Dissertationen zum Thema „Atmospheric reanalyses“

The proliferation of reanalysis models for the atmosphere in recent decades has allowed researchers to study Earth’s past climate in great detail. While much work has gone into understanding key climate indicators such as surface temperature and precipitation trends, there have been few studies dealing with heat stress. As climate change grows increasingly exigent, it is becoming vitally important to understand the thermal impacts on biological systems.

This study analyzed data from five reanalysis models (20CRv2, NARR, NNRA 1, NCEP DOE 2, and ERA-I) and found agreement in average surface temperature increases of 0.2–0.6°C per decade across the U.S. west coast and east coast since 1979. These trends were consistent with previous studies. Less agreement was found for the central U.S. The Temperature Humidity Index and the Heat Index were found to generally follow the temperature trends. An analysis of the role of moisture indicated that the effect of specific humidity on heat stress is dependent on climatology. Trends of heat stress over arid regions such as the desert southwest were found to be much more influenced by temperature trends than by moisture trends. In contrast, moisture seemed to play a stronger role in the more humid southeast. There appeared to be a more equal effect of temperature and moisture on heat stress in the northeast and Great Lake states.

Perhaps equally as important, the study provides a framework to reduce computational time but allows for more rigorous statistical methods that are not available in the typical suite of software and programming languages to analyze climate data. Functionality was developed to infer daily extrema from six-hourly reanalysis data. A shapefile was used to aggregate the data according to prescribed geographic boundaries and reduce the load of data for statistical analysis. Time series decomposition was performed on the aggregated daily data to determine linear trends which were then mapped out to visualize their spatial features.

Reanalysis products are widely used to study the land-atmosphere exchanges of energy, water, and carbon fluxes, and have been evaluated using in situ data above or below ground. Here measurements for several years at five flux tower sites in the U.S. (with a total of 315,576 hours of data) are used for the coupled evaluation of both below- and above-ground processes from three global reanalysis products and six global land data assimilation products. All products show systematic errors in precipitation, snow depth, and the timing of the melting and onset of snow. Despite the biases in soil moisture, all products show significant correlations with observed daily soil moisture for the periods with unfrozen soil. While errors in 2 meter air temperature are highly correlated with errors in skin temperature for all sites, the correlations between skin and soil temperature errors are weaker, particularly over the sites with seasonal snow. While net shortwave and longwave radiation flux errors have opposite signs across all products, the net radiation and ground heat flux errors are usually smaller in magnitude than turbulent flux errors. On the other hand, the all-product averages usually agree well with the observations on the evaporative fraction, defined as the ratio of latent heat over the sum of latent and sensible heat fluxes. This study identifies the strengths and weaknesses of these widely-used products, and helps understand the connection of their errors in above- versus below-ground quantities.

Hurricane Hazel struck North America on 15-16 October 1954, leaving a pattern of heavy rainfall and flooding in its wake. A complete analysis of the synoptic-scale conditions associated with the transformation of Hazel from its tropical phase into an extratropical cyclone was first undertaken to discern the dynamic and thermodynamic elements crucial to the intensification of this storm.
An analogue search was then conducted for Hazel using linear correlations of anomaly sea level pressure and 1000-500 hPa thickness. Three cases were found in 1985, 1995 and 1999. A comparison of these analogues to Hazel yielded the conclusion that Hazel is a unique event in recent meteorological history, as none of the analogues produces the extreme precipitation values in Hazel. The lack of significant hurricane circulations in all of the analogues is the important difference, as Hazel provides important moisture and latent heating that are absent in the analogues.
Finally a mesoscale modeling study was carried out to test the sensitivity of Hazel to improved surface vortex structure and increased horizontal resolution. Specification of the vortex led to a dramatic improvement in the simulation results, as precipitation and track closely mimicked the observed values. Enhancing the horizontal resolution to 12 km did not improve upon the 36 km specified vortex simulation. The movement of the storm slowed considerably as the development of an upper tropospheric cutoff circulation was diminished in the 12 km run. The parameterizations governing the interaction between the diabatic outflow from Hazel and dynamics of the midlatitude trough are poorly modeled in this situation, and lead to the severe time lag in the path of Hazel.

Global data and numerical models are used to investigate the climatology and variability in the upper-troposphere and lower-stratosphere where large-scale low-frequency vorticity modes dominate over high-frequency inertia-gravity waves. An algorithm based on the Empirical Normal Mode decomposition technique is proposed as a diagnostic tool. This technique combines dynamics (a wave-activity of linear dynamics defines the orthogonality between modes) with statistics (the wave-activity amplitude of each mode provides a measure of its statistical significance) and has the unbiased ability to capture neutral normal modes as well as singular modes.
First, the algorithm is applied to winter data provided by the National Centers for Environmental Prediction (NCEP) reanalyses and results indicate that most wave-activity is carried by large-scale, eastward-propagating modes centered at mid- and high-latitudes. The phase-speeds of some leading modes are in good agreement with theoretical predictions of linear dynamics. Some mid-latitude modes exhibit properties that can be explained by the theory of quasi-modes---for example, the leading wavenumber-5 mode with a dipolar pressure field near the tropopause, a propagation speed of 12 m s-1 and a decay rate of 3 days.
In a model study, we use data from two dynamical-core experiments of the Global Environmental Multiscale model: one with the forcing proposed by Held and Suarez, later modified by Williamson et al. (called HSW experiment); the other with the forcing by Boer and Denis (BD). Modes and spectra are similar to those found in the NCEP data study, although details depend on the forcing. For instance, wave-energy amplitudes are higher with the BD forcing and an approximate energy equipartition is observed in the spectrum of wavenumber-1 modes in the studies of NCEP data and BD experiment, but not in the HSW experiment.
The HSW forcing has a relatively strong relaxation acting on the complete temperature field, whereas the BD forcing only acts on the zonal-mean temperature letting the internal dynamics alone drive the wave-activity cascade through the rest of the spectrum. This difference seems to explain why the BD forcing is more successful in reproducing the observed atmospheric wave-activity.

