −3.1 to +0.2 percentage points in the Northeast. The largest-magnitude declines in P50(CF) ranging from −3.9 to −2 percentage points are projected for the southern West Coast. CF trends exhibit marked seasonality and are strongly linked to changes in the relative intensity of future synoptic patterns, with much less impact from shifts in the occurrence of synoptic types over time. Internal climate modes continue to play a significant role in inducing interannual variability in wind power production, even under high radiative forcing scenarios. Significance Statement We describe how future climate changes may affect wind resources and wind power generation. Near-term changes in projected wind power electricity generation potential at operating wind farms over North America are small, but by the end of the current century electricity production is projected to decrease in many areas but may increase in parts of the southern Great Plains. The amount of change in projected wind power production is a strong function of the Earth system model that is downscaled and also depends on the continued presence of internally forced climate variability. An additional dependence on the amount of greenhouse gas–induced global warming indicates the transition of the energy sector to low-carbon sources may assist in maintaining the abundant U.S. wind resource.

Neglecting the effect of long- and short-term erosion can lead to spurious coastal flood risk projections and maladaptation

Flooding and erosion are among the most relevant hazards for coastal regions and although they are linked, their inherent complexity has typically led them to be addressed separately, potentially leading to highly uncertain estimates. This paper has three aims: (a) to present a methodology for coupling coastal flood projections with shoreline changes; (b) to quantify the effects of neglecting flooding and erosion coupling on future projections at a case study location; and (c) to analyse the relative importance of the climate-related uncertainty sources. We use a suite of statistical, process-based, and physics-based models to generate and downscale storms, compute water levels affected by storm morphodynamics and long-term profile changes and propagate flooding over topo-bathymetries that are modified to incorporate the impact of sea-level rise, longshore sediment transport and storm-driven erosion. We sample climate uncertainty by considering storm variability (synthetic generation) and ensembles of radiative forcing scenarios, regional climate models, and sea-level rise trajectories. For illustration purposes, we consider a 40-km coastal stretch in the Spanish Mediterranean. We find that that if the effect of erosion is neglected, the mean values of the total water level and flooded area can be either over- or underestimated by up to 18% and 22%, and up to 7% and 85%, respectively, with respect to our coupled results. The factors that most influence total water levels are storm erosion and profile geometry, highlighting the relevance of using real profiles. In the flooded area, longshore transport can play a fundamental enhancing role. We also find that the coupling approach used can contribute more to the projection of flooded areas than the choice of climate models and sea-level rise trajectories even by 2 100 (up to 76% versus 8% and 16%, respectively). We conclude that neglecting erosion effects on coastal flooding can have management implications, especially for urban beaches, leading to poor adaptation planning and maladaptation. • We develop coupled coastal flooding and erosion projections for 2050 and 2 100. • We consider uncertainties in scenarios, climate models, storms, and sea-level rise. • Uncoupling erosion and flooding leads to biases in water levels and flooded areas. • Coupling erosion and flooding can affect flooded areas more than sea-level rise.

Projecting U.S. forest management, market, and carbon sequestration responses to a high-impact climate scenario

The impact of climate change on forest ecosystems remains uncertain, with wide variation in potential climate impacts across different radiative forcing scenarios and global circulation models, as well as potential variation in forest productivity impacts across species and regions. This study uses an empirical forest composition model to estimate the impact of climate factors (temperature and precipitation) and other environmental parameters on forest productivity for 94 forest species across the conterminous United States. The composition model is linked to a dynamic optimization model of the U.S. forestry sector to quantify economic impacts of a high warming scenario (Representative Concentration Pathway 8.5) under six alternative climate projections and two socioeconomic scenarios. Results suggest that forest market impacts and consumer impacts could range from relatively large losses (−$2.6 billion) to moderate gain ($0.2 billion) per year across climate scenarios. Temperature-induced higher mortality and lower productivity for some forest types and scenarios, coupled with increasing economic demands for forest products, result in forest inventory losses by end of century relative to the current climate baseline (3%–23%). Lower inventories and reduced carbon sequestration capacity result in additional economic losses of up to approximately $4.1 billion per year. However, our results also highlight important adaptation mechanisms, such forest type changes and shifts in regional mill capacity that could reduce the impact of high impact climate scenarios.

There is still time to reconcile forest management with climate-driven declines in habitat suitability for boreal caribou

