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The Indian Ocean Zonal Mode (IOZM) has gained considerable attention in the past decade due to its role in causing widespread flooding and droughts in the continents and islands surrounding the Indian Ocean. The IOZM has also been observed to vary on a low-frequency (multi-decadal) basis, making its behavior important to understand both for mid-range 21st century climate prediction and for paleoclimate studies. Despite efforts to reconstruct the IOZM using corals and other high-resolution proxies, nonstationarities in the response of paleoclimate proxies to the IOZM have also been noted, raising the possibility that the IOZM may be difficult to reconstruct or to predict in the long-term. It is therefore critical to assess the low-frequency component of the IOZM in observed, modeled, and paleoclimate data from the Indian Ocean region in order to identify nonstationary behavior and to assess its role in low-frequency climate variations. We present an analysis of low-frequency and nonstationary behavior in the IOZM on multi-decadal to centennial timescales using a combination of modeled, observed, and proxy reconstructions of δ 18 O/δD precip . In order to assess multiple timescales of low-frequency variability, we focus on two key time periods: the historical period (1870–2003), and the past millennium (1000 C.E.-present). We find significant nonstationarities in the relationships between the IOZM, precipitation amount, and δ 18 O precip /δD precip during the historical period. These relationships vary on a multi-decadal basis in our model and in observed/reanalysis datasets. Air-sea interactions in the Indo-Pacific Warm Pool and teleconnections to the Pacific Ocean, as well as the phase of the IOZM itself, may contribute to this nonstationary behavior. We examine the potential ramifications of nonstationary IOZM behavior using a synthesis of spatially distributed proxy archives of δ 18 O precip /δD precip from both sides of the IOZM region spanning the past millennium. Our findings indicate that during the past millennium, a strong IOZM-like connection exists in the proxy data network, with anti-correlation between East Africa and Indonesia. However, the links are spatially limited and in some cases timescale-dependent. Nonlinear behaviors in these links suggest that the IOZM may be difficult to detect on a consistent basis in proxy records from the past millennium. Based on our modeling results, the inconsistent links in the IOZM proxy network may arise from temporally and spatially variable relationships between the IOZM, precipitation, and δ 18 O precip /δD precip . We conclude that the IOZM's potential to influence the climate of the Indian Ocean region is inconsistently reflected in proxy data, and that due to the changing strength of the IOZM/δ 18 O precip /δD precip relationship, its spatial “footprint” may be restricted on multi-decadal to multi-centennial timescales.

Climate networks are constructed from climate time series data using correlation measures. It is widely accepted that the geographical proximity, as well as other geographical features such as ocean and atmospheric currents, have a large impact on the observable time-series similarity. Therefore it is to be expected that the spatial sampling will influence the reconstructed network. Here we investigate this by comparing analytical flow networks, networks generated with the START model and networks from temperature data from the Asian monsoon domain. We evaluate them on a regular grid, a grid with added random jittering and two variations of clustered sampling. We find that the impact of the spatial sampling on most network measures only distorts the plots if the node distribution is significantly inhomogeneous. As a simple diagnostic measure for the detection of inhomogeneous sampling we suggest the Voronoi cell size distribution.

Sedimentary proxy records constitute a significant portion of the recorded evidence that allow us to investigate paleoclimatic conditions and variability. However, uncertainties in the dating of proxy archives limit our ability to fix the timing of past events and interpret proxy record inter-comparisons. While there are various age-modeling approaches to improve the estimation of the age-depth relations of archives, relatively less focus has been given to the propagation of the age (and radiocarbon calibration) uncertainties into the final proxy record. We present a generic Bayesian framework to estimate proxy records along with their associated uncertainty starting with the radiometric age-depth and proxy-depth measurements, and a radiometric calibration curve if required. We provide analytical expressions for the posterior proxy probability distributions at any given calendar age, from which the expected proxy values and their uncertainty can be estimated. We illustrate our method using two synthetic datasets and then use it to construct the proxy records for groundwater inflow and surface erosion from Lonar lake in central India. Our analysis reveals interrelations between the uncertainty of the proxy record over time and the variance of proxy along the depth of the archive. For the Lonar lake proxies, we show that, rather than the age uncertainties, it is the proxy variance combined with calibration uncertainty that accounts for most of the final uncertainty. We represent the proxy records as probability distributions on a precise, error-free time scale that makes further time series analyses and inter-comparison of proxies relatively simpler and clearer. Our approach provides a coherent understanding of age uncertainties within sedimentary proxy records that involve radiometric dating. It can be potentially used within existing age modeling structures to bring forth a reliable and consistent framework for proxy record estimation.

