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Institute
Das Ziel dieser Studie ist es, die Möglichkeiten und Grenzen von hochauflösenden Klimaprojektionen in orographisch beeinflussten Gebieten an den Beispielen der europäischen Alpen und des Himalajas zu prüfen. Insbesondere wird die Fragestellung untersucht, ob beobachtete regionale Muster in den höher aufgelösten Daten besser wiedergegeben werden als in den antreibenden großskaligen Daten. Dazu werden regionale Klimasimulationen des COSMO-CLM Modells und Daten von zwei statistischen Regionalisierungsmethoden mit ERA40 Reanalysen sowie Daten des globalen Atmosphäre-Ozean Modells ECHAM5/MPIOM für verschiedene Parameter des Klimasystems verglichen. Ein Vergleich mit den Reanalysen anhand täglicher Niederschlagsstatistiken ergibt, dass die COSMO-CLM Niederschlagsdaten auf der 0.5° Skala vergleichbar sind mit ERA40 Niederschlägen und mit statistisch regionalisierten ERA40 Niederschlägen. Eine zusätzliche Fehlerkorrektur der COSMO-CLM Niederschläge liefert gute Ergebnisse. Dabei sind jedoch etwa 500 Regentage notwendig, um eine robuste Fehlerabschätzung zu gewährleisten. Für das südasiatische Gebiet ist eine realistische Wiedergabe des indischen Sommermonsuns (ISM) in den Modellen von hoher Relevanz. Betrachtet man nur die Mittelwerte und zeitlichen Variabilitäten von verschiedenen Indizes des ISM, so liefert das COSMO-CLM keinen Mehrwert im Vergleich zu den antreibenden Daten. Allerdings werden die räumlichen Strukturen von Niederschlag und vertikaler Windscherung, sowie die zeitliche Korrelation der modellierten Indizes gegenüber dem ECHAM5/MPIOM Modell verbessert. Die durchgeführten COSMO-CLM Projektionen für die Jahre 1960 bis 2100 zeigen negative Trends des ISM für die SRES Szenarien A2, A1B und B1. Die negativsten Trends sind dabei im Szenario A2 zu finden, gefolgt von A1B und B1. Fast keine Trends zeigen sich im commitment Szenario. Trotz großen zeitlichen Variabilitäten sind die Abnahmen in Niederschlagsmengen, ausgehender langwelliger Strahlung und Windscherung statistisch signifikant in großen Regionen des Simulationsgebietes. Für Nordwest-Indien weisen die Projektionen teilweise einen Rückgang der Monsunniederschläge von über 70% in 100 Jahren auf. Der Rückgang der Windscherung ist hauptsächlich auf Veränderungen in der oberen Troposphäre bei 200 hPa zurück zu führen. Während in den COSMO-CLM Projektionen alle Indizes des ISM synchrone Negativtrends aufweisen, sind die Trends für den Monsunregen über Indien im globalen ECHAM5/MPIOM Model positiv. Gemäß den Definitionen der verschiedenen Indizes, sind jedoch synchrone Trends wahrscheinlicher und das COSMO-CLM liefert zu den globalen ISM Projektionen ebenfalls einen Mehrwert. Insgesamt zeigen die Ergebnisse dieser Studie, dass das COSMO-CLM wertvolle regionale Zusatzinformationen zu den globalen Modellen in den beiden untersuchten Regionen liefert. Für die Einzugsgebiete der oberen Donau und des oberen Brahmaputra liefern die COSMO-CLM Projektionen einen signifikanten Anstieg der Temperatur für alle Jahreszeiten der Jahre 1960 bis 2100. Die Werte sind generell höher im Brahmaputragebiet, mit den größten Trends in der Region des tibetanischen Plateaus. Im Niederschlag zeigen die saisonalen Anteile ebenfalls klare Trends, beispielsweise eine Zunahme des Frühjahrsniederschlags im Einzugsgebiet der oberen Donau. Die größten Trends werden wiederum in der Region des tibetanischen Plateaus projiziert mit einem Anstieg von bis zu 50% in der Länge der Trockenperioden zwischen Juni und September und einem gleichzeitigen Anstieg von etwa 10% für die maximale Niederschlagsmenge an fünf aufeinander folgenden Tagen. Für die Region Assam in Indien, zeigen die Projektionen zudem eine Zunahme von 25% in der Anzahl der aufeinander folgenden trockenen Tage während der Monsunzeit
Die vorliegende Arbeit beschäftigt sich mit der Entwicklung von regionalen Klimasimulationen für die Region Ostasien. Hierfür werden zwei verschiedene Modellierungsansätze verwendet. Der dynamische Regionalmodellierungsansatz, vertreten durch COSMO CLM (CCLM), und der statistische Modellierungsansatz, vertreten durch STARS. Die Simulationen erfolgten unter den Rahmenbedingungen des Coordinated Regional Climate Downscaling Experiment (CORDEX). Beide Regionalmodelle wurden im Rahmen dieser Arbeit umfassend für die Region CORDEX-Ostasien kalibriert und evaluiert. Das statistische Modell STARS wurde hierbei erstmals auf kontinentaler Ebene angewendet. Auf Basis der kalibrierten Modelle wurden Projektionen der zukünftigen klimatischen Entwicklung der Region durchgeführt.
