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Abstract Human-induced climate change has increased the frequency and intensity of heavy precipitation1. Due to the complexity of runoff generation and the streamflow process, the historical impact of human-induced climate change on river flooding remains uncertain. Here, we address the question of whether anthropogenic climate change has altered the probability of the extreme river flood events for the period 1951–2010 based on simulated river discharge derived from large ensemble climate experiments with and without human-induced climate change. The results indicate that human-induced climate change altered the probabilities of 20 of the 52 analyzed flood events. Fourteen of these 20 flood events, which occurred mainly in Asia and South America, were very likely to have been enhanced by human-induced climate change due to an increase in heavy precipitation. Conversely, two flood events in North/South America and two flood events in Asia and two flood events in Europe were suppressed by human-induced climate change, perhaps as a result of lower snowfall. Human-induced climate change has enhanced flooding more prominently in recent years, providing important insights into potential adaptation strategies for river flooding.

Significant regional disparities exist in the time left to prepare for unprecedented drought and how much we can buy time depending on climate scenarios. Specific regions pass this timing by the middle of 21st century even with stringent mitigation.

Abstract Climate scenario data set are indispensable for assessing future climate impacts. In this study, we developed statistically downscaled climate scenarios in Japan using modified bias correction method based on five general circulation models selected from the Coupled Model Intercomparison Project (CMIP) Phase 6 to facilitate impact assessments and adaptation strategies. Modification of time window of the original correction method results in successful agreement with the observed seasonal change of variables in each grid. The original CMIP6 models have a relatively small bias compared to CMIP5 models. The advantage of CMIP6‐based bias‐corrected scenarios is its availability for multiple global circulation models, which covered wide uncertainty in CMIP6 ensembles, with various emissions scenarios for representative concentration pathway (RCP) including RCP4.5, RCP2.6, and RCP8.5. Several temperature‐related indices derived from the CMIP6‐based climate scenarios agreed well with observations. The number of extremely hot days and nights increased nonlinearly in the future with additional global warming. An increase in the global warming level from 1 to 2°C above the early 1900s would increase the probability of the number of extremely hot days per year exceeding the 2018 case by 4.1 times. The development of bias‐corrected climate scenarios facilitates the study of various climate impacts on a CMIP6 basis.

Multi-annual to decadal changes in climate are accompanied by changes in extreme events that cause major impacts on society and severe challenges for adaptation. Early warnings of such changes are now potentially possible through operational decadal predictions. However, improved understanding of the causes of regional changes in climate on these timescales is needed both to attribute recent events and to gain further confidence in forecasts. Here we document the Large Ensemble Single Forcing Model Intercomparison Project that will address this need through coordinated model experiments enabling the impacts of different external drivers to be isolated. We highlight the need to account for model errors and propose an attribution approach that exploits differences between models to diagnose the real-world situation and overcomes potential errors in atmospheric circulation changes. The experiments and analysis proposed here will provide substantial improvements to our ability to understand near-term changes in climate and will support the World Climate Research Program Lighthouse Activity on Explaining and Predicting Earth System Change.

Abstract The ongoing increases in anthropogenic radiative forcing have changed the global water cycle and are expected to lead to more intense precipitation extremes and associated floods. However, given the limitations of observations and model simulations, evidence of the impact of anthropogenic climate change on past extreme river discharge is scarce. Here, a large ensemble numerical simulation revealed that 64% (14 of 22 events) of floods analyzed during 2010-2013 were affected by anthropogenic climate change. Four flood events in Asia, Europe, and South America were enhanced within the 90% likelihood range. Of eight snow-induced floods analyzed, three were enhanced and four events were suppressed, indicating that the effects of climate change are more likely to be seen in the snow-induced floods. A global-scale analysis of flood frequency revealed that anthropogenic climate change enhanced the occurrence of floods during 2010-2013 in wide area of northern Eurasia, part of northwestern India, and central Africa, while suppressing the occurrence of floods in part of northeastern Eurasia, southern Africa, central to eastern North America and South America. Since the changes in the occurrence of flooding are the results of several hydrological processes, such as snow melt and changes in seasonal and extreme precipitation, and because a climate change signal is often not detectable from limited observation records, large ensemble discharge simulation provides insights into anthropogenic effects on past fluvial floods.

