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Mitigation and adaptation represent two solutions to the issue of global warming. While mitigation aims at reducing $$\hbox {CO}_2$$ emissions and preventing climate change, adaptation encompasses a broad scope of techniques used to reduce the impacts of climate change once they have occurred. Both have direct costs on a country's gross domestic product, but costs also arise from temperature increases due to inaction. This paper introduces a tipping point in a real options model and analyzes optimal investment choices in mitigation and their timing. [ABSTRACT FROM AUTHOR]

Egypt, a predominantly arid and hyper-arid country, is one of the environmentally most fragile regions of the world. The country became a hot spot for climatic extremes and aridity change in the global warming context. The unavailability of a detailed and reliable climate zonation map is a major hindrance to climatic studies in Egypt. This study attempted to generate a high-resolution climate zone map of Egypt based on a novel image analysis technique. For this purpose, a colored image representing Egypt's composite climatology was developed using three high-resolution (1-km) climate variables: rainfall, maximum temperature and minimum temperature during 1979–2013. A spherical evolution algorithm was used to classify the image into different climate zones. Subsequently, the climate zones representing similar climate distribution were merged to generate the climate map of Egypt. The study revealed that Egypt's distinguishable climate zones could be recognized when the land area was classified into nine zones using the image analysis technique. The statistical analysis of climate variables of each zone revealed similar climatology only in two pairs of zones. The merging of similar climate zones yielded seven climate zones having distinct climate characteristics. The validation of climate zonation using various statistical tests revealed the robustness of the proposed method in classifying climate. The climate zone map generated in the study can be used as a reference for climate change analysis in Egypt. [ABSTRACT FROM AUTHOR]

The study area, with arid to semi-humid climates, is located in northern Fars Province, southern Iran. Daily precipitation and temperature data from 22 models of the Coupled Model Inter-comparison Project Phase 5 (CMIP5) are used to study climate change for period 2026–2085 under RCP2.6, RCP4.5, and RCP8.5. Daily outputs of each CMIP5 model are downscaled to climatic stations over the study area using LARS-WG tool and transient change factors approach. Daily precipitation is downscaled to seven climatic stations while daily temperature downscaled to two stations. Downscaling results indicate that temperature will increase by + 1.85, + 2.5, and + 3.45 °C under the RCP2.6, RCP4.5, and RCP8.5, respectively, while precipitation will reduce by 14, 22, and 20.8%, respectively, within the study area. Standardized precipitation index (SPI) analysis, performed for 3-, 6-, 9-, and 12-month time scales, shows that frequency (duration) of normal periods will increase (decrease) under climate change, however, 12-month SPI analysis shows decreased frequency and increased duration for normal periods. Frequency, duration, severity, and intensity of wet periods may increase, decrease, or not change under climate change, depending on the SPI time scale. The study area will experience more frequent, longer-duration, severed, intensified droughts in the future due to global warming. Extreme storm analysis shows that precipitation depth of most frequent storms (2-year return period storms) will increase, but precipitation depth of less frequent storms (10- and 20-year return period storms) will decrease under climate change. Temperature enhancement, precipitation reduction, and longer-duration, severed, intensified droughts will increase water shortage in the future, resulting in water crisis and reduced security of food production over northern Fars Province. [ABSTRACT FROM AUTHOR]

Contrary to expectations that global warming will be accompanied by an increase in terrestrial evaporation, pan evaporation (PE) has decreased in several parts of the world during the last decades. This opposing relationship is known as the pan evaporation paradox phenomenon that can be affected by climate change. For this purpose, this study identifies spatial and temporal trends in PE and its associated climate variables (Cvars) in Iran at seasonal and annual time scales. Hence, PE and Cvars data from 68 meteorological stations distributed over Iran during the period 1987–2016 were selected. The analyses of the temporal and spatial trends in PE, Cvars, and relationships among them were examined using the statistical techniques to identify the causes of PE trends. Results indicated that stations located in the subtropical region (latitude below 32°), which is mostly located in the southern half of Iran, showed significant negative PE trends (about 20–30% of stations) in spite of an increasing trend in temperatures indicating the "pan paradox" phenomenon. However, stations with significant positive PE trends (about 40% of stations), which are mostly located in the northern half of Iran, are placed at latitudes above 32°. Trend analysis of other Cvars indicated significant increases in temperatures, sunshine duration, and wind speed in more than half of the stations on all time scales. In addition, minimum, maximum, and mean temperatures, and sunshine duration were the most dominant variables affecting PE in annual, spring, autumn, and summer, respectively, in Iran. [ABSTRACT FROM AUTHOR]

