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We investigated future frost risks in the Tohoku Region of Japan under climate change. We focused on the processes governing regional variations in the future frost risks over this region. Future changes in dates of development stages were determined by estimating the growing degree days. The last frost date was determined by analyzing the daily minimum temperature data. We found regional differences in future changes of the development stage at the last frost date. At sites where a greater advancement in the last frost date is projected, plants are expected to experience their last frost at an earlier development stage in comparison with their last frost experience at present. We showed that a seasonal warming rate (the temperature increase in the seasonal course from winter to summer) around the last frost date is closely linked to the magnitude of advancement in the last frost date. Sites with a low seasonal warming rate tend to show greater advancement in the last frost date, and plants at this site experience the last frost at an earlier development stage. We note that geographical proximity is not necessarily a key factor of similarity for determining the future changes in frost risk. Instead, the concept of “frost risk climatology,” based on classification by similarity of frost risk among sites, could be helpful for formulating measures to reduce future frost risk under climate change on a regional scale.

We investigated the first and last frost dates using meteorological observation data from Japan. First, we identified long-term trends of retardation (mean: +0.224 day/year) in the first frost date in fall and advancement (−0.228 day/year) in the last frost date in spring using historical frost observation data from 1951 to 2010. Trends determined over 20-year subperiods were distinct from long-term trends and were sensitive to decadal changes in daily minimum air temperature. Second, we proposed a scheme to infer the first and last frost dates from the time series of daily minimum air temperature. After optimization, the first (last) cold date when the daily minimum temperature fell below a temperature criterion of ca. 2 °C yielded the best estimate of the first (last) frost date with an error of 2 days in most cases. However, the overall root mean square errors were 13–16 days because some cases with significant misfits deteriorated the values. Both wind speed and humidity shifted the criterion. Sites with strong winds contributed to a decrease in the temperature criterion. Because this scheme only requires the daily minimum air temperature, it is widely applicable to the quantitative evaluation of the first and last frost dates from given meteorological or climate projection data.

We investigated meteorological conditions for frost occurrence using routine observation data during 1991–2010 from manned meteorological stations in Japan. The majority of the frost occurrence was observed when the daily minimum air temperature was in the range of − 5 °C to 5 °C. Due to strong radiative cooling of a frosted surface, frost was observed even when the daily minimum air temperature was above the dew-point temperature at a normal meteorological observation height. We proposed an analytical scheme to evaluate the probability of frost occurrence under given meteorological conditions. This scheme was based on a probability distribution of observed frost occurrence in a three-dimensional space of meteorological variables. The obtained distribution centered on − 1.9 °C for the daily minimum air temperature and 73% for the relative humidity, with an exponential decay with wind speed. The probability was halved when the daily minimum air temperature and relative humidity change by 3.3 °C and 24 percent-points, respectively, from the distribution center. Since this scheme gives probability of frost occurrence, it is advantageous for risk assessment under given meteorological conditions. Recent popularization of gridded meteorological datasets will help to expand the potential usage of the scheme.

Future changes in the climate system could have significant impacts on the natural environment and human activities, which in turn affect changes in the climate system. In the interaction between natural and human systems under climate change conditions, land use is one of the elements that play an essential role. On the one hand, future climate change will affect the availability of water and food, which may impact land-use change. On the other hand, human-induced land-use change can affect the climate system through biogeophysical and biogeochemical effects. To investigate these interrelationships, we developed MIROC-INTEG-LAND (MIROC INTEGrated LAND surface model version 1), an integrated model that combines the land surface component of global climate model MIROC (Model for Interdisciplinary Research on Climate) with water resources, crop production, land ecosystem, and land-use models. The most significant feature of MIROC-INTEG-LAND is that the land surface model that describes the processes of the energy and water balance, human water management, and crop growth incorporates a land use decision-making model based on economic activities. In MIROC-INTEG-LAND, spatially detailed information regarding water resources and crop yields is reflected in the prediction of future land-use change, which cannot be considered in the conventional integrated assessment models. In this paper, we introduce the details and interconnections of the submodels of MIROC-INTEG-LAND, compare historical simulations with observations, and identify various interactions between the submodels. By evaluating the historical simulation, we have confirmed that the model reproduces the observed states well. The future simulations indicate that changes in climate have significant impacts on crop yields, land use, and irrigation water demand. The newly developed MIROC-INTEG-LAND could be combined with atmospheric and ocean models to develop an integrated earth system model to simulate the interactions among coupled natural–human earth system components.

Although global- and catchment-scale hydrological models are often shown to accurately simulate long-term runoff time-series, far less is known about their suitability for capturing hydrological extremes, such as droughts. Here we evaluated simulations of hydrological droughts from nine catchment scale hydrological models (CHMs) and eight global scale hydrological models (GHMs) for eight large catchments: Upper Amazon, Lena, Upper Mississippi, Upper Niger, Rhine, Tagus, Upper Yangtze and Upper Yellow. The simulations were conducted within the framework of phase 2a of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). We evaluated the ability of the CHMs, GHMs and their respective ensemble means (Ens-CHM and Ens-GHM) to simulate observed hydrological droughts of at least one month duration, over 31 years (1971–2001). Hydrological drought events were identified from runoff-deficits and the Standardised Runoff Index (SRI). In all catchments, the CHMs performed relatively better than the GHMs, for simulating monthly runoff-deficits. The number of drought events identified under different drought categories (i.e. SRI values of -1 to -1.49, -1.5 to -1.99, and ≤-2) varied significantly between models. All the models, as well as the two ensemble means, have limited abilities to accurately simulate drought events in all eight catchments, in terms of their occurrence and magnitude. Overall, there are opportunities to improve both CHMs and GHMs for better characterisation of hydrological droughts.