This work approaches the topic of modeling the atmospheric boundary layer in four research projects, which are summarized below. i) The diurnal cycles of near-surface meteorological parameters over Antarctic sea ice in six widely used atmospheric reanalyses were validated against observations from Ice Station Weddell. The station drifted from February through May 1992 and provided the most extensive set of meteorological observations ever collected in the Antarctic sea ice zone. For the radiative and turbulent surface fluxes, both the amplitude and shape of the diurnal cycles varied considerably among different reanalyses. Near-surface temperature, specific humidity, and wind speed in the reanalyses all featured small diurnal ranges, which, in most cases, fell within the uncertainties of the observed cycle. A skill score approach revealed the superiority of the ERA-Interim reanalysis in reproducing the observed diurnal cycles. An explanation for the shortcomings in the reanalyses is their failure to capture the diurnal cycle in cloud cover fraction, which leads to errors in other quantities as well. Apart from the diurnal cycles, NCEP-CFSR gave the best error statistics. ii) The accuracy of prediction of stable atmospheric boundary layers depends on the parameterization of the surface layer which is usually derived from the Monin-Obukhov similarity theory. In this study, several surface-layer models in the format of velocity and potential temperature Deacon numbers were compared to observations from CASES-99, Cardington, and Halley datasets. The comparisons were hindered by a large amount of scatter within and among datasets. Tests utilizing R2 demonstrated that the Quasi-Normal Scale Elimination (QNSE) theory exhibits the best overall performance. Further proof of this was provided by 1D simulations with the Weather Research and Forecasting (WRF) model. iii) The increasing number of physics parameterization schemes adopted in numerical weather forecasting models has resulted in a proliferation of inter-comparison studies in recent years. Many of these studies concentrated on determining which parameterization yields results closest to observations rather than analyzing the reasons underlying the differences. In this work, the performance of two 1.5-order boundary layer parameterizations was studied, the QNSE and Mellor-Yamada-Janjić (MYJ) schemes, in the Weather Research and Forecasting (WRF) model. The objectives were to isolate the effect of stability functions on the near-surface values and vertical profiles of virtual temperature, mixing ratio and wind speed. The results demonstrate that the QNSE stability functions yield better error statistics for 2-m virtual temperature but higher up the errors related to QNSE are slightly larger for virtual temperature and mixing ratio. A surprising finding is the sensitivity of the model results to the choice of the turbulent Prandtl number for neutral stratification (Prt0): in the Monin-Obukhov similarity function for heat, the choice of Prt0 is sometimes more important than the functional form of the similarity function itself. There is a stability-related dependence to this sensitivity: with increasing near-surface stability, the relative importance of the functional form increases. In near-neutral conditions, QNSE exhibits too strong vertical mixing attributed to the applied turbulent kinetic energy subroutine and the stability functions including the effect of Prt0. iv) In recent years, many eddy-diffusivity mass flux (EDMF) planetary boundary layer (PBL) parameterizations have been introduced. Yet, most validations are based on idealized setups and/or single column models. To address this gap, this study focused on the effect the mass flux part has on the performance in the QNSE-EDMF PBL scheme in the WRF model by comparing the results to observations from the CASES-97 field campaign. In addition, two refined versions, one introducing the parameterized clouds to the WRF radiation scheme, and the second adding a different entrainment formulation, were evaluated. The introduction of mass flux reduced errors in the average moisture profile but virtual temperature and wind speed profiles did not change as much. The turbulent flux profiles for modeled virtual potential temperature were little affected, with consistent reasonable agreement with observations, if one allows for biases in the observed data and modeled surface fluxes. However, the water vapor flux divergences from QNSE tend to be more negative than observed, while including the mass flux part tends to make the divergences more positive, the latter at least partially due to deeper model PBLs resulting from excessive model surface virtual temperature fluxes. Further, both virtual potential temperature and water vapor flux profiles display spurious spikes attributed to the way the non-local and local terms interact in the model. The influence of the mass flux schemes extends to 60 – 100-km scale circulation features, which were greatly modified by both the inclusion of mass flux and the new entrainment formulation. Adding mass flux based clouds to the radiation calculation improved the time and space averaged modeled incoming shortwave flux. The choice of the representation for entrainment/detrainment often affected the results to the same extent as adding mass flux did.

