Climate projections and the parallel energy availability in the case of Kombolcha town, South Wollo Ethiopia

Climate projection is important for the exploring of climate variability and its purpose in ecology. Unrelated integration between ecology and climate science has limited the understanding of the available climate data and their appropriate use. Better focus on the climate projections is essential to update decisions about the choice of a climate model, emissions scenarios, baseline period, and the spatial and temporal resolution of the data (Harris et al. 2014). Based on the study by Hayhoe et al. (2017), the greenhouse gas concentration causes an increase in temperature by 0.6 °C, and the world temperature projection will increase from 0.3 to 0.7 °C, for the coming decades, the magnitude of climate change depends on cumulative emissions of greenhouse gases aerosol, global mean atmospheric carbon dioxide concentration increased to 400 ppm which occurred about 3 million years ago, the observed increase carbon emission in the past two decades has been consistent with higher scenarios. In 2014 and 2015, emission growth rates slowed as economic growth has become less carbon-intensive. Studies showed that climate change's impact on wind energy, hydropower, and bioenergy at a global level has mixed (both negative and positive) in different regions (Yalew et al. 2020). Using renewable energy is a vigorous solution to alleviate the hazard of global warming and climate change-related occurrences (Beyaztas et al. 2019). Developing and using hybrid technics for climate variability and energy increased the accuracy of forecasts. Hybrid network techniques have better performance than a single network (Shamshirband et al. 2019). Energy plays a vibrant part in the current society which quickens fiscal development and has been considered a serious matter in the past decades (Samadianfard et al. 2019).

The extreme precipitation in Adama city increased over the year 1965–2016. Under future climate change scenarios, the extreme precipitation would increase up to 2080 and the changes will be higher during 2050–2080. This extreme precipitation causes frequent and severe floods during the coming 60 years due to the change in global climate (Bulti et al. 2021). According to Chaemiso et al. (2016), there is an overall increasing trend in future annual temperature and significant variation of monthly and seasonal precipitation from 1985 to 2005. The annual potential evapotranspiration is showing an increasing trend for future climate change scenarios. Temperature and radiation showed an increased pattern in representative concentration pathway (RCP) climate models and cause increased water demand in the awash river basin (Yadeta et al. 2020). The African continent is an exceptionally vulnerable region to climate change and weather variability especially regions in semi-arid areas of the continent which are dependent on seasonal rainfall. In recent periods, severe drought causes famine and death in the Horn of Africa and climate models have difficulties in simulating key elements of climate (Hernández-Díaz et al. 2013). During the last 50 years, the global climate showed warming due to the greenhouse gases amount in the earth's atmosphere which has a big role in the day-to-day activities. From time to time, increments of solar radiation have been recorded in recent times around the world (Gharbia et al. 2016). Based on the observed and projected data drawn from climate models on precipitation and temperature analysis, there is a significant increase in temperature and less precipitation change. Different models showed different results of climate variability results due to the data they used for future climate systems (Asitatikie & Gebeyehu 2021). An increase in temperature in the world is observed in the late twentieth century related to the frequency and magnitude of extreme events and greenhouse gas emissions due to human activities in different countries. By the end of the 20th century, the maximum and minimum temperatures increased and caused stress on water bodies due to high evapotranspiration (Hassan & Nile 2021). The maximum temperature recorded showed increasing over the minimum temperature. Climatic parameters change has a role impact on horticulture and agricultural production which have consequences for world food security in the future. At the end of the areas covered forests in low-risk zones increased from 1 to 4.43% and for high-risk areas, the forest cover decreased from 8.2 to 4.7%. As a result, high-risk areas are in danger of hazard pruning in the future and proper action would be assigned for those areas (Rahimi et al. 2018). Evapotranspiration is a vital role in determining water flux in agricultural activities followed by temperature, humidity, wind speed, and solar radiation (Tyagi et al. 2019). The minimum mean temperature increased faster than the mean maximum temperature between 1981 and 2010 which cause the increased warming during nighttime in Ghana (Bessah et al. 2018). In long-term climate analysis in the Geba catchment, Ethiopia showed increased climate variables except for minimum temperature (Asnake et al. 2021). In assimilated water management, combined climate variable scenarios should include various climate parameters to resolve climate-related problems in water resources (Hussen et al. 2018). For water resources affected by different climate variability, restoration should be done including environmental, social, agricultural, hydrological, soil, climate, and ecological parameters. The energy due to the particle is directly proportional to temperature motion (Manatsa et al. 2008). The amount of energy due to temperature change is given by (1), where E is energy, K is constant and T is temperature. Despite the fact that more papers looked at climate impacts on energy systems: until now, relatively few comprehensive papers have been published on the impacts of climate change on energy systems as a whole. Papers focused on climate change impacts on agriculture activities and water. The use of varied methods in the energy sector studies also limits the comparability of climate change effects across different studies. We briefly discuss these systematic shortcomings next and recommend possible steps forwards. Since Kombolcha town is found between hills and an industrial town in Ethiopia, more energy-related research works are expected. Therefore, different research works in different areas showed that climate change causes a change in water resources demand, production, food security, environmental features, and health. As a result, there is no research works about energy with climate change and projection conducted in the study area. Therefore, this paper is designed, to assess climate projection and the parallel energy availability in Kombolcha town, south Wollo, Ethiopia by using the Marksim climate generator model.