We investigated the spring frost risk over the main apple production areas in Japan under future climates using multiple sets of global circulation models and scenarios. Frost risk was judged by the daily minimum air temperature. Apple phenology was estimated with a phenology model under future meteorological conditions. Since spring cold hardiness of apple trees depends on the phenophase, we took the effect into consideration by decomposing the season into three phenophases. April temperatures are projected to increase by 0.5–2 °C for 2031–2050 and 1.5–6 °C for 2081–2100 relative to that of 1981–2000, depending on the climate models and scenarios. Spring phenology will advance by 10 days or more for the highest temperature increase case for 2081–2100. Frost risk will not monotonically change with the future temperature increase. For the scenario with the temperature increase by 1–2 °C in spring, corresponding to the medium temperature increase case among our simulation cases, frost risk will be maximized in the southern Tohoku region and central highlands, where frost risk is relatively high under the current climates. Frost risk will tend to decrease for the highest temperature increase case. During bud break to foliation, apple trees will be in highest frost risk under future climates.

In this study, we aimed to evaluate the monthly precipitation forecasts of JMA/MRI-CPS2, a global dynamical seasonal climate forecast (Dyn-SCF) system operated in the Japan Meteorological Agency, by comparing them with the forecasts of a statistical SCF (St-SCF) system using climate indices systematically and globally. Accordingly, we developed a new global St-SCF system using 18 climate indices and compared the monthly precipitation of this system with those of JMA/MRI-CPS2. Consequently, it was found that JMA/MRI-CPS2 forecasts are superior to St-SCFs around the equator (10° S–10° N) even for six-month lead forecasts. For one-month lead forecasts, the accuracy of JMA/MRI-CPS2 forecasts was higher than that of St-SCFs when viewed globally. In contrast, for forecasts made two months or longer in advance, St-SCFs had an advantage in global forecasts. In addition to evaluating the accuracy of JMA/MRI-CPS2 forecasts, the slow dynamics of the ocean and atmosphere, not reproduced by the JMA/MRI-CPS2 system, were determined by comparing the evaluations, and it was concluded that this could contribute to improving Dyn-SCF systems.

Although global- and catchment-scale hydrological models are often shown to accurately simulate long-term runoff time-series, far less is known about their suitability for capturing hydrological extremes, such as droughts. Here we evaluated runoff simulations from nine catchment scale hydrological models (CHMs) and eight global scale hydrological models (GHMs) for eight large catchments: Upper Amazon, Lena, Upper Mississippi, Upper Niger, Rhine, Tagus, Upper Yangtze and Upper Yellow. The simulations were conducted within the framework of phase 2a of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). We evaluated the ability of the CHMs, GHMs and their respective ensemble means (Ens-CHM and Ens-GHM) to simulate observed monthly runoff and hydrological droughts over 31 years (1971–2001). Observed and simulated hydrological drought events were identified using the Standardised Runoff Index (SRI) and were classified based on intensity. Our results show that for all eight catchments, CHMs out-performed GHMs in monthly runoff estimation showing a better representation of observed runoff than GHMs. The number of drought events identified under different drought categories (i.e. SRI values of -1 to -1.49, -1.5 to -1.99, and ≤-2) varied significantly between models. All the models, as well as the two ensemble means present limited ability to accurately simulate severe drought events in all eight catchments, in terms of their timing and intensity. By analysing the monthly runoff time-series for several extreme droughts over the historical period, we identify room for improvement in the models so that extreme droughts may ultimately be better represented by both CHMs and GHMs.

It is now widely recognized that climate change affects multiple sectors in virtually every part of the world. Impacts on one sector may influence other sectors, including seemingly remote ones, which we call “interconnections of climate risks.” While a substantial number of climate risks are identified in the Intergovernmental Panel on Climate Change Fifth Assessment Report, there have been few attempts to explore the interconnections between them in a comprehensive way. To fill this gap, we developed a methodology for visualizing climate risks and their interconnections based on a literature survey. Our visualizations highlight the need to address climate risk interconnections in impact and vulnerability studies. Our risk maps and flowcharts show how changes in climate impact natural and socioeconomic systems, ultimately affecting human security, health, and well‐being. We tested our visualization approach with potential users and identified likely benefits and issues. Our methodology can be used as a communication tool to inform decision makers, stakeholders, and the general public of the cascading risks that can be triggered by climate change.

We investigated dam behaviours during high-flow events and their robustness against perturbations in meteorological conditions using the H08 global hydrological model. Differences in these behaviou...