L'homogénéisation est une étape importante et cruciale pour améliorer l'utilisation des données d'observation pour l'analyse du climat. Ce travail est motivé par l'analyse de les données journalières de Contenu Intégré en Vapeur d’Eau (CIVE) mesurées par GNSS (Global Navigation Satellite Systems), appelées GNSS CIVE (IWV Integrated Water Vapor en anglais) qui n'ont pas encore été utilisées dans ce contexte. Ces séries sont affectées par des inhomogénéités liées à des changements dans l'instrumentation, dans l'environnement et dans la procédure de traitement des données. En raison de la variabilité naturelle de la série, nous travaillons en fait sur la série chronologique des différences, en utilisant la réanalyse ERA-Interim comme référence pour le signal climatique. Une hypothèse de base est que les différences contiennent seulement la signature des changements brusques de la série GNSS qui peuvent être détectés au moyen d'un algorithme de segmentation. Une analyse minutieuse des résultats de la segmentation permet de trier les cas où cette hypothèse n'est en fait pas vraie. La principale contribution de cette thèse a été le développement d'une nouvelle méthode de segmentation dédiée à la détection des changements dans la moyenne de la série de différences GNSS-ERA-Interim CIVE. Ce modèle de segmentation intègre un biais périodique et une variance hétérogène, variable mensuellement, pour s'adapter correctement aux caractéristiques de la série. La méthode consiste à estimer d'abord la variance à l'aide d'un estimateur robuste puis à estimer les paramètres de segmentation (les positions des points de changement, les moyennes des segments) et le modèle de biais périodique de manière séquentielle. Les paramètres de segmentation et le modèle de biais périodique sont estimés de manière itérative pour un nombre fixe de points de changement. L'inférence est obtenue par la procédure classique du maximum de vraisemblance en utilisant l'algorithme de programmation dynamique pour l'estimation des paramètres de segmentation qui fournit la solution exacte dans un laps de temps raisonnable. La procédure est répétée pour tous les nombres de points de changement testés entre 0 et un maximum (environ 30). Enfin, le nombre optimal de points de changement est choisi en utilisant une stratégie de sélection de modèle pénalisée. Plusieurs critères sont testés. La méthode est implémentée dans le package R GNSSseg disponible sur CRAN. Les performances de la méthode proposée ont été évaluées par des simulations numériques. Une application pour un ensemble de données réel de 120 stations GNSS mondiales dans le réseau mondial IGS est présentée pour la période de janvier 1995 à décembre 2010. L'inspection des résultats révèle que les points de changement détectés contiennent une fraction (~20%) de valeurs aberrantes qui se caractérisent par des détections doubles avec deux grands décalages, généralement de signes opposés, rapprochés, p.ex. à quelques dizaines de jours d'intervalle. Afin de détecter et d'éliminer les valeurs aberrantes, une méthode de dépistage a été développée. L'ensemble final de points de changement est validé par rapport aux métadonnées GNSS qui contiennent des informations sur les changements d'équipement survenus dans les stations. Le pourcentage de validation reste modéré au niveau de 20% malgré tous les changements sont statistiquement significatifs. Certains des points de changement peuvent en fait être dus à la série de référence (ERA-Interim). Enfin, les informations de segmentation (dates des points de changement) sont incluses dans un algorithme de régression linéaire qui est utilisé pour estimer les tendances GNSS CIVE. Les tendances estimées sont testées pour leur signification et comparées aux tendances ERA-Interim. Une plus grande cohérence spatiale dans les tendances GNSS et une meilleure cohérence sont trouvées après l'homogénéisation avec ERA-Interim dans les régions où la réanalyse est connue pour ses performances
Homogenization is an important and crucial step to improve the use of observational data for climate analysis. This work is motivated by the analysis of long GNSS Integrated Water Vapor (IWV) data which have not yet been used in this context. These series are affected by inhomogeneities linked to changes in the instrumentation, in the environment, and in the data processing procedure. Due to the natural variability of the series we actually work on the time series of differences, using ERA-Interim reanalysis as reference for the climate signal. A base assumption is that the differences contain only the signature of the abrupt changes from the GNSS series which can be detected by means of a segmentation algorithm. Careful analysis of the segmentation results allows to sort the cases when this assumption is actually not true. The main contribution of this thesis was the development a novel segmentation method dedicated to detecting changes in the mean of the GNSS-ERA-Interim IWV difference series. This segmentation model integrates a periodic bias and a heterogeneous, monthly varying, variance to properly fit the characteristics of the series. The method consists of first estimating the variance using a robust estimator and then estimating the segmentation parameters (the positions of the change-points, the means of the segments) and the periodic bias model in a sequential way. The segmentation parameters and the periodic bias model are estimated iteratively for a fixed number of change-points. The inference is achieved by the classical maximum likelihood procedure using the dynamic programming algorithm for the estimation of the segmentation parameters which provides the exact solution in a reasonable amount of time. The procedure is repeated for all the numbers of change-points tested between 0 and a maximum (about 30). Finally, the optimal number of change-points is chosen using a penalized model selection strategy. Several criteria are tested. The method is implemented in the R GNSSseg package available on CRAN. The performance of the proposed method was evaluated by numerical simulations. An application for a real dataset of 120 global GNSS stations in the global IGS network is presented for the period from January 1995 to December 2010. Inspection of the results reveals that the detected change-points contain a fraction (~ 20 %) of outliers which are characterized by double detections with two large offsets, generally of opposite signs, close together, e.g. a few tens of days apart. In order to detect and eliminate the outliers a screening method was developed. The final set of change-points is validated with respect to GNSS metadata which contain information on equipment changes that occurred at the stations. The percentage of validation remains moderate at the level of 20 % despite all the changes are statistically significant. Some of the change-points may actually be due to the reference series (ERA-Interim). Finally, the segmentation information (dates of the change-points) is included in a linear regression algorithm which is used to estimate the GNSS IWV trends. The estimated trends are tested for significance and compared to the ERA-Interim trends. Higher spatial consistency in the GNSS trends and improved consistency is found after homogenisation with ERA-Interim in regions where the reanalysis is known to perform well. [...]

Weather and climate forecasts are almost exclusively produced by computer models, which use atmospheric measurements as starting points. It is a well-known and joked-about fact that model predictions can be incorrect at times. One of the reasons this happens is due to gaps in our knowledge of atmospheric conditions in areas where measurements don’t often taken place, such as the mesosphere, which stretches from roughly 45-90 km altitude. A lidar is a device that can shoot out short bursts of laser light to measure things such as atmospheric thickness at a distance. From this information one can then derive the temperature in the upper atmosphere. Using temperature measurements taken by lidar at Utah State University (41.74° N, 111.81° W) and temperatures from three popular atmospheric models, a comparison is made covering the period 1993-2004 at 45 km altitude. This comparison demonstrates poor predictive capabilities of the models at the target altitude and suggests the need for integrating datasets such as lidar data into future models. The modeling community depends on real measurement comparisons to bolster the reliability and credibility of their own work, and the comparison done here is intended to highlight an area in need of improvement.

Tropical cyclones (TCs) can make significant size changes during their lifetime. Being able to accurately forecast TC size change is important for predicting the onset of storm surge as well as the spatial extent of damaging winds. TC size changes can occur from internal storm dynamics, such as eyewall replacement cycle or from changes in the synoptic environment. In this study, the impacts of changing the atmospheric temperature and air-sea temperature difference on TC size and structure are investigated. The study is conducted in two parts: the first part uses the WRF-ARW model to test the sensitivity of TC size changes to simple changes in the environment; the second part to validates the results from the first part by characterizing the environments associated with real cases of TC size change in the North Atlantic basin. It is found that when the simulated atmosphere is cooled, the initial specific humidity and convective available potential energy (CAPE) decrease but the surface energy fluxes from the ocean increase. The higher surface fluxes produce a wider area of radially-inflowing air in the boundary layer, which supports a larger precipitation field and the formation of outer-core spiral rainbands. The larger precipitation field translates to a larger wind field, which is likely related to the diabatic production of potential vorticity. In contrast, when the atmosphere is warmed the surface energy fluxes reduce, which ultimately inhibits the growth of the TC wind field. The higher initial CAPE and moisture content, however, allow the TC to spin up more rapidly with a compact core of intense precipitation. Thus, it is not the temperature of the atmosphere that is causing the size changes, but instead it is the higher surface energy fluxes that arise from the increased air-sea temperature difference. Diagnostics show that fluxes of angular momentum from the environment are not responsible for the simulated TC size increases, even when the gradient in Earth vorticity is included. Rather, it is the production of energy due to the fluxes from the ocean that is responsible for the TC size increases in these simulations. Finally, a larger TC will increase in size more than a smaller TC in the same environment. In the second part of the study, the environments associated with real cases of TC size change in the North Atlantic Basin were characterized. Size changes were evaluated using the Tropical Cyclone Extended Best Track Dataset, and the environments associated with these size changes were examined using the 6-hourly, ERA-Interim global reanalysis dataset. Environmental composites show that the TCs that made size changes in the deep tropics were typically associated with more environmental, mid-level humidity and higher air-sea temperature difference. The TCs that made large size changes in the extratropics were associated with highly-baroclinic environments and high mid-level moisture south of the TC-circulation center. In general, the environments that were associated with TC size increases in the North Atlantic showed similar characteristics to the size change environments simulated in the first part of this study. In addition, the presence of high, mid-level moisture in both the deep tropics and extratropics was consistent with the results of other modeling studies that have explored the impact of environmental moisture on TC size changes.

