Theses supervised by Prof. Dr. Mehmet Levent Kurnaz
11 theses · Boğaziçi University
Forecasting the energy consumption of sectors under different NGFS scenarios and analyzing the effects on Türkiye's GDP
Physical and transition risks of climate change will have an impact on countries' economies and sectors. With proper planning and taking the necessary steps, these impacts can be mitigated. Therefore, academic studies and analyzes in this field are important. In this study, it is examined how T¨urkiye's GDP will be affected by physical risks and transition risks under different climate scenarios. In addition, within the scope of these scenarios, it has been forecasted how the energy consumption of the sectors in T¨urkiye will be in the future. In cases where current policies are continued or the necessary measures are not taken at the right time, the impact of climate change on T¨urkiye's GDP will be huge. At this point, it is of great importance to limit GHG emissions and not to increase the global average temperatures compared to the preindustrial revolution. Because the increase in the number of extreme weather events or the occurrence of irreversible physical events such as sea level rise can seriously affect the economies. The steps in transitioning to a low carbon economy and combating the effects of climate change will also be a huge burden for the economies. In this context, the use of renewable energy sources should be increased in Turkey and practices that can reduce emissions such as carbon tax should be introduced. In energy production, fossil fuel consumption should be reduced and alternative energy types should be used. It can be said that the Oil and Gas, Transportation and Automotive sectors will be more affected by this situation. In these sector renewable energy types may need to be used more in energy production.
Climate change projections of Cyprus using regcm4.4
The scientific motivation of this thesis is a substantial presence of climate change which is one of the most significant issues in the world. There have been happening many natural disasters because of climate change. In particularly, coastal regions and islands, such as Cyprus, are more vulnerable to any possible hazards. Humankind start to contribute the climate change, especially, after indus- trial revolution. Burning fossil fuels causes the increase of CO2 emission to the atmosphere. In fact, at the beginning of the 18th century, before the Industrial Revolution, CO2 concentration in the atmosphere was 280 ppm; however, today, this value is 410 ppm. The unprecedented CO2 increase in the atmosphere since Industrial Revolution gives rise to global climate change. The Mediterranean region is predicted to be affected by climate changes in terms of air temperature rise. In this study, air temperature (◦C) and precipitation (mm/day) estimations of Cyprus were investigated with the regional climate model. The climatology model is run for the three periods of 2011–2040, 2041–2070, and 2071–2100, with respect to the control period of 1971–2005 for Cyprus domain via regional climate model simulations. Regional Climate Model (RegCM4.4) of ICTP (International Centre for Theoretical Physics) was run by using two different global climate models. MPI- ESM-MR global climate model of the Max Planck Institute for Meteorology and HadGEM2 of the Met Office Hadley Center were dynamically downscaled to 10 km resolution by using double nesting. RCP4.5 and RCP8.5, the emission scenarios, of the IPCC (Intergovernmental Panel of Climate Change) were used for projections.
Changes in the extreme climatological events in the MENA region
Extreme weather events have been receiving increased attention because they provide striking examples of the changing climate. The last four years (2015-2018) have been the warmest years recorded. Temperature and precipitation records are being broken every year while the intensity, duration, and frequency of heatwaves, floods, and droughts are increasing. Decision-makers require every bit of information to be able to take the correct course of action. As a result, efforts of gathering high quality data for useful and accurate analysis and predictions have increased in the scientific community. We aim to document the frequency increase in extreme precipitation in the Middle East and North Africa and Turkey by fitting the gridded 3 hourly precipitation data generated by MPI and HadGEM general circulation models and ICTP's RegCM 4.4 to the exponential function, thus extracting the return period and intensity of extreme precipitation events. Results show a decrease in the return periods of 100 year events in 2010-2040, 2040-2070, and 2070-2100 with respect to 1970-2000.
Evaluation of different downscaling approaches for very-high-resolution climate data
Climate change leads to widespread changes in atmospheric and oceanic conditions, increasing the frequency of climate anomalies and negatively impacting ecosystems and human communities. It is crucial to understand climate change and make accurate predictions about it. Climate change studies focus on tools like General Circulation Models (GCMs); however, GCMs cannot accurately represent local climates, leading to uncertainties due to their coarse resolution. Statistical and dynamical downscaling techniques improve local climate projection accuracy. This study compared statistical and dynamical downscaling techniques for evaluating Turkey's climate change projections, using the MPI-ESM-MR as the main GCM, RegCM4.7.0 regional climate model for dynamical downscaling and the spatial delta method for statistical downscaling. 17 datasets were analyzed to investigate spatio-temporal correlations at resolutions of 1km, 5km, 10km, and 20km. Evaluated spatial correlation of precipitation and temperature showed low to moderate correlation coefficients with negative correlations and near-zero values for precipitation but higher correlation results for temperature. 10 and 20km resolution downscaling data showed more favorable results. The temporal correlation of precipitation showed superior consistency with reduced standard deviations and improved correlation coefficients. The study highlighted the temporal correlation of temperature, exhibiting exceptional precision due to its nature and alignment with annual seasonal cycles. This study's findings will significantly enhance understanding of the optimal methodology for downscaling climate change projections and the impacts of climate change on local communities.
