A commonly noted problem in the simulation of warm season convection in the North American monsoon region has been the inability of atmospheric models at the meso- scales (10 s to 100 s of kilometers) to simulate organized convection, principally mesoscale convective systems. With the use of convective parameterization, high precipitation biases in model simulations are typically observed over the peaks of mountain ranges. To address this issue, the Kain-Fritsch (KF) cumulus parameterization scheme has been modified with new diagnostic equations to compute the updraft velocity, the convective available potential energy closure assumption, and the convective trigger function. The scheme has been adapted for use in the Weather Research and Forecasting (WRF). A numerical weather prediction-type simulation is conducted for the North American Monsoon Experiment Intensive Observing Period 2 and a regional climate simulation is performed, by dynamically downscaling. In both of these applications, there are notable improvements in the WRF model-simulated precipitation due to the better representation of organized, propagating convection. The use of the modified KF scheme for atmospheric model simulations may provide a more computationally economical alternative to improve the representation of organized convection, as compared to convective-permitting simulations at the kilometer scale or a super-parameterization approach.

An ensemble of 11 dynamically downscaled CMIP3 GCMs under A2 projection scenario are first bias corrected for the historic (1971-2000) and scenario (2041-2070) period using a Scaled Distribution Mapping (SDM) technique, that preserves the relative change in the monthly mean and variance of precipitation and any model trends in temperature to generate an ensemble of streamflow projections across 3 catchments in the Colorado River basin - Upper Colorado at Lees Ferry, Salt and Verde. The hydroclimatic projections obtained from this method are compared against an existing ensemble of 15 Bias Corrected and Spatially Disaggregated (BCSD) CMIP3 models under A2 projection scenario developed by the Bureau of Reclamation (BOR). The confidence in the DD Ens. stems from its ability to represent historical flow quantiles better than BCSD Ens. Across all three basins, the mean of the dynamically downscaled ensemble (DD Ens.) projects a decrease in both monsoon and winter projected precipitation as compared to mean of the statistically downscaled ensemble (BCSD Ens.). For the Upper Colorado, both Ens. show a shift in peak hydrograph from June to May due to earlier snowmelt, but a projected decrease in precipitation (-5%) by DD Ens. as compared to a slight increase (+2%) by BCSD Ens. results in a lower April snow water equivalent (SWE) and reduced streamflows (14% by DD Ens. as compared to 5% by BCSD Ens.). The streamflow decrease over the Upper Colorado River basin, quantified by both the mean and the spread of the ensemble. is representative in high flows and flows during moist conditions. For smaller basins like Salt and Verde, DD Ens. shows a greater decrease (-11%) in precipitation than BCSD Ens. (-2%), which results in lower peak hydrograph during March and significantly reduced streamflows (-20%&-14% for Salt and Verde by DD Ens. as compared to -3% by BCSD Ens.). This decrease is more substantial in high flows, but occurs across all streamflow quantiles. The future streamflow projection, quantified by the spread of the DD Ens. presents the shifting of the streamflow range downward to be drier in the future.

Alterations to the land surface can be attributed to both human activity and natural variability. Human activities, such as urbanization and irrigation, can change the conditions of the land surface by altering albedo, soil moisture, aerodynamic roughness length, the partitioning of net radiation into sensible and latent heat, and other surface characteristics. On the other hand, natural variability, manifested through changes in atmospheric circulation, can also induce land surface changes. These regional scale land surface changes, induced either by humans or natural variability, can effectively modify atmospheric conditions through land-atmosphere interactions. However, only in recent decades have numerical models begun to include representations of the critical processes driving changes at the land surface, and their associated effects on the overlying atmosphere. In this work we explore three mechanisms by which changes to the land surface–both anthropogenic and naturally induced–impact the overlying atmosphere and affect regional hydroclimate. The first land-atmosphere interaction mechanism explored here is land-use and land-cover change (LULCC) due to urban expansion. Such changes alter the surface albedo, heat capacity, and thermal conductivity of the surface. Consequently, the energy balance in urban regions is different from that of natural surfaces. To evaluate the changes in regional hydroclimate that could arise due to projected urbanization in the Phoenix–Tucson corridor, Arizona, my first study applied the Weather Research and Forecasting (WRF) with an Urban Canopy Model (UCM; which includes a detailed urban radiation scheme) coupled to the Noah land surface model to this region. Land-cover changes were represented using land-cover data for 2005 and projections to 2050, and historical North American Regional Reanalysis (NARR) data were used to specify the lateral boundary conditions. Results suggest that temperature changes are well defined, reflecting the urban heat island (UHI) effect within areas experiencing LULCC, whereas changes in precipitation are less certain (statistically less robust). However, the study indicates the likelihood of reductions in precipitation over the mountainous regions northeast of Phoenix and decreased evening precipitation over the newly urbanized area. The second land-atmosphere interaction mechanism explored here is irrigation which, while being an important anthropogenic factor affecting the local to regional water cycle, is not typically represented in regional climate models. In this (second) study, I incorporated an irrigation