Climate, Volume 4, Issue 2 (June 2016) – 18 articles

In recent years, biofuels have emerged as a suitable alternative to hydrocarbon fuel due to their foreseen potential of being a future energy resource. Biofuel development initiatives have been successfully implemented in countries like Brazil, United States of America, European Union, Canada, Australia, and Japan. However, such programmes have been stagnant in Africa due to various constraints, such as financial barriers, technical expertise, land availability, and government policies. Nonetheless, some countries within the continent have realized the potential of biofuels and have started to introduce similar programmes and initiatives for their development. These include the bioethanol production initiatives and the plantation of jatropha oil seeds in most Sub-Saharan African countries for biodiesel production. Therefore, this paper examines the biofuel development initiatives that have been implemented in several countries across Sub-Saharan Africa over the past few years. It also discusses the opportunities and challenges of having biofuel industries in the continent. Finally, it proposes some recommendations that could be applied to accelerate their development in these Sub-Saharan African countries. Full article

This study aims to calibrate and validate the generic crop model (CROPGRO-Soybean) and estimate the soybean yield, considering simulations with different sowing times for the current period (1990–2013) and future climate scenario (2014–2030). The database used came from observed data, nine climate models of CORDEX (Coordinated Regional climate Downscaling Experiment)-Africa framework and MERRA (Modern Era Retrospective-Analysis for Research and Applications) reanalysis. The calibration and validation data for the model were acquired in field experiments, carried out in the 2009/2010 and 2010/2011 growing seasons in the experimental area of the International Institute of Tropical Agriculture (IITA) in Angónia, Mozambique. The yield of two soybean cultivars: Tgx 1740-2F and Tgx 1908-8F was evaluated in the experiments and modeled for two distinct CO₂ concentrations. Our model simulation results indicate that the fertilization effect leads to yield gains for both cultivars, ranging from 11.4% (Tgx 1908-8F) to 15% (Tgx 1740-2Fm) when compared to the performance of those cultivars under current CO₂ atmospheric concentration. Moreover, our results show that MERRA, the RegCM4 (Regional Climatic Model version 4) and CNRM-CM5 (Centre National de Recherches Météorologiques – Climatic Model version 5) models provided more accurate estimates of yield, while others models underestimate yield as compared to observations, a fact that was demonstrated to be related to the model’s capability of reproducing the precipitation and the surface radiation amount. Full article

Recent evidence suggests that urban forms and materials can help to mediate temporal variation of microclimates and that landscape modifications can potentially reduce temperatures and increase accessibility to outdoor environments. To understand the relationship between urban form and temperature moderation, we examined the spatial and temporal variation of air temperature throughout one desert city—Doha, Qatar—by conducting vehicle traverses using highly resolved temperature and GPS data logs to determine spatial differences in summertime air temperatures. To help explain near-surface air temperatures using land cover variables, we employed three statistical approaches: Ordinary Least Squares (OLS), Regression Tree Analysis (RTA), and Random Forest (RF). We validated the predictions of the statistical models by computing the Root Mean Square Error (RMSE) and discovered that temporal variations in urban heat are mediated by different factors throughout the day. The average RMSE for OLS, RTA and RF is 1.25, 0.96, and 0.65 (in Celsius), respectively, suggesting that the RF is the best model for predicting near-surface air temperatures at this study site. We conclude by recommending the features of the landscape that have the greatest potential for reducing extreme heat in arid climates. Full article

Tropical Cyclone (TC) systems affect global ocean heat transport due to mixing of the upper ocean and impact climate dynamics. A higher Sea Surface Temperature (SST), other influencing factors remaining supportive, fuels TC genesis and intensification. The atmospheric thermodynamic profile, especially the sea-air temperature contrast (SAT), also contributes due to heat transfer and affects TC’s maximum surface wind speed (V_max) explained by enthalpy exchange processes. Studies have shown that SST can approximately be used as a proxy for SAT. As a part of an ongoing effort in this work, we simplistically explored the connection between SST and V_max from a climatological perspective. Subsequently, estimated V_max is applied to compute Power Dissipation Index (an upper limit on TC’s destructive potential). The model is developed using long-term observational SST reconstructions employed on three independent SST datasets and validated against an established model. This simple approach excluded physical parameters, such as mixing ratio and atmospheric profile, however, renders it generally suitable to compute potential intensity associated with TCs spatially and weakly temporally and performs well for stronger storms. A futuristic prediction by the HadCM3 climate model under doubled CO₂ indicates stronger storm surface wind speeds and rising SST, especially in the Northern Hemisphere. Full article