Hydropower generation plays a key role in mitigating GHG emissions from the overall power supply. Although the maximum achievable hydropower generation (MAHG) will be affected by climate change, it is seldom incorporated in integrated assessment models. In this study, we first used the H08 global hydrological model to project MAHG under two physical climate change scenarios. Then, we used the Asia-Pacific Integrated Model/Computable General Equilibrium integrated assessment model to quantify the economic consequences of the presence or absence of mitigation policy on hydropower generation. This approach enabled us to quantify the physical impacts of climate change and the effect of mitigation policy—together and in isolation—on hydropower generation and the economy, both globally and regionally. Although there was little overall global change, we observed substantial differences among regions in the MAHG average change (from − 71% in Middle East to 14% in Former Soviet Union in RCP8.5). We found that the magnitude of changes in regional gross domestic product (GDP) was small negative (positive) in Brazil (Canada) by 2100, for the no mitigation policy scenario. These consequences were intensified with the implementation of mitigation policies that enhanced the price competitiveness of hydropower against fossil fuel-powered technologies. Overall, our results suggested that there would be no notable globally aggregated impacts on GDP by 2100 because the positive effects in some regions were canceled out by negative effects in other regions.

Global gridded crop models (GGCMs) are increasingly used for climate risk assessments and adaptation planning in agriculture. GGCM historical simulation performance is therefore crucial for such applications. However, GGCM performance is lower for rice than for other crops at an aggregated administrative unit level despite the lack of a clear difference in model performance at the site level. Here, we present key factors that need to be considered in the spatial and temporal aggregation of GGCM outputs to improve the evaluation of GGCM historical rice simulations at the country scale. The factors include an adjustment for the harvesting year in GGCM rainfed and irrigated simulations, the removal of misreports from reference data, a consideration of the quality of national crop statistics (census reports), and the explicit incorporation of a planting window. The effect of each individual factor is demonstrated by analyzing a multi-GGCM dataset and performing a planting date ensemble simulation of two GGCMs. We reveal that among others, aligning the harvesting year in the GGCM simulations with that in national reports is pivotal. Although our analysis focuses specifically on rice, the findings of this study are useful for improving the country-level evaluations of GGCM historical simulations for other crops.

Future changes in the climate system could have significant impacts on the natural environment and human activities, which in turn affect changes in the climate system. In the interaction between natural and human systems under climate change conditions, land use is one of the elements that play an essential role. Future climate change will affect the availability of water and food, which may impact land-use change. On the other hand, human land-use change can affect the climate system through bio-geophysical and bio-geochemical effects. To investigate these interrelationships, we developed MIROC-INTEG1 (MIROC INTEGrated terrestrial model version 1), an integrated model that combines the global climate model MIROC (Model for Interdisciplinary Research on Climate) with water resources, crop production, land ecosystem, and land use models. In this paper, we introduce the details and interconnections of the sub-models of MIROC-INTEG1, compare historical simulations with observations, and identify the various interactions between sub-models. MIROC-INTEG1 makes it possible to quantitatively evaluate the feedback processes or nexus between climate, water resources, crop production, land use, and ecosystem, and to assess the risks, trade-offs and co-benefits associated with future climate change and prospective mitigation and adaptation policies.

This study presents a novel application of machine learning to deliver optimised, multi-model combinations (MMCs) of Global Hydrological Model (GHM) simulations. We exemplify the approach using runoff simulations from five GHMs across 40 large global catchments. The benchmarked, median performance gain of the MMC solutions is 45% compared to the best performing GHM and exceeds 100% when compared to the ensemble mean (EM). The performance gain offered by MMC suggests that future multi-model applications consider reporting MMCs, alongside the EM and intermodal range, to provide end-users of GHM ensembles with a better contextualised estimate of runoff. Importantly, the study highlights the difficulty of interpreting complex, non-linear MMC solutions in physical terms. This indicates that a pragmatic approach to future MMC studies based on machine learning methods is required, in which the allowable solution complexity is carefully constrained.

We assessed whether the impacts of various increases in global mean temperature from preindustrial levels (∆GMT) on terrestrial ecosystems and water resources could be approximated by linear scaling of the impacts of ∆GMT = 2 °C at global and large regional scales. Impacts on net primary production, CO2 emissions from biomass burning, soil erosion, and surface runoff calculated by impact model simulations driven by multiple climate scenarios were assessed for a ∆GMT range of 1.5–4 °C. The results showed that the linear scaling was tolerable for net primary production, biomass burning, and surface runoff for a global average. However, for regional averages, the linear scaling was unacceptable for net primary production and biomass burning as well as for soil erosion at around 3 °C in numerous regions around the world. The linear scaling was judged to be tolerable for surface runoff in most regions where the impacts of 2 °C were statistically significant, but there were large uncertainties in future changes in surface runoff in many regions. Exploring the applicability of linear scaling could help simplify and streamline climate-change impact assessments at various ∆GMTs. Our approach leaves room for refinement, and further investigation will be worthwhile.