scheme into the Noah land surface scheme coupled to the WRF model. Using a newly developed water vapor tracer package (developed by Miguez-Macho et al. 2013), the study tracks the path of water vapor that evapotranspires from the irrigated regions. To assess the impact of irrigation over the California Central Valley (CCV) on the regional climate of the U.S. Southwest, I ran six simulations (for three dry and three wet years), both with and without the irrigation scheme. Incorporation of the irrigation scheme resulted in simulated surface air temperature and humidity that were closer to observations, decreased the depth of the planetary boundary layer over the CCV, and increased the convective available potential energy. The results indicated an overall increase in precipitation over the Sierra Nevada Range and the Colorado River Basin during the summer, with water vapor rising from the irrigated region moving mainly northeastward and contributing to precipitation in Nevada and Idaho. The results also indicate an increase in precipitation on the windward side of the Sierra Nevada Range and over the Colorado River Basin. The former is possibly linked to a sea-breeze type circulation near the CCV, while the latter is likely associated with a wave pattern related to latent heat release over the moisture transport belt. In the third study, I investigated the role of large-scale and local-scale processes associated with heat waves using the Modern Era-Retrospective Analysis for Research and Applications (MERRA) reanalysis, and evaluate the performance of the regional climate model ensemble used in the North America Regional Climate Change Program (NARCCAP) in reproducing these processes. The Continental US is divided into different climate divisions (following the convention of the National Climate Assessment) to investigate different mechanisms associated with heat waves. At the large scale, warm air advection from terrestrial sources is a driving factor for heat waves in the Northeast and Midwest. Over the western United States, reduced maritime cool air advection results in local warming. At the local scale, an antecedent precipitation deficit leads to the continuous drying of soil moisture, more energy being partitioned into sensible heat flux and acting to warm surface air temperatures, especially over the Great Plains. My analysis indicates that the NARCCAP simulated large-scale meteorological patterns and temporal evolution of antecedent local-scale terrestrial conditions are very similar to those of MERRA. However, NARCCAP overestimates the magnitude and underestimates the frequency of Northeastern and Midwestern US heat waves, partially due to anomalous heat advection through large-scale forcing. Overall, the aforementioned studies show that utilization of new parameterizations in land surface models, such as the urban canopy scheme and the irrigation scheme, allow us to understand the detailed physical mechanisms by which anthropogenic changes in the land surface can affect regional hydroclimate, and may thus help with informed decision making and climate adaptation/mitigation. In addition to anthropogenic changes of the land surface, humans are of course affecting the overlying atmosphere. Currently, NARCCAP is the best available tool we have to help us understand the effects of changes greenhouse gas induced climate change at the regional scale. The regional climate models participating in NARCCAP are able to realistically represent the dominant processes associated with heat waves, including the atmospheric circulation changes and the land-atmosphere interactions that drive heat waves. This lends credibility, when analyzing the projections of these models with increased GHG emissions, to the assessment of changes in heat waves under a future climate.

This dissertation aims to better understand how various climate modeling approaches affect the fidelity of the North American Monsoon (NAM), as well as the sensitivity of the future state of the NAM under a global warming scenario. Here, we improved over current fully-coupled general circulation models (GCM), which struggle to fully resolve the controlling dynamics responsible for the development and maintenance of the NAM. To accomplish this, we dynamically downscaled a GCM with a regional climate model (RCM). The advantage here being a higher model resolution that improves the representation of processes on scales beyond that which GCMs can resolve. However, as all RCM applications are subject to the transference of biases inherent to the parent GCM, this study developed and evaluated a process to reduce these biases. Pertaining to both precipitation and the various controlling dynamics of the NAM, we found simulations driven by these bias-corrected forcing conditions performed moderately better across a 32-year historical climatology than simulations driven by the original GCM data. Current GCM consensus suggests future tropospheric warming associated with increased radiative forcing as greenhouse gas concentrations increase will suppress the NAM convective environment through greater atmospheric stability. This mechanism yields later onset dates and a generally drier season, but a slight increase to the intensity during July-August. After comparing downscaled simulations forced with original and corrected forcing conditions, we argue that the role of unresolved GCM surface features such as changes to the Gulf of California evaporation lead to a more convective environment. Even when downscaling the original GCM data with known biases, the inclusion of these surface features altered and in some cases reversed GCM trends throughout the southwest United States. This reversal towards a wetter NAM is further magnified in future bias-corrected simulations, which suggest (1) fewer average number of dry days by the end of the 21st century (2) onset occurring up to two to three weeks earlier than the historical average, and (3) more extreme daily precipitation values. However, consistent across each GCM and RCM model is the increase in inter-annual variability, suggesting greater susceptibility to drought conditions in the future.