This study presents the results of the ERTDF S-12 project for searching an optimum reduction scenario of the short-lived climate pollutants (SLCPs) to simultaneously mitigate the global warming and environmental problems. The study utilized REAS emission inventory, Asia-Pacific Integrated Model-Enduse (AIM/Enduse), MIROC6 climate model, NICAM non-hydrostatic atmospheric model, and models for estimating environmental damages to health, agriculture, and flood risks. Results of various scenario search indicate that it is difficult to attain simultaneous reduction of global warming and environmental damages, unless a significant reduction of CO2 is combined with carefully designed SLCP reductions for CH4, SO2, black carbon (BC), NOx, CO, and VOCs. In this scenario design, it is important to take into account the impact of small BC reduction to the surface air temperature and complex atmospheric chemical interactions such as negative feedback between CH4 and NOx reduction. We identified two scenarios, i.e., B2a and B1c scenarios which combine the 2D-scenario with SLCP mitigation measures using End-of-Pipe (EoP) and new mitigation technologies, as promising to simultaneously mitigate the temperature rise by about 0.33 °C by 2050 and air pollution in most of the globe for reducing damages in health, agriculture, and flood risk. In Asia and other heavy air pollution areas, health-care measures have to be enhanced in order to suppress the mortality increase due to high temperature in hot spot areas caused by a significant cut of particulate matter. For this situation, the B1b scenario is better to reduce hot spot areas and high-temperature damage to the public health. [ABSTRACT FROM AUTHOR]

It has been challenging to project the tropical cyclone (TC) intensity, structure and destructive potential changes in a warming climate. Here, we compare the sensitivities of TC intensity, size and destructive potential to sea surface warming with and without a pre-storm atmospheric adjustment to an idealized state of Radiative-Convective Equilibrium (RCE). Without RCE, we find large responses of TC intensity, size and destructive potential to sea surface temperature (SST) changes, which is in line with some previous studies. However, in an environment under RCE, the TC size is almost insensitive to SST changes, and the sensitivity of intensity is also much reduced to 3% °C−1-4% °C−1. Without the pre-storm RCE adjustment, the mean destructive potential measured by the integrated power dissipation increases by about 25% °C−1 during the mature stage. However, in an environment under RCE, the sensitivity of destructive potential to sea surface warming does not change significantly. Further analyses show that the reduced response of TC intensity and size to sea surface warming under RCE can be explained by the reduced thermodynamic disequilibrium between the air boundary layer and the sea surface due to the RCE adjustment. When conducting regional-scale sea surface warming experiments for TC case studies, without any RCE adjustment the TC response is likely to be unrealistically exaggerated. The TC intensity-temperature sensitivity under RCE is very similar to those found in coupled climate model simulations. This suggests global mean intensity projections under climate change can be understood in terms of a thermodynamic response to temperature with only a minor contribution from any changes in large-scale dynamics. [ABSTRACT FROM AUTHOR]

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A large-ensemble climate simulation database, which is known as the database for policy decision-making for future climate changes (d4PDF), was designed for climate change risk assessments. Since the completion of the first set of climate simulations in 2015, the database has been growing continuously. It contains the results of ensemble simulations conducted over a total of thousands years respectively for past and future climates using high-resolution global (60 km horizontal mesh) and regional (20 km mesh) atmospheric models. Several sets of future climate simulations are available, in which global mean surface air temperatures are forced to be higher by 4 K, 2 K, and 1.5 K relative to preindustrial levels. Nonwarming past climate simulations are incorporated in d4PDF along with the past climate simulations. The total data volume is approximately 2 petabytes. The atmospheric models satisfactorily simulate the past climate in terms of climatology, natural variations, and extreme events such as heavy precipitation and tropical cyclones. In addition, data users can obtain statistically significant changes in mean states or weather and climate extremes of interest between the past and future climates via a simple arithmetic computation without any statistical assumptions. The database is helpful in understanding future changes in climate states and in attributing past climate events to global warming. Impact assessment studies for climate changes have concurrently been performed in various research areas such as natural hazard, hydrology, civil engineering, agriculture, health, and insurance. The database has now become essential for promoting climate and risk assessment studies and for devising climate adaptation policies. Moreover, it has helped in establishing an interdisciplinary research community on global warming across Japan. [ABSTRACT FROM AUTHOR]