A global water scarcity assessment for the 21st century was conducted under the latest socio-economic scenario for global change studies, namely Shared Socio-economic Pathways (SSPs). SSPs depict five global situations with substantially different socio-economic conditions. In the accompanying paper, a water use scenario compatible with the SSPs was developed. This scenario considers not only quantitative socio-economic factors such as population and electricity production but also qualitative ones such as the degree of technological change and overall environmental consciousness. In this paper, water availability and water scarcity were assessed using a global hydrological model called H08. H08 simulates both the natural water cycle and major human activities such as water abstraction and reservoir operation. It simulates water availability and use at daily time intervals at a spatial resolution of 0.5° × 0.5°. A series of global hydrological simulations were conducted under the SSPs, taking into account different climate policy options and the results of climate models. Water scarcity was assessed using an index termed the Cumulative Abstraction to Demand ratio, which is expressed as the accumulation of daily water abstraction from a river divided by the daily consumption-based potential water demand. This index can be used to express whether renewable water resources are available from rivers when required. The results suggested that by 2071–2100 the population living under severely water-stressed conditions for SSP1-5 will reach 2588–2793 × 106 (39–42% of total population), 3966–4298 × 106 (46–50%), 5334–5643 × 106 (52–55%), 3427–3786 × 106 (40–45%), 3164–3379 × 106 (46–49%) respectively, if climate policies are not adopted. Even in SSP1 (the scenario with least change in water use and climate) global water scarcity increases considerably, as compared to the present-day. This is mainly due to the growth in population and economic activity in developing countries, and partly due to hydrological changes induced by global warming.

Human activity has a profound influence on river discharges, hydrological extremes and water-related hazards. In this study, we compare the results of five state-of-the-art global hydrological models (GHMs) with observations to examine the role of human impact parameterizations (HIP) in the simulation of mean, high- and low-flows. The analysis is performed for 471 gauging stations across the globe for the period 1971-2010. We find that the inclusion of HIP improves the performance of the GHMs, both in managed and near-natural catchments. For near-natural catchments, the improvement in performance results from improvements in incoming discharges from upstream managed catchments. This finding is robust across the GHMs, although the level of improvement and the reasons for it vary greatly. The inclusion of HIP leads to a significant decrease in the bias of the long-term mean monthly discharge in 36%-73% of the studied catchments, and an improvement in the modeled hydrological variability in 31%-74% of the studied catchments. Including HIP in the GHMs also leads to an improvement in the simulation of hydrological extremes, compared to when HIP is excluded. Whilst the inclusion of HIP leads to decreases in the simulated high-flows, it can lead to either increases or decreases in the low-flows. This is due to the relative importance of the timing of return flows and reservoir operations as well as their associated uncertainties. Even with the inclusion of HIP, we find that the model performance is still not optimal. This highlights the need for further research linking human management and hydrological domains, especially in those areas in which human impacts are dominant. The large variation in performance between GHMs, regions and performance indicators, calls for a careful selection of GHMs, model components and evaluation metrics in future model applications.

Climate change and its impacts already pose considerable challenges for societies that will further increase with global warming (IPCC, 2014a, b). Uncertainties of the climatic response to greenhouse gas emissions include the potential passing of large-scale tipping points (e.g. Lenton et al., 2008; Levermann et al., 2012; Schellnhuber, 2010) and changes in extreme meteorological events (Field et al., 2012) with complex impacts on societies (Hallegatte et al., 2013). Thus climate change mitigation is considered a necessary societal response for avoiding uncontrollable impacts (Conference of the Parties, 2010). On the other hand, large-scale climate change mitigation itself implies fundamental changes in, for example, the global energy system. The associated challenges come on top of others that derive from equally important ethical imperatives like the fulfilment of increasing food demand that may draw on the same resources. For example, ensuring food security for a growing population may require an expansion of cropland, thereby reducing natural carbon sinks or the area available for bio-energy production. So far, available studies addressing this problem have relied on individual impact models, ignoring uncertainty in crop model and biome model projections. Here, we propose a probabilistic decision framework that allows for an evaluation of agricultural management and mitigation options in a multi-impact-model setting. Based on simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), we outline how cross-sectorally consistent multi-model impact simulations could be used to generate the information required for robust decision making. Using an illustrative future land use pattern, we discuss the trade-off between potential gains in crop production and associated losses in natural carbon sinks in the new multiple crop- and biome-model setting. In addition, crop and water model simulations are combined to explore irrigation increases as one possible measure of agricultural intensification that could limit the expansion of cropland required in response to climate change and growing food demand. This example shows that current impact model uncertainties pose an important challenge to long-term mitigation planning and must not be ignored in long-term strategic decision making.

