<p>Urban meteorology has developed in parallel to other sub-fields in the science, but in many ways remains poorly described. In particular, the study of urban rainfall modification remains behind compared to other comparable features. Urban rainfall modification refers to the change of a precipitation feature as it crosses an urban area. Typically, this manifests as rainfall initiation, local suppression, local invigoration, and/or storm morphology changes. Research in the prior decades have shown urban rainfall modification to arise from a combination of land-atmosphere and aerosol-cloud interaction. Urban areas create a greater surface roughness, which produces local convergence and divergence, modifying local thunderstorm inflow and morphology. The land surface also generates vertical velocity perturbations which can act to initiate or modify existing convection. Urban aerosols act as CCN to perturb existing cloud and precipitation characteristics. Higher CCN narrows the cloud droplet distribution, creating more smaller cloud droplets, and initially reducing precipitation efficiency by keeping more liquid water in the cloud than what would form into rain. The CCN-cloud interaction eventually increasing heavy rainfall production as graupel riming is enhanced by the narrower cloud droplet distribution, leading to more larger raindrops and higher rain in areas.</p><p>This dissertation addresses the observation and modeling of urban thunderstorm interaction from both the land surface and aerosol perspective. It reassesses the original urban rainfall anomaly: The La Porte Anomaly. First analyzed in the late 1960s, the La Porte Anomaly was ultimately dismissed by 1980 as either a temporary, biased, or otherwise unexplainable observation, as the process level understanding had yet to be explained. The contemporary analysis utilizes all existing data and objective optimal interpolation to show that a rainfall anomaly downwind of Chicago has indeed existed at least since the 1930s. The current rainfall anomaly exists as a broad region of warm season rainfall downwind of Chicago that is 20-30% greater than the regional average. Using synoptic parameters, the rainfall anomaly is shown to be independent of wind direction and most closely associated with local land surface forcing. Weekdays, where local aerosol loading has been measured at 40% or more greater than weekends, have up to 50% more warm season rainfall than weekends. The analysis is able to show that there is a land surface and aerosol contribution to the rainfall anomaly, but cannot unambiguously separate them.</p><p>In order to separate the land surface and aerosol effects on urban rainfall distribution, a numerical model was improved to better handle urban weather interaction. The Regional Atmospheric Modeling System (RAMS 6.0) was chosen for its base land surface and cloud physics parameterization. The Town Energy Budget (TEB) urban canopy model was coupled to RAMS to handle the urban land surface. The Simple Photochemical Module (SPM) was coupled with the cloud physics to handle conversion of surface emissions to CCN. The model utilized an external traffic simulation to create a realistic diurnal and weekly cycle of surface emissions, based on human behavior. The new Urban RAMS was used to study the land surface sensitivity of city size and of aerosol loading in two studies using the Real Atmosphere Idealized Land surface (RAIL) method, by which all non-urban features of the land surface are removed to isolate the urban effects. The city size study determined that the land surface of a given city eventually has a maximum effect on thunderstorm modifying potential, and that rainfall does not continue to increase or decrease locally for cities larger than a certain size based on that storm’s own motion. The aerosol-cloud analysis corroborated previous observations on the non-linear effects of aerosol loading on clouds. It also demonstrated that understanding the aerosol effect in an urban environment requires high resolution observations of precipitation change. In a single thunderstorm, regions can be both impacted by local rainfall rate increases and decreases from urban aerosols, leading to little total change in precipitation. But the rainfall rate changes can significantly affect soil moisture and drought potential in and around urban areas.Following the idealized studies, the historical and current La Porte Anomaly was simulated to separate the land surface from the aerosol factors near the Chicago area. The Urban RAMS model was deployed on a real land surface with full model physics. Simulations with 1932, 1962, 1992, and 2012 land covers were run over an exceptionally wet Aug. 2007 to approximate the rain variability for an entire summer season. Surface emissions were also varied in the 2012 land cover for variable aerosol loading. The simulations successfully reproduced the location of the downwind rainfall anomaly in each land cover scenario: farther east toward La Porte in 1932, moving southwestward to its current location by 2012. Doubling surface emissions eliminated the downwind anomaly, as was observed during the highest pollution decade of the 1970s. Eliminating surface emissions also decreased the downwind anomaly. As the land cover at the upwind edge of Chicago became more connected from the 1932 to 2012 land cover scenarios, a local upwind rainfall anomaly developed, moving westward with urban expansion. The results of these simulations enabled the conclusions that a) at the upwind edge, the land surface dominates urban rainfall modification, b) the aerosol loading sustains and increases the locally downwind rainfall increase, and c) that the total modification distance is static on given day and given urban footprint. A more expansive city does not produce a rainfall anomaly more distantly downwind, but rather the distance of rainfall modification moves to where the upwind edge of the city begins.