Life cycle analysis of different cuisines
The extensive global food system is responsible for approximately 30% of greenhouse gas emissions. While the steps of the global food system such as production, packaging, transportation, distribution, storage, and disposal are followed, environmental effects remain intangible. With the life cycle assessment (LCA), environmental impacts are seen in concrete form at every stage of the system. In this study, the environmental impacts of different cuisines were investigated through life cycle assessment. Three menus have been created, consisting of Turkish, Far East, and Mediterranean cuisines, which are known and have a wide variety of food. Each menu has been chosen in accordance with the culture of the cuisine it has. The menus consist of soup, main course, side course and dessert. As a result of the life cycle assessment made on the menus selected for 3 cuisines, it has been determined that the environmental impact of the Mediterranean cuisine is quite low. The reason why the environmental impact is very low compared to the Turkish and Far Eastern cuisines, mainly agricultural foods are included in the Mediterranean cuisine and animal- based meals are not preferred much. On a food basis, the environmental impact of animal- based foods is greater than that of plant foods. As a result of the study, Turkish cuisine, in which animal-based meals are predominant, is the cuisine with the most environmental impact.
Gridded distribution of daily non-uniform meteorological station data of Turkey
The climate modeling is a complex area to work on. It is not easy to represent the atmospheric events in a model, because there are a lot of drivers and variables which affects the local and global climate. Even if, somehow, some meteorological phenomenon can be fully represented mathematically, it is even harder to do the calculation through a computer or a super computer with the current technological developments. So, some assumptions, optimizations and simplifications must be done to work on climate. This study will examine the climate measurement station data distributed in Turkey. The randomly distributed real station data all around Turkey gathered from the Turkish State Meteorological Service database. Our goal is to obtain uniformly distributed grid data for temperature and precipitation. We have used interpolation, one of the most ancient and common computational method with a scale that allows us to create a high-resolution temperature and precipitation map of Turkey. With the help of MATLAB build-in tools and functions, the scattered station data turned into a uniformly gridded data. The station data was very hard to work on, since the data begins from 1960. Meanwhile, the meteorology stations moved, closed, opened which caused a lot of incomplete and disconnected data. So, some normalization must be done, and the data become suitable for interpolation. To do the interpolation calculation, we used the MATLAB build-in function, scatteredInterpolant. It allowed us to calculate the scattered data into a gridded data. Once the interpolation completed, another MATLAB toolbox used to project the results on a regional map. The mapping toolbox is very powerful in printing the results on a map, in this case on Turkey regional map. Finally, we have managed to obtain Turkey temperature and precipitation station data map between 1960-2016 in daily basis.
Computational study on allostery in bacterial ribosome
Molecular machines in a cell have signal processing to perform their function using specific sites such as active or allosteric sites. Ligand binding to active sites or signal transferring from allosteric sites affect their function and dynamics. In this thesis, firstly crystal structure of bacterial ribosome (4kdk-4kdj) and its conformers which are generated by ClustENM are investigated to determine allosteric communication pathways. Targets on ribosome are determined as the Decoding Center (DC) – the Sarcin Ricin Loop (SRL), DC - the Peptidyl Transferase Center (PTC) and the PTC – Tunnel. On the allosteric pathways between DC and SRL, EF-G stands out with critical sites on its domains IV which has a significant function in blocking back translocation of tRNA. Some significant nucleotide and aminoacid like A1493 and Met580 appear on pathways between DC-SRL, which help EF-G hydrolysis. On the DC-PTC pathways drug binding site is observed. On the PTC-ribosomal tunnel pathway has a highly conserved non-Watson-Crick base pair and binding pocket for antibiotic is found. Secondly, the bacterial ribosome of E.coli, 4v5h, is analyzed to investigate allostery between Secretion Monitor (SecM)–PTC and TF-Ribosomal Tunnel. A76 of tRNA and nascent chain which consists of alanines seems significant between U2585 and A2451 to provide allosteric communication between SecM and PTC. On the shortest pathways on the TF-Ribosomal Tunnel, GLY91 from L22 has a high frequency of occurrence on all pathways. GLY91 is significant for elongation arrest and for the turn of the β-hairpin of L22 which is important since antibiotic resistance appears when a mutation on the β-hairpin occurs.