Zur Auswertung der einzelnen Kalibrierungsläufe wurde ein komplexes Evaluierungsschema, mit einem Gütekennzahlensystem basierend auf einer linearisierten Form der relativen Modelldifferenz, entwickelt. Neben den etablierten univariaten statistischen Kennwerten (Mittelwert, Varianz, Trend) enthält das Gütekennzahlensystem auch ein bivariates statistisches Maß, welches die zweidimensionalen Stichprobenverteilungen zweier Variablen (beispielsweise Temperatur und Niederschlag) bewertet.
Im Rahmen der Kalibrierung konnte ein Großteil des Parameterraums des statistischen Modells STARS systematisch untersucht werden. Es zeigte sich, dass nur wenige Parameter einen Einfluss auf die Simulationen haben. Die meisten Parameter zeigten eine geringe und teilweise unsystematische Beeinflussung. Es konnte zudem eine Schwachstelle des Modells in Bezug auf die Variablenkorrelationen identifiziert werden. Bei der Kalibrierung des dynamischen Regionalmodells CCLM zeigte sich, dass aufgrund der groben horizontalen Auflösung des Modells eine signifikante Verbesserung der Simulationen durch eine Anpassung der physikalischen Parametrisierungen erfolgen kann.
Im Rahmen einer abschließenden Evaluierung wurden beide Modelle hinsichtlich ihres räumlichen Bias, des simulierten Jahresgangs und der Abbildung des asiatischen Monsunphänomens untersucht. Im ersten Punkt ergab sich kein qualitativer Unterschied zwischen CCLM und STARS. Beide Modelle zeigen eine deutliche Überschätzung der 2m-Temperatur im Winter über dem nördlichen Teil CORDEX-Ostasiens und eine Überschätzung des Luftdrucks über dem Hochland von Tibet im Sommer. Unterschiede zwischen beiden Modellen ergaben sich hingegen beim simulierten Jahresgang.
In Bezug auf die Modellierung des Monsunphänomens zeigt CCLM eine Unterschätzung der Intensität des indischen Sommermonsuns und eine Überschätzung des Sommermonsuns über dem westlichen Nordpazifik. Das statistische Modell STARS zeigte eine Auffälligkeit bei der Simulation des Jahresgangs sowie der räumlichen und zeitlichen Entwicklung des Sommermonsuns. Aufgrund der Konzeption des Modells ergab sich in einzelnen Regionen eine systematische Deformation des Jahresgangs. Trotz der identifizierten Schwachstellen von CCLM und STARS, bilden beide Modelle das Klima über der Region CORDEX-Ostasien qualitativ ähnlich gut ab wie aktuelle Reanalysen (ERA-Interim).
Auf Basis der kalibrierten und evaluierten Modelle wurden Klimaprojektionen für einen nahen (2020-2046), mittleren (2041-2070), und späten (2071-2100) Projektionszeitraum unter den Emissionsszenarien RCP2.6, RCP4.5 und RCP8.5 durchgeführt. Aufgrund von Modellbeschränkungen begrenzen sich die Rechnungen des Modells STARS auf den nahen Projektionszeitraum und die Emissionsszenarien RCP2.6 und RCP4.5. Die Projektionen beider Modelle zeigen eine deutliche und statistisch signifikante Erhöhung der 2m-Temperatur über der gesamten Region mit einer stärkeren Erwärmung über dem Kontinent gegenüber dem Meer. Aufgrund der relativ großen interannulären Variabilität des Niederschlags und des Luftdrucks werden statistisch nicht signifikante Änderungssignale und teils widersprüchliche Änderungen für den nahen Projektionszeitraum simuliert. Für den späten Projektionszeitraum ergeben sich jedoch deutliche Änderungssignale in den Simulationen des Modells CCLM. Insbesondere über dem Hochland von Tibet wird für den Zeitraum von 2071-2100 eine Temperaturerhöhung von über 7.0°C simuliert. Der Luftdruck und der Niederschlag zeigen räumlich heterogene Änderungssignale. Die spezifische Ausprägung der Luftdruckänderungen deutet auf eine Abschwächung der indischen Sommermonsunzirkulation und eine deutlichen Intensivierung des Sommermonsun über dem westlichen Nordpazifik hin. Die Niederschlagsänderungen über dem ostasiatischen Monsungebiet lassen auf eine Entkopplung der östlichen Monsunsysteme schließen. Trotz der heterogenen Änderungssignale im Niederschlag wird in den meisten Regionen eine Zunahme der Intensität von Extremniederschlägen simuliert. Dies gilt selbst für Regionen mit einer simulierten Abnahme der jährlichen Niederschlagssumme wie Westindonesien.
Gridded maps of meteorological variables are needed for the evaluation of weather and climate models and for climate change monitoring. In order to produce them, values at locations where no observing stations are available need to be estimated from point-wise observations. For the interpolation of meteorological observations deterministic and stochastic methods are often combined. Deterministic methods can account for ancillary information such as elevation, continentality or satellite observations. Stochastic methods such as kriging reproduce observed values at the station locations and also account for spatial variability. In the first two studies of this thesis, a flexible interpolation method for the gridding of locally observed daily extreme temperatures is developed that also provides an optimal estimate of the interpolation ncertainty. In the third study, an observational dataset is created using this interpolation method and then applied to evaluate a climate simulation for Africa.
