Thesis (M.Sc.)--University of Alberta, 2010. / Title from PDF file main screen (viewed on July 2, 2010). A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science, Department of Earth and Atmospheric Sciences, University of Alberta. Includes bibliographical references.

Parameterization development and evaluation ideally takes a two-step approach (Lohmann et al., 2007). Insight into new processes, and initial parameterization formulation should be guided by theory, process-level observations (laboratory experiments or field studies) or, if these are unavailable, by high-resolution modelling. However, once implemented into large-scale atmospheric models, a thorough testing and evaluation is required in order to assure that the parameterization works satisfactorily for all weather situations and at the scales the model is applied to. Satellite observations are probably the most valuable source of information for this purpose, since they offer a large range of parameters over comparatively long time series and with a very large, to global, coverage. However, satellites usually retrieve parameters in a rather indirect way, and some quantities (e.g., vertical wind velocities) are unavailable. It is thus essential for model evaluation 1. to assure comparability; and, 2. to develop and apply metrics that circumvent the limitations of satellite observations and help to learn about parameterizations. In terms of comparability, the implementation of so-called \"satellite simulators\" has emerged as the approach of choice, in which satellite retrievals are emulated, making use of model information about the subgrid-scale variability of clouds, and creating summary statistics (Bodas-Salcedo et al., 2011; Nam and Quaas, 2012; Nam et al., 2014). In terms of process-oriented metrics, a large range of approaches has been developed, e.g. investigating the life cycle of cirrus from convective detrainment (Gehlot and Quaas, 2012), or focusing on the details of microphysical processes (Suzuki et al., 2011). Besides such techniques focusing on individual parameterizations, the data assimilation technique might be exploited, by objectively adjusting convection parameters and learning about parameter choices and parameterizations in this way (Schirber et al., 2013).In this chapter, we will first introduce the available satellite data, consider their limitations and the approaches to account for these, and then discuss observations-based process-oriented metrics that have been developed so far.

The mesoscale organization and structure of convective clouds is thought to be rooted in the thermodynamic properties of the atmosphere and in the turbulent to mesoscale dynamics of the flow. Such structure may contribute to the transition between shallow and deep convection. The thermodynamic state of the boundary layer is forced by the amount of surface fluxes from below. Conversely, landscape patterns and land-cover heterogeneity may equally give rise to focused regions for deep convection triggering, in particular when patch sizes exceed 10 km. Since the convective boundary layer has a mediating function between the surface and deep storm clouds, the connection between surface and upper atmosphere is not straightforward. It is generally believed to involve local erosion of the capping inversion layer, the build-up of a moist energy supply, gradual humidification of the lower-free troposphere that reduces dry air entrainment into burgeoning deeper clouds, and thermal mesoscale circulations that can generate moisture convergence and locally forced ascent. To what extent microscale realistic surface heterogeneity and an interactive surface response matter to shallow and deep convection and its organization remains an open question. In this dissertation, we describe the coupling of a physiology-based vegetation model (HYBRID) and of a sea surface flux algorithm (COARE) to the cloud-resolving Active Tracer High-resolution Atmospheric Model (ATHAM). We investigate the full diurnal cycle of convection based on the example of the Hector storm over Tiwi Islands, notably the well-characterized event on 30th November 2005. The model performs well in terms of timing and cloud dynamics in comparison to a range of available observations. Also, ATHAM-HYBRID seems to do well in terms of flux partitioning. Whilst awaiting more thorough flux validation, we remain confident that the interactive surface response of both HYBRID and COARE is suited for the purpose of simulating convective-scale processes. We find the storm system evolution in 3D simulations to be robust with respect to differences in surface configuration and initialization. Within our 3D sensitivity runs, we could not identify a strong dependence on either realistic surface heterogeneity in the island landscape or on the interactive surface response. We conclude that in our case study at least, atmospheric (turbulent) dynamics likely dominate over surface heterogeneity effects, provided that the bulk magnitude of the surface energy fluxes, and their partitioning into sensible and latent heat (Bowen ratio), remain unaltered. This is consistent with 2D sensitivity studies, where we find model grid-spacing and momentum diffusion, governing the dynamics, to have an important influence on the overall evolution of deep convection. Fine grid-spacing is necessary, as the median width of updraught cores mostly does not exceed 1000 m. We associate this influence with the dry air entrainment rate in the wake of rising parcels, and with how resolution and diffusion act on coherent structures in the flow. In 2D sensitivity studies with differences in realistic heterogeneities of surface properties, we find little evidence for a clear deterministic influence of these properties on the transition between shallow and deep convection, in spite of largely different storm evolutions across the various runs. In these runs, we tentatively ascribe triggering to stochastic features in the flow, without discarding the relevance of convergence lines produced by mesoscale density currents, such as the sea breeze and cold pool storm outflows.

Parameterization development and evaluation ideally takes a two-step approach (Lohmann et al., 2007). Insight into new processes, and initial parameterization formulation should be guided by theory, process-level observations (laboratory experiments or field studies) or, if these are unavailable, by high-resolution modelling. However, once implemented into large-scale atmospheric models, a thorough testing and evaluation is required in order to assure that the parameterization works satisfactorily for all weather situations and at the scales the model is applied to. Satellite observations are probably the most valuable source of information for this purpose, since they offer a large range of parameters over comparatively long time series and with a very large, to global, coverage. However, satellites usually retrieve parameters in a rather indirect way, and some quantities (e.g., vertical wind velocities) are unavailable. It is thus essential for model evaluation 1. to assure comparability; and, 2. to develop and apply metrics that circumvent the limitations of satellite observations and help to learn about parameterizations. In terms of comparability, the implementation of so-called \"satellite simulators\" has emerged as the approach of choice, in which satellite retrievals are emulated, making use of model information about the subgrid-scale variability of clouds, and creating summary statistics (Bodas-Salcedo et al., 2011; Nam and Quaas, 2012; Nam et al., 2014). In terms of process-oriented metrics, a large range of approaches has been developed, e.g. investigating the life cycle of cirrus from convective detrainment (Gehlot and Quaas, 2012), or focusing on the details of microphysical processes (Suzuki et al., 2011). Besides such techniques focusing on individual parameterizations, the data assimilation technique might be exploited, by objectively adjusting convection parameters and learning about parameter choices and parameterizations in this way (Schirber et al., 2013).In this chapter, we will first introduce the available satellite data, consider their limitations and the approaches to account for these, and then discuss observations-based process-oriented metrics that have been developed so far.

<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