In this study, the hourly air temperature differences between City hall (urban) and Okoafo (rural) in Lagos, Nigeria, were calculated using one year of meteorological observations, from June 2014 to May 2015. The two sites considered for this work were carefully selected to represent their climate zones. The city core, City hall, is within the Local Climate Zone (LCZ 2) (Compact midrise) while the rural location, Okoafo, falls within LCZ B (Scattered Trees) in the south-western part on the outskirt of the city. This study is one of very few to investigate urban temperature conditions in Lagos, the largest city in Africa and one of the most rapidly urbanizing megacities in the world; findings show that maximum nocturnal UHI magnitudes in Lagos can exceed 7 °C during the dry season, and during the rainy season, wet soils in the rural environment supersede regional wind speed as the dominant control over UHI magnitude. Full article

The precise role of air pollution on the climate and local weather has been an issue for quite a long time. Among the diverse issues, the effects of air pollution on lightning are of recent interest. Exploration over several years (2004 to 2011) has been made over Gangetic West Bengal of India using lightning flash data from TRMM-LIS (Tropical Rainfall Measuring Mission-Lightning Imaging Sensor), atmospheric pollutants, and rainfall data during pre-monsoon (April and May) and monsoon (June, July, August and September) seasons. Near-surface pollutants such as PM₁₀ and SO₂ have a good positive association with aerosol optical depth (AOD) for both the pre-monsoon and monsoon months. High atmospheric aerosol loading correlates well with pre-monsoon and monsoon lightning flashes. However, rainfall has a dissimilar effect on lightning flashes. Flash count is positively associated with pre-monsoon rainfall (r = 0.64), but the reverse relation (r = −0.4) is observed for monsoon rainfall. Apart from meteorological factors, wet deposition of atmospheric pollutant may be considered a crucial factor for decreased lightning flash count in monsoon. The variation in the monthly average tropospheric column amount of NO₂, from the Tropospheric Emission Monitoring Internet Service (TEMIS), is synchronic with average lightning flash rate. It has a good linear association with flash count for both pre-monsoon and monsoon seasons. The effect of lightning on tropospheric NO₂ production is evident from the monthly average variation in NO₂ on lightning and non-lightning days. Full article

The impact of global climate change on Lebanon’s society, environment, and economy is expected to be tremendous. Indices have been developed to help in the identification and monitoring of drought and characterization of its severity. In this context, this work aimed at assessing the temporal variability of the Standardized Precipitation Index in Lebanon for improved understanding of drought occurrence. This is expected to help in mitigation and response actions to future drought circumstances across the country. The methodology of work involved the calculation of the Standardized Precipitation Index over different time series from four regions across the country using both the Variability Analysis of Surface Climate Observations (VASClimO) gridded rainfall dataset for the period 1951–2000 and the European rainfall dataset E-OBS for the period 1950–2014. In general, higher precipitation values were recorded by the VASClimO dataset than those coming from the E-OBS dataset. Intra-annual precipitation changes showed increasing precipitation starting in September-October and decreasing precipitation starting in February. The VASClimO dataset showed a 50% increase in the frequency of severe drought conditions, while the E-OBS dataset indicated a 60% increase in the frequency of moderate drought conditions. In addition, it was observed that the winter of 2014, characterized by extreme drought conditions, was the driest in the past 56 years. Although specific years were commonly characterized by severe to extreme drought conditions with the use of both datasets, considerable differences between the two datasets were observed with respect to the identification of the degree of wet and dry conditions for some other years. Overall, trend lines for the Standardized Precipitation Index values, as derived from VASClimO and E-OBS datasets, commonly point to a relatively slight increase in drought conditions mainly in the winter-spring season; however, the situation on the ground could vary greatly given that many other environmental factors (e.g., changes in land cover) may also play an important role in affecting drought conditions. Full article

Climate change adds an additional layer of complexity that needs to be considered in business strategy. For firms in the food industry, many of the important climate impacts are not directly related to food processing so a value chain approach to adaptation is recommended. However, there is a general lack of operational tools to support this. In this study, carbon and water footprints were conducted at a low-precision screening level in three case studies in Australia: Smith’s potato chips, OneHarvest Calypso™ mango and selected Treasury Wine Estates products. The approach was cost-effective when compared to high-definition studies intended to support environmental labels and declarations, yet provided useful identification of physical, financial, regulatory and reputational hotspots related to climate change. A combination of diagnostic footprinting, downscaled climate projection and semi-quantitative value chain analysis is proposed as a practical and relevant toolkit to inform climate adaptation strategies. Full article