In the first study, the Regression-Kriging-Kriging (RKK) method is tested for the interpolation of daily minimum and maximum temperatures (Tmin and Tmax) in different regions in Europe. RKK accounts for elevation, continentality index and zonal mean temperature and is applicable in regions of differing station density and climate. The accuracy of RKK is compared to Inverse Distance Weighting, a common deterministic interpolation method, and to Ordinary Kriging, a common stochastic interpolation method. The first step in RKK is to use regression kriging, in which multiple linear regression accounts for topographical effects on the temperature field and kriging minimizes the regression error, to interpolate climatological means. In the second step daily deviations from the monthly climatology are interpolated using simple kriging. Owing to the large climatological differences across the investigation area the interpolation is performed in homogeneous subregions defined according to the Köppen-Geiger climate classification. Cross validation demonstrates the superiority of RKK over the simpler algorithms in terms of accuracy and preservation of spatial variability. The interpolation performance however strongly varies across Europe, being considerably higher over Central Europe (highest station density) than over Greenland (few stations along the coast line). This illustrates the strong impact of the station density on the accuracy of the interpolation result. Satellites provide comprehensive observations of climate variables such as land surface temperature (LST) and cloud cover (CC). However, LST is associated with high uncertainty (standard error ~ 1-2°C), preventing its direct application in meteorology and climatology. The second study investigates the usefulness of LST and CC as predictors for the gridding of daily Tmin and Tmax. The RKK algorithm is compared with similar interpolation methods that apply LST and CC in addition to the predictors used with the RKK algorithm. The investigation is conducted in two regions, Central Europe and the Iberian Peninsula, which differ strongly in average cloud cover (Central Europe is approximately 30% cloud free and the Iberian Peninsula approximately 60 % cloud free). RKKLST (in which monthly mean LST is used as an additional predictor) yields for Central Europe no clear improvement over RKK, yet it reduces the interpolation error over the Iberian Peninsula. This finding can be explained by the higher percentage of cloud free pixels over that region in summer which enables a more robust determination of monthly mean LST. Adding a regression step for daily anomalies (using the predictor CC) yields the RKRK method and improves the preservation of spatial variability over the Iberian Peninsula. Moreover, a successive reduction of the station number (from 140 to 10 stations) reveals an increasing superiority of RKKLST and RKRK over RKK in both regions.
The application of a gridded observational dataset for climate monitoring or climate model validation requires knowledge of the uncertainties associated with the dataset. The estimation of the interpolation uncertainty, here the inter quartile range is the used uncertainty measure, is therefore an important issue within the frame of this thesis. By means of cross validation it is shown that the largest uncertainties occur in regions of low station density (e.g. Greenland), in mountainous regions and along coastlines (in these regions model evaluation results should be interpreted carefully). The magnitude of the interpolation error mainly depends on the station density, while the complexity of terrain has substantially less influence. On average over all regions and investigation days the target precision of the uncertainty estimate is reached. However, on local scales and for single days it can be clearly over- or underestimated. The application of satellite-derived predictors (LST and CC) yields no noteworthy improvement of the uncertainty estimate.
In the last study two regional climate simulations for Africa using the ERA-Interim driven COSMO-CLM (CCLM) model at two different horizontal resolutions (0.22° and 0.44°) are validated. It is assessed whether observed patterns and statistical properties of daily Tmin and Tmax are correctly represented in the model. The ERA-Interim reanalysis and a specially created observational dataset are used as reference. The observational dataset is generated by applying the RKRK algorithm (developed within the second study). The investigations show an occasionally large bias in Tmin and Tmax. The hemispheric summers are generally too warm and the temporal variability in temperature is too high, particularly over extra tropical Africa. The diurnal temperature range is overestimated by about 2°C in the northern subtropics but underestimated by about 2°C over large parts of the African tropics. CCLM reproduces the observed frequency distribution of daily Tmin and Tmax in all African climate regions, and the extreme values in the lower percentiles (5, 10, 20%) for Tmin are well simulated. The higher percentiles (80, 90, 95%) for Tmax are however overestimated by 2-5°C. For both Tmin and Tmax the 0.22° simulation is on average 0.5°C warmer than the 0.44° simulation. Additionally, the higher percentiles are about 1°C warmer for both Tmin and Tmax in the higher resolution run, while the lower percentiles in both runs match very well. Although the temperature pattern is represented in more detail along the coastlines and in topographically complex regions, the higher resolution simulation yields no qualitative improvement.
To summarize, the choice of the appropriate algorithm mainly depends on the interpolation conditions. In cases where the station density is high across the target region and the predictor space is adequately covered by observing stations, the computationally less demanding RK algorithm should be preferred. In regions where the station density is low the more robust RKRK algorithm should be the first choice. Due to the strong physical relation of both CC and LST to Tmin and Tmax the missing information is at least partially compensated for. The estimation of the interpolation uncertainty could be improved by applying a normal score transformation to the data prior to a kriging step. This is because the kriging assumption that the increments of the variable of interest are second order stationary can be approximately met by a normal score transformation.
Mistral and Tramontane are wind systems in southern France and the western Mediterranean Sea. Both are caused by similar synoptic situations and channeled in valleys. Their relevance for the climate of the western Mediterranean region motivated this work. The representation of Mistral and Tramontane in regional climate simulations was surveyed with the models ALADIN, WRF, PROMES, COSMO-CLM, RegCM, and LMDZ. ERA-Interim and global CMIP5 simulations (MPI-ESM, CMCC-CM, HadGEM2-ES, and CNRM-CM5) provided the lateral boundary data for the regional simulations regarding the 20th century and two representative concentration pathways for the 21st century (RCP4.5 and RCP8.5).
