Multivariate wave climate using self-organizing maps

ABSTRACT: The visual description of wave climate is usually limited to two-dimensional conditional histograms. In this work, self-organizing maps (SOMs), because of their visualization properties, are used to characterize multivariate wave climate. The SOMs are applied to time series of sea-state parameters at a particular location provided by ocean reanalysis databases. Trivariate (significant wave height, mean period, and mean direction), pentavariate (the previous wave parameters and wind velocity and direction), and hexavariate (three wave parameters of the sea and swell components; or the wave, wind, and storm surge) classifications are explored. This clustering technique is also applied to wave and wind data at several locations to analyze their spatial relationship. Several processes are established in order to improve the results, the most relevant being a preselection of data by means a maximum dissimilarity algorithm (MDA). Results show that the SOM identifies the relevant multivariate sea-state types at a particular location spanning the historical variability, and provides an outstanding analysis of the dependency between the different parameters by visual inspection. In the case of wave climate characterizations for several locations the SOM is able to extract the qualitative spatial sea-state patterns, allowing the analysis of the spatial variability and the relationship between different locations. Moreover, the distribution of sea states over the reanalysis period defines a probability density function on the lattice, providing a visual interpretation of the seasonality and interannuality of the multivariate wave climate.The work was partially funded by projects GRACCIE (CSD2007-00067, CONSOLIDERINGENIO 2010) from the Spanish Ministry of Science and Technology, MARUCA(200800050084091) from the Spanish Ministry of Public Works, and C3E(E17/08) from the Spanish Ministry of Environment, Rural and Marine Environs. The authors thank Puertos del Estado (Spanish Ministry of Public Works) for providing the reanalysis database

ENSO-Driven skill of ENSEMBLES STREAM 2 multimodel seasonal precipitation hindcasts over the globe

Trabajo presentado al European Geosciences Union General Assembly 2010 celebrado en Viena (Austria) del 2 al 7 de Mayo de 2010.Seasonal forecasting is a promising research field with enormous potential impact in different socio-economic sectors. The ability to forecast unusual climate conditions such as droughts or hot spells a few months in advance is particularly interesting in agriculture, energy requirements and other human affairs in order to avoid severe consequences. This study assesses the skill of state-of-the-art seasonal forecast considering five coupled atmosphere-ocean general circulation models from the Stream 2 multimodel experiment of the European ENSEMBLES Project. The methodology applied by Frías et al 2010 over Spain is here extended to the world to present a map of regions with a significant seasonal predictability related to ENSO. To this aim, seven month hindcast simulations produced four times per year (November, February, May and August initializations) in the period 1961-2000 are analyzed. In a first step, validations are carried out separately for each season; winter (DJF), spring (MAM), summer (JJA) and autumn (SON), considering both one and four-months lead time predictions (e.g. initializations of November and August are considered for winter). Then, since ENSO is considered to globally dominate interannual climate variability, the same process is repeated, but restricting validations exclusively to the years of the strongest El Niño and La Niña events.Peer reviewe

Analysis and downscaling multi-model seasonal forecasts in Peru using self-organizing maps

We present an application of self-organizing maps (SOMs) for analysing multi-model ensemble seasonal forecasts from the DEMETER project in the tropical area of Northern Peru. The SOM is an automatic data-mining clustering technique, which allows us to summarize a high-dimensional data space in terms of a set of reference vectors (cluster centroids). Moreover, it has outstanding analysis and visualization properties, because the reference vectors can be projected into a two-dimensional lattice, preserving their high-dimensional topology. In the first part of the paper, the SOM is applied to analyse both atmospheric patterns over Peru and local precipitation observations at two nearby stations. First, the method is applied to cluster the ERA40 reanalysis patterns on the area of study (Northern Peru), obtaining a two-dimensional lattice which represents the climatology. Then, each particular phenomenon or event (e.g. El Niño or La Niña) is shown to define a probability density function (PDF) on the lattice, which represents its characteristic ‘location’ within the climatology. On the other hand, the climatological lattice is also used to represent the local precipitation regime associated with a given station. For instance, we show that the precipitation regime is strongly associated with El Niño events for one station, whereas it is more uniform for the other. The second part of the paper is devoted to downscaling seasonal ensemble forecasts from the multi-model DEMETER ensemble to local stations. To this aim, the PDF generated on the lattice by the patterns predicted for a particular season is combined with the local precipitation lattice for a given station. Thus, a probabilistic or numeric local forecast is easily obtained from the resulting PDF. Moreover, a measure of predictability for the downscaled forecast can be computed in terms of the entropy of the ensemble PDF. We present some evidence that accurate local predictions for accumulated seasonal precipitation can be obtained some months in advance for strong El Niño episodes. Finally, we compare the multi-model ensemble with single-model ensembles, and show that the best results correspond to different models for different years; however, the best global performance over the whole period corresponds to the multi-model ensemble

Downscaling multi-model climate projection ensembles with deep learning (DeepESD): contribution to CORDEX EUR-44

