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)

The ECOMS User Data Gateway: Towards seasonal forecast data provision and research reproducibility in the era of Climate Services

Sectorial applications of seasonal forecasting require data for a reduced number of variables from different datasets, mainly (gridded) observations, reanalysis, and predictions from state-of-the-art seasonal forecast systems (such as NCEP/CFSv2, ECMWF/System4 or UKMO/GloSea5). Whilst this information can be obtained directly from the data providers, the resulting formats, temporal aggregations, and vocabularies may not be homogeneous across datasets. Moreover, different data policies hold for the different databases, being only some of them publicly available. Therefore, obtaining and harmonizing multi-model seasonal forecast data for sector-specific applications is an error-prone, time consuming task. In order to facilitate this, the ECOMS User Data Gateway (ECOMS-UDG) was developed in the framework of the ECOMS initiative as a one-stop-service for climate data. To this aim, the variables required by end users were identified, downloaded from the data providers and locally stored as virtual datasets in a THREDDS Data Server (TDS), implementing fine-grained user management and authorization via the THREDDS Access Portal (TAP). As a result, users can retrieve the subsets best suited to their particular research needs in a user-friendly manner using the standard TDS data services. Moreover, an open source, R-based interface for data access and postprocessing was developed in the form of a bundle of packages implementing harmonized data access (one single vocabulary), data collocation, bias adjustment and downscaling, and forecast visualization and validation. This provides a unique comprehensive framework for end-to-end applications of seasonal predictions, hence favoring the reproducibility of the ECOMS scientific outcomes, extensible to the whole scientific community.We thank the European Union’s Seventh Framework Program [FP7/2007–2013] under Grant Agreements 308291 (EUPORIAS Project) and 308378 (SPECS Project). This project took advantage of THREDDS Data Server (TDS) software developed by UCAR/Unidata (http://doi.org/10.5065/D6N014KG). We would like to thank the two anonymous reviewers for their suggestions and comments

On the reliability of global seasonal forecasts: sensitivity to ensemble size, hindcast length and region definition

One of the key quality aspects in a probabilistic prediction is its reliability. However, this property is difficult to estimate in the case of seasonal forecasts due to the limited size of most of the hindcasts that are available nowadays. To shed light on this issue, this work presents a detailed analysis of how the ensemble size, the hindcast length and the number of points pooled together within a particular region affect the resulting reliability estimates. To do so, we build on 42 land reference regions recently defined for the IPCC-AR6 and assess the reliability of global seasonal forecasts of temperature and precipitation from the European Center for Medium Weather Forecasts SEAS5 prediction system, which is compared against its predecessor, System4. Our results indicate that whereas longer hindcasts and larger ensembles lead to increased reliability estimates, the number of points that are pooled together within a homogeneous climate region is much less relevant.This research has been partially supported by the AfriCultuReS (“Enhancing Food Security in African Agricultural Systems with the Support of Remote Sensing”) and FOCUS-Africa projects, which received funding from the European Union's Horizon 2020 Research and Innovation Framework Programme under grant agreements No. 77465 and 869575, respectively.Peer ReviewedPostprint (published version

Integrated Systems Modeling to Improve Watershed Habitat Management and Decision Making

Regulated rivers provide opportunities to improve habitat quality by managing the times, locations, and magnitudes of reservoir releases and diversions across the watershed. To identify these opportunities, managers select priority species and determine when, where, and how to allocate water between competing human and environmental users in the basin. Systems models have been used to recommend allocation of water between species. However, many models consider species’ water needs as constraints on instream flow that is managed to maximize human beneficial uses. Many models also incorporate uncertainty in the system and report an overwhelmingly large number of management alternatives. This dissertation presents three new novel models to recommend the allocation of water and money to improve habitat quality. The new models also facilitate communicating model results to managers and to the public. First, a new measurable and observable habitat metric quantifies habitat area and quality for priority aquatic, floodplain, and wetland habitat species. The metric is embedded in a systems model as an ecological objective to maximize. The systems model helps managers to identify times and locations at which to apply scarce water to most improve habitat area and quality for multiple competing species. Second, a cluster analysis approach is introduced to reduce large dimensional uncertainty problems in habitat models and focus management efforts on the important parameters to measure and monitor more carefully. The approach includes manager preferences in the search for clusters. It identifies a few, easy-to-interpret management options from a large multivariate space of possible alternatives. Third, an open-access web tool helps water resources modelers display model outputs on an interactive web map. The tool allows modelers to construct node-link networks on a web map and facilitates sharing and visualizing spatial and temporal model outputs. The dissertation applies all three studies to the Lower Bear River, Utah, to guide ongoing habitat conservation efforts, recommend water allocation strategies, and provide important insights on ways to improve overall habitat quality and area