Statistical return periods of extreme weather events
The Middle East & North Africa (MENA) region is one of the most populated areas on Earth in terms of city population. There are 643 cities in MENA region with population of urban agglomerations with 300,000 or more in 2018 according to United Nations. In this thesis, return periods of extreme temperature events are calculated using the probability density functions. For this purpose, Global Climate Model (GCM) outputs of Max Plank Institute Earth System Model Mixed Resolution (MPI-ESM-MR) and Hadley Global Environment Model 2 - Earth System (HadGEM2-ES) are dynamically downscaled to 50 km for the MENA region by using the Regional Climate Model v4.4 (RegCM4.4) for 2 different Representative Concentration Pathways scenarios, namely RCP 4.5 and RCP 8.5. Elevation correction is applied to each point for sea level. Temperatures at city centers are calculated from the nearest 4 grid points using inverse distance squared interpolation method. Daily maximum temperatures histograms are plotted for each city and future predictions of return periods are compared with the reference period of 1971-2000 using the means and standard deviations obtained from Gaussian Mixture Model. The results show that the frequency of extreme events increases for all cities between 2070 and 2099. Peaks in temperature distribution are diverging from each other which will cause more severe extreme events. This divergence would cause cities to have shorter transition seasons and their climate would transform into only 2 seasons.
Predicting countries' vulnerability to climate change
Many countries are subject to the consequences of global climate change at varying degrees, and their vulnerability varies according to socioeconomic and environmental factors. Individuals, societies, and countries must be aware of the effects of climate change. Knowledge and understanding of how exposed they are to hazards, their level of vulnerability, and what must be done are critical for the continuation of basic vital activities. Accurately predicting how much a country will be affected by climate change in the future or which life-supporting sectors will suffer is crucial for countries to take precautions. Therefore, in this study, The Notre Dame Global Adaptation Initiative (ND-GAIN) Country Index's data is used for predicting countries' vulnerability to climate change. It's an open-source index that displays how vulnerable a nation is to climate disruptions. The data for this index includes scores for vulnerability and six areas that support life, including food, water, health, ecosystem services, human habitat, and infrastructure, for 182 nations during a 26-year period from 1995 to 2020. Long short-term memory (LSTM) network-based model is built to predict seven countries' six years of data from 2021 to 2026. These countries are Turkey, Australia, Germany, Portugal, Sudan, Georgia, and Kyrgyzstan. The results showed that the model predicts an increase in the vulnerability scores of all countries except Sudan for 2021, a slight decrease in Germany and Australia, and a decrease in Turkey, Portugal, and Kyrgyzstan after 2021. The model predicts a decrease in Sudan and an increase in Georgia for all years. The model's successes are tested using data from 2010 to 2020. Although time series forecasting is challenging, forecasted values are close to actual values. This study is novel since no other studies have predicted countries' future years' vulnerability to climate change.
A comparative evaluation of machine learning algorithms for statistical downscaling of monthly mean temperature data over a European region
Climate change is the most vital environmental change that has already started to affect many ecosystems. It is caused by greenhouse gas emissions which are increasing since the pre-industrial era, and populated areas become more vulnerable to disasters due to climate change. It has never been more crucial to model the climate effects on local regions. Organizations like Intergovernmental Panel on Climate Change (IPCC) use global climate models (GCMs) to project future changes in climate on a continental scale. Although these models are becoming more accurate, downscaling these models to smaller scales is an important task that is studied by climate scientists. The two main downscaling methods are dynamical and statistical downscaling. Statistical downscaling studies are more reachable and important to develop when compared to dynamical downscaling due to its lower costs. The use of machine learning algorithms in statistical downscaling is a new area. Studies that implement machine learning to make local scale projections of surface temperature are numbered. In this paper, four different machine learning algorithms were tested on downscaling of two different surface temperature datasets over a European region with different resolutions. The best performing algorithm was also tested augmenting elevation data. The results show that Gaussian process regression performs the best with MAE of 0.04 - 0.51 as compared to the other machine learning algorithms tested. In conclusion, machine learning algorithms such as Gaussian process regression can be a suitable approach when downscaling spatial monthly mean surface temperature data.
The effect of wind pattern change on the environmental/climatic conditions within cities
The labor force demand by the Industrial Revolution has led to an increase in the population of cities. This situation was followed by rapid development of urban areas with starting of nineteenth century to accommodate labor force around the industry. This situation has ended up with altering more land surfaces to buildings, car parks, roads or other structure types. However, it cannot be said that all the urbanization steps are taken properly. Thus, these anthropogenic developments have affected wind flow in urban. Arbitrary built urban areas restrict the wind flow in street canyons and make it slower than the flow above the building blocks, hence reduce the cooling effect of wind at near-ground levels. However, increasing the wind speed in streets can improve thermal comfort of people by the means of convective heat transfer through the skin. Thermal comfort and wind flow patterns are important environmental issues when designing new urban areas. Starting from this point, this thesis focuses on computational fluid dynamics (CFD) models to observe wind flow and thermal comfort of arbitrary built urban areas in Mecidiyeköy, Istanbul. Mecidiyeköy is an arbitrarily urbanized and one of the most crowded hubs of Istanbul, which is modelled as it is and compared with alternative design scenarios in wind flow and thermal comfort results. In general, this thesis analyzes the impact of buildings on wind flow, hence thermal comfort in cities.