Deep learning (DL) has recently emerged as an innovative tool to downscale climate variables from large-scale atmospheric fields under the perfect-prognosis (PP) approach. Different convolutional neural networks (CNNs) have been applied under present-day conditions with promising results, but little is known about their suitability for extrapolating future climate change conditions. Here, we analyze this problem from a multi-model perspective, developing and evaluating an ensemble of CNN-based downscaled projections (hereafter DeepESD) for temperature and precipitation over the European EUR-44i (0.5º) domain, based on eight global circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5). To our knowledge, this is the first time that CNNs have been used to produce downscaled multi-model ensembles based on the perfect-prognosis approach, allowing us to quantify inter-model uncertainty in climate change signals. The results are compared with those corresponding to an EUR-44 ensemble of regional climate models (RCMs) showing that DeepESD reduces distributional biases in the historical period. Moreover, the resulting climate change signals are broadly comparable to those obtained with the RCMs, with similar spatial structures. As for the uncertainty of the climate change signal (measured on the basis of inter-model spread), DeepESD preserves the uncertainty for temperature and results in a reduced uncertainty for precipitation. To facilitate further studies of this downscaling approach, we follow FAIR principles and make publicly available the code (a Jupyter notebook) and the DeepESD dataset. In particular, DeepESD is published at the Earth System Grid Federation (ESGF), as the first continental-wide PP dataset contributing to CORDEX (EUR-44).This research has been supported by the Spanish Government (MCIN/AEI /10.13039/501100011033) through project CORDyS (grant no. PID2020-116595RB-I00)

WRF4G: WRF experiment management made simple

his work presents a framework, WRF4G, to manage the experiment workflow of the Weather Research and Forecasting (WRF) modelling system. WRF4G provides a flexible design, execution and monitoring for a general class of scientific experiments. It has been designed with the aim of facilitating the management and reproducibility of complex experiments. Furthermore, the concepts behind the design of this framework can be straightforwardly extended to other modelsThis work has been supported by the Spanish National R&D Plan under projects WRF4G (CGL2011-28864, co-funded by the European Regional Development Fund –ERDF–) and CORWES (CGL2010-22158-C02-01) and the IS-ENES2 project from the 7FP of the European Commission (grant agreement no. 312979). C. Blanco acknowledges financial support 5 from programa de Personal Investigador en Formación Predoctoral from Universidad de Cantabria, co-funded by the regional government of Cantabria. The authors are thankful to the developers of third party software (e.g. GridWay, WRFV3, python and NetCDF), which was intensively used in this work

An R package to visualize and communicate uncertainty in seasonal climate prediction

Interest in seasonal forecasting is growing fast in many environmental and socio-economic sectors due to the huge potential of these predictions to assist in decision making processes. The practical application of seasonal forecasts, however, is still hampered to some extent by the lack of tools for an effective communication of uncertainty to non-expert end users. visualizeR is aimed to fill this gap, implementing a set of advanced visualization tools for the communication of probabilistic forecasts together with different aspects of forecast quality, by means of perceptual multivariate graphical displays (geographical maps, time series and other graphs). These are illustrated in this work using the example of the strong El Niño 2015/16 event forecast. The package is part of the climate4R bundle providing transparent access to the ECOMS-UDG climate data service. This allows a flexible application of visualizeR to a wide variety of specific seasonal forecasting problems and datasets.This work has been funded by the European Union 7th Framework Program [FP7/20072013] under Grant Agreement 308291 (EUPORIAS Project). We are grateful to the EUPORIAS team on Communicating levels of con dence (Work Package 33)

Statistical downscaling with the downscaleR package (v3.1.0): contribution to the VALUE intercomparison experiment

The increasing demand for high-resolution climate information has attracted growing attention to statistical downscaling (SDS) methods, due in part to their relative advantages and merits as compared to dynamical approaches (based on regional climate model simulations), such as their much lower computational cost and their fitness for purpose for many local-scale applications. As a result, a plethora of SDS methods is nowadays available to climate scientists, which has motivated recent efforts for their comprehensive evaluation, like the VALUE initiative (http://www.value-cost.eu, last access: 29 March 2020). The systematic intercomparison of a large number of SDS techniques undertaken in VALUE, many of them independently developed by different authors and modeling centers in a variety of languages/environments, has shown a compelling need for new tools allowing for their application within an integrated framework. In this regard, downscaleR is an R package for statistical downscaling of climate information which covers the most popular approaches (model output statistics ? including the so-called ?bias correction? methods ? and perfect prognosis) and state-of-the-art techniques. It has been conceived to work primarily with daily data and can be used in the framework of both seasonal forecasting and climate change studies. Its full integration within the climate4R framework (Iturbide et al., 2019) makes possible the development of end-to-end downscaling applications, from data retrieval to model building, validation, and prediction, bringing to climate scientists and practitioners a unique comprehensive framework for SDS model development. In this article the main features of downscaleR are showcased through the replication of some of the results obtained in VALUE, placing an emphasis on the most technically complex stages of perfect-prognosis model calibration (predictor screening, cross-validation, and model selection) that are accomplished through simple commands allowing for extremely flexible model tuning, tailored to the needs of users requiring an easy interface for different levels of experimental complexity. As part of the open-source climate4R framework, downscaleR is freely available and the necessary data and R scripts to fully replicate the experiments included in this paper are also provided as a companion notebook.We thank the European Union Cooperation in Science and Technology (EU COST) Action ES1102 VALUE (http://www.value-cost.eu) for making publicly available the data used in this article and the tools implementing the comprehensive set of validation measures and indices. We also thank the THREDDS Data Server (TDS) software developed by UCAR/Unidata (https://doi.org/10.5065/D6N014KG, Unidata, 2006) and all R developers and their supporting community for providing free software facilitating open science. We acknowledge the World Climate Research Program’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the EC-EARTH Consortium for producing and making available their model output used in this paper. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led the development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We are very grateful to the two anonymous referees participating in the interactive discussion for their insightful comments, helping us to considerably improve the original paper. Financial support. The authors acknowledge partial funding from the MULTI-SDM project (MINECO/FEDER, CGL2015-66583-R) and from the project INDECIS, part of the European Research Area for Climate Services Consortium (ERA4CS) with co-funding by the uropean Union (grant no. 690462)