</p><p></p><p>The modeling work ends with a two-city simulation in the southeast United States, of a bow-echo forming near Memphis, TN and crossing Birmingham, AL before splitting. Simulations were performed on different surface emissions rates, land covers where Birmingham did not exist, and a novel approach with two inner emitting grids over both Birmingham and Memphis. A storm tracking algorithm enabled one-to-one comparisons of point simulated storm characteristics between scenarios. The results of most scenarios only corroborated previous research, showing how increased aerosol loading changes cloud and rainfall characteristics until the highest aerosol loading shuts down riming and rainfall enhancement. However, the two most accurate simulations, where the storm forms and splits over Birmingham, were a non-urban higher rural aerosol scenario and the scenario with Memphis also emitting pollution. In order to split the storm over Birmingham, the upwind cloud characteristics were primed by higher upwind aerosols, either from a realistic city upwind or unrealistically high rural aerosols. The conclusions produced by this study demonstrated the importance of aerosol cloud interaction, perhaps equal with land surface, but also the need for far upwind information for a storm in a given city. Memphis and Birmingham are separated by over 300km, far exceeding the threshold thought to connect two cities by mutual rainfall modification.</p><p>The overall conclusions of the research presented in this dissertation shows a more unified approach to the effects of urban rainfall modification. The upwind edge of a city is a fixed location, and a thunderstorm begins modifying at that point. The thunderstorm usually produces a local rainfall maximum at the upwind edge, due to the vertical velocity of the urban land surface. The urban aerosols proceed to narrow the cloud droplet distribution, locally reducing rainfall as the storm passes over the urban area. Eventually the enhanced rainfall from enhanced riming produces a maximum somewhere downwind. However, “downwind” is a location relative to the storm’s motion and could exist anywhere over the urban footprint or downwind in a rural region. The climatological location of increased rainfall is an average of every storm in a season and beyond. The results of each part of the study provide a way to continue the research presented here.</p><br>

Vegetation controls the exchange of heat, mass and momentum between the land surface and the atmosphere, and is also the primary producer that sustains life on Earth. We combine theoretical analyses, satellite and in-situ observations, and Earth system model simulations in this dissertation to illustrate the key role of vegetation in the climate system and human society. Specifically, this is accomplished via three studies, described below. First, we address the problem of how to retrieve Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FPAR) from a novel satellite Bidirectional Reflectance Factor product derived from the Multi-Angle Implementation of Atmospheric Correction algorithm. The LAI/FPAR retrieval is done via a radiative transfer model using the recently developed theory of spectral invariants. Our analyses show that the LAI/FPAR data sets developed in this study have higher accuracy and better stability relative to the existing products, especially in cloudy conditions and under high aerosol loadings. Second, we analyze the long-term trend in LAI derived from the Moderate Resolution Imaging Spectroradiometer observations and identify its main driver. We find that over a third of the terrestrial vegetation shows statistically significant increasing trends in LAI (i.e., Earth greening) during the 21st century. Both remote sensing and inventory data show that land-use management is the key driver of this greening, arising primarily from large-scale tree planting and intensive agriculture in emerging countries like China and India. This finding highlights the need for a more realistic representation of land-use practices in Earth system models. Third, we use a new method based on the concept of “two-resistances” and the Community Land Model (CLM5) runs with prescribed satellite-derived LAI to quantify the impacts of Earth greening on land surface temperature (LST). We find that over 90% of the Earth greening can lead to a local cooling effect at the annual scale. Further attribution analysis with multiple data sources reveals that aerodynamic resistance is the dominant factor controlling the LST change. The greening produces a decrease in aerodynamic resistance, which favors increased heat dissipation by turbulent fluxes, including the latent heat flux. These studies that span LAI data production, long-term trends and their impacts highlight the importance of vegetation dynamics in the natural and human systems.