A Mistral and Tramontane time series, a principal component analysis of pressure fields, and a Bayesian network were combined to develop a classification algorithm to identify pressure patterns in favor of Mistral and Tramontane. The regional climate models were able to reproduce the observed climatology of Mistral and Tramontane. Compared to observational data (SAFRAN and QuikSCAT), the simulations underestimate the wind speed over the Mediterranean Sea, mainly at the borders of the main flow. Simulations with smaller grid spacing showed better agreement with the observations.
A sensitivity study tested the influence of the Charnock parameter on the Mistral wind field. Its value impacted both wind speed and wind direction. Decreasing the orographic resolution in idealized simulations using COSMO-CLM caused a reduction in wind speed and a broader flow area. Including a parameterization for subgrid scale orography improved the simulation. However, an accurate simulation of Mistral and Tramontane still requires a high-resolution orography.
The classification algorithm also was applied to pressure fields from regional climate simulations driven by global simulation data. At the end of the 21st century, only small, non-significant changes in the number of Mistral days per year occur in the projection simulations. The number of Tramontane days per year decreased significantly.
Air-sea feedbacks between the Mediterranean Sea and the atmosphere on various temporal and spatial scales play a major role in the Mediterranean regional climate system and beyond. The Mediterranean Sea is a source of moisture due to excess evaporation and, on a long-term average, is associated with a warming of the lower atmosphere in contact with the sea surface due to heat loss at the air-sea interface. The complex air-sea interactions and feedbacks in the Mediterranean basin strongly modulate the sea surface fluxes and favor several cyclogenetic activities under certain meteorological conditions. Examples of such cyclonic activities are medicanes (Mediterranean hurricanes) and Vb-cyclones. Medicanes are mesoscale, marine, and warm-core Mediterranean cyclones that exhibit some similarities to tropical cyclones, while Vb-cyclones are extra-tropical cyclones, that propagate from the Western Mediterranean Sea and travel across the Eastern European Alps into the Central European region. Extremely strong winds and heavy precipitation associated with these cyclones can lead to severe destruction and flooding. Changes in the intensity and frequency of these cyclones are also projected under changing future climate conditions, where the Mediterranean region has been identified as a hotspot in terms of rising temperatures.
The development of high-resolution regional climate models (RCMs) has progressed our understanding of the processes characterizing the Mediterranean climate. However, large uncertainties still exist regarding the estimates of air-sea fluxes, which, in turn, affect the simulation of the Mediterranean climate. Several factors can be attributed to such discrepancies, such as data quality, temporal and spatial resolution, and the misrepresentation of physical processes. To overcome some of these inconsistencies and deficiencies of the existing climate simulations, a new high-resolution atmosphere-ocean regional coupled model (AORCM) has been developed to simulate the air-sea feedback mechanisms. This coupled model incorporates the coupling of RCM COSMO-CLM (CCLM) and the regional ocean model NEMO-MED12 for the Mediterranean Sea (MED) as well as NEMO-NORDIC for the North- and Baltic Sea (NORDIC). Several experiments were performed using both the coupled and uncoupled models to investigate the impact of air-sea interactions and feedbacks on sea surface heat fluxes, wind speed, and on the formation of Mediterranean cyclones (i.e., medicanes and Vb-cyclones). These experiments were performed using different horizontal atmospheric grid resolutions to analyze the effect of resolution on sea surface heat fluxes, wind speed, and the development of medicanes.
The results of the present study indicate that a finer atmospheric grid resolution ([is as appreciated as]9 vs. [is as appreciated as]50 km) improved the wind speed simulations (particularly near coastal areas) and subsequently improved the simulations of the turbulent heat fluxes. Both parameters were better simulated in the coupled simulations than in the uncoupled simulations, but coupling introduced a warm SST bias in winter. Radiation fluxes were slightly better represented in coarse-grid simulations than in fine-grid simulations. However, the higher-resolution coupled model could reproduce the observed net outgoing total surface heat flux over the Mediterranean Sea. In addition to that sub diurnal SST variations have a strong effect on sub-daily heat fluxes and wind speed but minor effects at longer timescales. Regarding the impact of atmospheric grid resolution ([is as appreciated as]50, 25, and [is as appreciated as]9 km) and ocean coupling on medicanes, it was detected that the coupled model with a finer atmospheric grid ([is as appreciated as]9 km) was able to not only reproduce most medicane events, but also improved the track length, warm core, and wind speed compared to the uncoupled model. The coupled model with the coarse-grid ([is as appreciated as]50 and [is as appreciated as]25 km) did not show any improvement in simulating medicanes compared to the uncoupled model. The spectral nudging technique, applied on the wind components above 850 hPa in the interior domain to keep large-scale circulation close to the driving data (i.e., ERAInterim reanalysis), improved the accuracy of the times and locations of generated medicanes, but no improvement was found in the track length and intensity.
Concerning the role of the Mediterranean Sea coupling on Vb cyclones, the investigation showed that atmosphere-ocean coupling had an overall positive impact, although with a strong case-by-case variation, on the trajectories and intensity of Vb-cyclones as a result of the variation in moisture source for each event. In general, all model configurations could replicate Vbcyclones, their trajectories, and associated precipitation fields. The average structure of the precipitation field was best represented in the coupled simulations. Coupling of the North- and Baltic Seas also showed an improvement in some of the simulated Vb-cyclones.
The atmosphere-ocean coupling showed an overall positive impact on the simulation of sea surface heat fluxes and Mediterranean cyclones (medicanes and Vb-cyclones). Moreover, the representation of sea surface heat fluxes, wind speed, and medicane features was more realistic when using a finer atmospheric grid resolution (less than 10 km). The present study suggests that the combination of a finer atmospheric grid resolution together with atmosphere-ocean coupling is advantageous in simulating the Mediterranean climate system.