Weather forecasting has improved greatly since the middle of the 20th century, thanks to better forecasting models, an evolved weather observing system, and improved ways of assimilating the observation data. However, these large systematical improvements make it difficult to use the weather data for climatological studies. Furthermore, observations are scarce, and they cannot be made everywhere. One way to solve this problem is to produce reanalyses, where a fixed version of a numerical weather prediction (NWP) model is used to produce gridded analysis and forecast data with detailed descriptions of the weather by assimilating observation data for a determined time period. One of the newest regional reanalyses is UERRA (Uncertainties in Ensembles of Regional Re-Analyses), which spans over the time period 1961-2015 and covers the whole Europe. By using a fixed NWP model, the only two factors that might influence the temporal quality of a regional reanalysis dataset are the varying number and quality of weather observations, and the quality of the global driving model which gives information about boundaries and large-scale features. In this report, data from one of the UERRA products has been used with the aim to investigate the temporal consistency of the 30-hour forecast skill regarding three parameters; temperature at 2 meters height (t2m), wind speed at 100 meters height (ws100) and 500 hPa geopotential (Φ500). The work has been focused on only land points over Europe during winters and summers, as this enables to investigate the model behaviour at the lowest and highest temperatures. The 30-hour forecast skill was estimated throughout the time period from how well it performed compared to the 6-hour forecast. Temporal inconsistencies were found throughout the reanalysis, with the largest temporal differences being present for Φ500, followed by ws100. UERRA shifts its global driving model in 1979 from ERA-40 (ECMWF Re-Analysis 40) to ERA-Interim (ECMWF Interim Re-Analysis), which ends up as a significant improvement of forecast skill for all investigated parameters. Furthermore, ws100 also shows a significant skill improvement in wintertime from 1979 onwards, while Φ500 shows a systematical improvement for both seasons. In general, the forecast skill is lower in wintertime than in summertime, which might be a result from higher natural variability of the weather in winters. A quick study of forecast data from ERA-Interim shows that the same improving trend in Φ500 can be seen also in that dataset, while the two model drifts differ completely. It was concluded that the addressed issues with temporal inconsistency should be communicated to end users utilizing the UERRA datasets, as knowledge about this can be greatly beneficial when studying climatological trends and patterns and when using the model to reforecast weather events.
Väderprognostisering har utvecklats betydligt sedan mitten på 1900-talet, tack vare bättre prognosmodeller, fler väderobservationer och förbättrade sätt att samla in och nyttja observationerna. Den snabba utvecklingen gör det dock svårt att på ett tillförlitligt sätt kunna jämföra väderdata från olika tidsperioder med varandra, då det är svårt att säkerställa kvaliteten på observationer från flera decennier tillbaka. Ett sätt att lösa det här problemet är att framställa så kallade återanalyser, vilka använder en enskild väderprognosmodell för att uppskatta vädret historiskt i varje punkt i ett förutbestämt rutnät, som sträcker sig över en enskild kontinent eller hela Jorden. En av de nyaste återanalyserna är UERRA, vilket är en regional återanalys över Europa som sträcker sig över tidsperioden 1961–2015. Då en och samma modell används för att beräkna vädret över hela perioden så påverkas inte kvaliteten på datat av den historiska utvecklingen av prognosmodeller. De enda två faktorerna som kan påverka datakvaliteten är den varierande tillgängligheten till väderobservationer, samt kvaliteten på den globala modellen vilken ger information om vädret utanför Europa. För att undersöka om det finns tidsmässiga skillnader i hur konsistent eller inkonsistent kvaliteten på UERRA-återanalysen är, har väderdatat från denna analyserats med avseende på temperatur, vindstyrka och lufttryckshöjd. Arbetet har fokuserats på enbart landpunkter över Europa för sommar och vinter, då detta möjliggör att kunna se hur bra modellen presterar vid de allra lägsta och högsta temperaturerna. Datat har utvärderats genom att undersöka hur tillförlitlig en prognos för 30 timmar framåt är jämfört med en prognos för 6 timmar framåt. Resultaten visar att kvaliteten på återanalysdatat i UERRA inte är konsistent genom hela tidsperioden, där de största skillnaderna hittades för lufttryckshöjden följt av vindstyrkan. För alla tre parametrar hittades betydande kvalitetsskillnader från vilken typ av global modell som används för att ge väderinformation utanför Europa, då UERRA byter global modell under år 1979. För lufttryckshöjden sågs även att datakvaliteten ökar konsekvent även efter 1979 och framåt, vilket därmed är ett resultat från den ökande mängden väderobservationer. Generellt sågs en högre prognoskvalitet sommartid än vintertid, vilket tros vara ett resultat från att vädret varierar mycket mer vintertid vilket därmed bör göra det mer svårprognostiserat. Dessa skillnader i datakvaliteten bör tydliggöras för alla användare av UERRA-återanalysen, då det är viktigt att ha kännedom om detta före eventuella slutsatser dras från återanalysdatat om hur vädret har varit historiskt sett.

Radio signals from Global Navigation Satellite Systems (GNSS) satellites suffer delay as they propagate through the atmosphere (neutral and non-neutral) and this delay is partially driven by the water vapour content in the atmosphere. The delay component due to the non-neutral atmosphere (ionosphere) is removed through the use of dual frequency GNSS receivers. The main tropospheric parameter is the zenith tropospheric (or total) delay (ZTD), which is a widely accepted parameter with which to express the total delay in the signal from all satellites due to the neutral atmosphere. The ZTD is a measure of the integrated tropospheric condition over a GNSS receiver station. Accordingly, the integrated water vapour or precipitable water vapour (PWV) can be obtained from a portion of the ZTD, if the atmospheric pressure and temperature at the station are known through a concept often referred to as GNSS meteorology. A number of GNSS receivers have been deployed for mapping and geodetic services in Nigeria under the African reference frame initiative, but unfortunately most of these receivers do not have co-located meteorological sensors for pressure and temperature measurements. The prospect of incorporating GNSS meteorology into weather monitoring and climate analysis in Nigeria was investigated and is reported in this thesis. During the first task of this research, the technical basis for ground-based GNSS meteorology was reviewed and the potentials and challenges of the approach to meteorological activities in Africa (including Nigeria) were identified. Thereafter an in-depth analysis of the spatial and temporal variability of ZTD over Nigeria for the period of 2010-2014 was conducted; results revealed weak spatial dependence among the stations. Tidal oscillations (of the diurnal and semidiurnal components) were observed at the GNSS stations of which the diurnal ZTD cycles exhibited significant seasonal dependence, affirming the prospective relevance of ground-based GNSS data to atmospheric studies. Also in this research, the accuracy and suitability of using reanalysis datasets (ERA-Interim and NCEP/NCAR) and a GPT2 neutral model in retrieving PWV from GNSS observations over Nigeria were investigated; results showed that PWV can be retrieved to within a precision of about 1 mm, provided GNSS-derived ZTD is of high precision. A fundamental issue for GNSS meteorology in the West African region was yet again addressed in this research; this is the development of a weighted tropospheric mean temperature model for use in current and future GNSS meteorology activities in the region. A multitechnique comparison of PWV estimates showed good agreement between GNSS estimates and other techniques (i.e. the atmospheric infrared sounder, and ERAInterim reanalysis). This result is suggestive of the potential of assimilating GNSS atmospheric products into reanalysis and climate models. Diurnal and seasonal variability of GNSS PWV estimates exhibits strong correlation with weather events that influence the region (i.e. solar activity and rainfall events); this further demonstrated the immense contribution of the approach to efficient weather forecasting and climate monitoring for Nigeria.
Thesis (PhD)--University of Pretoria, 2017.
Geography, Geoinformatics and Meteorology
PhD
Unrestricted