Environmental monitoring of Earth from space has provided invaluable information for understanding the land-atmosphere water and energy exchanges. However, the use of satellite observations in hydrologic applications is often limited by coarse space-time resolutions. This study aims to develop a data assimilation system that integrates remotely-sensed precipitation and soil moisture observations into physically-based models to produce fine-scale precipitation, soil moisture, and other relevant hydrometeorological variables. This is particularly useful with the active Global Precipitation Measurement and Soil Moisture Active Passive missions. The system consists of two major components: (1) a framework for dynamic downscaling of satellite precipitation products using the Weather Research and Forecasting (WRF) model with four-dimensional variational data assimilation (4D-Var) and (2) a variational data assimilation system using spatio-temporally varying background error covariance for directly assimilating satellite soil moisture data into the Noah land surface model coupled with the WRF model. The WRF 4D-Var system can effectively assimilate and downscale six-hour precipitation products of a spatial resolution of about 20 km (i.e., those derived from the National Centers for Environmental Prediction Stage IV data and the Tropical Rainfall Measuring Mission (TRMM) 3B42 dataset) to hourly precipitation with a spatial resolution of less than 10 km. The system is able to assimilate and downscale daily soil moisture products at a gridded 36-km resolution obtained from the Soil Moisture and Ocean Salinity (SMOS) mission to produce hourly 4-by-4 km surface soil moisture forecasts with a reduction of mean absolute error by 35% on average. The results from the system with coupled components show that assimilation of the TRMM 3B42 precipitation improves the quality of both downscaled precipitation and soil moisture analyses, while the effect of SMOS soil moisture data assimilation is largely on the soil moisture analyses. The downscaled WRF precipitation, with and without assimilation of TRMM precipitation, was preliminarily tested with a spatially distributed simulation of streamflow using the TIN (Triangular Irregular Network)-based Real-time Integrated Basin Simulator (tRIBS).

In dieser Studie wird ein neues Modul “CLM-Palm” für mehrjährige Nutzpflanzen zur Modellierung einer funktionellen Gruppe (plant functional type) für Ölpalmen im Rahmen des Community Land Models (CLM4.5) entwickelt, um die Auswirkungen der Transformation eines tropischen Waldes in eine Ölpalmenplantage auf die Kohlenstoff-, Wasser- und Energieflüsse zwischen Land und Atmosphäre zu quantifizieren. Um die Morphologie der Ölpalme möglichst detailgetreu darzustellen (das heißt, dass ungefähr 40 Phytomere einen mehrschichtigen Kronenraum formen), wird in dem Modul CLM-Palm eine phänologische und  physiologische Parametrisierung auf Skalen unterhalb des Kronraums eingeführt, so dass jedem Phytomer sein eigenes prognostisches Blattwachstum und seine Erntekapazität zugeordnet wird, während Stamm und Wurzeln gemeinsam genutzt werden. Das Modul CLM-Palm wurde ausschließlich für Ölpalmen getestet, ist aber auch für andere Palmarten (z. B. Kokospalmen) interessant.  Im ersten Kapitel dieser Arbeit werden Hintergrund und Motivation dieser Arbeit vorgestellt. In Kapitel 2 wird die Entwicklung des Haupt- bzw. Kernmodells beschrieben,  inklusive Phänologie und Allokationsfunktionen zur Simulation des Wachstums und des Ertrags der Palme PFT, wodurch die Basis zur Modellierung  der biophysikalischen und biogeochemicalischen Kreisläufe innerhalb dieser Monokultur bereitgestellt wird. Die neuen Parameter für die Phänologie und die Allokation wurden sorgfältig mit Feldmessungen des Blattflächenindexes (LAI), des Ertrags und der Nettoprimärproduktion (NPP) verschiedener Ölpalmenplantagen auf Sumatra (Indonesien) kalibriert und validiert. Die Validierung zeigte die Eignung von CLM-Palm zur adäquaten Vorhersage des mittleren Blattwachstums und Ertrags für verschiedene Standorte und repräsentiert in ausreichendem Maß die signifikante Variabilität bezüglich des Stickstoffs und Alters von Standort zu Standort.  