Abstract In extreme event attribution, which aims to answer whether and to what extent a particular extreme weather event can be attributed to global warming, the probability of an event is generally estimated through large ensemble simulations, using an atmospheric general circulation model (AGCM). In islands, such as Japan, it has been considered that surface air temperature (SAT) can be significantly affected by the surrounding sea surface temperature (SST), which mostly is affected by atmospheric circulation at mid- and high-latitudes. Therefore, the absence of SST responses to atmospheric variability in AGCMs impacts the estimation of the occurrence of extreme events, such as heat waves in Japan. In this study, we examined the impact of air–sea coupling on the probability of occurrence of severe heat waves that occurred in Japan in the summer of 2010 by analyzing the probability differences obtained from AGCM and coupled general circulation model (CGCM) large-ensemble experiments. The observed ocean temperature, salinity, and sea ice were assimilated in the 100-member CGCM experiments, as they were assigned as boundary conditions in the 100-member AGCM experiments. The SAT around Japan in the northern summer is largely related to the Bonin high, whose interannual variability is largely affected by the Silk Road and Pacific-Japan (PJ) pattern teleconnections in the real world. The SAT anomaly over Japan was related to the pressure variability due to the Silk Road and PJ patterns in the CGCM experiment. By contrast, the SAT over Japan simulated by AGCM was less sensitive to such pressure variability, and the SAT ensemble spread became narrower in AGCM. The results suggest that the probability of occurrence of the 2010 heat wave in Japan would tend to be underestimated by the AGCM ensemble compared to the CGCM ensemble, provided that the ensemble averages of the SAT anomalies were equal between CGCM and AGCM experiments. This study raised the issue of the absence of SST response to atmospheric variability in AGCMs, which can critically impact the estimation of extreme event probability, particularly in mid-latitude islands, such as Japan.

Abstract Large-ensemble climate experiments were performed to simulate future climates for a + 1.5 K rise in the global mean surface air temperatures relative to preindustrial levels as a subset of the database for Policy Decision making for future climate change (d4PDF), using the Non-Hydrostatic Regional Climate Model (NHRCM) with 20 km grid spacing. Along with present climate, + 2 K and + 4 K experimental outputs from the d4PDF already available, we investigated responses of surface air temperature (SAT) and precipitation on regional scales over Japan to global warming. The reproducibility of the present climate experiment is satisfactory to investigate future changes in the Japanese climate, and dynamical downscaling from the global to the regional climate states improves the frequency of heavy daily precipitation. In the future, SAT over Japan rises linearly with and faster than the global mean SAT. The meridional contrast of SAT rises becomes more pronounced as global warming progresses. Winter precipitation decreases/increases linearly in the western/eastern Japan, reflected by weakening of future winter monsoons. Annual maximum daily precipitation (R1d) shows a closely linear increase with the global SAT rise, but annual precipitation is mostly unchanged. The global mean SAT change from + 1.5 to + 2 K enhances R1d by 2.7% over the Japanese Islands, although the increase of R1d varies by regions. The increase in R1d is 5% in northern Japan, where the SAT increases are greater than those in other regions.

