North Atlantic climate variability from a self-organizing map perspective

[1] North Atlantic variability in general, and the North Atlantic Oscillation (NAO) in particular, is a long-studied, very important but still not well-understood problem in climatology. The recent trend to a higher wintertime NAO index was accompanied by an additional increase in the Azores High not coupled to changes in the Icelandic Low, as shown by a self-organizing maps (SOMs) analysis of monthly mean DJF mean sea level pressure data from 1957 to 2002. SOMs are a nonlinear tool to optimally extract a user-specified number of patterns or icons from an input data set and to uniquely relate any input data field to an icon, allowing analyses of occurrence frequencies and transitions complementary to principal component analysis (PCA). SOMs analysis of ERA-40 data finds a North Atlantic monopole roughly colocated with the mean position of the Azores High, as well as the well-known NAO dipole involving the Icelandic Low and the subtropical high. Little trend is shown in December, but the Azores High increased along with the NAO in January and February over the study interval, with implications for storminess in northwestern Europe. In short, our SOM-based analyses of winter MSLP have both confirmed prior knowledge and expanded it through the relative ease of use and power with nonlinear systems of the SOM-based approach to climatological analysis

Climate information websites: an evolving landscape

The climate change agenda is populated by actors and agencies with different objectives, values, and motivations, yet many seek decision scale climate information to inform policy and adaptation responses. A central element of this network of activity is the climate information website (CIW) that has seen a rapid and organic growth, yet with variable content and quality, and unfettered by any code of practice. This builds an ethical–epistemic dilemma that warrants assessment as the presence of CIWs contribute to real-world consequences and commitment. This study considers the context of CIW growth, and reviews a representative sample of CIWs to draw out key issues for consideration in CIW development. We assess content, function, and use-case value through a dual approach of a typology and user experience narratives to evaluate the general efficacy of a CIW. The typology reveals strong contrasts in content, complicated interfaces, and an overload of choice making it difficult to converge on a stable outcome. The narratives capture user experience and highlight barriers that include navigation difficulties, jargon laden content, minimal or opaque guidance, and inferred information without context about uncertainty and limits to skill. This illuminates four concerns: (1) the ethics of information provision in a context of real-world consequences; (2) interfaces that present barriers to achieving robust solutions; (3) weak capacity of both users and providers to identify information of value from the multimodel and multimethod data; and (4) inclusion of data that infer skill. Nonetheless, results provide a positive indication of a community of practice that is still maturing. WIREs Clim Change 2017, 8:e470. doi: 10.1002/wcc.470. For further resources related to this article, please visit the WIREs website

Interpreting self-organizing maps through space--time data models

Self-organizing maps (SOMs) are a technique that has been used with high-dimensional data vectors to develop an archetypal set of states (nodes) that span, in some sense, the high-dimensional space. Noteworthy applications include weather states as described by weather variables over a region and speech patterns as characterized by frequencies in time. The SOM approach is essentially a neural network model that implements a nonlinear projection from a high-dimensional input space to a low-dimensional array of neurons. In the process, it also becomes a clustering technique, assigning to any vector in the high-dimensional data space the node (neuron) to which it is closest (using, say, Euclidean distance) in the data space. The number of nodes is thus equal to the number of clusters. However, the primary use for the SOM is as a representation technique, that is, finding a set of nodes which representatively span the high-dimensional space. These nodes are typically displayed using maps to enable visualization of the continuum of the data space. The technique does not appear to have been discussed in the statistics literature so it is our intent here to bring it to the attention of the community. The technique is implemented algorithmically through a training set of vectors. However, through the introduction of stochasticity in the form of a space--time process model, we seek to illuminate and interpret its performance in the context of application to daily data collection. That is, the observed daily state vectors are viewed as a time series of multivariate process realizations which we try to understand under the dimension reduction achieved by the SOM procedure.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS174 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Call for an ethical framework for climate services

Call for an ethical framework for climate service

Evaluation and projections of extreme precipitation over southern Africa from two CORDEX models

The study focus on the analysis of extreme precipitation events of the present and future climate over southern Africa. Parametric and non-parametric approaches are used to identify and analyse these extreme events in data from the Coordinated Regional Climate Downscaling Experiment (CORDEX) models. The performance of the global climate model (GCM) forced regional climate model (RCM) simulations shows that the models are able to capture the observed climatological spatial patterns of the extreme precipitation. It is also shown that the downscaling of the present climate are able to add value to the performance of GCMs over some areas and depending on the metric used. The added value over GCMs justify the additional computational effort of RCM simulation for the generation relevant climate information for regional application. In the climate projections for the end of twenty-first Century (2069-2098) relative to the reference period (1976-2005), annual total precipitation is projected to decrease while the maximum number of consecutive dry days increases. Maximum 5-day precipitation amounts and 95th percentile of precipitation are also projected to increase significantly in the tropical and sub-tropical regions of southern Africa and decrease in the extra-tropical region. There are indications that rainfall intensity is likely to increase. This does not equate to an increase in total rainfall, but suggests that when it does rain, the intensity is likely to be greater. These changes are magnified under the RCP8.5 when compared with the RCP4.5 and are consistent with previous studies based on GCMs over the region.Water Research Commission-Project K5-2240.http://link.springer.com/journal/105842017-04-30hb2016Geography, Geoinformatics and Meteorolog

A tale of two futures: contrasting scenarios of future precipitation for West Africa from an ensemble of regional climate models

The results of a large ensemble of regional climate models lead to two contrasting but plausible scenarios for the precipitation characteristics over West Africa; one where mean precipitation is projected to decrease significantly over the Gulf of Guinea in spring and the Sahel in summer, and the other one where summer precipitation over both regions is projected to increase. Dry and wet models show similar patterns of the dynamic and thermodynamic terms of the moisture budget, although their magnitudes are larger in the dry models. Largest discrepancies are found in the strength of the land-atmosphere coupling, with dry models showing a marked decrease in soil moisture and evapotranspiration. Some changes in precipitation characteristics are consistent for both sets of models. In particular, precipitation frequency is projected to decrease in spring over the Gulf of Guinea and in summer over the Sahel, but precipitation is projected to become more intense

The vulnerability, impacts, adaptation and climate services advisory board (VIACS AB v1.0) contribution to CMIP6

This paper describes the motivation for the creation of the Vulnerability, Impacts, Adaptation and Climate Services (VIACS) Advisory Board for the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6), its initial activities, and its plans to serve as a bridge between climate change applications experts and climate modelers. The climate change application community comprises researchers and other specialists who use climate information (alongside socioeconomic and other environmental information) to analyze vulnerability, impacts and adaptation of natural systems and society in relation to past, ongoing and projected future climate change. Much of this activity is directed toward the co-development of information needed by decision-makers for managing projected risks. CMIP6 provides a unique opportunity to facilitate a two-way dialogue between climate modelers and VIACS experts who are looking to apply CMIP6 results for a wide array of research and climate services objectives. The VIACS Advisory Board convenes leaders of major impact sectors, international programs, and climate services to solicit community feedback that increases applications relevance of the CMIP6-Endorsed Model Intercomparison Projects (MIPs). As an illustration of its potential, the VIACS community provided CMIP6 leadership with a list of prioritized climate model variables and MIP experiments of greatest interest to the climate model applications community, indicating the applicability and societal relevance of climate model simulation outputs. The VIACS Advisory Board also recommended an impacts version of Obs4MIPs, and indicated user needs for the gridding and processing of model output