The aim of this study is a better understanding of radiation processes in regional climate models (RCMs) in order to quantify their impact and to reduce possible errors. A first important task in finding an answer to this question was to examine the accuracy of the components of the radiation budget in regional climate simulations. To this end, the simulated radiation budgets of two regional climate simulations for Europe were compared with a satellite-based reference. In the simulations with the RCM COSMO-CLM there were some serious under- and overestimations of short- and long-wave net radiation in Europe. However, taking into account the differences in the reference datasets, the results of the COSMO-CLM were quite satisfactory.
Using statistical methods, the influence of potential sources of uncertainties was estimated. Uncertainties in the cloud cover and surface albedo had a significant impact on uncertainties in short-wave net radiation, the explained variance of uncertainties in cloud cover was two to three times higher than that of uncertainties in surface albedo. Uncertainties in the cloud cover resulted in significant errors in the net long-wave radiation. However, the influence of uncertainties in soil temperature on errors in the long-wave radiation budget was low or even negligible. These results were confirmed in a comparison with simulations of the REMO and ALADIN regional climate models. It is reasonable to expect that a better parameterization of relatively simple parameters such as cloud cover and surface albedo is a means of significantly improving the simulation of radiation budget components in the COSMO-CLM.
An important question for the application of RCMs is to examine whether the results of radiation uncertainties and their impact factors are comparable if the model is applied in a region that is not the one for which it was originally created. Comparisons of the simulated radiation budgets of different RCMs for West Africa showed that problems in the simulation of short- and long-wave radiation fluxes were a widespread problem. Most of the tested models showed some considerable under- or overestimation of the short- and long-wave radiation fluxes.
Similar to Europe uncertainties in cloud cover were also in the simulations for Africa a significant factor affecting uncertainties in the simulated radiation fluxes. However, for the African simulations uncertainties in the parameterization of surface albedo were much more important than in Europe. On average, overland uncertainties in the cloud cover and surface albedo were of similar importance. Uncertainties in soil temperature simulations were of higher importance in Africa, and reached overland similar values of the mean explained variance (R2 ≈ 0.2) such as uncertainties in the cloud cover. This indicates a geographical dependence of the model error. This study confirmed the assumption that an improved parameterization of relatively simple parameters such as the surface albedo in RCMs leads to a significant improvement in the modeled radiation budget, particularly in Africa.
The influence of errors in the simulated radiation budget components on the simulation of climate processes, such as the West-African monsoon (WAM), was investigated in a next step. The evaluation of ERA-Interim and ECHAM5 driven COSMO-CLM simulations for Africa showed that the main features of the WAM were well reproduced by the model, but there were only slight improvements compared to the driving data. The index of convective activity in the model simulations was much too high and precipitation was underestimated in large parts of tropical Africa. The partly considerable differences between the ERA-Interim and ECHAM5 driven simulations demonstrated the sensitivity of the RCM to the boundary conditions and in particular to the sea surface temperature. An excessive northwards shift of the monsoon in the model was influenced by the land-sea temperature gradient and the strength of the Saharan heat low. Consequently, a part of the error was due to the driving data and the model itself produced another part.
By modifying the parameterization of the bare soil albedo the errors in the radiation budget and 2 m temperature in the Sahara region were significantly reduced. Similarly, the overesti-mation of precipitation and convection has been reduced in the Sahel. The effect of this modifi-cation on the examined WAM area was low. This confirmed that especially in desert regions, errors in the surface albedo were a driving factor for errors in the radiation budget. However, there are other important factors not yet sufficiently understood that have a strong influence on the quality of the simulation of the WAM.
The analysis of the actual state, the quantification of error sources and the highlighting of connections made it possible to find means to reduce uncertainties in the simulated radiation in RCMs and to have a better understanding of radiation processes. However, the magnitude of the errors found, the number of possible influencing factors, and the complexity of interactions, indicate that there is still a need for further research in this area.
In dieser Dissertation wird die Parametrisierung von subgitterskaligen (SGS) Prozessen in Atmosphärenmodellen untersucht. Die Arbeit befasst sich mit den stochastisch angetriebenen Flachwassergleichungen, im ersten Teil in einer räumlichen Dimension und im zweiten Teil in zwei Dimensionen. Die Einteilung in aufgelöste und SGS-Variable erfolgt in beiden Fällen über lokale räumliche Mittel der Ursprungsvariable und deren Abweichungen vom lokalen Mittel.
Im eindimensionalen Fall liegt zwischen den Variablen eine deutliche Separation der charakteristischen Zeitskalen vor, wodurch die Anwendung der stochastischen Moden Reduktion (SMR) ermöglicht wird. Die SMR generiert ein reduziertes Modell der aufgelösten Variable mit einer stochastischen SGS-Parametrisierung, im Folgenden auch Schließung genannt. Die SMR-Schließung basiert auf den Grundgleichungen des Flachwassermodells und ist numerisch effizient einsetzbar, da sie nur eine geringe Anzahl von benachbarten Zellen koppelt. Sie verbessert die Ergebnisse des reduzierten Modells und übertrifft die Ergebnisse zweier zum Vergleich untersuchter empirischer stochastischer Schließungen. Den größten Zugewinn liefert sie im Energiespektrum, insbesondere für kleine Skalen. Das Ergebnis der SMR-Schließung kann verbessert werden, indem die Amplitude der stochastischen Schließungskomponente gedämpft wird. Die SMR-Schließung ist skalenabhängig im Sinne der räumlichen Modellauflösung. Untersucht wird die Schließung bei Halbierung und Viertelung der räumlichen Auflösung, wo sie ihre Überlegenheit gegenüber den empirischen Schließungen wiederholt bestätigt.