Abstract The present paper presents results of seasonal‐to‐decadal climate predictions based on a coupled climate model called the Model for Interdisciplinary Research on Climate version 6 (MIROC6) contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6). MIROC6 is initialized every year for 1960–2018 by assimilating observed ocean temperature and salinity anomalies and full fields of sea ice concentration and by prescribing atmospheric initial states from reanalysis data. The impacts of updating the system on prediction skill are then evaluated by comparing hindcast experiments between the MIROC6 prediction system and a previous system based on MIROC version 5 (MIROC5). Skill of seasonal prediction is overall improved in association with representation and initialization of El Niño/Southern Oscillation (ENSO), the Quasi‐Biennial Oscillation (QBO), and the Northern Hemisphere sea ice concentration in MIROC6. In particular, the QBO is skillfully predicted up to 3 years ahead with a maximum anomaly correlation exceeding r = 0.8. The prediction skill for the North Atlantic Oscillation in winter is also enhanced, but the prediction still suffers from model's inherent errors. On decadal timescales, MIROC6 has a larger fraction of areas of the globe with better surface temperature skill at all lead times than MIROC5, and it has predictive skill in the annual‐mean sea surface temperature (SST) in the North Atlantic and the Pacific. In particular, MIROC6 hindcasts at 2–5 years lead time are able to capture the spatial structure of SST changes in the North Pacific and the eastern tropical Pacific associated with the 1970s regime shift better than MIROC5 hindcasts.

Since many new generation Earth system models (ESMs) have been suggested to overestimate future global warming, the latest report of the Intergovernmental Panel on Climate Change used the constrained range of global warming instead of that in the raw ensemble. However, it is not clear how the constraints of climate change projections potentially reduce the uncertainty of impact assessments. Here, we show that the climate-related uncertainty of the economic impact of climate change in the world can be constrained. By applying an impact emulator, we estimate the economic impacts in nine sectors based on 67 ESMs’ future climate change projections and find that the impacts in eight sectors are closely related to the recent past trend of global mean temperature, which is the metric used for the constraint of global warming projections. Observational constraints lower the upper bound of the aggregate economic impact simulated by the single emulator from 2.9% to 2.5% of the world gross domestic product (the relative reduction of variance is 31%) under the medium greenhouse gas concentration scenarios. Our results demonstrate how advances in climate science can contribute to reducing climate-related uncertainties in impact assessments, while we do not examine uncertainties of emulators and impact models.

Increased aridity and drought risks are significant global concerns. However, there are few comprehensive studies on the related risks with regard to the differences between relatively weak levels of warming, including the recent targets of the United Nations Framework Convention on Climate Change (UNFCCC) of 1.5 °C or 2 °C. The present study investigates the impacts of 1.5 °C and 2 °C warming on aridification and their non-linearity based on the relationship between available water and energy at the Earth’s terrestrial surface. Large multi-model ensembles with a 4000-model-year in total are sourced from the Half a degree Additional warming, Prognosis, and Projected Impacts (HAPPI) project. Results demonstrate that 2 °C warming results in more frequent dry states in the Amazon Basin, western Europe, and southern Africa, and a limited warming to 1.5 °C will mitigate aridification and increase the frequency of extreme dry-year in these regions. In the Mediterranean region, a significant acceleration of aridification is found from the 1.5 °C to 2 °C warming projections, which indicates a need to limit the warming by 1.5 °C. A substantial portion of Asia is projected to become increasingly humid under both 1.5 °C and 2 °C warming scenarios. In some geographic regions, such as Australia, a strong nonlinear shift of aridification is found as 2 °C warming results in shift to wetter state contrast to significant increases in aridity and dry-year frequency at the weaker level of warming. The results suggest that the responses of regional precipitation to global warming cause the aridity changes, but their nonlinear behaviors along with different warming levels should be assessed carefully, in particular, to incorporate the additional 0.5 °C warming.

