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Register a new userModeling of raining season onset and cessation of tropical rainfall for climate change adaptation in Agriculture
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This study investigates the trend in Rainfall Onset Dates (ROD), Rainfall Cessation Dates (RCD), Length of Growing Seasons (LGS) and Rainfall Amount at Onset of Rainfall (RAO) using linear regression, Mann-Kendall, Sen Slope and Hurst Exponent for four locations in tropical Nigeria and the development of a Fourier based model for ROD and RCD. Daily data was obtained from the Nigerian Meteorological Agency for thirty-four (34) years (1979 - 2013). ROD and RCD were computed using the method of cumulative percentage mean rainfall values. Maiduguri, Gusau and Ikom showed positive trends in ROD and RCD while Ibadan exhibited negative trends in the two parameters. Anti-persistence was observed in ROD, RCD and LGS for three locations (Maiduguri, Gusau and Ibadan). A Fourier based model with seven (7) coefficients was developed to model ROD and RCD for all the locations. The model developed performed very well in all locations with the best performance obtained in Gusau and Ibadan for ROD and RCD respectively. The effects of climate change on agricultural output for the four (4) locations under consideration were highlighted and adaption techniques suggested for mitigating the impact on agricultural output and livelihood of citizens in the areas.
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Currently, agriculture in Africa contributes only a tenth to global Green House Gas (GHG) emissions from agriculture. Despite its relatively low contribution to GHG, a conundrum of climate justice, adverse impacts of climate change disproportionately threaten Africas agriculture, the Continents main economic sector. Consequently, we seek to review the effects of climate change on Agriculture.
The Centre for Climate Change Research (CCCR;http://cccr.tropmet.res.in) at the Indian Institute of Tropical Meteorology (IITM; http://www.tropmet.res.in), Pune, launched in 2009 with the support of the Ministry of Earth Sciences (MoES), Government of India, focuses on the development of new climate modelling capabilities in India and South Asia to address issues concerning the science of climate change. CCCR-IITM has the mandate of developing an Earth System Model and to make the regional climate projections. An important achievement was made by developing an Earth System Model at IITM, which is an important step towards understanding global and regional climate response to long-term climate variability and climate change. CCCR-IITM has also generated an ensemble of high resolution dynamically downscaled future projections of regional climate over South Asia and Indian monsoon, which are found useful for impact assessment studies and for quantifying uncertainties in the regional projections. A brief overview of these core climate change modeling activities of CCCR-IITM was presented in an Interim Report on Climate Change over India (available at http://cccr.tropmet.res.in/home/reports.jsp)
Quantifying the impact of climate change on future air quality is a challenging subject in air quality studies. An ANN model is employed to simulate hourly O3 concentrations. The model is developed based on hourly monitored values of temperature, solar radiation, nitrogen monoxide, and nitrogen dioxide which are monitored during summers (June, July, and August) of 2009-2012 at urban air quality stations in Tehran, Iran. Climate projections by HadCM3 GCM over the study area, driven by IPCC SRES A1B, A2, and B1 emission scenarios, are downscaled by LARS-WG5 model over the periods of 2015-2039 and 2040-2064. The projections are calculated by assuming that current emissions conditions of O3 precursors remain constant in the future. The employed O3 metrics include the number of days exceeding one-hour (1-hr) (120 ppb) and eight-hour (8-hr) (75 ppb) O3 standards and the number of days exceeding 8-hr Air Quality Index (AQI). The projected increases in solar radiation and decreases in precipitation in future summers along with summertime daily maximum temperature rise of about 1.2 and 3 celsius in the first and second climate periods respectively are some indications of more favorable conditions for O3 formation over the study area in the future. Based on pollution conditions of the violation-free summer of 2012, the summertime exceedance days of 8-hr O3 standard are projected to increase in the future by about 4.2 days in the short term and about 12.3 days in the mid-term. Similarly, based on pollution conditions of the polluted summer of 2010 with 58 O3 exceedance days, this metric is projected to increase about 4.5 days in the short term and about 14.1 days in the mid-term. Moreover, the number of Unhealthy and Very Unhealthy days in 8-hr AQI is also projected to increase based on pollution conditions of both summers.
This paper examines how subsistence farmers respond to extreme heat. Using micro-data from Peruvian households, we find that high temperatures reduce agricultural productivity, increase area planted, and change crop mix. These findings are consistent with farmers using input adjustments as a short-term mechanism to attenuate the effect of extreme heat on output. This response seems to complement other coping strategies, such as selling livestock, but exacerbates the drop in yields, a standard measure of agricultural productivity. Using our estimates, we show that accounting for land adjustments is important to quantify damages associated with climate change.
Artificial neural-networks have the potential to emulate cloud processes with higher accuracy than the semi-empirical emulators currently used in climate models. However, neural-network models do not intrinsically conserve energy and mass, which is an obstacle to using them for long-term climate predictions. Here, we propose two methods to enforce linear conservation laws in neural-network emulators of physical models: Constraining (1) the loss function or (2) the architecture of the network itself. Applied to the emulation of explicitly-resolved cloud processes in a prototype multi-scale climate model, we show that architecture constraints can enforce conservation laws to satisfactory numerical precision, while all constraints help the neural-network better generalize to conditions outside of its training set, such as global warming.
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