@phdthesis{Block2007, author = {Block, Alexander}, title = {Unsicherheiten in Oberfl{\"a}chen- und Bodenparametern und ihre Auswirkungen auf die Ergebnisse regionaler Klimasimulationen}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus-3636}, school = {BTU Cottbus - Senftenberg}, year = {2007}, abstract = {In regionalen Klimamodellen wird die untere Randbedingung durch Bodenmodelle bereitgestellt. Diese beinhalten Modellformulierungen, die eine große Anzahl an Eingabeparametern ben{\"o}tigen. Die exakte Bestimmung dieser Parameter stellt ein Problem dar, da sie f{\"u}r ein großes Gebiet in hoher r{\"a}umlicher und teilweise zeitlicher Aufl{\"o}sung erfolgen muss. Die unterschiedlichen Bestimmungsmethoden tragen dazu bei, dass f{\"u}r viele Oberfl{\"a}chen- und Bodenparameter nur ein m{\"o}glicher Wertebereich angegeben werden kann. Die Auswirkungen dieser ungenauen Kenntnis der Parameter auf die Ergebnisse regionaler Klimasimulationen mit dem CLM wurden untersucht. F{\"u}r den LAI, den Vegetationsbedeckungsgrad, die Wurzeltiefe, die Vegetationsalbedo, den Stomatawiderstand, die Bodenart und die W{\"a}rmeleitf{\"a}higkeit konnten Ver{\"a}nderungen der Modellergebnisse im Jahresmittel von 2-6 W/m^2 der Energiebilanzgr{\"o}ßen und 0,25 K der 2m-Temperatur nachgewiesen werden. Im Monatsmittel ergeben sich Ver{\"a}nderungen bis 20 W/m^2 f{\"u}r die turbulenten W{\"a}rmefl{\"u}sse und bis 1,5 K f{\"u}r die 2m-Temperatur. Es konnte gezeigt werden, dass die resultierenden Unsicherheiten in den Modellergebnissen dabei r{\"a}umlich und zeitlich variieren und stark vom Bodenwassergehalt beeinflusst werden. Die Benutzung zweier neuer Parameterdatens{\"a}tze f{\"u}hrte in den Simulationen zu einer Erw{\"a}rmung bis 0,5 K im Jahresmittel und konnte damit die gegen{\"u}ber Beobachtungsdaten vorhandene Temperaturabweichung von 1,5 K des Modells reduzieren.}, subject = {Klima / Modell; Boden; Energiebilanz; Regionale Klimamodelle; Bodenmodelle; Unsicherheiten; Regional climate simulation; Soil model; Uncertainties}, language = {de} } @phdthesis{Wilberforce2007, author = {Wilberforce, Nana Takyi}, title = {Climate change detection in Central Africa}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus-3583}, school = {BTU Cottbus - Senftenberg}, year = {2007}, abstract = {Central Africa is a band of land stretching from the Atlantic Ocean to the Horn of Africa between 18º N 0.75º S and 18º W 44º E. The climate of this region shows a transition zone from the Sahara Desert in the north to the wet tropical Guinean coast and equatorial continental interior in its south. In such transition zones, drought is a common occurrence; of great concern is the progressive drying of the region from the 1970s and into the 1980s. This thesis aims to investigate whether climate change has occurred in Central Africa in the 2nd half of the 20th century. The reanalysis data from the National Centres for Environmental Prediction (NCEP) was used to analyse trends of temperature and precipitation over Central Africa with the linear regression model and patterns of precipitation using principal component analysis. The results of the trend analyses revealed an average increase in temperature of 0.15K/ decade and an average decrease in precipitation of -91 mm/decade for the period 1948-2004 over the whole study area. A regional trend analysis of box averaged data (2.5° in latitude and longitude) revealed an increased precipitation over the Northern part," the Sahel", and a marked decrease in precipitation and an increase in temperature over the Eastern parts of the study areas especially in countries such as Somalia, Sudan and Ethiopia. To quantify the changes in temperature and precipitation for different climatic periods (1951-1980, 1961-1990 and 1971-2000), 30-year trend analyses were also performed. Results revealed a relatively stable temperature trend and fluctuations in the precipitation trend over the three 30 year climate periods. Principal component analysis produces a decomposition of the data field into spatial patterns (eigenvectors) and a temporal time series describing the temporal importance of these patterns. For precipitation over Central Africa, three patterns stand out for 60.39\% of the total variance in the precipitation over Central Africa. The individual principal components explain 37.6\%, 12.1\%, and 10.7\% respectively of the total variance. The first principal component captures progressive drying in Central Africa, the second