Meteorological extreme events have great potential for damaging railway infrastructure and posing risks to the safety of train passengers. In the future, climate change will presumably have serious implications on meteorological hazards in the Alpine region. Hence, attaining insights on future frequencies of meteorological extremes with relevance for the railway operation in Austria is required in the context of a comprehensive and sustainable natural hazard management plan of the railway operator. In this study, possible impacts of climate change on the frequencies of so-called critical meteorological conditions (CMCs) between the periods 1961–1990 and 2011–2040 are analyzed. Thresholds for such CMCs have been defined by the railway operator and used in its weather monitoring and early warning system. First, the seasonal climate change signals for air temperature and precipitation in Austria are described on the basis of an ensemble of high-resolution Regional Climate Model (RCM) simulations for Europe. Subsequently, the RCM-ensemble was used to investigate changes in the frequency of CMCs. Finally, the sensitivity of results is analyzed with varying threshold values for the CMCs. Results give robust indications for an all-season air temperature rise, but show no clear tendency in average precipitation. The frequency analyses reveal an increase in intense rainfall events and heat waves, whereas heavy snowfall and cold days are likely to decrease. Furthermore, results indicate that frequencies of CMCs are rather sensitive to changes of thresholds. It thus emphasizes the importance to carefully define, validate, and—if needed—to adapt the thresholds that are used in the weather monitoring and warning system of the railway operator. For this, continuous and standardized documentation of damaging events and near-misses is a pre-requisite. Full article

Climate influences geographic differences of vegetation phenology through both contemporary and historical variability. The latter effect is embodied in vegetation heterogeneity underlain by spatially varied genotype and species compositions tied to climatic adaptation. Such long-term climatic effects are difficult to map and therefore often neglected in evaluating spatially explicit phenological responses to climate change. In this study we demonstrate a way to indirectly infer the portion of land surface phenology variation that is potentially contributed by underlying genotypic differences across space. The method undertaken normalized remotely sensed vegetation start-of-season (or greenup onset) with a cloned plants-based phenological model. As the geography of phenological model prediction (first leaf) represents the instantaneous effect of contemporary climate, the normalized land surface phenology potentially reveals vegetation heterogeneity that is related to climatic adaptation. The study was done at the continental scale for the conterminous U.S., with a focus on the eastern humid temperate domain. Our findings suggest that, in an analogous scenario, if a uniform contemporary climate existed everywhere, spring vegetation greenup would occur earlier in the north than in the south. This is in accordance with known species-level clinal variations—for many temperate plant species, populations adapted to colder climates require less thermal forcing to initiate growth than those in warmer climates. This study, for the first time, shows that such geographic adaption relationships are supported at the ecosystem level. Mapping large-scale vegetation climate adaptation patterns contributes to our ability to better track geographically varied phenological responses to climate change. Full article

Climate change and Land-Use Land-Cover change (LULC) has significantly displaced the local rainfall patterns and weather conditions in Pakistan. This has resulted in a different climate-related problem, particularly vector borne diseases. Dengue transmission has emerged as one of the most devastating and life threatening disease in Pakistan, causing hundreds of deaths since its first outbreak. This study is designed to understand and analyze the disease patterns across two distinct study regions, using Geographic Information System (GIS), Satellite Remote Sensing (RS) along with climate and socio-economic and demographics datasets. The datasets have been analyzed by using GIS statistical analysis techniques. As a result, maps, tables and graphs have been plotted to estimate the most significant parameters. These parameters have been assigned a contribution weight value to prepare a model and Threat Index Map (TIM) for the study areas. Finally, the model has been tested and verified against existing datasets for both study areas. This model can be used as a disease Early Warning System (EWS). Full article