To assess the anthropogenic influence on the summer 2013 heat wave in Korea, this study employed a fraction of attributable risk (FAR) approach to three Atmospheric General Circulation Models (AGCMs) with a large ensemble simulation, participating in the C20C+ Detection and Attribution Project. Monthly and daily temperatures were compared between two experiments. The real world (ALL) experiments were simulated under the observed variations in sea surface temperature, sea ice, greenhouse gas, and aerosol concentrations, while the counterfactual world (NAT) experiments were performed under adjusted boundary conditions by removing anthropogenic warming and with preindustrial levels of greenhouse gases and aerosols. Results from the three AGCMs consistently show that anthropogenic influences had an important role in the extreme heat event over Korea, increasing the chance of the occurrence of extreme warming in summer mean temperature as observed in 2013 by at least 20 times, which supports results from the Coupled Model Intercomparison Project Phase 5 (CMIP5) coupled GCMs (CGCMs). A comparison of individual CMIP5 CGCMs suggests that inter-model difference in FAR values is highly correlated with the amplitude of surface warming centered over Korea, which is also supported by the three AGCMs. Further analysis of individual forcing experiments suggests that the inter-model difference in the regional surface warming is closely linked to the model's response to the aerosol forcing, with stronger influence than that of greenhouse gas forcing. Anthropogenic influences also result in a 5–6 times greater likelihood of extreme daily heat events as observed in 2013, which supports a robust mean-extreme relation in the attribution of extreme heat waves. Generally good agreement between AGCM and CGCM results increases the robustness of the conclusion of anthropogenic influences on the summer 2013 Korean heat wave.

We examine the effect of the 20th and recent 21st century anthropogenic climate change on high temperature extremes as simulated by four global atmospheric general circulation models submitted to the Climate of the 20th Century Plus Detection and Attribution project. This coordinated experiment is based upon two large ensembles simulations for each participating model. The “world that was” simulations are externally forced as realistically as possible. The “world that might have been” is identical except that the influence of human forcing is removed but natural forcing agents and variations in ocean and sea ice are retained. We apply a stationary generalized extreme value analysis to the annual maxima of the three day average of the daily maximum surface air temperature, finding that long period return values have been increased by human activities between 1 and 3 °C over most land areas. Corresponding changes in the probability of achieving long period non-industrial return values in the industrialized world are also presented. We find that most regions experience increases in the frequency and intensity of extremely hot three day periods, but anthropogenic sulfate aerosol forcing changes locally can decrease these measures of heat waves in some models.

A growing field of research aims to characterise the contribution of anthropogenic emissions to the likelihood of extreme weather and climate events. These analyses can be sensitive to the shapes of the tails of simulated distributions. If tails are found to be unrealistically short or long, the anthropogenic signal emerges more or less clearly, respectively, from the noise of possible weather. Here we compare the chance of daily land-surface precipitation and near-surface temperature extremes generated by three Atmospheric Global Climate Models typically used for event attribution, with distributions from six reanalysis products. The likelihoods of extremes are compared for area-averages over grid cell and regional sized spatial domains. Results suggest a bias favouring overly strong attribution estimates for hot and cold events over many regions of Africa and Australia, and a bias favouring overly weak attribution estimates over regions of North America and Asia. For rainfall, results are more sensitive to geographic location. Although the three models show similar results over many regions, they do disagree over others. Equally, results highlight the discrepancy amongst reanalyses products. This emphasises the importance of using multiple reanalysis and/or observation products, as well as multiple models in event attribution studies.

There is a growing research interest in understanding extreme weather in the context of anthropogenic climate change, posing a requirement for new tailored climate data products. Here we introduce the Climate of the 20th Century Plus Detection and Attribution project (C20C + D&A), an international collaboration generating a product specifically intended for diagnosing causes of changes in extreme weather and for understanding uncertainties in that diagnosis. The project runs multiple dynamical models of the atmosphere-land system under observed historical conditions as well as under naturalised versions of those observed conditions, with the latter representing how the climate system might have evolved in the absence of anthropogenic interference. Each model generates large ensembles of simulations with different initial conditions for each historical scenario, providing a large sample size for understanding interannual variability, long-term trends, and the anthropogenic role in rare types of weather. This paper describes the C20C + D&A project design, implementation, strengths, and limitations, and also discusses various activities such as this special issue of Weather and Climate Extremes dedicated to “First results of the C20C + Detection and Attribution project”. Keywords: Detection and attribution, Event attribution, Climate models