Specific humidity inversions are extremely frequent in the Arctic atmosphere, and they are strongly intertwined with the energy budget, by affecting downward longwave radiation and cloud processes. Despite their importance, they have been the focus of a limited number of studies, and our knowledge of their characteristics is limited because of the lack of in situ data in the region, and the presence of large errors in moisture variables in reanalyses. Retrievals from satellite infrared sounders could fill this knowledge gap. In this thesis, the capability of two benchmark satellite sounders, the Infrared Atmospheric Sounding Interferometer (IASI) and the Atmospheric Infrared Sounder (AIRS), to detect specific humidity inversions at an Arctic site, Ny-Ålesund, is systematically assessed for the first time. Additionally, an accurate characterization of specific humidity inversions above Ny-Ålesund is performed employing radiosonde data, and the capability to detect Arctic specific humidity inversions of the next-generation reanalysis ERA5 is also assessed. The assessment is performed by comparing specific humidity inversion statistics from radiosondes to analogous statistics obtained from co-located IASI and AIRS retrievals, as well as ERA5 fields. Results show that ERA5 provides a good representation of specific humidity inversion characteristics, while IASI and AIRS retrievals display strong limitations in capturing Arctic specific humidity inversions.

The eastern North Pacific Ocean has the highest density of tropical cyclone genesis events of any tropical basin in the world, and many of these systems form near land before moving westward. However, despite the level of tropical cyclone activity in this basin, and the proximity of the main genesis region to land, tropical cyclone behavior in the eastern North Pacific has been relatively unexplored. When synoptic conditions are favorable, moisture from northward-moving tropical cyclones can be advected into northern Mexico and the southwestern United States, often leading to the development of summertime thunderstorms during the North American monsoon season. An interaction with a mid-latitude trough produces the most rainfall, and the spatial variability of precipitation is greatly affected by the complex topography of the region. Moisture can be advected from a tropical cyclone around the subtropical ridge in place for much of the eastern North Pacific hurricane season and contribute to precipitation. This ridge, when it extends westward over the Pacific Ocean, can also prevent tropical cyclone moisture from impacting the southwestern United States. Northward-moving tropical cyclones often enter an environment with decreasing sea surface temperatures, increasing vertical wind shear, and meridional air temperature and moisture gradients. These key ingredients for extratropical transition are generally present in the eastern North Pacific, but the subtropical ridge prevents many named systems from moving northward, and only 9% of eastern North Pacific tropical cyclones from 1970 to 2011 complete ET according to cyclone phase space. However, over half of the systems that do not complete ET dissipate as cold core cyclones, a structural change that has yet to be explored in other tropical basins. It is difficult to estimate tropical cyclone intensity in a vast ocean area with few direct measurements available. The deviation angle variance technique, an objective method independent of the current techniques widely used today, was successfully applied to seven years of eastern North Pacific tropical cyclones. The RMS error of 13.5 kt for all seven years is comparable to the RMS errors found for other basins.

Since their development in the 1990s, gridded reanalysis data sets have proven quite useful for a broad range of synoptic climatological analyses, especially those utilizing a map pattern classification approach. However, their use in broad-scale, surface weather typing classifications and applications have not yet been explored. This research details the development of such a gridded weather typing classification (GWTC) scheme using North American Regional Reanalysis data for 1979-2010 for the continental United States.

Utilizing eight-times daily observations of temperature, dew point, pressure, cloud cover, u-wind and v-wind components, the GWTC categorizes the daily surface weather of 2,070 locations into one of 11 discrete weather types, nine core types and two transitional types, that remain consistent throughout the domain. Due to the use of an automated deseasonalized z-score initial typing procedure, the character of each type is both geographically and seasonally relative, allowing each core weather type to occur at every location, at any time of the year. Diagnostic statistics reveal a high degree of spatial cohesion among the weather types classified at neighboring locations, along with an effective partitioning of the climate variability of individual locations (via a Variability Skill Score metric) into these 11 weather types. Daily maps of the spatial distribution of GWTC weather types across the United States correspond well to traditional surface weather maps, and comparisons of the GWTC with the Spatial Synoptic Classification are also favorable.

While the potential future utility of the classification is expected to be primarily for the resultant calendars of daily weather types at specific locations, the automation of the methodology allows the classification to be easily repeatable, and therefore, easily transportable to other locations, atmospheric levels, and data sets (including output from gridded general circulation models). Further, the enhanced spatial resolution of the GWTC may also allow for new applications of surface weather typing classifications in mountainous and rural areas not well represented by airport weather stations.

Clouds regulate the Earth’s radiation budget, both by reflecting part of the incoming sunlight leading to cooling and by absorbing and emitting infrared radiation which tends to have a warming effect. Globally averaged, at the top of the atmosphere the cloud radiative effect is to cool the climate, while at the Arctic surface, clouds are thought to be warming. Here we compare a passive instrument, the AVHRR-based retrieval from CM-SAF, with recently launched active instruments onboard CloudSat and CALIPSO and the widely used ERA-Interim reanalysis. We find that in particular in winter months the three data sets differ significantly. While passive satellite instruments have serious difficulties, detecting only half the cloudiness of the modeled clouds in the reanalysis, the active instruments are in between. In summer, the two satellite products agree having monthly means of 70–80 percent, but the reanalysis are approximately ten percent higher. The monthly mean long- and shortwave components of the surface cloud radiative effect obtained from the ERAInterim reanalysis are about twice that calculated on the basis of CloudSat’s radar-only retrievals, while ground based measurements from SHEBA are in between. We discuss these differences in terms of instrument-, retrieval- and reanalysis characteristics, which differ substantially between the analyzed datasets.