Weather and climate models applied with sufficiently fine mesh grids to enable a large part of atmospheric deep convection to be explicitly resolved have shown a significantly improved representation of local, short-duration and intense precipitation events compared to coarser scale models. In this thesis, two studies are presented aimed at exploring the dependence of horizontal resolution and of parameterization of convection on the simulation of precipitation. The first examined the ability of HARMONIE Climate (HCLIM) regional climate model to reproduce the recent climate in Europe with two different horizontal resolutions, 15 and 6.25 km. The latter is part of the ”grey-zone” resolution interval corresponding to approximately 3-10 km. Particular focus has been given to rainfall and its spatial and temporal variability and other characteristics, for example intensity distributions. The model configuration with the higher resolution is much better at simulating days of large accumulated precipitation amounts, most evident when the comparison is made against high-resolution observations. Otherwise, the two simulations show similar skill, including the representation of the spatial structure of individual rainfall areas of primarily convective origin. The results suggest a ”scale-awareness” in HCLIM, which supports a central feature of the model’s description of deep convection as it is designed to operate independently of the horizontal resolution. In the second study, summer season precipitation over the Alps region, as simulated by HCLIM at different resolutions, is investigated. Similar model configurations as in the previous study were used, but in addition a simulation at the ”convection-permitting” 2 km resolution has been made over Central Europe. The latter considerably increases the realism compared to the former regarding the distribution and intensities of precipitation, as well as other important characteristics including the duration of rain spells, particularly on sub-daily time scales and for extreme events. The simulations with cumulus parameterization active underestimate short-duration heavy rainfall, and rainspells with low peak intensities are too persistent. Furthermore, even though the 6.25 km simulation generally reduces the biases seen in the 15 km run, definitive conclusions of the benefit of ”grey-zone” resolution is difficult to establish in context of the increased requirement of computer resources for the higher-resolution simulation.

The recent elevated North Atlantic hurricane activity has generated considerable interests in the interaction between tropical cyclones (TCs) and climate change. The possible connection between TCs and the changing climate has been indicated by observational studies based on historical TC records; they indicate emerging trends in TC frequency and intensity in some TC basins, but the detection of trends has been hotly debated due to TC track data issues. Dynamical climate modeling has also been applied to the problem, but brings its own set of limitations owing to limited model resolution and uncertainties. The final goal of this study is to project the future changes of North Atlantic TC behavior with global warming for the next 50 years using the Nested Regional Climate Model (NRCM). Throughout the course of reaching this goal, various uncertainties and limitations in simulating TCs by the NRCM are identified and explored. First we examine the TC tracking algorithm to detect and track simulated TCs from model output. The criteria and thresholds used in the tracking algorithm control the simulated TC climatology, making it difficult to objectively assess the model's ability in simulating TC climatology. Existing tracking algorithms used by previous studies are surveyed and it is found that the criteria and thresholds are very diverse. Sensitivity of varying criteria and thresholds in TC tracking algorithm to simulated TC climatology is very high, especially with the intensity and duration thresholds. It is found that the commonly used criteria may not be strict enough to filter out intense extratropical systems and hybrid systems. We propose that a better distinction between TCs and other low-pressure systems can be achieved by adding the Cyclone Phase technique. Two sets of NRCM simulations are presented in this dissertation: One in the hindcasting mode, and the other with forcing from the Community Climate System Model (CCSM) to project into the future with global warming. Both of these simulations are assessed using the tracking algorithm with cyclone phase technique. The NRCM is run in a hindcasting mode for the global tropics in order to assess its ability to simulate the current observed TC climatology. It is found that the NRCM is capable of capturing the general spatial and temporal distributions of TCs, but tends to overproduce TCs particularly in the Northwest Pacific. The overpredction of TCs is associated with the overall convective tendency in the model added with an outstanding theory of wave energy accumulation leading to TC genesis. On the other hand, TC frequency in the tropical North Atlantic is under predicted due to the lack of moist African Easterly Waves. The importance of high-resolution is shown with the additional simulation with two-way nesting. The NRCM is then forced by the CCSM to project the future changes in North Atlantic TCs. An El Nino-like SST bias in the CCSM induced a high vertical wind shear in tropical North Atlantic, preventing TCs from forming in this region. A simple bias correction method is applied to remove this bias. The model projected an increase both in TC frequency and intensity owing to enhanced TC genesis in the main development region, where the model projects an increased favorability of large-scale environment for TC genesis. However, the model is not capable of explicitly simulating intense (Category 3-5) storms due to the limited model resolution. To extrapolate the prediction to intense storms, we propose a hybrid approach that combines the model results and a statistical modeling using extreme value theory. Specifically, the current observed TC intensity is statistically modeled with the General Pareto distribution, and the simulated intensity changes from the NRCM are applied to the statistical model to project the changes in intense storms. The results suggest that the occurrence of Category 5 storms may be increased by approximately 50% by 2055.

