Muestreo de pseudo-ausencias en modelos de distribución de especies y transferibilidad en condiciones de cambio climático

186 p.Los Modelos de Distribución de Especies (SDMs), son herramientas estadísticas utilizadas para la generación de predicciones probabilísticas de la presencia de poblaciones de especies en el espacio geográfico (mapas de idoneidad de hábitat). Dada la amenaza que supone el cambio climático, una aplicación popular de estos modelos es la proyección futura de las distribuciones potenciales de las especies con el fin de evaluar temas claves en la conservación del medio ambiente. Sin embargo, hay fuentes importantes de incertidumbre que afectan la credibilidad de las predicciones. Entre ellas, en esta Tesis se destacan dos, la elección del algoritmo de modelización y la utilización de datos de pseudo-ausencia. Para ello se analiza el muestreo de pseudo-ausencias como un factor determinante para caracterizar la estabilidad y transferibilidad de los SDMs en condiciones de cambio climático, mediante la evaluación de la incertidumbre en conjuntos de predicciones futuras. Además, se ha desarrollado una herramienta de modelización que implementa diferentes técnicas para generar datos de pseudo-ausencia y analizar la incertidumbre de las predicciones, dirigidos a producir estimaciones óptimas de la idoneidad de hábitats futuros y facilitar el acceso y preparación de datos climáticos

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

Background sampling and transferability of species distribution model ensembles under climate change

Species Distribution Models (SDMs) constitute an important tool to assist decision-making in environmental conservation and planning. A popular application of these models is the projection of species distributions under climate change conditions. Yet there are still a range of methodological SDM factors which limit the transferability of these models, contributing significantly to the overall uncertainty of the resulting projections. An important source of uncertainty often neglected in climate change studies comes from the use of background data (a.k.a. pseudo-absences) for model calibration. Here, we study the sensitivity to pseudo-absence sampling as a determinant factor for SDM stability and transferability under climate change conditions, focusing on European wide projections of Quercus robur as an illustrative case study. We explore the uncertainty in future projections derived from ten pseudo-absence realizations and three popular SDMs (GLM, Random Forest and MARS). The contribution of the pseudo-absence realization to the uncertainty was higher in peripheral regions and clearly differed among the tested SDMs in the whole study domain, being MARS the most sensitive ? with projections differing up to a 40% for different realizations ? and GLM the most stable. As a result we conclude that parsimonious SDMs are preferable in this context, avoiding complex methods (such as MARS) which may exhibit poor model transferability. Accounting for this new source of SDM-dependent uncertainty is crucial when forming multi-model ensembles to undertake climate change projections.We acknowledge the ENSEMBLES project, funded by the European Commission's EU 6th Framework Programme through contract GOCE-CT-2003-505539. The first author has a research contract from the EU-funded project FP7- SEC-2013-1 (INTACT)

Testing bias adjustment methods for regional climate change applications under observational uncertainty and resolution mismatch

Systematic biases in climate models hamper their direct use in impact studies and, as a consequence, many statistical bias adjustment methods have been developed to calibrate model outputs against observations. The application of these methods in a climate change context is problematic since there is no clear understanding on how these methods may affect key magnitudes, for example, the climate change signal or trend, under different sources of uncertainty. Two relevant sources of uncertainty, often overlooked, are the sensitivity to the observational reference used to calibrate the method and the effect of the resolution mismatch between model and observations (downscaling effect). In the present work, we assess the impact of these factors on the climate change signal of temperature and precipitation considering marginal, temporal and extreme aspects. We use eight standard and state-of-the-art bias adjustment methods (spanning a variety of methods regarding their nature—empirical or parametric—, fitted parameters and trend-preservation) for a case study in the Iberian Peninsula. The quantile trend-preserving methods (namely quantile delta mapping (QDM), scaled distribution mapping (SDM) and the method from the third phase of ISIMIP-ISIMIP3) preserve better the raw signals for the different indices and variables considered (not all preserved by construction). However, they rely largely on the reference dataset used for calibration, thus presenting a larger sensitivity to the observations, especially for precipitation intensity, spells and extreme indices. Thus, high-quality observational datasets are essential for comprehensive analyses in larger (continental) domains. Similar conclusions hold for experiments carried out at high (approximately 20 km) and low (approximately 120 km) spatial resolutions. © 2020 The Authors. Atmospheric Science Letters published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society