Tackling Uncertainties of Species Distribution Model Projections with Package mopa

Species Distribution Models (SDMs) constitute an important tool to assist decision-making in environmental conservation and planning in the context of climate change. Nevertheless, SDM projections are affected by a wide range of uncertainty factors (related to training data, climate projections and SDM techniques), which limit their potential value and credibility. The new package mopa provides tools for designing comprehensive multi-factor SDM ensemble experiments, combining multiple sources of uncertainty (e.g. baseline climate, pseudo-absence realizations, SDM techniques, future projections) and allowing to assess their contribution to the overall spread of the ensemble projection. In addition, mopa is seamlessly integrated with the climate4R bundle and allows straightforward retrieval and post-processing of state-of-the-art climate datasets (including observations and climate change projections), thus facilitating the proper analysis of key uncertainty factors related to climate data.We acknowledge the ENSEMBLES project (GOCE-CT-2003-505539), supported by the European Commission’s 6th Framework Program for providing publicly the RCM simulations and observational data used in this study. We are also grateful to Rémy Petit and François Ehrenmann for providing the distribution of Oak phylogenies

Improving estimates and change detection of forest above-ground biomass using statistical methods

Forests store approximately as much carbon as is in the atmosphere, with potential to take in or release carbon rapidly based on growth, climate change and human disturbance. Above-ground biomass (AGB) is the largest carbon pool in most forest systems, and the quickest to change following disturbance. Quantifying AGB on a global scale and being able to reliably map how it is changing, is therefore required for tackling climate change by targeting and monitoring policies. AGB can be mapped using remote sensing and machine learning methods, but such maps have high uncertainties, and simply subtracting one from another does not give a reliable indication of changes. To improve the quantification of AGB changes it is necessary to add advanced statistical methodology to existing machine learning and remote sensing methods. This review discusses the areas in which techniques used in statistical research could positively impact AGB quantification. Nine global or continental AGB maps, and a further eight local AGB maps, were investigated in detail to understand the limitations of techniques currently used. It was found that both modelling and validation of maps lacked spatial consideration. Spatial cross validation or other sampling methods, which specifically account for the spatial nature of this data, are important to introduce into AGB map validation. Modelling techniques which capture the spatial nature should also be used. For example, spatial random effects can be included in various forms of hierarchical statistical models. These can be estimated using frequentist or Bayesian inference. Strategies including hierarchical modelling, Bayesian inference, and simulation methods can also be applied to improve uncertainty estimation. Additionally, if these uncertainties are visualised using pixelation or contour maps this could improve interpretation. Improved uncertainty, which is commonly between 30% and 40%, is in addition needed to produce accurate change maps which will benefit policy decisions, policy implementation, and our understanding of the carbon cycle

Applications of Time-lapse Imagery for Monitoring and Illustrating Ecological Dynamics in a Water-stressed System

Understanding and perceiving the natural world is a key part of management, policy, conservation, and inevitably for our future. Increased demand on natural resources has heightened the importance of documenting ecosystem changes, and knowledge-sharing to foster awareness. The advancement of digital technologies has improved the efficiency of passive monitoring, connectivity among systems, and expanded the potential for innovative and communicative approaches. From technological progression, time-lapse imagery has emerged a valuable tool to capture and depict natural systems. I sought to enhance our understanding of a water-stressed system by analyzing imagery, in addition to integrating images with data visualization to illustrate the complexity of a river basin in central Nebraska. Image analysis was used to quantify wetland water inundation and vegetation phenology. These measurements from visible changes were combined with less visible data from additional passive monitoring to examine the relationship between vegetation phenology and bat activity, as well as wetland inundation and water quality. Moreover, time-lapse data sequences were constructed by integrating time-lapse imagery with data visualization in an interactive digital framework to examine the applications for communicating social-ecological dynamics. Findings suggest vegetation phenology was differentially associated with seasonal bat activity, possibly related to migratory versus resident life history strategies. In regards to examining wetland hydrology, water inundation was found to be correlated with nitrate, dissolved oxygen, and DEA, and negatively correlated with water temperature, indicating the importance of understanding water levels. AEM-RDA analysis identified several significant temporal patterns occurring with the wetland as well as the river site. Similarities between river and wetland patterns were suggestive of regional conditions driving fluctuations, while discrepancies were indicative of structural, biological, and local differences within individual sites. In examining communicative applications, time-lapse data sequences depicted a range of ecological dynamics while linking visible and invisible occurrences. The framework shows potential to offer a tangible context with explanatory content to aid in understanding environmental changes that are often too subtle to see or beyond the temporal scale of unaided human observation. Overall, cumulative findings suggest time-lapse imagery is of dual utility and has high potential for collecting data and illustrating ecological dynamics. Advisor: Craig R. Alle

