Climate, Volume 7, Issue 10 (October 2019) – 9 articles

Irrigated production around the world has significantly increased over the last decade. However, climate change is a new threat that could seriously aggravate the irrigation water supplies and request. In this study, the data is derived from the IPCC Fifth Assessment Report (AR5). For the climate change scenarios, five Global Climate Models (GCMs) have been used. By using the CROPWAT approach of Smith, the net irrigation water requirement (IRnet) was calculated. For the estimation of the potential evapotranspiration (Epot), the method in Raziei and Pereira was used. According to representative concentration pathway (RCP) 4.5, these increases vary between 0.74% (North America) and 20.92% (North America) while the RCP 8.5 predict increases of 4.06% (sub-Saharan Africa) to more than 68% (North America). The results also show that the region of Latin America is the region with the large amount of IRnet with coprime value between 1.39 km³/yr (GFDL 4.5) and 1.48 km³/yr (CSIRO 4.5) while sub-Saharan Africa has the smallest IRnet amount between 0.13 km³/yr (GFDL 8.5) and 0.14 km³/yr (ECHAM 8.5). However, the most affected countries by this impact are those in sub-Saharan Africa. This study will probably help decision-makers to make corrections in making their decision. Full article

Changes in climate can be favorable as well as detrimental for natural and anthropogenic systems. Temperatures in Central Asia have risen significantly within the last decades whereas mean precipitation remains almost unchanged. However, climatic trends can vary greatly between different subregions, across altitudinal levels, and within seasons. Investigating in the seasonally and spatially differentiated trend characteristics amplifies the knowledge of regional climate change and fosters the understanding of potential impacts on social, ecological, and natural systems. Considering the known limitations of available climate data in this region, this study combines both high-resolution and long-term records to achieve the best possible results. Temperature and precipitation data were analyzed using Climatic Research Unit (CRU) TS 4.01 and NASA’s Tropical Rainfall Measuring Mission (TRMM) 3B43. To study long-term trends and low-frequency variations, we performed a linear trend analysis and compiled anomaly time series and regional grid-based trend maps. The results show a strong increase in temperature, almost uniform across the topographically complex study site, with particular maxima in winter and spring. Precipitation depicts minor positive trends, except for spring when precipitation is decreasing. Expected differences in the development of temperature and precipitation between mountain areas and plains could not be detected. Full article

Climate change has been one of the most debated topics in the past few decades, but a number of challenges have hindered the development of robust policies and strategies by nations. At the same time, social media platforms—such as Instagram, Twitter, and Facebook—have given the opportunity for the general public to share opinions and engage with the issue of climate change like never before. This phenomenon is considered to be a new form of soft power which can provide input into the discussion and possibly affect the current international political mechanisms. The present paper aims to (1) define the forms and characteristics of social media as a soft power method, (2) analyze its influence on the awareness of societies, and (3) assess if increased public awareness could influence the official political and policy processes. In order to assess if social media has influence on people’s relative awareness, we have focused on analyzing the links between a few highly visible climate change related events and the trends in people’s searches on the Internet in connection to those events. The study finds that even though it is difficult to assess the effects of social media as a soft power tool with certainty, there are visible links between social media and changing public perceptions, with the possibility of public opinion influencing political decision-making. Full article

The assessment of the effect of the electricity price on energy production is important when studying the profitability and benefits of energy systems. The demand and price of electricity depends upon societal and economic development, but it is subject to a seasonal, weather-dependent variability, and possibly to long-term variation under climate change. Here, we developed a methodology to model the energy demand and electricity price in response to gross domestic product (GDP), temperatures, and random factors, usable for the purpose of cost/benefit analysis of production systems. The method was applied to the case study of the Italian electricity market, showing acceptable capacity of modelling recently observed price fluctuations. Then, we gathered climate projections until 2100 from three global climate models of the IPCC AR5, under RCP2.6, RCP4.5, and RCP8.5, and we produced future scenarios of price fluctuations for two reference decades, half-century 2040–2049, and end-of-century 2090–2099. Our scenarios displayed a potential for the reduction of energy demand in winter, and an increase in summer and spring, and for the similarly-changing electricity price throughout the 21st century. We discuss the application of our model with the specific aim of the projection of future hydropower production, as controlled by climate, hydrology, demand, and price constraints, with examples from recent studies. Our results provide a tool for modelling the behaviour of energy systems based upon knowledge of external factors, usable for further investigation of energy systems, such as hydropower, and others, taking into account the key variables affecting energy production and energy price. Full article