Actual land evapotranspiration (ET) is a key component of the global hydrological cycle and anessential variable determining the evolution of hydrological extreme events under different climate change scenarios. However, recently available ET products show persistent uncertainties thatare impeding a precise attribution of human-induced climate change. Here, we aim at comparing arange of independent global monthly land ET estimates with historical model simulations from theglobal water, agriculture, and biomes sectors participating in the second phase of the Inter-SectoralImpact Model Intercomparison Project (ISIMIP2a). Among the independent estimates, we use theEartH2Observe Tier-1 dataset (E2O), two commonly used reanalyses, a pre-compiled ensembleproduct (LandFlux-EVAL), and an updated collection of recently published datasets thatalgorithmically derive ET from observations or observations-based estimates (diagnostic datasets). Acluster analysis is applied in order to identify spatio-temporal differences among all datasets and tothus identify factors that dominate overall uncertainties. The clustering is controlled by several factorsincluding the model choice, the meteorological forcing used to drive the assessed models, the datacategory (models participating in the different sectors of ISIMIP2a, E2O models, diagnostic estimates,reanalysis-based estimates or composite products), the ET scheme, and the number of soil layers inthe models. By using these factors to explain spatial and spatio-temporal variabilities in ET, we findthat themodel choicemostly dominates (24%–40%of variance explained), except for spatio-temporalpatterns of total ET, where the forcing explains the largest fraction of the variance (29%). The mostdominant clusters of datasets are further compared with individual diagnostic and reanalysis-basedestimates to assess their representation of selected heat waves and droughts in the Great Plains,Central Europe and western Russia. Although most of the ET estimates capture these extreme events,the generally large spread among the entire ensemble indicates substantial uncertainties.

Water scarcity is rapidly increasing in many regions. In a novel, multi-model assessment, we examine how human interventions (HI: land use and land cover change, man-made reservoirs and human water use) affected monthly river water availability and water scarcity over the period 1971–2010. Here we show that HI drastically change the critical dimensions of water scarcity, aggravating water scarcity for 8.8% (7.4–16.5%) of the global population but alleviating it for another 8.3% (6.4–15.8%). Positive impacts of HI mostly occur upstream, whereas HI aggravate water scarcity downstream; HI cause water scarcity to travel downstream. Attribution of water scarcity changes to HI components is complex and varies among the hydrological models. Seasonal variation in impacts and dominant HI components is also substantial. A thorough consideration of the spatially and temporally varying interactions among HI components and of uncertainties is therefore crucial for the success of water scarcity adaptation by HI., Water scarcity threatens a growing number of global catchments. Here, the authors examine how human interventions (HI) affected water scarcity between 1971 and 2010 and find that HI caused increases in the average duration and occurrence of water scarcity for 32% and 34% of the global population, respectively.

By introducing two scalar quantities, namely, the Gini and Lorenz asymmetry coefficients, we examined their characteristics and applicability to the global analysis of changes in river flow regimes under future climate change. First, by applying these coefficients to river discharge data, we showed that various types of flow-duration curves can be interpreted quantitatively in terms of the seasonal inequality in the discharge (i.e., the unevenness of the temporal distribution of river discharge). Their statistical characteristics, based on five theoretical distribution functions frequently used in hydrological analysis, were also shown. Next we used these coefficients to evaluate the seasonal inequality of major global rivers using the global hydrological model H08 for four 30 year time spans (1960–1989, 2010–2039, 2040–2069, and 2070–2099) under four climate-change scenarios. We used ensembles of hydrological simulation results with five general circulation models. From the analysis of the Gini coefficient, future changes in seasonal inequality show a contrasting geographical pattern: a decreasing trend at high northern latitudes and an increasing trend in most other areas. The Lorenz asymmetry coefficient shows large changes at high northern latitudes, attributable to major shifts in the flow regime accompanied by different snow-melting properties under different future climate scenarios. Although a flow-duration curve is a pictorial representation of river discharge suitable for one specific site, by depicting the geographical distribution of these two coefficients along river channels, different characteristics of flow-duration curves at different sites can be detected, even within the same river basin.

Climate change due to anthropogenic greenhouse gas emissions is expected to increase the frequency and intensity of precipitation events, which is likely to affect the probability of flooding into the future. In this paper we use river flow simulations from nine global hydrology and land surface models to explore uncertainties in the potential impacts of climate change on flood hazard at global scale. As an indicator of flood hazard we looked at changes in the 30-y return level of 5-d average peak flows under representative concentration pathway RCP8.5 at the end of this century. Not everywhere does climate change result in an increase in flood hazard: decreases in the magnitude and frequency of the 30-y return level of river flow occur at roughly one-third (20–45%) of the global land grid points, particularly in areas where the hydrograph is dominated by the snowmelt flood peak in spring. In most model experiments, however, an increase in flooding frequency was found in more than half of the grid points. The current 30-y flood peak is projected to occur in more than 1 in 5 y across 5–30% of land grid points. The large-scale patterns of change are remarkably consistent among impact models and even the driving climate models, but at local scale and in individual river basins there can be disagreement even on the sign of change, indicating large modeling uncertainty which needs to be taken into account in local adaptation studies.

We compare ensembles of water supply and demand projections from 10 global hydrological models and six global gridded crop models. These are produced as part of the Inter-Sectoral Impacts Model Intercomparison Project, with coordination from the Agricultural Model Intercomparison and Improvement Project, and driven by outputs of general circulation models run under representative concentration pathway 8.5 as part of the Fifth Coupled Model Intercomparison Project. Models project that direct climate impacts to maize, soybean, wheat, and rice involve losses of 400-1,400 Pcal (8-24% of present-day total) when CO2 fertilization effects are accounted for or 1,400-2,600 Pcal (24-43%) otherwise. Freshwater limitations in some irrigated regions (western United States; China; and West, South, and Central Asia) could necessitate the reversion of 20-60 Mha of cropland from irrigated to rainfed management by end-of-century, and a further loss of 600-2,900 Pcal of food production. In other regions (northern/eastern United States, parts of South America, much of Europe, and South East Asia) surplus water supply could in principle support a net increase in irrigation, although substantial investments in irrigation infrastructure would be required.