The aim of this thesis is to study the desert dust aerosols and their transport in the atmosphere and in particular to gain insight into the desert dust distribution over North Africa, Middle East and Europe region during a ten-year period, using dust model reanalysis products of various optical and physical dust particle properties. Atmospheric desert dust is one of the major contributors to global aerosol loading and is the dominant component of atmospheric aerosols over large areas of Earth, strongly affecting Earth’s radiative budget and ecosystems. Mineral dust particles, suspended in the atmosphere from arid and semi-arid regions, can remain in the atmosphere from several days to about a week, depending on their size. Huge amounts of dust can be transported over great distances under favorable meteorological conditions, affecting regions hundreds to thousands of kilometers away. For countries in and downwind of arid regions, airborne sand and dust poses a serious threat to human and animal health and to various socio-economic sectors, such as aviation, ground transportation, agriculture, infrastructure, and the solar energy and other industries. An advanced regional model reanalysis for Northern Africa, Middle East and Europe was produced specifically for the desert dust component in the framework of the DustClim project using the MONARCH atmospheric chemistry model, based on the assimilation of dust retrievals obtained from aerosol satellite observations over a ten-year period 2007-2016. The reanalysis products cover a wide range of dust-related atmospheric parameters, including optical and physical dust properties, - such as dust optical depth, dust extinction coefficient and dust mass concentration - along with dust deposition and solar radiation variables. Such kind of reanalysis can be used as suitable tool for having a complete picture of the desert dust distribution, at high temporal and spatial resolution, overcoming the gap into observations and providing reliable climatological information on sand and dust storm trends and current conditions. Additionally, combined dust products can be investigated as suitable for quantifying the impact of desert dust on aviation (visibility), air quality (dust concentration exposure), solar energy plants (deposition and solar energy attenuation). The main objective of this thesis is to perform a thorough evaluation of the reanalysis performance using a wide variety of observations and data from experimental campaigns. The evaluation of the model simulations, performed by comparing the model-predicted values against selected observed values, is critical to assess the quality and uncertainty of the simulations and to establish the model’s reliability. In the most recent years, the advancement in observing techniques and the synergistic use of passive and active sensors and of satellite and ground based measurements are opening new possibilities like the identification of the dust particle contribution to measured optical properties through lidar measurements of particle depolarization ratio and estimation of the dust concentration through inversion methods when collocated lidar and photometer measurements are available. Moreover, a general overview of the desert dust climatology produced by the MONARCH reanalysis is provided in this work. Dust climatology aims to provide reliable reference values describing the spatial and temporal distribution of airborne dust, both near the Earth’s surface and in upper levels. In a later stage, the data analyzed and processed during this thesis, could be exploited for investigating the impact of the desert dust on different sectors. This kind of study could be of interest for developing mitigation and resilient actions to desert dust impacts. This thesis is structured in the following way: The 1st Chapter is an introduction to basic concepts revolving the atmospheric desert dust, its cycle – emission, transport, deposition – and its impacts on various socio-economic sectors. Moreover, datasets of observations and global and regional models, specifically for the desert dust component, are introduced. The 2nd Chapter provides a description of the MONARCH dust reanalysis, its novelties and its products. It also presents the approach used to evaluate the reanalysis products and describes in detail the main characteristics of the observational datasets used for the evaluation along with the assessment of satellite data quality. A description of the methodology which was applied in order to retrieve the dust component of the observations and the results from the evaluation procedure are presented for each variable separately. The 3rd Chapter provides an analysis of the inter- and intra-annual variability and climatology of the atmospheric dust load produced by the reanalysis. In addition, an assessment of dust climatological tendencies is attempted. In the final Chapter 4 the main findings and conclusions are summarized.

Philosophiae Doctor - PhD (Biodiversity and Conservation Biology)
Seawater temperature from regional to global scale is central to many measures of biodi- versity and continues to aid our understanding of the evolution and ecology of biolog- ical assemblages. Therefore, a clear understanding of the relationship between marine biodiversity and thermal structures is critical for effective conservation planning. In the an- thropocene, an epoch characterised by anthropogenic forcing on the climate system, future patterns in biodiversity and ecological functioning may be estimated from projected climate scenarios however; absent from many of these scenarios is the inclusion of extreme thermal events, known as marine heatwaves (MHWs). There is also a conspicuous absence in knowl- edge of the drivers for all but the most notorious of these events. Before the drivers of MHWs along the coast of South Africa could be determined, it was first necessary to validate the 129 in situ coastal seawater temperature time series that could be used to this end. In doing so it was found that time series created with older (longer), lower precision (0.5 Degrees Celsius) instruments were more useful than newer (shorter) time series produced with high precision (0.001 Degrees Celsius) instruments. With the in situ data validated, a history of the occurrence of MHWs along the coastline (nearshore) was created and compared against MHWs detected by remotely sensed data (offshore). This comparison showed that the forcing of offshore temperatures onto the nearshore was much lower than anticipated, with the rates of co-occurrence for events between the datasets along the coast ranging from 0.2 to 0.5. To accommodate this lack of consistency between datasets, a much larger mesoscale area was then taken around southern Africa when attempting to determine potential mesoscale drivers of MHWs along the coast. Using a self organising-map (SOM), it was possible to organise the synoptic scale oceanographic and atmospheric states during coastal MHWs into discernible groupings. It was found that the most common synoptic oceanographic pattern during coastal MHWs was Agulhas Leakage, and the most common atmospheric pattern was anomalously warmoverland air temperatures.With these patterns known it is now necessary to calculate how often they occur when no MHW has been detected. This work may then allow for the development of predictive capabilities that could help mitigate the damage caused by MHWs.