A critical challenge in paleoclimate data analysis is the fact that the proxy data are heterogeneously distributed in space, which affects statistical methods that rely on spatial embedding of data. In the paleoclimate network approach nodes represent paleoclimate proxy time series, and links in the network are given by statistically significant similarities between them. Their location in space, proxy and archive type is coded in the node attributes. We develop a semi-empirical model for Spatio-Temporally AutocoRrelated Time series, inspired by the interplay of different Asian Summer Monsoon (ASM) systems. We use an ensemble of transition runs of this START model to test whether and how spatio–temporal climate transitions could be detectable from (paleo)climate networks. We sample model time series both on a grid and at locations at which paleoclimate data are available to investigate the effect of the spatially heterogeneous availability of data. Node betweenness centrality, averaged over the transition region, does not respond to the transition displayed by the START model, neither in the grid-based nor in the scattered sampling arrangement. The regionally defined measures of regional node degree and cross link ratio, however, are indicative of the changes in both scenarios, although the magnitude of the changes differs according to the sampling. We find that the START model is particularly suitable for pseudo-proxy experiments to test the technical reconstruction limits of paleoclimate data based on their location, and we conclude that (paleo)climate networks are suitable for investigating spatio–temporal transitions in the dependence structure of underlying climatic fields.

Complex network approaches have recently been applied to continuous spatial dynamical systems, like climate, successfully uncovering the system's interaction structure. However the relationship between the underlying atmospheric or oceanic flow's dynamics and the estimated network measures have remained largely unclear. We bridge this crucial gap in a bottom-up approach and define a continuous analytical analogue of Pearson correlation networks for advection-diffusion dynamics on a background flow. Analysing complex networks of prototypical flows and from time series data of the equatorial Pacific, we find that our analytical model reproduces the most salient features of these networks and thus provides a general foundation of climate networks. The relationships we obtain between velocity field and network measures show that line-like structures of high betweenness mark transition zones in the flow rather than, as previously thought, the propagation of dynamical information.

Paleoclimate time series are often irregularly sampled and age uncertain, which is an important technical challenge to overcome for successful reconstruction of past climate variability and dynamics. Visual comparison and interpolation-based linear correlation approaches have been used to infer dependencies from such proxy time series. While the first is subjective, not measurable and not suitable for the comparison of many datasets at a time, the latter introduces interpolation bias, and both face difficulties if the underlying dependencies are nonlinear. In this paper we investigate similarity estimators that could be suitable for the quantitative investigation of dependencies in irregular and age uncertain time series. We compare the Gaussian-kernel based cross correlation (gXCF, Rehfeld et al., 2011) and mutual information (gMI, Rehfeld et al., 2013) against their interpolation-based counterparts and the new event synchronization function (ESF). We test the efficiency of the methods in estimating coupling strength and coupling lag numerically, using ensembles of synthetic stalagmites with short, autocorrelated, linear and nonlinearly coupled proxy time series, and in the application to real stalagmite time series. In the linear test case coupling strength increases are identified consistently for all estimators, while in the nonlinear test case the correlation-based approaches fail. The lag at which the time series are coupled is identified correctly as the maximum of the similarity functions in around 60–55% (in the linear case) to 53–42% (for the nonlinear processes) of the cases when the dating of the synthetic stalagmite is perfectly precise. If the age uncertainty increases beyond 5% of the time series length, however, the true coupling lag is not identified more often than the others for which the similarity function was estimated. Age uncertainty contributes up to half of the uncertainty in the similarity estimation process. Time series irregularity contributes less, particularly for the adapted Gaussian-kernel based estimators and the event synchronization function. The introduced link strength concept summarizes the hypothesis test results and balances the individual strengths of the estimators: while gXCF is particularly suitable for short and irregular time series, gMI and the ESF can identify nonlinear dependencies. ESF could, in particular, be suitable to study extreme event dynamics in paleoclimate records. Programs to analyze paleoclimatic time series for significant dependencies are included in a freely available software toolbox.