Im Unterschied zum eindimensionalen Fall ist in zwei Dimensionen auch die Corioliskraft enthalten und eine räumliche Divergenz der Schwerewellen möglich. Zwischen der aufgelösten und der SGS-Variable kommt es erneut zu einer Separation der charakteristischen Zeitskalen. Die Separation ist allerdings weniger stark ausgeprägt als im eindimensionalen Fall. Grund hierfür ist das Auftreten einer lang korrelierten geostrophisch balancierten Mode, welche auch auf die SGS-Variable projiziert. Das Vorgehen zur Bestimmung der SMR-Schließung für das zweidimensionale Modell verläuft analog zum eindimensionalen Fall. Es werden die Ergebnisse des hoch aufgelösten Referenzmodells und zweier Modelle ohne SGS-Schließung verglichen.
Hydro-climatic causes of widespread floods in central Europe : on rain-on-snow and Vb-cyclone events
(2021)
The presented work investigates the hydro-meteorological and hydro-climatological drivers of widespread floods in Central Europe during the past century. Due to the strong seasonality of the detected flood drivers, the thesis is divided into two parts: the first part focuses on widespread winter floods and the second one on extreme summer floods. For analysing past flood events, we profited from the dynamically downscaled centennial ERA-20C reanalysis (continuously from 1901—2010). The downscaling was performed over Europe with a coupled regional atmosphere-ocean model (COSMO-CLM+NEMO) to represent the water cycle more realistic. These high resolution atmospheric data allowed us to study the four-dimensional atmospheric state during selected floods during the early decades of the 20th century for the first time with such a high temporal and spatial resolution.
During the winter half-year, the observed floods were particularly widespread. High peak discharges were recorded simultaneously in the Rhine, Elbe, and Danube catchments. Most of these trans-basin floods were compound events caused by rainfall during extensive snowmelt (i.e., rain-on-snow events). Interestingly, the winter flood time series exhibited a remarkable high flood frequency during the 1940s and 1980s, while other decades were flood-poor. We detected a synchronization of the inter-annual flood frequency with the superposition of the North Atlantic Oscillation (NAO) and the Scandinavian pattern (SCA). The negative NAO phase is often associated with large snowfall and cyclone tracks over southern Europe, while the negative SCA pattern correlates with total precipitation in the affected river catchments.
During the summer half-year, most extreme floods in Central Europe were caused by so-called Vb-cyclones propagating from the Mediterranean Sea north-eastward to Central Europe. So far in the literature, only a few Vb-events, which occurred during the past two decades, have been analysed. We extended the previous case studies by several past Vb-cyclone floods since 1900. We investigated the processes that intensify Vb-cyclone precipitation with Lagrangian moisture-source diagnostics and the parametric transfer entropy measure TE-linear. Overall, an enhanced and dynamically driven moisture uptake over the Mediterranean Sea was found to be characteristic for Vb-events with heavy precipitation. This is supported by high information exchange from evaporation over the western basin of the Mediterranean Sea towards heavy precipitation in the Odra catchment. The dominating moisture uptake regions during the investigated events were, however, the European continent and the North Sea. A possible cause could be the pre-moistening of non-saturated continental moisture sources upstream of the affected river catchments as indicated by significant information exchange from land surface evaporation and soil moisture content along the Vb-cyclone pathway. Besides, evaporation over the Mediterranean Sea might contribute to Vb-cyclone intensification in the early stages of their development through latent heat release. On the catchment scale, orographic rainfall and convective precipitation further enhance the flood triggering rainfall. As expected, the Vb-cyclones mainly trigger precipitation along west-east orientated mountain ranges such as the Alps or Ore mountains due to their meridional pathway. Remarkably, during summer, we detected a convective fraction of up to 90% during the afternoons of individual days and up to 23% on average (based on convective cell tracking and convection-permitting simulations of selected flood events since 1900).
The presented analyses deepened the knowledge on atmospheric and hydroclimatic drivers of widespread floods in Central Europe. This will serve as a basis for future studies on the predictability of floods induced by rain-on-snow and Vb-cyclone precipitation events in the context of a changing climate.
Clouds and precipitation are essential climate variables. Because of their high spatial and temporal variability, their observation and modeling is difficult. In this thesis multiple observational data sources, including satellite data and station data are globally analyzed to understand the distribution and variability of clouds and precipitation, while a special focus is on the diurnal cycle of both variables. Substantial diurnal cycles of clouds and precipitation are observed in the tropics, with different properties over land and ocean. But also in Europe cloud diurnal cycles are observed in the summer season. Overall the maximum cloud cover and also the maximum precipitation is observed in the afternoon over land, and in the morning over ocean. The analyzed climate model simulations and the model-based reanalysis fail to simulate the observed diurnal cycles. Owing to their limited resolution, models can not fully resolve the processes responsible for the existence of diurnal cycles of clouds and precipitation.