Crop irrigation is responsible for 70% of humanity’swater demand. Since the late 1990s, the expansion ofirrigated areas has been tapering off, and this trend isexpected to continue in the future. Future irrigation waterdemand (IWD) is, however, subject to large uncertaintiesdue to anticipated climate change. Here, we use a set ofseven global hydrological models (GHMs) to quantify theimpact of projected global climate change on IWD oncurrently irrigated areas by the end of this century, and toassess the resulting uncertainties arising from both theGHMs and climate projections. The resulting ensembleprojections generally show an increasing trend in futureIWD, but the increase varies substantially depending onthe degree of global warming and associated regionalprecipitation changes. Under the highest greenhouse gasemission scenario (RCP8.5), IWD will considerably increaseduring the summer in the Northern Hemisphere (>20% by2100), and the present peak IWD is projected to shift onemonth or more over regions where ≥80% of the globalirrigated areas exist and 4 billion people currently live.Uncertaint ies arising from GHMs and global climatemodels (GCMs) are large, with GHM uncertainty dominatingthroughout the century and with GCM uncertaintysubstantially increasing from the midcentury, indicating thechoice of GHM outweighing by far the uncertainty arisingfrom the choi ce of GCM and associated emission scenario.

A novel global water scarcity assessment for the 21st century is presented in a two-part paper. In this first paper, water use scenarios are presented for the latest global hydrological models. The scenarios are compatible with the socio-economic scenarios of the Shared Socio-economic Pathways (SSPs), which are a part of the latest set of scenarios on global change developed by the integrated assessment, the IAV (climate change impact, adaptation, and vulnerability assessment), and the climate modeling community. The SSPs depict five global situations based on substantially different socio-economic conditions during the 21st century. Water use scenarios were developed to reflect not only quantitative socio-economic factors, such as population and electricity production, but also key qualitative concepts such as the degree of technological change and overall environmental consciousness. Each scenario consists of five factors: irrigated area, crop intensity, irrigation efficiency, and withdrawal-based potential industrial and municipal water demands. The first three factors are used to estimate the potential irrigation water demand. All factors were developed using simple models based on a literature review and analysis of historical records. The factors are grid-based at a spatial resolution of 0.5° × 0.5° and cover the whole 21st century in five-year intervals. Each factor shows wide variation among the different global situations depicted: the irrigated area in 2085 varies between 2.7 × 106 and 4.5 × 106 km2, withdrawal-based potential industrial water demand between 246 and 1714 km3 yr−1, and municipal water between 573 and 1280 km3 yr−1. The water use scenarios can be used for global water scarcity assessments that identify the regions vulnerable to water scarcity and analyze the timing and magnitude of scarcity conditions.

We performed a twofold intercomparison of river discharge regulated by dams under multiple meteorological forcings among multiple global hydrological models for a historical period by simulation. Paper II provides an intercomparison of river discharge simulated by five hydrological models under four meteorological forcings. This is the first global multimodel intercomparison study on dam-regulated river flow. Although the simulations were conducted globally, the Missouri-Mississippi and Green- Colorado Rivers were chosen as case-study sites in this study. The hydrological models incorporate generic schemes of dam operation, not specific to a certain dam. We examined river discharge on a longitudinal section of river channels to investigate the effects of dams on simulated discharge, especially at the seasonal time scale. We found that the magnitude of dam regulation differed considerably among the hydrological models. The difference was attributable not only to dam operation schemes but also to the magnitude of simulated river discharge flowing into dams. That is, although a similar algorithm of dam operation schemes was incorporated in different hydrological models, the magnitude of dam regulation substantially differed among the models. Intermodel discrepancies tended to decrease toward the lower reaches of these river basins, which means model dependence is less significant toward lower reaches. These case-study results imply that, intermodel comparisons of river discharge should be made at different locations along the river's course to critically examine the performance of hydrological models because the performance can vary with the locations.

Ideally, the results from models operating at different scales should agree in trend direction and magnitude of impacts under climate change. However, this implies that the sensitivity to climate variability and climate change is comparable for impact models designed for either scale. In this study, we compare hydrological changes simulated by 9 global and 9 regional hydrological models (HM) for 11 large river basins in all continents under reference and scenario conditions. The foci are on model validation runs, sensitivity of annual discharge to climate variability in the reference period, and sensitivity of the long-term average monthly seasonal dynamics to climate change. One major result is that the global models, mostly not calibrated against observations, often show a considerable bias in mean monthly discharge, whereas regional models show a better reproduction of reference conditions. However, the sensitivity of the two HM ensembles to climate variability is in general similar. The simulated climate change impacts in terms of long-term average monthly dynamics evaluated for HM ensemble medians and spreads show that the medians are to a certain extent comparable in some cases, but have distinct differences in other cases, and the spreads related to global models are mostly notably larger. Summarizing, this implies that global HMs are useful tools when looking at large-scale impacts of climate change and variability. Whenever impacts for a specific river basin or region are of interest, e.g. for complex water management applications, the regional-scale models calibrated and validated against observed discharge should be used.