Many boreal populations of woodland caribou in Canada are declining, mainly due to the prevalence of anthropogenic disturbances which alter predator-prey dynamics. Climate change is expected to exert an additional negative influence on caribou populations in coming decades, but it is unclear whether or how human activities and climate change will interact to influence habitat suitability for caribou, and how important these agents of change will be relative to each other. In this study, we used the LANDIS-II forest landscape model to forecast boreal caribou habitat suitability across its distribution within the harvestable boreal forest in Québec for the period 2020–2100, under three increasing anthropogenic radiative forcing scenarios (baseline, Representative Concentration Pathways [RCP] 4.5 and 8.5), and two contrasting harvest scenarios (with and without harvest). Our simulations revealed that harvesting was the dominant agent explaining future variations in caribou habitat suitability, although climate change also decreased habitat suitability, especially under RCP 8.5. Climate-induced decreases in habitat suitability mostly originated from increases in wildfires that burned mature conifer-dominated forests, i.e., high-quality habitat for caribou. Habitat suitability by 2100 was also predicted to vary spatially, with the northeastern and northwestern parts of the study area supporting better caribou habitat conditions regardless of scenarios. We show that reducing harvest activities in areas where habitat suitability is currently high could help maintain high-quality caribou habitat even under the most intense climate change scenario. Our results also suggest that highly-disturbed regions which currently provide low-quality habitat may not improve in the future unless active habitat restoration is performed. Our study helps disentangle the potential future effects of forest management and climate change as threats to caribou habitat, emphasizing the urgency of reconciling forest management with the conservation of this species at risk in Canada.

A weakened AMOC may prolong greenhouse gas–induced Mediterranean drying even with significant and rapid climate change mitigation

The Mediterranean region has been identified as a climate hot spot, with models projecting a robust warming and rainfall decline in response to increasing greenhouse gases. The projected rainfall decline would have impacts on agriculture and water resources. Can such changes be reversed with significant reductions in greenhouse gases? To explore this, we examine large ensembles of a high-resolution climate model with various future radiative forcing scenarios, including a scenario with substantial reductions in greenhouse gas concentrations beginning in the mid-21st century. In response to greenhouse gas reductions, the Mediterranean summer rainfall decline is reversed, but the winter rainfall decline continues. This continued winter rainfall decline results from a persistent atmospheric anticyclone over the western Mediterranean. Using additional numerical experiments, we show that the anticyclone and continued winter rainfall decline are attributable to greenhouse gas-induced weakening of the Atlantic Meridional Overturning Circulation (AMOC) that continues throughout the 21st century. The persistently weak AMOC, in concert with greenhouse gas reductions, leads to rapid cooling and sea ice growth in the subpolar North Atlantic. This cooling leads to a strong cyclonic atmospheric circulation anomaly over the North Atlantic subpolar gyre and, via atmospheric teleconnections, to the anticyclonic circulation anomaly over the Mediterranean. The failure to reverse the winter rainfall decline, despite substantial climate change mitigation, is an example of a "surprise" in the climate system. In this case, a persistent AMOC change unexpectedly impedes the reversibility of Mediterranean climate change. Such surprises could complicate pathways toward full climate recovery.

Summer ozone pollution in China affected by the intensity of Asian monsoon systems

Ozone in the troposphere is harmful to human health and ecosystems. It has become the most severe air pollutant in China. Here, based on global atmospheric chemistry model simulations during 1981–2019 and nation-wide surface observations, the impacts of interannual variations in Asian summer monsoon (ASM), including East Asian summer monsoon (EASM) and South Asian summer monsoon (SASM), on surface O3 concentrations during June-July-August (JJA) in China are investigated. EASM intensity has a significant positive correlation with the surface O3 concentration in south-central China (97.5°–117.5°E, 20°–35°N) with a correlation coefficient of 0.6. Relative to the weak EASM years, O3 concentrations in strong EASM years increased by up to 5 ppb (10 % relative to the average) due to the weakened transboundary transport of O3 resulting from the decrease in prevailing southwesterlies. SASM can be divided into two components. The one near East Asia has a similar relation with O3 in southern China (100°–117.5°E, 22°–32°N) as that of EASM. The other component of SASM is negatively correlated with surface O3 concentration in eastern China (110°–117.5°E, 22°–34°N) and the maximum difference in O3 concentrations exceeded 5 ppb (10 %) between the strong and weak monsoon years, which can be explained by the O3 divergence caused by the anomalous southerlies blowing pollutants away from the northern boundary of eastern China. This study shows that the ASM has an important impact on the O3 concentrations in China, primarily through changing transboundary transport related to the variability of large-scale circulations, which has great implications for air pollution prevention and mitigation in China. Future projections of ASM suggests that the sustainable and medium development scenarios are the perfect pathways that can help to mitigate O3 pollution, while high social vulnerability and radiative forcing scenarios could enhance future O3 pollution in China.

Multi-driver ensemble to evaluate the water utility business interruption cost induced by hydrological drought risk scenarios in Brazil

ABSTRACT Climate change and increasing water demand in urban environments necessitate planning water utility companies’ finances. Traditionally, methods to estimate the direct water utility business interruption costs (WUBIC) caused by droughts have not been clearly established. We propose a multi-driver assessment method. We project the water yield using a hydrological model driven by regional climate models under radiative forcing scenarios. We project water demand under stationary and non-stationary conditions to estimate drought severity and duration, which are linked with pricing policies recently adopted by the Sao Paulo Water Utility Company. The results showed water insecurity. The non-stationary trend imposed larger differences in the drought resilience financial gap, suggesting that the uncertainties of WUBIC derived from demand and climate models are greater than those associated with radiative forcing scenarios. As populations increase, proactively controlling demand is recommended to avoid or minimize reactive policy changes during future drought events, repeating recent financial impacts.