The analysis of palaeoclimate time series is usually affected by severe methodological problems, resulting primarily from non-equidistant sampling and uncertain age models. As an alternative to existing methods of time series analysis, in this paper we argue that the statistical properties of recurrence networks – a recently developed approach – are promising candidates for characterising the system's nonlinear dynamics and quantifying structural changes in its reconstructed phase space as time evolves. In a first order approximation, the results of recurrence network analysis are invariant to changes in the age model and are not directly affected by non-equidistant sampling of the data. Specifically, we investigate the behaviour of recurrence network measures for both paradigmatic model systems with non-stationary parameters and four marine records of long-term palaeoclimate variations. We show that the obtained results are qualitatively robust under changes of the relevant parameters of our method, including detrending, size of the running window used for analysis, and embedding delay. We demonstrate that recurrence network analysis is able to detect relevant regime shifts in synthetic data as well as in problematic geoscientific time series. This suggests its application as a general exploratory tool of time series analysis complementing existing methods.

Two techniques for target motion characterisation based on 4DCT images were evaluated: calculation of the conformality index and a technique based on image subtraction. The outputs for both techniques were shown to correlate with target displacement, indicating the applicability of these techniques for motion characterisation. While conformality index calculation requires target delineation (a time-consuming and subjective process), image subtraction is automatic and objective. In addition, the image subtraction method produces a visual representation of the areas where motion is present between different phases. The procedure has potential for automatic internal target volume definition for radiotherapy treatment purposes.

Purpose: To develop a high throughput method for quantitative analysis of organ motion characterization based on 4DCT image reconstruction and identify breathing phases corresponding to the most extreme target motion. Method and Materials: A novel algorithm was developed that utilizes subtraction of paired images obtained under 4DCT protocol and analyzes the resulting image residuals to characterize the extent of organ motion. The cumulative pixel value (CPV) of the image residual is calculated for all possible combinations of the paired image sets. The maximum CPV is associated with the greatest separation of the target in two image sets, and extreme “breathing” phases are identified. The algorithm was validated on a set of 4DCT images obtained for a dynamic phantom (CIRS, Inc., Norfolk, VA) for different target sizes and motion characteristics. Initial tests on 4 patients with different degrees of motion were conducted. Results: The extent of target motion was correctly described for all pairs of image sets by calculated CPV values. The phases known to correspond to extremes of motion were correctly identified for the phantom study. Phantom studies using targets of different sizes (1 to 3 cm) and different amplitudes of motion (1 to 2cm) produced similar results. Using patient images, our method correctly predicted the degree of observable motion in a blinded test. Conclusions: An automated algorithm for target motion evaluation was developed and tested using 4DCT image sets obtained with a dynamic phantom. The algorithm predictions were accurate for a range of target sizes and amplitudes of motions. The method is objective and robust and therefore may be effectively used in the clinic as a quantitative measure of organ motion. Preliminary results of using patient data are very promising.

This report focuses on the risks of climate change to development in Sub-Saharan Africa, South East Asia and South Asia. Building on the 2012 report, Turn Down the Heat: Why a 4°C Warmer World Must be Avoided, this new scientific analysis examines the likely impacts of present day, 2°C and 4°C warming on agricultural production, water resources, and coastal vulnerability for affected populations. It finds many significant climate and development impacts are already being felt in some regions, and in some cases multiple threats of increasing extreme heat waves, sea level rise, more severe storms, droughts and floods are expected to have further severe negative implications for the poorest. Climate related extreme events could push households below the poverty trap threshold. High temperature extremes appear likely to affect yields of rice, wheat, maize and other important crops, adversely affecting food security. Promoting economic growth and the eradication of poverty and inequality will thus be an increasingly challenging task under future climate change. Immediate steps are needed to help countries adapt to the risks already locked in at current levels of 0.8°C warming, but with ambitious global action to drastically reduce greenhouse gas emissions, many of the worst projected climate impacts could still be avoided by holding warming below 2°C.

Reliable age models are fundamental for any palaeoclimate reconstruction. Available interpolation procedures between age control points are often inadequately reported, and very few translate age uncertainties to proxy uncertainties. Most available modeling algorithms do not allow incorporation of layer counted intervals to improve the confidence limits of the age model in question. We present a framework that allows detection and interactive handling of age reversals and hiatuses, depth-age modeling, and proxy-record reconstruction. Monte Carlo simulation and a translation procedure are used to assign a precise time scale to climate proxies and to translate dating uncertainties to uncertainties in the proxy values. The presented framework allows integration of incremental relative dating information to improve the final age model. The free software package COPRA1.0 facilitates easy interactive usage.