Droughts are anticipated to intensify in many parts of the world due to climate change. However, the issue of drought definition, namely the diversity of drought indices, makes it difficult to compare drought assessments. This issue is widely known, but its relative importance has never been quantitatively evaluated in comparison to other sources of uncertainty. Here, encompassing three drought categories (meteorological, agricultural, and hydrological droughts) with four temporal scales of interest, we evaluated changes in the drought frequency using multi-model and multi-scenario simulations to identify areas where the definition issue could result in pronounced uncertainties and to what extent. We investigated the disagreement in the signs of changes between drought definitions and decomposed the variance into four main factors: drought definitions, greenhouse gas concentration scenarios, global climate models, and global water models, as well as their interactions. The results show that models were the primary sources of variance over 82% of the global land area. On the other hand, the drought definition was the dominant source of variance in the remaining 17%, especially in parts of northern high-latitudes. Our results highlight specific regions where differences in drought definitions result in a large spread among projections, including areas showing opposite signs of significant changes. At a global scale, 7% of the variance resulted independently from the definition issue, and that value increased to 44% when 1st and 2nd order interactions were considered. The quantitative results suggest that by clarifying hydrological processes or sectors of interest, one could avoid these uncertainties in drought assessments to obtain a clearer picture of future drought change.

Future flood and drought risks have been predicted to transition from moderate to high levels at global warmings of 1.5 °C and 2.0 °C above pre-industrial levels, respectively. However, these results were obtained by approximating the equilibrium climate using transient simulations with steadily warming. This approach was recently criticised due to the warmer global land temperature and higher mean precipitation intensities of the transient climate in comparison with the equilibrium climate. Therefore, it is unclear whether floods and droughts projected under a transient climate can be systematically substituted for those occurring in an equilibrated climate. Here, by employing a large ensemble of global hydrological models (HMs) forced by global climate models, we assess the validity of estimating flood and drought characteristics under equilibrium climates from transient simulations. Differences in flood characteristics under transient and equilibrium climates could be largely ascribed to natural variability, indicating that the floods derived from a transient climate reasonably approximate the floods expected in an equally warm, equilibrated climate. By contrast, significant differences in drought intensity between transient and equilibrium climates were detected over a larger global land area than expected from natural variability. Despite the large differences among HMs in representing the low streamflow regime, we found that the drought intensities occurring under a transient climate may not validly represent the intensities in an equally warm equilibrated climate for approximately 6.7% of the global land area.

In climate change research, future scenarios of greenhouse gas and air pollutant emissions generated by integrated assessment models (IAMs) are used in climate models (CMs) and earth system models to analyze future interactions and feedback between human activities and climate. However, the spatial resolutions of IAMs and CMs differ. IAMs usually disaggregate the world into 10-30 aggregated regions, whereas CMs require a grid-based spatial resolution. Therefore, downscaling emissions data from IAMs into a finer scale is necessary to input the emissions into CMs. In this study, we examined whether differences in downscaling methods significantly affect climate variables such as temperature and precipitation. We tested two downscaling methods using the same regionally aggregated sulfur emissions scenario obtained from the Asian-Pacific Integrated Model/Computable General Equilibrium (AIM/CGE) model. The downscaled emissions were fed into the Model for Interdisciplinary Research on Climate (MIROC). One of the methods assumed a strong convergence of national emissions intensity (e.g., emissions per gross domestic product), while the other was based on inertia (i.e., the base-year remained unchanged). The emissions intensities in the downscaled spatial emissions generated from the two methods markedly differed, whereas the emissions densities (emissions per area) were similar. We investigated whether the climate change projections of temperature and precipitation would significantly differ between the two methods by applying a field significance test, and found little evidence of a significant difference between the two methods. Moreover, there was no clear evidence of a difference between the climate simulations based on these two downscaling methods.