We have studied future changes in the atmospheric and hydrological environments in Northeast Thailand from the viewpoint of risk assessment of future cultural environments in crop fields. To obtain robust and reliable estimation for future climate, ten general circulation models under three warming scenarios, B1, A1B, and A2, were used in this study. The obtained change trends show that daily maximum air temperature and precipitation will increase by 2.6°C and 4.0%, respectively, whereas soil moisture will decrease by c.a. 1% point in volumetric water content at the end of this century under the A1B scenario. Seasonal contrasts in precipitation will intensify: precipitation increases in the rainy season and precipitation decreases in the dry season. Soil moisture will slightly decrease almost throughout the year. Despite a homogeneous increase in the air temperature over Northeast Thailand, a future decrease in soil water content will show a geographically inhomogeneous distribution: Soil will experience a relative larger decrease in wetness at a shallow depth on the Khorat plateau than in the surrounding mountainous area, reflecting vegetation cover and soil texture. The predicted increase in air temperature is relatively consistent between general circulation models. In contrast, relatively large intermodel differences in precipitation, especially in long-term trends, produce unwanted bias errors in the estimation of other hydrological elements, such as soil moisture and evaporation, and cause uncertainties in projection of the agro-climatological environment. Offline hydrological simulation with a wide precipitation range is one strategy to compensate for such uncertainties and to obtain reliable risk assessment of future cultural conditions in rainfed paddy fields in Northeast Thailand.

We have developed a method for estimating hourly global solar radiation (GSR) from hourly sunshine duration data. This procedure requires only hourly sunshine duration as the input data and utilizes hourly precipitation and daily snow cover as auxiliary data to classify time intervals into six cases according to weather conditions. To obtain hourly GSR using a simple algebraic form, a quadratic function of the solar elevation angle and the sunshine duration ratio is used. Daily GSR is given by a sum of hourly GSRs. We evaluated the performance of the newly developed method using data obtained at 67 meteorological stations and found that the estimated GSR is highly consistent with that observed. Hourly and daily root-mean-square misfits are approximately 0.2 MJ/m2/h (~55 W/m2) and 1.4 to 1.5 MJ/m2/day (~16 to 17 W/m2), respectively. Our classification of weather conditions is effective for reducing estimation errors, especially under cloudy skies. Since the sunshine duration is observed at more meteorological stations than GSR, the proposed new method is a powerful tool for obtaining solar radiation with hourly resolution and a dense geographical distribution. One of the proposed methods, GSRgrn, can be applicable to hourly GSR estimations at different observation sites by setting local parameters (the precipitable water, surface albedo, and atmospheric turbidity) suitable to the sites. The hourly GSR can be applied for various micrometeorological studies, such as the heat budget of crop fields.

We estimated monthly atmospheric turbidity from hourly observation data of global solar radiation under cloudless skies at 67 stations all over Japan. Using an empirical formula for calculating global solar radiation, we determined atmospheric turbidity as the best-fit parameter. The estimated turbidity is consistent with the observation by the Japan Meteorological Agency. Sub-nationwide increases in atmospheric turbidity, caused by forest fires or Asian dust events, are detected in time series. Some temporal changes in atmospheric turbidity are also found in a small area. Such local phenomena are hardly detectable by sparse distribution of the stations for direct solar radiation observation. The accuracy of the global solar radiation estimation can be improved by considering the seasonal variation of the turbidity. Since Angstrom’s turbidity coefficient is directly linked with the volumetric aerosol content, the present method is useful for monitoring the spatial distribution of aerosols. In addition, the present method using the densely distributed observation network of global solar radiation has an advantage in compensating for the sparseness of the current atmospheric turbidity observation in Japan.

In order to achieve climate change mitigation, long-term decisions are required that must be reconciled with other societal goals that draw on the same resources. For example, ensuring food security for a growing population may require an expansion of crop land, thereby reducing natural carbon sinks or the area available for bio-energy production. Here, we show that current impact-model uncertainties pose an important challenge to long-term mitigation planning and propose a new risk-assessment and decision framework that accounts for competing interests. Based on cross-sectorally consistent simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) we discuss potential gains and limitations of additional irrigation and trade-offs of the expansion of agricultural land as two possible response measures to climate change and growing food demand. We describe an illustrative example in which the combination of both measures may close the supply demand gap while leading to a loss of approximately half of all natural carbon sinks. We highlight current limitations of available simulations and additional steps required for a comprehensive risk assessment.

The impacts of global climate change on different aspects of humanity’s diverse life-support systems are complex and often difficult to predict. To facilitate policy decisions on mitigation and adaptation strategies, it is necessary to understand, quantify, and synthesize these climate-change impacts, taking into account their uncertainties. Crucial to these decisions is an understanding of how impacts in different sectors overlap, as overlapping impacts increase exposure, lead to interactions of impacts, and are likely to raise adaptation pressure. As a first step we develop herein a framework to study coinciding impacts and identify regional exposure hotspots. This framework can then be used as a starting point for regional case studies on vulnerability and multifaceted adaptation strategies. We consider impacts related to water, agriculture, ecosystems, and malaria at different levels of global warming. Multisectoral overlap starts to be seen robustly at a mean global warming of 3 °C above the 1980–2010 mean, with 11% of the world population subject to severe impacts in at least two of the four impact sectors at 4 °C. Despite these general conclusions, we find that uncertainty arising from the impact models is considerable, and larger than that from the climate models. In a low probability-high impact worst-case assessment, almost the whole inhabited world is at risk for multisectoral pressures. Hence, there is a pressing need for an increased research effort to develop a more comprehensive understanding of impacts, as well as for the development of policy measures under existing uncertainty.