Tese de mestrado, Ciências Geofísicas, Universidade de Lisboa, Faculdade de Ciências, 2019
A temperatura da superfície terrestre (SKT) é um dos parâmetros-chave nas trocas terra-atmosfera de energia e água. Além disso, é cada vez mais importante na assimilação de dados e na parametrização de variáveis em modelos. Estudos anteriores apontam para subestimações consideráveis da SKT em reanálises, principalmente durante o dia e em regiões semi-áridas e áridas. Na primeira parte desta tese, a SKT de duas reanálises (ERA-Interim e ERA5) do Centro Europeu de Previsão de Tempo a Médio-Prazo (ECMWF) é avaliada, tendo como produto de referência a temperatura da superfície terrestre (LST) obtida por satélite pelos Serviços de Aplicações de Satélite a Análises da Superfície Terrestre (LSA-SAF). De igual forma, é avaliada a SKT de simulações offline de uma versão do modelo HTESSEL do ECMWF muito semelhante à utilizada na concepção da ERA5. Este estudo é realizado entre 2004 e 2015 (período em que a LST reprocessada se encontra disponível) sobre a Península Ibérica, numa resolução espacial de 0.25º × 0.25º. Como a LST de satélite tem uma resolução maior (~5 km), é realizado o upscaling dos dados de satélite para se poder fazer a comparação com as reanálises. Além disso, esta base de dados realiza medições num determinado pixel apenas quando não há nuvens sobre esse pixel. Por causa disso, aplica-se um limite máximo de cobertura nebulosa de 0.3 em cada pixel e para cada produto, de forma a evitar uma contaminação elevada dos dados por nuvens e, ao mesmo tempo, manter uma percentagem considerável de dados válidos. Quatro métricas de desempenho são aplicadas à SKT máxima e mínima diária: erro médio, desvio-padrão do erro, correlação temporal e a raiz quadrada do erro médio quadrático. Para facilitar a organização dos resultados, aplica-se o algoritmo K-Means de forma a dividir o domínio em diferentes clusters. Os clusters são determinados a partir do ciclo diurno médio da LST no Verão dos doze anos considerados no estudo. Os resultados mostram, em todos os produtos, uma subestimação considerável da SKT de dia e uma sobrestimação pouco significativa de noite, em linha com estudos anteriores, sendo essa disparidade superior em regiões mais áridas. No geral, a ERA5 apresenta um produto com qualidade superior em relação à ERA-Interim, pois é a reanálise que mais se aproxima das observações de satélite. Mostra-se também que existe uma correlação razoavelmente elevada entre o erro na representação da cobertura vegetal no modelo (ao comparar a fracção de cobertura vegetal do modelo pela observada pela base de dados da Copernicus) e o erro médio na simulação da SKT. Na segunda parte deste trabalho, são aplicadas mudanças a alguns parâmetros do modelo HTESSEL e o seu impacto na simulação da SKT é avaliado pelas observações de satélite da LST, num domínio mais restrito centrado em Évora (quatro pontos na resolução original de 0.25º × 0.25º). A implementação de uma representação mais realista de coberto vegetal no modelo é obtida a partir da base de dados de cobertura terrestre da ESA-CCI, mantendo os tipos de vegetação alta/baixa originais considerados pelo modelo mas alterando a respectiva fracção de cobertura vegetal. O domínio passa a ser coberto maioritariamente por vegetação baixa, ao contrário do domínio original que era coberto quase na totalidade por vegetação alta. Esta nova representação da vegetação tem um impacto positivo na SKT durante o dia, e uma nova discretização do solo (nove camadas em vez de quatro) reduz ainda mais o erro, embora com um impacto menos acentuado do que a introdução da nova vegetação. Ainda assim, mesmo depois destas alterações, o viés nocturno e a diferença de fase durante o dia mantêm-se no ciclo diurno médio da SKT. É importante referir que o tipo de vegetação baixa considerado pelo modelo HTESSEL não é directamente equivalente ao obtido com os dados da ESA-CCI e ao utilizar outros tipos de vegetação baixa no modelo, estes originam resultados menos positivos. Isto dever-se-á ao facto da cobertura vegetal ser menor no tipo de vegetação baixa originalmente considerado pelo modelo quando comparado com os restantes tipos existentes, o que implica um aumento da fracção de cobertura vegetal e, consequentemente, um aumento do viés da SKT quando se muda para outro tipo de vegetação baixa. Finalmente, um estudo de sensibilidade é aplicado ao pârametro cveg, que representa a cobertura vegetal do modelo, de forma a verificar a sua relação com o viés na simulação da SKT, utilizando a representação de vegetação original do modelo HTESSEL e, seguidamente, a representação revista. O parâmetro cveg é perturbado com valores entre 0.1 e 1, para os tipos de vegetação alta e baixa, formando no total 100 pares de cveg. Esses pares são obtidos através de uma distribuição quasi-aleatória, distribuição de Sobol, que permite preencher na totalidade o domínio considerado, sem introduzir correlações entre as diferentes perturbações e sem criar clusters e espaços vazios no domínio. Este estudo reafirma a importância da representação da vegetação no modelo, pois existe uma correlação entre cveg e o viés da SKT máxima diária quando se considera a vegetação revista (ao contrário do que acontece com a vegetação original do modelo, em que não se verifica qualquer correlação com o erro). As simulações desacopladas (offline) permitem avaliar o impacto dos parâmetros de superfície na simulação de SKT, mas é igualmente importante estudar o efeito da vegetação revista em simulação acopladas com a atmosfera. Também é importante referir que as mudanças na cobertura de vegetação afectam o balanço de água (que não é estudado neste trabalho) e causam outras alterações noutras estações do ano. Contudo, a pouca disponibilidade de dados de LST de satélite e de outras observações (como, por exemplo, fluxos e temperatura e humidade do solo) dificultam eventuais estudos adicionais. Ainda que a ERA5 demonstre ser um produto com melhor qualidade que a ERA-Interim, os resultados sugerem a necessidade de uma revisão da vegetação no modelo HTESSEL sobre a Península Ibérica, nomeadamente as fracções de cobertura de vegetação baixa / alta. Do mesmo modo, a própria definição dos tipos de vegetação baixa / alta e o valor de cveg associado poderão também precisar de uma revisão.
The surface skin temperature (SKT) is a key variable in surface-atmosphere energy and water exchanges. In the first part of this work, the SKT from two reanalyses (ERA-Interim and ERA5) of the European Centre for Medium-Range Weather Forecasts (ECMWF) is evaluated against satellite-based Land Surface Temperature (LST) retrieved by the Satellite Application Facility on Land Surface Analysis (LSA-SAF), during the 2004-2015 period over the Iberian Peninsula. Offline simulations by the Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL) model are also assessed. We apply four performance metrics to the daily maximum and minimum SKT: the mean error, standard deviation of the error, temporal correlation and root mean squared error. The results show an underestimation of the daytime SKT and a small overestimation of nighttime SKT in all the products, which is in line with previous studies. In general, ERA5 presents a consistent improvement over ERA-Interim by showing an overall better agreement with the satellite observations. There is also a reasonably high correlation between the misrepresentation of vegetation cover in the HTESSEL model and the daily maximum SKT bias. In the second part of the thesis, we apply changes to some parameters of the HTESSEL model. The parameters’ impact in the simulation of SKT is then assessed by comparing them to the satellite-LST in a confined domain centred in Évora. The implementation of a revised model representation of vegetation cover (based on the ESA-CCI Land Cover Dataset) is shown to have a positive impact on SKT, especially during daytime. A new soil discretization scheme, on the other hand, does not significantly impact the simulation of SKT. Finally, a sensitivity study applied to the cveg parameter (the model vegetation cover) reaffirms the importance of the representation of vegetation in the model, as there is a correlation between cveg and the daily maximum SKT bias with the revised model vegetation (while the same correlation cannot be reproduced with the original model vegetation).