Clarifying characteristics of hazards and risks of climate change at 2 °C and 1.5 °C global warming is important for understanding the implications of the Paris Agreement. We perform and analyze large ensembles of 2 °C and 1.5 °C warming simulations. In the 2 °C runs, we find substantial increases in extreme hot days, heavy rainfalls, high streamflow and labor capacity reduction related to heat stress. For example, about half of the world’s population is projected to experience a present day 1-in-10 year hot day event every other year at 2 °C warming. The regions with relatively large increases of these four hazard indicators coincide with countries characterized by small CO _2 emissions, low-income and high vulnerability. Limiting global warming to 1.5 °C, compared to 2 °C, is projected to lower increases in the four hazard indicators especially in those regions.

This study investigates the impacts of global warming of 1.5 °C and 2.0 °C above pre-industrial conditions (Paris Agreement target temperatures) on the South Asian and East Asian monsoon rainfall using five atmospheric global climate models participating in the ‘Half a degree Additional warming, Prognosis and Projected Impacts’ (HAPPI) project. Mean and extreme precipitation is projected to increase under warming over the two monsoon regions, more strongly in the 2.0 °C warmer world. Moisture budget analysis shows that increases in evaporation and atmospheric moisture lead to the additional increases in mean precipitation with good inter-model agreement. Analysis of daily precipitation characteristics reveals that more-extreme precipitation will have larger increase in intensity and frequency responding to the half a degree additional warming, which is more clearly seen over the South Asian monsoon region, indicating non-linear scaling of precipitation extremes with temperature. Strong inter-model relationship between temperature and precipitation intensity further demonstrates that the increased moisture with warming (Clausius-Clapeyron relation) plays a critical role in the stronger intensification of more-extreme rainfall with warming. Results from CMIP5 coupled global climate models under a transient warming scenario confirm that half a degree additional warming would bring more frequent and stronger heavy precipitation events, exerting devastating impacts on the human and natural system over the Asian monsoon region.

Changes in the hydrological cycle are among the aspects of climate change most relevant for human systems and ecosystems. Besides trends in overall wetting or drying, changes in temporal characteristics of wetting and drying are of crucial importance in determining the climate hazard posed by such changes. This is particularly the case for tropical regions, where most precipitation occurs during the rainy season and changes in rainy season onset and length have substantial consequences. Here we present projections for changes in tropical rainy season lengths for mean temperature increase of 1.5 °C and 2 °C above pre-industrial levels. Based on multi-ensemble quasi-stationary simulations at these warming levels, our analysis indicates robust changes in rainy season characteristics in large parts of the tropics despite substantial natural variability. Specifically, we report a robust shortening of the rainy season for all of tropical Africa as well as north-east Brazil. About 27% of West Africa is projected to experience robust changes in the rainy season length with a mean shortening of about 7 days under 1.5 °C. We find that changes in the temporal characteristics are largely unrelated to changes in overall precipitation, highlighting the importance of investigating both separately.

Since the late 1990s, land surface temperatures over Japan have increased during the summer and autumn, while global mean temperatures have not risen in this duration (i.e., the global warming hiatus). In contrast, winter and spring temperatures in Japan have decreased. To assess the impact of both global warming and global-scale decadal variability on this enhanced seasonal temperature contrast, we analyzed the outputs of 100 ensemble simulations of historical and counterfactual non-warming climate simulations conducted using a high-resolution atmospheric general circulation model (AGCM). Our simulations showed that atmospheric fields impacted by the La Nina-like conditions associated with Interdecadal Pacific Oscillation (IPO) have predominantly contributed to the seasonal temperature contrast over Japan. Compared with the impact of negative IPO, the influence of global warming on seasonal temperature contrasts in Japan was small. In addition, atmospheric variability has also had a large impact on temperatures in Japan over a decadal timescale. The results of this study suggest a future increase in heatwave risk during the summer and autumn when La Nina-like decadal phenomena and atmospheric perturbations coincide over a background of global warming.