The Marksim weather generator model is an agricultural model developed for decision support system for agricultural technology transfer (DSSAT). It has 20 ensemble models and 4 RCPs; RCP 2.6 for projection of carbon dioxide, RCP 4.5 for non-renewable energy sources, RCP 6 for high irradiative force stabilization and RCP 8.5 for projected temperature and sea level. Marksim weather generator is used for temperature projection with hybrid models and revealed more accurate outputs Ayugi et al. (2021). Marksim weather generator is selected because it gives point data for the study area and is easy to feed and draw the model output. Since temperature projection and the parallel energy availability are related to RCP 8.5 scenario, it is selected with all ensemble models.

Observational data (minimum and maximum temperature) for the time 2010–2020 are collected from Ethiopian Meteorology Institute and the model output data (minimum and maximum temperature) for the same period (2010–2020) are generated from the model output. Statistical analysis is commonly used for the depiction of water-related monthly variables and events (Chebana & Ouarda 2021). To study the characteristics of climate extremes (flood and drought) frequency analysis should be applied. Popular Hydrological phenomena are described by correlation characteristics (Burgan et al. 2017). For the time 2010–2020, historical data are compared and corrected. The correction factor for temperature is calculated from the observation data and model output CF = T_O − T_M, where CF is the correction factor, T_O is the monthly mean observation temperature, and T_M is the monthly mean model output. The bias is corrected based on model output and baseline data. Consequently on the corrected temperature data future projections are predicted. Projected model output data for the time 2050–2095 is forecasted and corrected by the correction factor. Forecasting is planned to be decadal and 2020 is selected as a recent year for the first half-century of 2100. The next 4.5 decades 2050–2095 are considered. With the projected temperature the energy availability is estimated.

Data Flow chart

Based on Table 1, the decadal climate variability of the monthly mean minimum temperature shows a maximum rate of change than the monthly mean maximum temperature and precipitation. For the last 4.5 (2050–2095) decades, the rate of change of mean monthly maximum temperature, mean monthly minimum temperature and, mean monthly rainfall is 8.4, 14.85, and 6.6%, respectively. Therefore, the mean monthly minimum temperature showed a faster rate of change and precipitation showed a lower rate of change. Since the minimum temperature is recorded in the morning and nighttime, its increase will affect the sea breath during nighttime. Subsequently, kinetic energy is directly proportional to the amount of temperature, the increase in temperature results in an energy increment.

As compared to the past, present, and, future temperature and precipitation in central north Ethiopia, Kombolcha town by using the general circulation model, the measured parameters showed an increased pattern with a faster change of rate in monthly mean minimum temperature. During the baseline period (2010–2020) the historical data (model output and observation) are tested and showed good linkage. Based on the correction factor, the future projection (2050–2095) was corrected. As a result, the minimum and maximum temperature increase over time and the parallel energy availability forecasted showed an amplified pattern. Therefore, projected temperature enables future energy estimation.

This study is conducted based on the climate data from the climate model and meanwhile, Kombolcha is an industrial town in the country, for future studies industries' effects should be considered to see their effects on climate change in Kombolcha. Grounded on the result to overcome temperature-related effects on the area appropriate actions should be done to reduce climate change-related effects on societies. On the other hand, more energy availability is estimated over the study area due to temperature projection and more technological devices will be used to convert the energy into useful forms for the society. Since Kombolcha is a semi-desert area, industry-related climate change and variability are not considered in this study and the society is in lower life standard, more research and project works related to renewable energy and industry-related climate impact should be conducted to use wind, hydropower and solar radiation energies wisely and alleviate climate-related hazards in the area. Originated on this, wind energy projects should be proposed around the town (free hills) and technologies should be practised in the town for solar radiation.

The authors thank Wollo University for research fund, Ethiopia Meteorology Institute for station data and Marksim General Circulation model for data availability.

The authors contribute in material collection data gathering, data organizing, paper reviewing, manuscript preparation, editing, data analysis, and discussion.

This research work is funded by Wollo University.

All relevant data are available from an online repository or repositories: (https://www.bing.com/search?pglt=41&q=marksim+weather+generato).

The authors declare there is no conflict.