principal component captures the different seasons within Central Africa and the third principal component captures the movement of the Intertropical Convergence Zone over Central Africa. Apart from explaining the current precipitation patterns over Central Africa, the use of principal component analysis in this study has demonstrated that a limited number of spatial patterns are basis for African weather, coordinate system for present day climate with maximum variance contribution along the first axis, maximum of the remaining variance along the second axis with subsequent axes explaining less variance can be used to explain precipitation variability within Central Africa. The same method can be used to explain temporal differences between climate change scenarios and present day climate.}, subject = {Zentralafrika; Klima{\"a}nderung; Wiederschlaf; Temperatur; Zentralafrika; Klimawandel; Precipitation; Temperature; Central Africa; Climate Change}, language = {en} } @phdthesis{Nawdiyal2014, author = {Nawdiyal, Manish}, title = {Effect of mining on regional climate}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-34631}, school = {BTU Cottbus - Senftenberg}, year = {2014}, abstract = {Intensive coal mining over the region of Lusatia covers a large area in southern Brandenburg and Saxony. Post mining this region has been filled with water resulting in lake formation. People living in the vicinity of mining area believe that the lake formation will increase convective precipitation in the neighboring area which shall lead to weathering of their structures. This was the prime reason to investigate the Lusatia region. The study about the impact of lake was carried out using data and CCLM simulation analyses. For data analysis, stations Senftenberg, Cottbus and Elsterheide-Geierswalde were selected. For the analysis, six 10 years running time series, CPDF were analyzed for all the three stations. Last three time series included the period of lake formation at station Elsterheide-Geierswalde. The CPDF time series showed little deviation for station Elsterheide-Geierswalde as compared to that for other two stations during lake formation periods. But in absence of parameters (temperature, wind data) data analysis was inconclusive. To observe effect of LULC (Land use and land cover) change on precipitation, three different simulations have been carried out, 1. VAT010: This simulation represents the potential vegetation of the whole domain. This describes the pre mining stage of the area when there was no mining. 2. VAT020: This simulation represents the introduction of lake points in the model domain. This describes the situation of area post mining, when lakes are formed.}, subject = {Mining effect; Regional climate; CCLM simulation; Data analysis; Bergbaut{\"a}tigkeit; Regionales Klima; CCLM-Simulation; Datenanalyse; Lausitz; Braunkohlenrevier; Rekultivierung; Klimaanalyse}, language = {en} } @phdthesis{An2015, author = {An, Ni}, title = {Statistical analysis of extreme climate events in Brandenburg}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-36117}, school = {BTU Cottbus - Senftenberg}, year = {2015}, abstract = {The main idea of this paper is to evaluate the performance of the fine resolution CLM model in the state of Brandenburg Germany, create a bias correction method and reanalyze the climate signals of the corrected simulations on projections of the 21st century. The bias correction method is a distribution oriented method and the analysis is mainly focused on the extreme events such as 95 and 5 percentile daily temperatures and 95 percentile daily precipitations in a period of 30 years, but also includes seasonal temperature, daily temperature difference and monthly precipitation to demonstrate the effectiveness of the correction method. The results showed that the correction method was very effective on different variables. After correction, the extreme daily temperature bias was reduced from a cold bias of 1-2K to less than 0.1K. The corrected projection simulations suggested that the extreme temperatures were about to increase both in intensity and in frequency. The extreme precipitation after correction was meant to become more severe and frequent as well.}, subject = {Bias correction; Climate simulation; CLM model evaluation; Climate signal analysis; Bias Korrektur; Klima Simulation; CLM Modell Auswertung; Klima Signal Analyse; Brandenburg; Klima; Modell; Statistische Analyse; Simulation}, language = {en} }