Attribution of climate change to causal factors has been based predominantly on simulations using physical climate models, which have inherent limitations in describing such a complex and chaotic system. We propose an alternative, data centric, approach that relies on actual measurements of climate observations and human and natural forcing factors. Specifically, we develop a novel method to infer causality from spatial-temporal data, as well as a procedure to incorporate extreme value modeling into our method in order to address the attribution of extreme climate events, such as heatwaves. Our experimental results on a real world dataset indicate that changes in temperature are not solely accounted for by solar radiance, but attributed more significantly to CO2 and other greenhouse gases. Combined with extreme value modeling, we also show that there has been a significant increase in the intensity of extreme temperatures, and that such changes in extreme temperature are also attributable to greenhouse gases. These preliminary results suggest that our approach can offer a useful alternative to the simulation-based approach to climate modeling and attribution, and provide valuable insights from a fresh perspective.

A fingerprint-based method for climate change detection and attribution with some novel features is proposed. The method is based on a functional ANOVA (ANalysis Of VAriance) decomposition of a time and space signal, further decomposed into global time-trend and time-trend anomaly as a function of space. The method estimates the signal as a component of forced minus background climate model output, and then uses a partial spline model to estimate and test for the existence of signal in historical data. The method is based on the classical detection of signal in noise, however there are several features apparently novel to the fingerprint literature, in particular, the analysis takes place directly in observation space, anomalies are tted directly and there is possibility for estimating certain parameters of covariance models for the historical data as part of the analysis. Simulation studies using climate model runs from GFDL and NCAR and historical data for NH Winter average...

. Classic wavelet methods were developed in the Euclidean spaces for multiscale representation and analysis of regularly sampled signals (time series) and images. This paper introduces a method of representing scattered spherical data by multiscale spherical wavelets. The method extends the recent pioneering work of Narcowich and Ward [Appl. Comput. Harmon. Anal., 3 (1996), pp. 324--336] by employing multiscale rather than single-scale spherical basis functions and by introducing a bottom-up procedure for network design and bandwidth selection. Decomposition and reconstruction algorithms are proposed for e#cient computation. An analytical investigation confirms the localization property of the resulting spherical wavelets. The proposed method is illustrated by numerical examples. It is also employed to analyze and compress a real-data set consisting of the surface air temperatures observed on a global network of weather stations. Key words. approximation, climatology, data compression,...

this paper we then (i) review the use of model errors as dual variables, (ii) review the GCV and generalized maximum likelihood (GML) tuning methods, and pinpoint sensitivity issues as tunable parameters are sprinkled liberally throughout the weak 4D-Var problem, noting that they can be studied in the influence matrix (or influence operator in the nonlinear case). Then (iii) we describe some simple models for correlated model errors and the simultaneous consideration of systematic (bias), short memory and long memory correlation. We end with (iv) a summary of some representer theory in reproducing kernel Hilbert space (RKHS) relevant to the weak 4D-Var setting. Let t = 1; \Delta \Delta \Delta ; T denote discrete time and let \Psi t ; t = 1; \Delta \Delta \Delta T be a sequence of state vectors representing (some part of) nature that evolves according to

This TR contains two brief reviews which will appear in the Proceedings of the 13th IFAC Symposium on System Identification (SYSID 2003), Rotterdam, August 2003.

Abstract: This TR contains two brief reviews which will appear in the Proceedings of the

Abstract: In this paper, we introduce a model-based method for clustering multiple curves or functionals under spatial dependence specified up to a set of unknown parameters. The functionals are decomposed using a semi-parametric model where the fixed effects account for the large-scale clustering association and the random effects for the small scale spatialdependence variability. The clustering model assumes the clustering membership as a realization from a Markov random field. Within our estimation framework, the emphasis is on a large number of functionals/spatial units with sparsely sampled time points. To overcome the computational cost resulting from large dependence matrix operations, the estimation algorithm includes a two-stage approximation: low-ranked kernel-based decomposition of the dependence matrix and Incomplete Choslesky Factorization of the kernel matrix. We assess the performance of our clustering approach within a simulation study. The simulation results show enhanced clustering estimation accuracy of our method compared with other existing model-based clustering methods under a series of settings: small number of time points, low signal-to-noise ratio and different spatial dependence structures. Many case studies will fall within our clustering framework, but we focus on obtaining fine-grid spatial clusters for demographics trends including ethnicity and income for five southern states of US over the past 11 years. 1 1

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