In Kapitel 3 wird die weitere Modellentwicklung und die Implementierung eines Norman-Mehrschichtmodells für den Strahlungstransport vorgestellt, das an den  mehrschichtigen Kronenraum der Ölpalme angepasst ist. Dieses Norman-Mehrschichtmodell des Strahlungstransports zeigte im Vergleich zu dem in CLM4.5 implementierten Standardmodell (basierend auf großen Blättern) bei der Simulation der Licht-Photosynthese-Kurve leichte Verbesserungen und hat  lediglich marginale Vorteile gegenüber dem ebenfalls in CLM4.5 implementierten alternativen statistischen Mehrschichtmodell.  Dennoch liefert das Norman-Modell eine detailliertere und realistischere Repräsentation des Belaubungszustands wie etwa dem dynamischen LAI, der Blattwinkelverteilung in verschiedenen Höhen, und ein ausgewogeneres Profil der absorbierten photosynthetisch aktiven Strahlung (PAR). Die Validierung mit Hilfe der Eddy-Kovarianz Flussdaten zeigte die Stärke von CLM-Palm bei der Simulation der Kohlenstoffflüsse, offenbarte aber auch Abweichungen in der simulierten Evapotranspiration (ET), dem sensiblen und dem latenten Wärmefluss (H und LE). Eine Reihe von hydrologischen Messungen im Kronenraum wird in Kapitel 4 beschrieben. Dies beinhaltet eine Adaption des in CLM4.5 eingebauten Standardmodells für Niederschlag, Interzeption und Speicherfunktionen für die speziellen Merkmale eines Ölpalmen-Kronenraums. Die überarbeitete Hydrologie des Kronenraums behob die Probleme bei der Simulation der Wasserflüsse (ET und Transpiration im Kronenraum) und verbesserte die Energieaufteilung zwischen H und LE. Kapitel 5 dokumentiert die Implementierung eines neuen dynamischen Modells für Stickstoff (nitrogen, N) in CLM-Palm zur Verbesserung der Simulation der C- und N-Dynamik, insbesondere mit Bezug auf den N-Düngeeffekte in landwirtschaftlich genutzten Systemen. Das dynamische N-Modell durchbricht die Limitierung des Standardmodells in CLM4.5, mit fixierter C-N-Stöchiometrie und erlaubt die Variation des C:N-Verhältnisses in lebendem Gewebe in Abhängigkeit der N-Verfügbarkeit und dem N-Bedarf der Pflanze.  Eine Reihe von Tests bezüglich der Düngung zeigte beispielhaft die Vorteile des dynamischen N-Modells, wie zum Beispiel die Verbesserung des Netto-Ökosystemaustauschs (net ecosystem exchange, NEE), ein realistischeres C:N-Verhältnis im Blatt, eine verbesserte Repräsentation der Effizienz des Stickstoffeinsatzes (nitrogen-use efficiency, NUE), sowie der Effekte von Düngung auf Wachstum und Ertrag. Abschließend wird in Kapitel 6 eine Anwendungsstudie gezeigt, in der die zentralen Modellentwicklungen aus den vorangegangenen Kapiteln verwendet werden. Eine junge und eine  erntereife Ölpalmenplantage sowie ein Primärregenwald wurden simuliert und verglichen. Sie wiesen klare Unterschiede in den C-Flüssen und in den biophysikalischen Merkmalen (z.B. ET und Oberflächentemperatur) auf. Ölpalmenplantagen können durch Wachstumsentwicklung (im Alter von etwa 4 Jahren)  ebenso hohe und darüber hinausgehende C-Assimilation und Wassernutzungsraten erreichen wie Regenwälder, haben jedoch im Allgemeinen eine höhere Oberflächentemperatur als eine bewaldete Fläche – dies gilt auch für erntereife Plantagen. Eine Simulation des Übergangs, die zwei Rotationsperioden mit Neubepflanzungen alle 25 Jahre umspannt, zeigte dass der Anbau von Ölpalmen auf längeren Zeitskalen lediglich in etwa die Hälfte des ursprünglichen C-Speichers der bewaldeten Fläche vor dem Kahlschlag  rückspeichern kann. Das im Boden gespeicherte C nimmt in einer bewirtschafteten Plantage aufgrund des begrenzten Streurücklaufs langsam und graduell ab. Insgesamt reduziert die Umwandlung eines Regenwaldes in eine Ölpalmenplantage die langfristigen C-Speicher und die Kapazität der Fläche zur C-Sequestrierung und trägt potentiell zur Erwärmung der Landoberfläche bei – trotz des schnellen Wachstums und der hohen C-Assimilationsrate einer stark gedüngten Plantage. Zur Einschätzung der regionalen und globalen Effekte der Ausbreitung der Kultivierung von Ölpalmen auf die Austauschprozesse zwischen Land und Atmosphäre und auf das Klima ist es notwendig eine Upscaling-Studie durchzuführen.