The application of atmospheric circulation classifications in the interpretation of climate model outputs Mgr. Jan Stryhal Automated (computer-assisted) classifications of atmospheric circulation patterns (circulation classifications, for short) constitute a tool widely used in synoptic and dynamic climatology to study atmospheric circulation and its link to various atmospheric, environmental, and societal phenomena. The application of circulation classifications to output of dynamical models of the atmosphere has developed considerably since the pioneering studies about three decades ago, reflecting rapid development in statistics, computing technology, and-naturally-climatological research, increasingly more and more dependent on simulations of the atmosphere, facing the paradigm of anthropogenic climate change. An uncoordinated use of various statistical approaches to analyzing output of global climate models (GCM) or their various ensembles, and an arbitrary selection of circulation variables, spatial and temporal domains, and reference datasets, have contributed to a need for a comparative study, which would shed some light on the sensitivity of studies dealing with an intercomparison of circulation classifications in two datasets to subjective choices. The present thesis responds to this need...

abstract: The numerical climate models have provided scientists, policy makers and the general public, crucial information for climate projections since mid-20th century. An international effort to compare and validate the simulations of all major climate models is organized by the Coupled Model Intercomparison Project (CMIP), which has gone through several phases since 1995 with CMIP5 being the state of the art. In parallel, an organized effort to consolidate all observational data in the past century culminates in the creation of several "reanalysis" datasets that are considered the closest representation of the true observation. This study compared the climate variability and trend in the climate model simulations and observations on the timescales ranging from interannual to centennial. The analysis focused on the dynamic climate quantity of zonal-mean zonal wind and global atmospheric angular momentum (AAM), and incorporated multiple datasets from reanalysis and the most recent CMIP3 and CMIP5 archives. For the observation, the validation of AAM by the length-of-day (LOD) and the intercomparison of AAM revealed a good agreement among reanalyses on the interannual and the decadal-to-interdecadal timescales, respectively. But the most significant discrepancies among them are in the long-term mean and long-term trend. For the simulations, the CMIP5 models produced a significantly smaller bias and a narrower ensemble spread of the climatology and trend in the 20th century for AAM compared to CMIP3, while CMIP3 and CMIP5 simulations consistently produced a positive trend for the 20th and 21st century. Both CMIP3 and CMIP5 models produced a wide range of the magnitudes of decadal and interdecadal variability of wind component of AAM (MR) compared to observation. The ensemble means of CMIP3 and CMIP5 are not statistically distinguishable for either the 20th- or 21st-century runs. The in-house atmospheric general circulation model (AGCM) simulations forced by the sea surface temperature (SST) taken from the CMIP5 simulations as lower boundary conditions were carried out. The zonal wind and MR in the CMIP5 simulations are well simulated in the AGCM simulations. This confirmed SST as an important mediator in regulating the global atmospheric changes due to GHG effect.
Dissertation/Thesis
Ph.D. Mechanical Engineering 2013

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
Satellite and model atmospheric composition data are stored in different platforms, using heterogeneous file formats, varying spatiotemporal resolutions and noncompatible metadata. Comparing these datasets is not a trivial task, but required in data assimilation, validation and mutual coverage studies. This thesis investigates the prevailing methods used to compare sensor observations with data from the forecast and reanalysis system developed by the Copernicus Atmospheric Monitoring Service (CAMS). These are implemented in the development of the first prototype of the Atmospheric Datasets Comparison (ADC) Toolbox. This toolbox, which is the core part of the project, contains a set of tools that facilitate the file interoperability, binning and regridding, computation of levels pressure, conversion of units, application of the averaging kernels, datasets merge, geostatistical comparison and trend analysis. The contribution of this work is twofold: a toolbox is developed to merge and compare atmospheric composition datasets systematic and automatically for any region and time, and its applicability is shown in a case study, where the NO2 emissions in Spain in the last decade are analyzed using satellite and model data.

Changing land cover from prairie grasslands to intensive, primarily cereal agriculture, over the North American Great Plains since the mid-19th century, has had a hydrological and climatological impact on that ecosystem (Pielke, Sr., et al., 2011). Agriculture has introduced timed harvest seasons, irrigation, and C3 photosynthesizing crops with poorer water efficiency than the grasses it replaced. All of these changes have been linked to exacerbated drought conditions and warmer temperatures; however, few studies have quantified this relationship at the continental scale. In order to evaluate the change imposed by this shift in land use and land cover, the observation based 20th Century Reanalysis Project (20CR) was used to quantify the climatological differences in temperature and humidity between areas of natural prairie and agriculture over the 20th century. An additional analysis used the Observation Minus Reanalysis (OMR) technique to isolate the surface climate signal found in the 20CR. We find indications that changing land cover had an impact on climate. However, using observation based data returned no evidence of a statistically significant change due to the small land use and land cover change (LULCC) signal within the larger climate noise. Therefore, an idealised modelling experiment was undertaken using the Geophysical Fluid Dynamics Laboratory (GFDL) AM2-LM2 atmosphere-land model to remove these other influences. This experiment compared the results of two model simulations: one where the entirety of the prairie was preserved as grassland (GRASS), and another where the entire prairies had been converted into an agricultural area (AGRIC). Relative to GRASS, the AGRIC simulation has reduced surface albedo and root zone depth, and increased roughness length over the prairies, which collectively cause a significant summer drying. This occurs when the shallower rooting zone limited potential evapotranspiration (PET) forcing the additional energy created by turbulent mixing and a lower surface albedo to warm the air, surpassing PET and reaching drier conditions faster. While not conclusive, the results presented in this thesis represent a step towards filling the gaps in understanding land-atmosphere interactions and connecting LULCC to climate.