The study examined the spatiotemporal distribution of drought in the Maasai rangelands of Kenya. The implications of this distribution, in concert with the documented existing and/or projected social and biophysical factors, on critical rangeland resources in Maasai-pastoralism are discussed using an integrated approach. Participatory interviews with the Maasai, retrieval from archives, and acquisition from instrument measurements provided data for the study. Empirical evidence of the current study reveals that drought occurrences in this rangeland have been recurrent, widespread, cyclic, sometimes temporally clustered, and have manifested with varying intensities across spatial, temporal, and, occasionally, social scales; and they have more intensity in lower than higher agroecological areas. An estimated 86% of drought occurrences in this rangeland, over the last three decades alone, were of major drought category. The 2000s, with four major drought events including two extreme droughts, are an important drought period. A strong consensus exists among the Maasai regarding observed drought events. In Maasai-pastoralism, the phenomenon called drought, pastoralist drought, is simultaneously multivariate and multiscalar: its perception comprises the simultaneous manifestation of cross-scale meteorological, socioeconomic, and environmental factors and processes, and their various combinations. The inherent simultaneous multivariate and scalar nature of the pastoralist drought distinguishes it from the conventional drought types, particularly the meteorological drought that predominantly guides drought and resource management in the rangelands of Kenya. In Maasai-pastoralism, the scarcely used (33%) meteorological drought is construed as rainfall delay/failure across spatial and/or temporal scale, and never its reduced amount. Collectively, the current findings reveal that knowledge about drought affects the way the manifestation of this climatic hazard is perceived, communicated, and characterized; hence, ceteris paribus, alongside its spatiotemporal distribution, shapes the nature of the adaptive capacity of and resource management in Maasai-pastoralism. Studies that anticipate enhancing the drought-adaptive capacity of the Maasai should account for cross-scale social and biophysical factors, their processes, and interactions; they must engage the affected inhabitants, and utilize and integrate multiple data sources and approaches. These necessities become more crucial for informing adaptation under the present spatiotemporal distribution of drought as well as in relation to the projected increase in occurrence and intensity of this climatic hazard as the climate continues to change, and as pressures from socioeconomic globalization persistently proliferate into the Maasai’s social and biophysical landscapes. Full article

Bangladesh has been experiencing increased temperature and change in precipitation regime, which might adversely affect the important ecosystems in the country differentially. The river flows and groundwater recharge over space and time are determined by changes in temperature, evaporation and crucially precipitation. These again have a spatio-temporal dimension. This geospatial modeling research aimed at investigating spatial patterns and changing trends of temperature and rainfall within the geographical boundary of Bangladesh. This would facilitate better understanding the change pattern and their probable impacts on the ecosystem. The southeastern region, which is one of the most important forest ecosystem zones in the country, is experiencing early onset and withdrawal of rain but increasing trends in total rainfall except in the Monsoon season. This means that the region is experiencing a lower number of rainy days. However, total rainfall has not changed significantly. The differential between maximum and minimum showed an increasing trend. This changing pattern in average max and min temperature along with precipitation might cause a situation in which the species that are growing now may shift to suitable habitats elsewhere in the future. Consequently, the biodiversity, watersheds and fisheries, productivity of land, agriculture and food security in the region will be affected by these observed changes in climate. Full article

Farmers in China’s tea-growing regions report that monsoon dynamics and other weather factors are changing and that this is affecting tea harvest decisions. To assess the effect of climate change on tea production in China, this study uses historical weather and production data from 1980 to 2011 to construct a yield response model that estimates the partial effect of weather factors on tea yields in China, with a specific focus on East Asian Monsoon dynamics. Tea (Camellia sinensis (L.) Kunze) has not been studied using these methods even though it is an important crop for human nutrition and the economic well-being of rural communities in many countries. Previous studies have approximated the monsoon period using historical average onset and retreat dates, which we believe limits our understanding of how changing monsoon patterns affect crop productivity. In our analysis, we instead estimate the monsoon season across China’s tea growing regions empirically by identifying the unknown breakpoints in the year-by-province cumulative precipitation. We find that a 1% increase in the monsoon retreat date is associated with 0.481%–0.535% reduction in tea yield. In the previous year, we also find that a 1% increase in the date of the monsoon retreat is associated with a 0.604% decrease in tea yields. For precipitation, we find that a 1% increase in average daily precipitation occurring during the monsoon period is associated with a 0.184%–0.262% reduction in tea yields. In addition, our models show that 1% increase in the average daily monsoon precipitation from the previous growing season is associated with 0.258%–0.327% decline in yields. We also find that a 1% decrease in solar radiation in the previous growing season is associated with 0.554%-0.864% decrease in tea yields. These findings suggest the need for adaptive management and harvesting strategies given climate change projections and the known negative association between excess rainfall and delayed monsoon retreat on tea quality and yield. Full article