我々は、大きな軌道離心率を持つ天体が、内側を円軌道で回る天体から摂動を受けているとき、その解析運動理論を構築した。また我々は、数値積分結果との比較により、この解析理論が天体の運動を高い精度で記述していることを確認した。 惑星をはじめとする太陽系天体の運動は摂動論の発展とともに解析的に記述されてきた。一般に惑星や衛星は離心率や軌道傾斜角が小さいため、これらのパラメータが微小量となり得る。すなわち摂動関数をこれらの冪級数に展開し、必要とする精度より高次の微小量を無視することによって解くことになる。しかし、離心率・軌道傾斜角ともに大きい天体に対してはこの手法が適用できない。冪級数の収束が極めて遅くなるためである。 我々は、離心率の冪級数展開によらない解析運動理論を構築した。対象天体の近点離角を表す独立変数には真近点離角を採用した。当理論は小さな軌道離心率を持つ天体から大きな軌道離心率を持つ天体にまで適用することができる。なお、当理論は中心天体に対する内側天体の質量の比が小さいものとして構築されている。 理論の構築にはHoriの正準摂動論を用いた。当問題において基本となる摂動の周期は、短いものから順に内側天体の公転周期・外側天体の公転周期・外側天体の近点回転周期の3つである。この摂動論の特徴は各周期摂動成分の時間平均が0となるように正準変換を繰り返し、最終的には解析的に解ける正準方程式を作り出すことである。作用変数は永年項を含まない。数値積分では初期時刻からの時間経過に伴ない、丸め誤差の蓄積による計算位置精度の劣化が避けられないが、解析解では長期間にわたる精度の保持ができる。解析理論式を導出する際の繁雑な式変形には数式処理言語による計算機処理をした。 なお得られた運動理論の精度を評価するため、我々は外挿法（Bulirsch-toer法）による数値積分結果との比較を行った。 解析理論結果は数値積分結果と良い一致を示した。軌道要素などのパラメータを変えても、広範な条件下で高い精度が実現されることが確認された。理論の精度は対象天体と摂動天体との軌道配置などにより異なる。内側天体に対する外側天体の軌道長半径の比が大きいほど、離心率が大きくても高い精度を維持することができる。 その一方で、当理論の精度維持が困難となる事例についても解析した。対象天体の軌道長半径を固定した場合、離心率が大きくなると急激に精度が悪くなる。この原因の1つに、短周期摂動に関する正準変換の際、微小パラメータに関する収束が悪くなることが挙げられる。ここでは外側天体と内側天体の平均運動の比が微小パラメータになっているが、離心率が大きいときはこの微小パラメータの働きを弱めるため、解析解（特に短周期摂動）の精度が悪化する原因となる。 もう1つの原因として対象天体が近点を通過する際、軌道要素の時間変動に階段状の急変動（オフセット）が生じることが挙げられる。このオフセットについて、数値積分結果をもとに精査した。このオフセットは2天体の空間的な配置に依存して変化する。対象天体の受ける摂動の影響が近点通過の前後で非対称になるとき、オフセットが生じる。なおオフセットは制限三体問題の保存量（Jacobi積分ないしTisserand判定式）を満たしており、数値積分の誤差によるものではない。 オフセットは短周期摂動より長い周期性を持ちながら、その大きさは内側天体の経度にも依存した量となっている。なお木星摂動下で運動する離心率の大きな周期彗星についても、その軌道要素の時間変動にオフセットと類似した現象が報告されている。 そのほか、我々は解析理論における打ち切り誤差等についても評価した。 当理論は質量や軌道要素などのパラメータに値を代入することで、様々な天体に適用できる。解析運動理論の利点を活かした応用が期待される。例えば太陽系内の衛星で最も大きな軌道離心率（0.75）を有する海王星の衛星Nereidに対して、内側を円軌道で運動するTritonを摂動天体とした問題に利用できる。（なお我々は、主にこのNereidの問題をケーススタディとして取り上げ、本文における理論の計算適用例にはこの系のパラメータを使用した。） また近年、太陽系外惑星の発見が相次いでいる。現在のところ惑星の存在が確認されている天体は、主星の視線速度観測で検出できるものに限られている。しかし発見された太陽系外惑星について、主星の近傍を回る惑星ほど軌道離心率は小さい傾向がある。このような軌道長半径の小さな円軌道を持つ木星質量程度の惑星（いわゆる“Hot Jupiter”）の外側に、地球質量程度の惑星が将来発見されることも十分に考えられる。幅広い離心率に対して適用可能な当理論の特色を活かして、その惑星の運動を求めることも可能である。