Applications of Time-lapse Imagery for Monitoring and Illustrating Ecological Dynamics in a Water-stressed System

Understanding and perceiving the natural world is a key part of management, policy, conservation, and inevitably for our future. Increased demand on natural resources has heightened the importance of documenting ecosystem changes, and knowledge-sharing to foster awareness. The advancement of digital technologies has improved the efficiency of passive monitoring, connectivity among systems, and expanded the potential for innovative and communicative approaches. From technological progression, time-lapse imagery has emerged a valuable tool to capture and depict natural systems. I sought to enhance our understanding of a water-stressed system by analyzing imagery, in addition to integrating images with data visualization to illustrate the complexity of a river basin in central Nebraska. Image analysis was used to quantify wetland water inundation and vegetation phenology. These measurements from visible changes were combined with less visible data from additional passive monitoring to examine the relationship between vegetation phenology and bat activity, as well as wetland inundation and water quality. Moreover, time-lapse data sequences were constructed by integrating time-lapse imagery with data visualization in an interactive digital framework to examine the applications for communicating social-ecological dynamics. Findings suggest vegetation phenology was differentially associated with seasonal bat activity, possibly related to migratory versus resident life history strategies. In regards to examining wetland hydrology, water inundation was found to be correlated with nitrate, dissolved oxygen, and DEA, and negatively correlated with water temperature, indicating the importance of understanding water levels. AEM-RDA analysis identified several significant temporal patterns occurring with the wetland as well as the river site. Similarities between river and wetland patterns were suggestive of regional conditions driving fluctuations, while discrepancies were indicative of structural, biological, and local differences within individual sites. In examining communicative applications, time-lapse data sequences depicted a range of ecological dynamics while linking visible and invisible occurrences. The framework shows potential to offer a tangible context with explanatory content to aid in understanding environmental changes that are often too subtle to see or beyond the temporal scale of unaided human observation. Overall, cumulative findings suggest time-lapse imagery is of dual utility and has high potential for collecting data and illustrating ecological dynamics. Advisor: Craig R. Alle

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)

Penggunaan Prakiraan Musim untuk Pertanian di Indonesia: Status Terkini dan Tantangan Kedepan

Prakiraan musim sangat penting dalam pengambilan keputusan usaha tani karena variabilitas iklim yang semakin meningkat sebagai dampak dari perubahan iklim. Prakiraan musim memiliki potensi untuk bisa membantu pengambil kebijakan dan keputusan pertanian. Kajian empirik dan literatur serta kuantifikasi nilai ekonomi pemanfaatan prakiraan iklim di Indonesia untuk pertanian masih sangat terbatas. Tulisan ini merupakan tinjauan mengenai pemanfaatan prakiraan musim dalam sistem usahatani, perkembangan terkini prakiraan musim, nilai ekonomi prakiraan musim, kendala dan tantangan ke depan. Kajian nilai ekonomi prakiraan musim untuk pertanian masih belum banyak dilakukan, kuantifikasi manfaat prakiraan musim sangat penting untuk meyakinkan pengambil kebijakan atau petani bahwa prakiraan musim memberikan manfaat bagi pertanian. Prakiraan musim masih sulit dipahami oleh petani bahkan penyuluh. Beberapa user interface telah dikembangkan untuk memudahkan memanfaatkan prakiraan musim untuk pertanian. Peningkatan akurasi prakiraan musim ke depan harus menjadi prioritas pembangunan pertanian dan merupakan salah satu investasi penting dalam adaptasi variabilitas dan perubahan iklim. Peningkatan akurasi prakiraan musim sangat tergantung pada kapasitas sumber daya manusia serta sarana pendukung seperti sistem pemantauan dan pengamatan data iklim, pengembangan model prakiraan musim .Pengembangan berbagai user interface yang lebih mudah dipahami dan diaplikasikan oleh petani harus dilakuka