Anticipating seasonal climate anomalies is essential for defining short-term adaptation measures. To be actionable, many stakeholders require seasonal forecasts at the regional scale to be properly coupled to region-specific vulnerabilities. In this study, we present and preliminarily evaluate a regional-scale Seasonal Forecast System (SFS) over Central Italy. This system relies on a double dynamical downscaling performed through the Regional-scale Climate Model (RCM) RegCM4.1. A twelve-member ensemble of the NCEP-CFSv2 provides driving fields for the RegCM. In the first step, the RegCM dynamically downscales NCEP-CFSv2 predictions from a resolution of 100 to 60 km over Europe (RegCM-d1). This first downscaling drives a second downscaling over Central Italy at 12 km (RegCM-d2). To investigate the added value of the downscaled forecasts compared to the driving NCEP-CFSv2, we evaluate the driving CFS, and the two downscaled SFSs over the same (inner) domain. Evaluation involves winter temperatures and precipitations over a climatological period (1982–2003). Evaluation for mean bias, statistical distribution, inter-annual anomaly variability, and hit-rate of anomalous seasons are shown and discussed. Results highlight temperature physical values reproduction benefiting from the downscaling. Downscaled inter-annual variability and probabilistic metrics show improvement mainly at forecast lead-time 1. Downscaled precipitation shows an improved spatial distribution with an undegraded but not improved seasonal forecast quality. Full article

The population that lives in cities has surpassed the one that lives in the countryside. Cities are recognized as apriority source of pollution. The degradation of air quality and the phenomenon of Urban Heat Island (UHI) are some of the most well-known consequences of urban development. The adaptation of the cities is emerging as one of the greatest challenges that urban planners will face in this century. Urban Green Infrastructures (GIs) could help cities adapt to climate change, and the strategy of expansion of greening in urban planning could play an important role in enhancing the sustainability and resilience of cities and communities. Many studies have shown the benefits of GIs to climate change mitigation and adaptation in urban areas and their role as an important urban planning tool to satisfy environmental, social, and economic needs of urban areas. The objective of this article is to propose a methodological approach to evaluate the social perception of citizens regarding urban green areas. The proposed methodology, applied to the reality of the “urban green system” of Catania, is based on an integrated approach between participatory planning and the methods social multi criteria evaluation to guiding the city’s government to realize a new urban resilient development. Full article

Concerns exists regarding natural disasters, but what about the resulting power outages? This study investigates the characteristics of a digital visual narrative depicting the loss of electrical power after a natural disaster to identify how such situations are represented in images found on the internet. A qualitative approach with an exploratory scope was taken using digital methods. Six events in different places were selected, and 4691 images were analyzed using the Google Cloud Vision API. A constant comparison method was used to identify categories from these images. Then, a manual analysis was performed on a sample of the images of each event, and then categorized. It was found that more than half of the images refer to categories such as infrastructure, nature, and hazards, while the energy category was represented in 13.02% of the images. Most images were photographs; however, the non-photographic images found contained useful information regarding energy. Even when all events featuring power outages, few focused on the lack of energy and more on impacts to the infrastructure, despite energy being required for cities’ recovery. Full article

We examined the effects of elevated CO₂ and elevated O₃ concentrations on net CO₂ assimilation and growth of Fagus crenata in a screen-aided free-air concentration-enrichment (FACE) system. Seedlings were exposed to ambient air (control), elevated CO₂ (550 µmol mol⁻¹ CO₂, +CO₂), elevated O₃ (double the control, +O₃), and the combination of elevated CO₂ and O₃ (+CO₂+O₃) for two growing seasons. The responses in light-saturated net CO₂ assimilation rates per leaf area (A_growth-CO2) at each ambient CO₂ concentration to the elevated CO₂ and/or O₃ treatments varied widely with leaf age. In older leaves, A_growth-CO2 was lower in the presence of +O₃ than in untreated controls, but +CO₂+O₃ treatment had no effect on A_growth-CO2 compared with the +CO₂ treatment. Total plant biomass increased under conditions of elevated CO₂ and was largest in the +CO₂+O₃ treatment. Biomass allocation to roots decreased with elevated CO₂ and with elevated O₃. Elongation of second-flush shoots also increased in the presence of elevated CO₂ and was largest in the +CO₂+O₃ treatment. Collectively, these results suggest that conditions of elevated CO₂ and O₃ contribute to enhanced plant growth; reflecting changes in biomass allocation and mitigation of the negative impacts of O₃ on net CO₂ assimilation. Full article

A large population relies on water input to the Indus basin, yet basinwide precipitation amounts and trends are not well quantified. Gridded precipitation data sets covering different time periods and based on either station observations, satellite remote sensing, or reanalysis were compared with available station observations and analyzed for basinwide precipitation trends. Compared to observations, some data sets tended to greatly underestimate precipitation, while others overestimate it. Additionally, the discrepancies between data set and station precipitation showed significant time trends in many cases, suggesting that the precipitation trends of those data sets were not consistent with station data. Among the data sets considered, the station-based Global Precipitation Climatology Centre (GPCC) gridded data set showed good agreement with observations in terms of mean amount, trend, and spatial and temporal pattern. GPCC had average precipitation of about 500 mm per year over the basin and an increase in mean precipitation of about 15% between 1891 and 2016. For the more recent past, since 1958 or 1979, no significant precipitation trend was seen. Among the remote sensing based data sets, the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) compared best to station observations and, though available for a shorter time period than station-based data sets such as GPCC, may be especially valuable for parts of the basin without station data. The reanalyses tended to have substantial biases in precipitation mean amount or trend relative to the station data. This assessment of precipitation data set quality and precipitation trends over the Indus basin may be helpful for water planning and management. Full article