Diese Dissertation beschäftigt sich mit regionaler Klimamodellierung über Südamerika, der Analyse von Modellsensitivitäten bezüglich Wolkenparametrisierungen und der Entwicklung neuer Methoden zur Modellevaluierung mithilfe von Klimanetzwerken. Im ersten Teil untersuchen wir Simulationen mit dem COnsortium for Small scale MOdeling model in CLimate Mode (COSMO-CLM) und stellen die erste umfassende Evaluierung dieses dynamischen regionalen Klimamodells über Südamerika vor. Dabei untersuchen wir insbesondere die Abhängigkeit simulierter tropischer Niederschläge von Parametrisierungen subgitterskaliger cumuliformer und stratiformer Wolken und finden starke Sensitivitäten bezüglich beider Wolkenparametrisierungen über Land. Durch einen simultanen Austausch der entsprechenden Schemata gelingt uns eine beträchtliche Reduzierung von Fehlern in klimatologischen Niederschlags- und Strahlungsmitteln, die das COSMO-CLM über tropischen Regionen für lange Zeit charakterisierten. Im zweiten Teil führen wir neue Metriken für die Evaluierung von Klimamodellen bezüglich räumlicher Kovariabilitäten ein. Im Kern bestehen diese Metriken aus Unähnlichkeitsmaßen für den Vergleich von simulierten mit beobachteten Klimanetzwerken. Wir entwickeln lokale und globale Unähnlichkeitsmaße zum Zwecke der Darstellung lokaler Unähnlichkeiten in Form von Fehlerkarten sowie der Rangordnung von Modellen durch Zusammenfassung lokaler zu globalen Unähnlichkeiten. Die neuen Maße werden dann für eine vergleichende Evaluierung regionaler Klimasimulationen mit COSMO-CLM und dem Statistical Analogue Resampling Scheme über Südamerika verwendet. Dabei vergleichen wir die sich ergebenden Modellrangfolgen mit solchen basierend auf mittleren quadratischen Abweichungen klimatologischer Mittelwerte und Varianzen und untersuchen die Abhängigkeit dieser Rangfolgen von der betrachteten Jahreszeit, Variable, dem verwendeten Referenzdatensatz und Klimanetzwerktyp. / This dissertation is about regional climate modeling over South America, the analysis of model sensitivities to cloud parameterizations, and the development of novel model evaluation techniques based on climate networks. In the first part we examine simulations with the COnsortium for Small scale MOdeling weather prediction model in CLimate Mode (COSMO-CLM) and provide the first thorough evaluation of this dynamical regional climate model over South America. We focus our analysis on the sensitivity of simulated tropical precipitation to the parameterizations of subgrid-scale cumuliform and stratiform clouds. It is shown that COSMO-CLM is strongly sensitive to both cloud parameterizations over tropical land. Using nondefault cumulus and stratus parameterization schemes we are able to considerably reduce long-standing precipitation and radiation biases that have plagued COSMO-CLM across tropical domains. In the second part we introduce new performance metrics for climate model evaluation with respect to spatial covariabilities. In essence, these metrics consist of dissimilarity measures for climate networks constructed from simulations and observations. We develop both local and global dissimilarity measures to facilitate the depiction of local dissimilarities in the form of bias maps as well as the aggregation of those local to global dissimilarities for the purposes of climate model intercomparison and ranking. The new measures are then applied for a comparative evaluation of regional climate simulations with COSMO-CLM and the STatistical Analogue Resampling Scheme (STARS) over South America. We compare model rankings obtained with our new performance metrics to those obtained with conventional root-mean-square errors of climatological mean values and variances, and analyze how these rankings depend on season, variable, reference data set, and climate network type.