• Energy Research

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.

Abstract Recent studies have detected anthropogenic influences due to increases in greenhouse gases on extreme temperature changes during the latter half of the twentieth century at global and regional scales. Most of the studies, however, were based on a limited number of climate models and also separation of anthropogenic influence from natural factors due to changes in solar and volcanic activities remains challenging at regional scales. Here, the authors conduct optimal fingerprinting analyses using 12 climate models integrated under anthropogenic-only forcing or natural plus anthropogenic forcing. The authors compare observed and simulated changes in annual extreme temperature indices of coldest night and day (TNn and TXn) and warmest night and day (TNx and TXx) from 1951 to 2000. Spatial domains from global mean to continental and subcontinental regions are considered and standardization of indices is employed for better intercomparisons between regions and indices. The anthropogenic signal is detected in global and northern continental means of all four indices, albeit less robustly for TXx, which is consistent with previous findings. The detected anthropogenic signals are also found to be separable from natural forcing influence at the global scale and to a lesser extent at continental and subcontinental scales. Detection occurs more frequently in TNx and TNn than in other indices, particularly at smaller scales, supporting previous studies based on different methods. A combined detection analysis of daytime and nighttime temperature extremes suggests potential applicability to a multivariable assessment.

Recent studies have assessed the impacts of climate change at specific global temperature targets using relatively short (30 year ) transient time-slice periods which are characterized by a steady increase in global mean temperature with time. The Inter-Sectoral Impacts Model Intercomparison Project Phase 2b (ISIMIP2b) provides trend-preserving bias-corrected climate model datasets over six centuries for four global climate models (GCMs) which therefore can be used to evaluate the potential effects of using time-slice periods from stabilized climate state rather than time-slice periods from transient climate state on climate change impacts. Using the H08 global hydrological model, the impacts of climate change, quantified as the deviation from the pre-industrial era, and the signal-to-noise (SN) ratios were computed for five hydrological variables, namely evapotranspiration (EVA), precipitation (PCP), snow water equivalent (SNW), surface temperature (TAR), and total discharge (TOQ) over 20 regions comprising the global land area. A significant difference in EVA for the transient and stabilized climate states was systematically detected for all four GCMs. In addition, three out of the four GCMs indicated that significant differences in PCP, TAR, and TOQ for the transient and stabilized climate states could also be detected over a small fraction of the globe. For most regions, the impacts of climate change toward EVA, PCP, and TOQ are indicated to be underestimated using the transient climate state simulations. The transient climate state was also identified to underestimate the SN ratios compared to the stabilized climate state. For both the global and regional scales, however, there was no indication that surface areas associated with the different classes of SN ratios changed depending on the two climate states (t-test, p > 0.01). Transient time slices may be considered a good approximation of the stabilized climate state, for large-scale hydrological studies and many regions and variables, as: (1) impacts of climate change were only significantly different from those of the stabilized climate state for a small fraction of the globe, and (2) these differences were not indicated to alter the robustness of the impacts of climate change.

Climate warming due to human activities will be accompanied by hydrological cycle changes. Economies, societies and ecosystems in South America are vulnerable to such water resource changes. Hence, water resource impact assessments for South America, and corresponding adaptation and mitigation policies, have attracted increased attention. However, substantial uncertainties remain in the current water resource assessments that are based on multiple coupled Atmosphere Ocean General Circulation models. This uncertainty varies from significant wetting to catastrophic drying. By applying a statistical method, we characterized the uncertainty and identified global-scale metrics for measuring the reliability of water resource assessments in South America. Here, we show that, although the ensemble mean assessment suggested wetting across most of South America, the observational constraints indicate a higher probability of drying in the Amazon basin. Thus, over-reliance on the consensus of models can lead to inappropriate decision making.

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.

AbstractHuman-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.