Holarchical Development: Discovering and Applying Missing Drives from Ken Wilber’s Twenty Tenets

Ken Wilber’s AQAL model offers a way to synthesize the partial truths of many theories across various fields of knowledge such as evolutionary biology and sociology, developmental psychology, and perennial and contemporary philosophy to name only a few. Despite its reconciling power and influence, the model has been validly criticized for its static nature and its overemphasis on the ascendant, versus descendant, path of development. This paper points out areas of Wilber’s writing that suggest a way to overcome these criticisms. Doing so allows for the refinement of AQAL’s Twenty Tenets for an extension of its formal, dynamic features. This is accomplished first by relating Wilber’s original dynamic drives to the quadrants and levels enabling the quadrants and levels to then predict additional drives not specified by Wilber. The full set of drives then suggests clarifications of assumptions and applications of the model regarding transcendence and inclusion in order for the refined model to be internally consistent. The result helps correct for AQAL’s ascending bias, a bias which overemphasizes a linear path from lower to higher stages of development. Instead, more possibilities emerge such as those in which ascending development is overly dependent on a higher capacity with inclusion of only basic, lower core capacities. This is in contrast to more fully realizing the potential for development of individuals or societies in the more fundamental, lower levels, through deeper inclusion within higher capacities. Also, given the other horizontal drives that are predicted by the model, further possibilities are explored for differing directions of, and emphasis in, development

Content in the Context of 4D-Var Data Assimilation. II: Application to Global Ozone Assimilation

Data assimilation obtains improved estimates of the state of a physical system by combining imperfect model results with sparse and noisy observations of reality. Not all observations used in data assimilation are equally valuable. The ability to characterize the usefulness of different data points is important for analyzing the effectiveness of the assimilation system, for data pruning, and for the design of future sensor systems. In the companion paper [Sandu et al.(2011)] we derived an ensemble-based computational procedure to estimate the information content of various observations in the context of 4D-Var. Here we apply this methodology to quantify two information metrics (the signal and degrees of freedom for signal) for satellite observations used in a global chemical data assimilation problem with the GEOS-Chem chemical transport model. The assimilation of a subset of data points characterized by the highest information content, gives analyses that are comparable in quality with the one obtained using the entire data set

A Practical Method to Estimate Information Content in the Context of 4D-Var Data Assimilation. I: Methodology

Data assimilation obtains improved estimates of the state of a physical system by combining imperfect model results with sparse and noisy observations of reality. Not all observations used in data assimilation are equally valuable. The ability to characterize the usefulness of different data points is important for analyzing the effectiveness of the assimilation system, for data pruning, and for the design of future sensor systems. This paper focuses on the four dimensional variational (4D-Var) data assimilation framework. Metrics from information theory are used to quantify the contribution of observations to decreasing the uncertainty with which the system state is known. We establish an interesting relationship between different information-theoretic metrics and the variational cost function/gradient under Gaussian linear assumptions. Based on this insight we derive an ensemble-based computational procedure to estimate the information content of various observations in the context of 4D-Var. The approach is illustrated on linear and nonlinear test problems. In the companion paper [Singh et al.(2011)] the methodology is applied to a global chemical data assimilation problem

A Practical Method to Estimate Information Content in the Context of 4D-Var Data Assimilation. II: Application to Global Ozone Assimilation

Data assimilation obtains improved estimates of the state of a physical system by combining imperfect model results with sparse and noisy observations of reality. Not all observations used in data assimilation are equally valuable. The ability to characterize the usefulness of different data points is important for analyzing the effectiveness of the assimilation system, for data pruning, and for the design of future sensor systems. In the companion paper (Sandu et al., 2012) we derive an ensemble-based computational procedure to estimate the information content of various observations in the context of 4D-Var. Here we apply this methodology to quantify the signal and degrees of freedom for signal information metrics of satellite observations used in a global chemical data assimilation problem with the GEOS-Chem chemical transport model. The assimilation of a subset of data points characterized by the highest information content yields an analysis comparable in quality with the one obtained using the entire data set

Geostationary Coastal and Air Pollution Events (GEO-CAPE) Sensitivity Analysis Experiment

Geostationary Coastal and Air pollution Events (GEO-CAPE) is a NASA decadal survey mission to be designed to provide surface reflectance at high spectral, spatial, and temporal resolutions from a geostationary orbit necessary for studying regional-scale air quality issues and their impact on global atmospheric composition processes. GEO-CAPE's Atmospheric Science Questions explore the influence of both gases and particles on air quality, atmospheric composition, and climate. The objective of the GEO-CAPE Observing System Simulation Experiment (OSSE) is to analyze the sensitivity of ozone to the global and regional NOx emissions and improve the science impact of GEO-CAPE with respect to the global air quality. The GEO-CAPE OSSE team at Jet propulsion Laboratory has developed a comprehensive OSSE framework that can perform adjoint-sensitivity analysis for a wide range of observation scenarios and measurement qualities. This report discusses the OSSE framework and presents the sensitivity analysis results obtained from the GEO-CAPE OSSE framework for seven observation scenarios and three instrument systems

Empirical validation of dynamic thermal computer models of buildings

A methodology for the validation of dynamic thermal models of buildings has been presented. The three techniques, analytical verification, inter-model comparisons and empirical validation have been described and their relative merits assessed by reference to previous validation work on ESP, SERIR'S, DEROB and BLAST. Previous empirical validation work on these models has been reviewed. This research has shown that numerous sources of error have existed in previous studies leading to uncertainty in model predictions. The effects of these errors has meant that none of the previous empirical validation studies would have produced conclusive evidence of internal errors in the models themselves. An approach towards developing tests to empirically validate dynamic thermal models is given

A hierarchical statistical framework for emergent constraints: application to snow-albedo feedback

Emergent constraints use relationships between future and current climate states to constrain projections of climate response. Here, we introduce a statistical, hierarchical emergent constraint (HEC) framework in order to link future and current climate with observations. Under Gaussian assumptions, the mean and variance of the future state is shown analytically to be a function of the signal-to-noise (SNR) ratio between data-model error and current-climate uncertainty, and the correlation between future and current climate states. We apply the HEC to the climate-change, snow-albedo feedback, which is related to the seasonal cycle in the Northern Hemisphere. We obtain a snow-albedo-feedback prediction interval of $(-1.25, -0.58)$ \%$K^{-1}$. The critical dependence on SNR and correlation shows that neglecting these terms can lead to bias and under-estimated uncertainty in constrained projections. The flexibility of using HEC under general assumptions throughout the Earth System is discussed.Comment: 19 pages, 5 Figure