The main objective of this PhD work is to assess the impact of fine-scale air-sea interaction on the performance of a regional climate prediction model in marginal sea regions. Focus is on the North and Baltic Seas, the largest marginal sea area in the mid-latitudes. Motivation for this work is to better understand the interaction between the different components of the climate system, namely atmosphere, ocean and sea-ice. In addition to that, the sea regions of interest, the North and Baltic Seas, are orographically complex and cannot be resolved by a global ocean model. The ice coverage on the Baltic Sea is underestimated in the stand-alone atmospheric model COSMO-CLM due to the low water freezing temperature value assumed, which is not applicable for such brackish water body. To fulfil the thesis goal, a new regional coupled atmosphere-ocean-ice system was developed for these two seas, named COSMO-CLM/NEMO. The two-way coupling system involves active feedback from both component models: the limited-area climate model COSMO-CLM and the regional ocean model NEMO-NORDIC.
The coupled system COSMO-CLM/NEMO for the North and Baltic Seas was used to study the impact of sea surface temperature and sea ice on the atmosphere on diffrent topics. The long term impact of the North and Baltic Seas was studied through 15- year long simulations driven by European Center for Medium-Range Weather Forecasts (ECMWF) Interim reanalysis (ERA-Interim) data. Furthermore, to see whether the marginal sea modelling can advance the simulation of extreme climate events, the coupled model was used to reproduce six extreme snowband phenomena over the Baltic Sea in simulations driven by ERA-interim data. Last but not least, the role of the North and Baltic Sea model in improving long-term regional climate prediction was examined. Two sets of experiments with coupled and uncoupled models, each set has five independent decadal hindcasts forced by global climate model, were carried out.
All results were compared with observations and the stand-alone atmospheric model COSMO-CLM results. In all experiments, COSMO-CLM/NEMO showed good agreement with observations. Improvements compared with the uncoupled COSMO-CLM were also found. Coupling was found to affect the air temperature not only around the coupled sea region but also inland. The convective snowbands over the Baltic Sea were successfully reproduced by the coupled model. The high contrast of temperature in the air column, as well as considerably high amounts of surface heat fluxes exchanged between air and sea could not be simulated by COSMO-CLM without the help of reanalysis data. The coupled model also provided better forecasts in decadal scales compared with the uncoupled model and the global model. The added predictability came from the initialized regional seas and better simulated sea surface temperatures by the ocean model.
The impact of the North and Baltic Seas on the climate of the surrounding regions is in certain phases dominated by the North Atlantic Oscillation (NAO) activity. In this thesis, the relation between the NAO and the marginal sea influences was studied. It is confirmed by this study that, in strong phases, the NAO can overpower the impact of the local seas. During dominant phases of NAO, the European climate is mainly governed by large-scale circulation. On the other hand, the local seas play an important role in determining the European climate when NAO is in weak phases.
The added value of the coupled model raises promising perspectives for research in this field. It points to a potential benefit of using the coupled atmosphere-ocean-ice system for climate prediction in the region surrounding the North and Baltic Seas. Along with that, it is still a challenge to complete the model representation of the climate system by adding more climate components (such as a hydrological model). Further improvement of the coupled system can be achieved by coupling for a larger sea region, or by trying to reduce remaining low performance of the coupled model in some areas with a better configuration of the current system.
Extreme convective precipitation events are among the most severe hazards in central Europe and are expected to intensify under global warming. However, the degree of intensification and the underlying processes are still uncertain. In this thesis, recent advances in continuous, radar-based precipitation monitoring and convection-permitting climate modeling are used to investigate Lagrangian properties of convective rain cells such as precipitation intensity, cell area, and precipitation sum and their relationship to large-scale, environmental conditions.
Firstly, convective precipitation objects are tracked in a gauge-adjusted radar-data set and the properties of these cells are related to large-scale environmental variables to investigate the observed super-Clausius-Clapeyron (CC) scaling of convective extreme precipitation. The Lagrangian precipitation sum of convective cells increases with dew point temperature at rates well above the CC-rate with increasing rates for higher dew point temperatures. These varying, high rates are caused by a covarying increase of CAPE with dew point temperature as well as the effect of high vertical wind shear causing an increase in cell area and thus precipitation sum. At the same time, cells move faster at high vertical wind shear so that Eulerian scaling rates are lower than Lagrangian but still above the CC-rate. The results show that wind shear and static instability need to be taken into account when transferring precipitation scaling under current climate conditions to future conditions. Secondly, the representation of convective cell properties in the convection-permitting climate model COSMO-CLM is evaluated. The model can simulate the observed frequency distributions of cell properties such as lifetime, area, mean and maximum intensity, and precipitation sum. The increase of area and intensity with lifetime is also well captured despite an underestimation of the intensity of the most severe cells. Furthermore, the model can represent the temperature scaling of intensity, area, and precipitation sum but fails to simulate the observed increase of lifetime. Thus, the model is suitable to study climatologies of convective storms in Germany. Thirdly, two COSMO-CLM projections at the end of the century under emission scenario RCP8.5 were investigated. While the number of convective cells and their lifetime remain approximately constant compared to present conditions, intensity and area increase strongly. The relative increase of intensity and area is largest for the highest percentiles meaning that extreme events intensify the most. The characteristic afternoon maximum of convective precipitation is damped, and shifted to later times of day which leads to an increase of nighttime precipitation in the future. Scaling rates of cell properties with dew point temperature are nearly identical in present and future in the simulation driven by the EC-Earth model which means that the upper limit of cell properties like intensity, area, and precipitation sum could be predicted from near-surface dew point temperature. However, this result could not be reproduced by the simulation driven by MIROC5 and needs further investigation.