<p>Large-scale coherent structures are systematically investigated in terms of their geometric attributes, importance toward describing turbulent exchange of energy, momentum and mass as well as their relationship to landscape features in the context of land-atmosphere interaction. In the first chapter, we present the motivation of this work as well as a background review of large-scale coherent structures in land-atmosphere interaction. In the second chapter, the methodology of large-eddy simulation (LES) and the proper orthogonal decomposition (POD) is introduced. LES was used to serve as a virtual laboratory to simulate typical scenarios in land-atmosphere interaction and the POD was used as the major technique to educe the coherent structures from turbulent flows in land-atmosphere interaction. In the third chapter, we justify the use of the LES to simulate the realistic coherent structures in the atmospheric boundary layer (ABL) by comparing results obtained from LES of the ABL and direct numerical simulation (DNS) of channel flow. In the fourth chapter, we investigate the effects of a wide range of vegetation density on the coherent structures within the air space within and just above the canopy (the so-called canopy sublayer, CSL). The fifth chapter presents an analysis of the coherent structures across a periodic forest-clearing-forest transition in the steamwise direction. The sixth chapter focuses on the role of coherent structures in explaining scalar dissimilarity in the CSL. The seventh chapter summarizes this dissertation and provides suggestions for future study.</p> / Dissertation

Land-atmosphere interaction includes complex feedbacks among radiative, hydrological, and ecological processes, and the understanding of it is hindered by many factors such as the heterogeneity of land surface properties, the chaotic nature of the atmosphere, and the lack of observational data. In this study, several different methods are used to investigate the land-atmosphere interaction processes and their relationship with climate variability. Firstly, a simple one-dimensional model is developed to simulate the dominant soil-vegetation-atmosphere interaction processes in the warm climate. Although the physical processes are described coarsely, the model can be more easily used to find some relationships which may be drown out or distorted by noise. The influence of land on climate variability mainly lies in it memory, which is greatly related with the atmospheric forcing, so this model is used to investigate the influence of different forcing strengths on land-atmosphere interaction and its difference at different land covers. The findings from the simple model can provide guidance for other studies. The second part of the study compares a lagged soil moisture-precipitation (S-P) correlation (soil moisture in current day and precipitation in future 30 days) in three atmospheric reanalysis products (ERA-40, NCEP/DOE reanalysis-2, and North American Regional Reanalysis (NARR)), Global Soil Wetness Project Phase 2 (GSWP-2) data, and NCAR CAM3 simulations. Different datasets and model simulations come to a similar negative-dominant S-P correlation pattern. This is different from the traditional view that the soil moisture should have positive influence on future precipitation. Further analysis shows that this correlation pattern is not caused by the soil moisture feedback but due to the combined effect of the precipitation oscillation and the memory of soil moisture. Theoretical analysis confirms the above results and finds that the precipitation time series with the strongest oscillation at 32-60 day period is most likely to induce a significantly negative S-P correlation, and regions with longer soil water retention time are more likely to have a significantly negative S-P correlation. This study illustrates that a lagged correlation does not always indicate a causal relation.