Ensembles of general circulation model (GCM) integrations yield predictions for meteorological conditions in future months. Such predictions have implicit uncertainty resulting from model structure, parameter uncertainty, and fundamental randomness in the physical system. In this work, we build probabilistic models for long-term forecasts that include the GCM ensemble values as inputs but incorporate statistical correction of GCM biases and different treatments of uncertainty. Specifically, we present, and evaluate against observations, several versions of a probabilistic forecast for gridded air temperature 1 month ahead based on ensemble members of the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2). We compare the forecast performance against a baseline climatology based probabilistic forecast, using average information gain as a skill metric. We find that the error in the CFSv2 output is better represented by the climatological variance than by the distribution of ensemble members because the GCM ensemble sometimes suffers from unrealistically little dispersion. Lack of ensemble spread leads a probabilistic forecast whose variance is based on the ensemble dispersion alone to underperform relative to a baseline probabilistic forecast based only on climatology, even when the ensemble mean is corrected for bias. We also show that a combined regression based model that includes climatology, temperature from recent months, trend, and the GCM ensemble mean yields a probabilistic forecast that outperforms approaches using only past observations or GCM outputs. Improvements in predictive skill from the combined probabilistic forecast vary spatially, with larger gains seen in traditionally hard to predict regions such as the Arctic. Full article

It is widely accepted by the scientific community that the world has begun to warm as a result of human influence. The accumulation of greenhouse gases in the atmosphere, arising primarily from the combustion of carbon fossil fuels and agricultural activities, generates changes in the climate. Indeed various studies have assessed the potential impacts of climate change on human health (both negative and positive). The increased frequency and intensity of heat waves, the reduction in cold-related deaths, the increased floods and droughts, and the changes in the distribution of vector-borne diseases are among the most frequently studied effects. On the other hand, climate change differs from many other environmental health problems because of its gradual onset, widespread rather than localized effect, and the fact that the most important effects will probably be indirect. Some recent and important publications show that only the collaboration between the meteorological and the public health communities can help us to thoroughly study the link between climate and health, thus improving our ability to adapt to these future changes. The aim of this editorial is to give different perspectives on a widely discussed topic, which is still too complicated to be addressed to a satisfactory extent. Moreover, it is necessary to underline the importance of using new biometeorological indices (i.e. thermal indexes, etc.) for future projections, in order to reduce the impacts of negative outcomes, protecting the population through adaptation measures and public awareness. Full article

Global climate change has local implications. Focusing on datasets from the topographically-challenging Karnali river basin in Western Nepal, this research provides an overview of hydro-climatic parameters that have been observed during 1981–2012. The spatial and temporal variability of temperature and precipitation were analyzed in the basin considering the seven available climate stations and 20 precipitation stations distributed in the basin. The non-parametric Mann–Kendall test and Sen’s method were used to study the trends in climate data. Results show that the average precipitation in the basin is heterogeneous, and more of the stations trend are decreasing. The precipitation shows decreasing trend by 4.91 mm/year, i.e., around 10% on average. Though the increasing trends were observed in both minimum and maximum temperature, maximum temperature trend is higher than the minimum temperature and the maximum temperature trend during the pre-monsoon season is significantly higher (0.08 °C/year). River discharge and precipitation observations were analyzed to understand the rainfall-runoff relationship. The peak discharge (August) is found to be a month late than the peak precipitation (July) over the basin. Although the annual precipitation in most of the stations shows a decreasing trend, there is constant river discharge during the period 1981–2010. Full article

The aim of the presented study is to assess the impacts of climate change on hydropower production of the Toce Alpine river basin in Italy. For the meteorological forcing of future scenarios, time series were generated by applying a quantile-based error-correction approach to downscale simulations from two regional climate models to point scale. Beside a general temperature increase, climate models simulate an increase of mean annual precipitation distributed over spring, autumn and winter, and a significant decrease in summer. A model of the hydropower system was driven by discharge time series for future scenarios, simulated with a spatially distributed hydrological model, with the simulation goal of defining the reservoirs management rule that maximizes the economic value of the hydropower production. The assessment of hydropower production for future climate till 2050 respect to current climate (2001–2010) showed an increase of production in autumn, winter and spring, and a reduction in June and July. Significant change in the reservoir management policy is expected due to anticipation of the date when the maximum volume of stored water has to be reached and an increase of the reservoir drawdown during August and September to prepare storage capacity for autumn inflows. Full article