Derivation and characterization of a new filter for nonlinear high-dimensional data assimilation
(2015)
Data assimilation (DA) combines model forecasts with real-world observations to achieve an optimal estimate of the state of a dynamical system. The quality of predictions in nonlinear and chaotic systems such as atmospheric or oceanic circulation is strongly sensitive to the initial conditions. Therefore, beyond the consistent reconstruction of past states, a primary relevance of advanced DA methods concerns the proper model initialization. The ensemble Kalman filter (EnKF) and its deterministic variants, mostly square root filters such as the ensemble transform Kalman filter (ETKF), represent a popular alternative to variational DA schemes. They are applied in a wide range of research and operations. Their forecast step employs an ensemble integration that fully respects the nonlinear nature of the analyzed system. In the analysis step, they implicitly assume the prior state and observation errors to be Gaussian. Consequently, in nonlinear systems, the mean and covariance of the analysis ensemble are biased and these filters remain suboptimal. In contrast, the fully nonlinear, non-Gaussian particle filter (PF) relies on Bayes' theorem without further assumptions, which guarantees an exact asymptotic behavior. However, it is exposed to weight collapse, particularly in higher-dimensional settings, known as the curse of dimensionality.
This work presents a new method to obtain an analysis ensemble with mean and covariance that exactly match the corresponding Bayesian estimates. This is achieved by a deterministic matrix square root transformation of the forecast ensemble, and subsequently a suitable random rotation that significantly contributes to filter stability while preserving the required second-order statistics. The forecast step remains as in the ETKF. The algorithm, which is fairly easy to implement and computationally efficient, is referred to as the nonlinear ensemble transform filter (NETF). The limitation with respect to fully-nonlinear filtering is that the NETF only considers the mean and covariance of the Bayesian analysis density, neglecting higher-order moments.
The properties and performance of the proposed algorithm are investigated via a set of experiments. The results indicate that such a filter formulation can increase the analysis quality, even for relatively small ensemble sizes, compared to other ensemble filters in nonlinear, non-Gaussian scenarios. They also confirm that localization enhances the applicability of this PF-inspired scheme in larger-dimensional systems. Finally, the novel filter is coupled to a large-scale ocean general circulation model with a realistic observation scenario. The NETF remains stable with a small ensemble size and shows a consistent behavior. Additionally, its analyses exhibit low estimation errors, as revealed by a comparison with a free ensemble integration and the ETKF. The results confirm that, in principle, the filter can be applied successfully and as simple as the ETKF in high-dimensional problems. No further modifications are needed, even though the algorithm is only based on the particle weights. Thus, it is able to overcome the curse of dimensionality, even in deterministic systems. This proves that the NETF constitutes a promising and user-friendly method for nonlinear high-dimensional DA.
The climate system is one of the classical examples of a complex dynamical system consisting of interacting sub-systems through mass, momentum, and energy exchange across various spatial and temporal scales. This thesis aims to detect and quantify sub-component interactions from an information exchange (IE) perspective. For this purpose, IE estimators derived from information theory are explored and applied to the available climate data obtained from observations, reanalysis, global and regional climate models. Specifically, this thesis investigates the usefulness of information theory methods for process-oriented climate model evaluation.
Firstly, methods derived from the concepts of information theory such as transfer entropy and information flow along with their linear and non-linear estimation techniques are initially tested and applied to idealized two-dimensional dynamical systems. The results revealed an expected direction and magnitude of IE providing insights into underlying dynamics. However, as expected the linear estimators are robust for linear systems but fail for non-linear systems. Though the non-linear estimators (kernel and kraskov) showed expected results for all the idealized systems, their free tuning parameters are to be tested for consistent results. Moreover, these methods are sensitive to the available time series length.
A real world example case study involving the dynamics between the Indian and Pacific oceans revealed a physically consistent bi-directional IE. However, unexpected IE was detected in the example of North Atlantic and European air temperatures indicating hidden drivers. Though IE provides insights into system dynamics, the availability of time series length and the system at hand must be carefully taken into account before inferring any possible interpretations of the results.
Quantifying the IE from El-Ni\~{n}o southern oscillation (ENSO) and Indian Ocean Dipole (IOD) to the Indian Summer Monsoon Rainfall (ISMR) with the observational and reanalysis data sets revealed that both ENSO and IOD are synergistic predictors for the inter-annual variability of the ISMR over central India i.e., the monsoon core region. Though the investigated three Global Climate Models (GCM) could not reveal the underlying IE dynamics of ENSO, IOD, and ISMR, a Regional Climate Model (RCM) simulation downscaling one of the GCMs with realistic large scale signals across the lateral boundaries showed good agreement with the observations.
Evaluating a coupled regional climate modeling system driven by two different global data sets with IE estimators revealed significant differences between the process chains linking the north-west Mediterranean sea surface temperatures, evaporation, wind speed, and the Vb-cyclone induced precipitation over Danube, Odra, and Elbe catchments in the historical period (1951-2005). Detailed investigation revealed that the north-west Mediterranean Sea in the coupled regional simulation driven by ERA-20C reanalysis corresponded to the Vb-cyclone precipitation over the three catchments while no such correspondence is noted in the EC-EARTH driven simulation. This discrepancy is attributed to the inheritance of the simulation biases from GCM into the RCM. In the future period (1965-2099), no significant changes in the processes are noted from the simulation.
Overall, this thesis used IE estimators in investigating the underlying dynamics of climate system and climate models. The estimators proved useful in providing insights into climate system dynamics assisting in a process based climate model evaluation.