<p>Accurate modeling of land-atmosphere interaction would help us understand the persistent weather conditions and further contribute to the skill of seasonal climate prediction. In this study, seasonal variations in radiation and precipitation forcing are included in a stochastic soil water balance model to explore the seasonal evolution of soil moisture probabilistic structure. The theoretical results show soil moisture tends to exhibit bimodal behavior only in summer when there are strong positive feedback from soil moisture to subsequent rainfall. Besides the statistical analysis of soil moisture – rainfall feedback, simplified mixed-layer models, coupled with soil-plant-atmosphere continuum, are also used to study heat flux partitioning, cloud initiation, and strength of moist convection. Approximate analytical solutions to the mixed-layer model are derived by applying Penman-Monteith approach, which help explain the roles of equilibrium evaporation and vapor pressure deficit in controlling the diurnal evolution of boundary layer. Results from mixed-layer model also define four regimes for possible convection in terms of cloud/no-cloud formation and low/high convection intensity. Finally, cloud-topped mixed-layer model is developed to simulate the boundary-layer dynamics after the cloud formation, when the evaporative and radiative cooling other than surface heat flux may significantly contribute to the growth of the boundary layer.</p> / Dissertation

<p>Methane (CH₄) is the second most powerful greenhouse gas (GHG) behind carbon dioxide (CO₂), and is able to trap a large amount of long-wave radiation, leading to surface warming. Carbon monoxide (CO) plays an important role in controlling the oxidizing capacity of the atmosphere by reacting with OH radicals that affect atmospheric CH₄ dynamics. Terrestrial ecosystems play an important role in determining the amount of these gases into the atmosphere. However, global quantifications of CH₄ emissions from wetlands and its sinks from uplands, and CO exchanges between land and the atmosphere are still fraught with large uncertainties, presenting a big challenge to interpret complex atmospheric CH₄ dynamics in recent decades. In this dissertation, I apply modeling approaches to estimate the global CH₄ and CO exchanges between land ecosystems and the atmosphere and analyze how they respond to contemporary and future climate change.</p> <p>Firstly, I develop a process-based biogeochemistry model embedded in Terrestrial Ecosystem Model (TEM) to quantify the CO exchange between soils and the atmosphere at the global scale (Chapter 2). Parameterizations were conducted by using the CO <i>in situ</i> data for eleven representative ecosystem types. The model is then extrapolated to global terrestrial ecosystems. Globally soils act as a sink of atmospheric CO. Areas near the equator, Eastern US, Europe and eastern Asia will be the largest sink regions due to their optimum soil moisture and high temperature. The annual global soil net flux of atmospheric CO is primarily controlled by air temperature, soil temperature, SOC and atmospheric CO concentrations, while its monthly variation is mainly determined by air temperature, precipitation, soil temperature and soil moisture. </p> <p>Secondly, to better quantify the global CH₄ emissions from wetlands and their uncertainties, I revise, parameterize and verify a process-based biogeochemical model for methane for various wetland ecosystems (Chapter 3). The model is then extrapolated to the global scale to quantify the uncertainty induced from four different types of uncertainty sources including parameterization, wetland type distribution, wetland area distribution and meteorological input. Spatially, the northeast US and Amazon are two hotspots of CH₄ emissions, while consumption hotspots are in the eastern US and eastern China. The relationships between both wetland emissions and upland consumption and El Niño and La Niña events are analyzed. This study highlights the need for more in situ methane flux data, more accurate wetland type and area distribution information to better constrain the model uncertainty.</p> <p>Thirdly, to further constrain the global wetland CH₄ emissions, I develop a predictive model of CH₄ emissions using an artificial neural network (ANN) approach and available field observations of CH₄ fluxes (Chapter 4). Eleven explanatory variables including three transient climate variables (precipitation, air temperature and solar radiation) and eight static soil property variables are considered in developing the ANN models. The models are then extrapolated to the global scale to estimate monthly CH₄ emissions from 1979 to 2099. Significant interannual and seasonal variations of wetland CH₄ emissions exist in the past four decades, and the emissions in this period are most sensitive to variations in solar radiation and air temperature. This study reduced the uncertainty in global CH₄ emissions from wetlands and called for better characterizing variations of wetland areas and water table position and more long-term observations of CH₄ fluxes in tropical regions.</p> <p>Finally, in order to study a new pathway of CH₄ emissions from palm tree stem, I develop a two-dimensional diffusion model. The model is optimized using field data of methane emissions from palm tree stems (Chapter 5). The model is then extrapolated to Pastaza-Marañón foreland basin (PMFB) in Peru by using a process-based biogeochemical model. To our knowledge, this is among the first efforts to quantify regional CH₄ emissions through this pathway. The estimates can be improved by considering the effects of changes in temperature, precipitation and radiation and using long-period continuous flux observations. Regional and global estimates of CH₄ emissions through this pathway can be further constrained using more accurate palm swamp classification and spatial distribution data of palm trees at the global scale.</p>