In the midsummer of 2013, Central and Eastern China (CEC) was hit by an extraordinary heat event, with the region experiencing the warmest July–August on record. To explore how human-induced greenhouse gas emissions and natural internal variability contributed to this heat event, we compare observed July–August mean surface air temperature with that simulated by climate models. We find that both atmospheric natural variability and anthropogenic factors contributed to this heat event. This extreme warm midsummer was associated with a positive high-pressure anomaly that was closely related to the stochastic behavior of atmospheric circulation. Diagnosis of CMIP5 models and large ensembles of two atmospheric models indicates that human influence has substantially increased the chance of warm mid-summers such as 2013 in CEC, although the exact estimated increase depends on the selection of climate models.

Initialization based on data assimilations using historical observations possibly improves near-term climate predictions. Significant volcanic activity in the future is unpredictable and not assumed in future climate predictions. To examine the possible influence of unpredictable future volcanic activity on the decadal potential predictability of the natural variability, we performed a 2006–2035 climate prediction experiment with the assumption that the 1991 Mt. Pinatubo eruption would take place again in 2010. The Pinatubo forcing induced not only significant cooling responses but also considerable noises in the natural variability. The errors due to the Pinatubo forcing grew faster than that arising from imperfect knowledge of the observed state, leading to a rapid reduction of the decadal potential predictability of the natural variability.

Effects of climate change on the worst case scenario of a storm surge induced by a super typhoon in the present climate are investigated through the case study of Typhoon Haiyan. We present the results of our investigation on super-typhoon Haiyan by using a super high resolution (1 km grid) regional model that explicitly handles cloud microphysical processes. As the parent model, we adopted the operational weekly ensemble experiments (60 km grid) of the Japan Meteorological Agency, and compared experiments using sea surface temperatures and atmospheric environmental parameters from before the beginning of anthropogenic climate change (150 years ago) with those using observed values throughout the typhoon. We were able not only to represent the typhoon’s intensity but also to evaluate the influences of climate change on worst case storm surges in the Gulf of Leyte due to a typhoon with high robustness. In 15 of 16 ensemble experiments, the intensity of the simulated worst case storm in the actual conditions was stronger than that in a hypothetical natural condition without historical anthropogenic forcing during the past 150 years. The intensity of the typhoon is translated to a disaster metric by simulating the storm surge height by using a shallow-water long-wave model. The result indicates that the worst case scenario of a storm surge in the Gulf of Leyte may be worse by 20%, though changes in frequency of such events are not accounted for here.

Uncontrolled biomass burning in Indonesia during drought periods damages the landscape, degrades regional air quality, and acts as a disproportionately large source of greenhouse gas emissions. The expansion of forest fires is mostly observed in October in Sumatra favored by persistent droughts during the dry season from June to November. The contribution of anthropogenic warming to the probability of severe droughts is not yet clear. Here, we show evidence that past events in Sumatra were exacerbated by anthropogenic warming and that they will become more frequent under a future emissions scenario. By conducting two sets of atmospheric general circulation model ensemble experiments driven by observed sea surface temperature for 1960–2011, one with and one without an anthropogenic warming component, we found that a recent weakening of the Walker circulation associated with tropical ocean warming increased the probability of severe droughts in Sumatra, despite increasing tropical-mean precipitation. A future increase in the frequency of droughts is then suggested from our analyses of the Coupled Model Intercomparison Project Phase 5 model ensembles. Increasing precipitation to the north of the equator accompanies drier conditions over Indonesia, amplified by enhanced ocean surface warming in the central equatorial Pacific. The resultant precipitation decrease leads to a ∼25% increase in severe drought events from 1951–2000 to 2001–2050. Our results therefore indicate the global warming impact to a potential of wide-spreading forest fires over Indonesia, which requires mitigation policy for disaster prevention.