Darin Comeau

Tectonic and structural control of fluvial channel morphology in metamorphic core complexes: The example of the Catalina-Rincon core complex, Arizona

Fluvial channels in metamorphic core complexes are preferentially oriented parallel and perpendicular to the direction of tectonic extension. This pattern has been variably attributed to such causes as tectonic tilting during extension, channel elongation by slip along the range-bounding detachment fault, and the exploitation of extension-related joint sets during channel incision. In this paper we use field measurements, digital elevation model analyses, and numerical modeling to test hypotheses for the tectonic and structural control of fluvial channels in metamorphic core complexes, using the Catalina-Rincon core complex in southern Arizona, USA, as a type example. Field measurements and aerial photographic analyses indicate that channels of all sizes exploit steeply dipping joint sets during fluvial incision. As a consequence, channels become preferentially aligned along those joint sets. First and second Strahler-order channels preferentially exploit a joint set oriented perpendicular to the extension direction, while higher-order channels preferentially exploit a joint set oriented parallel to the extension direction. While these observations support the joint-exploitation hypothesis for structural control of drainage architecture, numerical modeling indicates that the spatial distribution of rock uplift during the initial phase of extension plays a crucial role by determining which joint set is preferentially exploited by channels of which Strahler orders. Numerical models indicate that higher-order channels exploit the joint set that is most closely aligned with the direction of initial tectonic tilting, even if that tilting is active for only a short period of time following the initiation of uplift. We conclude that the drainage architecture in the Catalina-Rincon core complex is the result of a combination of joint exploitation and tectonic tilting mechanisms. Structure also plays an important role in controlling the longitudinal profiles of channels in metamorphic core complexes. Channels in the Catalina-Rincon core complex are characterized by structurally controlled knickpoints with a wide distribution of heights and spacings. Field observations indicate that the occurrence of structurally controlled knickpoints and the resulting variability in longitudinal profile form is related to spatial variations in joint density. Numerical models that incorporate spatial variations in joint density using a stochastic bedrock erodibility coefficient are capable of reproducing the statistical properties of longitudinal profiles in the Catalina-Rincon core complex, including the power spectrum of longitudinal profiles and the frequency size distribution of structurally controlled knickpoints. The results of this study illustrate the important roles played by both jointing and the spatial distribution of rock uplift on the geomorphic evolution of metamorphic core complexes. More broadly, the study provides a recipe for how to incorporate joint-related structural controls into landscape evolution models.

Defining a trend for time series using the intrinsic time-scale decomposition

We propose criteria that define a trend for time series with inherent multi-scale features. We call this trend the tendency of a time series. The tendency is defined empirically by a set of criteria and captures the large-scale temporal variability of the original signal as well as the most frequent events in its histogram. Among other properties, the tendency has a variance no larger than that of the original signal; the histogram of the difference between the original signal and the tendency is as symmetric as possible; and with reduced complexity, the tendency captures essential features of the signal. To find the tendency we first use the intrinsic time-scale decomposition (ITD) of the signal, introduced in 2007 by Frei and Osorio, to produce a set of candidate tendencies. We then apply the criteria to each of the candidates to single out the one that best agrees with them. While the criteria for the tendency are independent of the signal decomposition scheme, it is found that the ITD is a simple and stable methodology, well suited for multi-scale signals. The ITD is a relatively new decomposition and little is known about its outcomes. In this study we take the first steps towards a probabilistic model of the ITD analysis of random time series. This analysis yields details concerning the universality and scaling properties of the components of the decomposition.

Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting

Predicting Arctic sea ice extent is a notoriously difficult forecasting problem, even for lead times as short as one month. Motivated by Arctic intraannual variability phenomena such as reemergence of sea surface temperature and sea ice anomalies, we use a prediction approach for sea ice anomalies based on analog forecasting. Traditional analog forecasting relies on identifying a single analog in a historical record, usually by minimizing Euclidean distance, and forming a forecast from the analog’s historical trajectory. Here an ensemble of analogs is used to make forecasts, where the ensemble weights are determined by a dynamics-adapted similarity kernel, which takes into account the nonlinear geometry on the underlying data manifold. We apply this method for forecasting pan-Arctic and regional sea ice area and volume anomalies from multi-century climate model data, and in many cases find improvement over the benchmark damped persistence forecast. Examples of success include the 3–6 month lead time prediction of Arctic sea ice area, the winter sea ice area prediction of some marginal ice zone seas, and the 3–12 month lead time prediction of sea ice volume anomalies in many central Arctic basins. We discuss possible connections between KAF success and sea ice reemergence, and find KAF to be successful in regions and seasons exhibiting high interannual variability.