On the need of bias adjustment for more plausible climate change projections of extreme heat

ABSTRACT: The assessment of climate change impacts in regions with complex orography and land-sea interfaces poses a challenge related to shortcomings of global climate models. Furthermore, climate indices based on absolute thresholds are especially sensitive to systematic model biases. Here we assess the effect of bias adjustment (BA) on the projected changes in temperature extremes focusing on the number of annual days with maximum temperature above 35°C. To this aim, we use three BA methods of increasing complexity (from simple scaling to empirical quantile mapping) and present a global analysis of raw and BA CMIP5 projections under different global warming levels. The main conclusions are (1) BA amplifies the magnitude of the climate change signal (in some regions by a factor 2 or more) achieving a more plausible representation of future heat threshold-based indices; (2) simple BA methods provide similar results to more complex ones, thus supporting the use of simple and parsimonious BA methods in these studies.Agencia Estatal de Investigación, Grant/Award Numbers: MdM-2017-0765, PID2019-111481RB-I00; H2020-ERA4CS INDECIS Consejería de Universidades, Igualdad, Cultura y Deporte del Gobierno de Cantabri

Testing bias adjustment methods for regional climate change applications under observational uncertainty and resolution mismatch

ABSTRACT: Systematic biases in climate models hamper their direct use in impact studies and, as a consequence, many statistical bias adjustment methods have been developed to calibrate model outputs against observations. The application of these methods in a climate change context is problematic since there is no clear understanding on how these methods may affect key magnitudes, for example, the climate change signal or trend, under different sources of uncertainty. Two relevant sources of uncertainty, often overlooked, are the sensitivity to the observational reference used to calibrate the method and the effect of the resolution mismatch between model and observations (downscaling effect). In the present work, we assess the impact of these factors on the climate change signal of temperature and precipitation considering marginal, temporal and extreme aspects. We use eight standard and state-of-the-art bias adjustment methods (spanning a variety of methods regarding their nature-empirical or parametric-, fitted parameters and tren-preservation) for a case study in the Iberian Peninsula. The quantile tren-preserving methods (namely quantile delta mapping (QDM), scaled distribution mapping (SDM) and the method from the third phase of ISIMIP-ISIMIP3) preserve better the raw signals for the different indices and variables considered (not all preserved by construction). However, they rely largely on the reference dataset used for calibration, thus presenting a larger sensitivity to the observations, especially for precipitation intensity, spells and extreme indices. Thus, high-quality observational datasets are essential for comprehensive analyses in larger (continental) domains. Similar conclusions hold for experiments carried out at high (approximately 20 km) and low (approximately 120 km) spatial resolutions.Participation of S. Herrera and J.M. Gutiérrez was partially supported by the project AfriCultuReS (European Union's Horizon 2020 program, grant agreement no, 774652). S. Lange acknowledges funding from the European Union's Horizon 2020 research and innovation program under grant agreement no. 641816 (CRESCENDO

Weather Variables Associated with Spore Dispersal of Lecanosticta acicola Causing Pine Needle Blight in Northern Spain

In the last decade, the impact of needle blight fungal pathogens on the health status of forests in northern Spain has marked a turning point in forest production systems based on Pinus radiata species. Dothistroma needle blight caused by Dothistroma septosporum and D. pini, and brown spot needle blight caused by Lecanosticta acicola, coexist in these ecosystems. There is a clear dominance of L. acicola with respect to the other two pathogens and evidence of sexual reproduction in the area. Understanding L. acicola spore dispersal dynamics within climatic determinants is necessary to establish more efficient management strategies to increase the sustainability of forest ecosystems. In this study, spore counts of 15 spore traps placed in Pinus ecosystems were recorded in 2019 and spore abundance dependency on weather data was analysed using generalised additive models. During the collection period, the model that best fit the number of trapped spores included the daily maximum temperature and daily cumulative precipitation, which was associated to higher spore counts. The presence of conidia was detected from January and maximum peaks of spore dispersal were generally observed from September to November.This research was funded by the Spanish Ministry of Science and INIA, grant number: RTA 2017-00063-C04-03, LIFE programme, grant number: LIFE14 ENV/ES/000179 and by the Department of Economic Development, Sustainability and Environment (Basque Government), grant reference: FUNGITRAP2019.S

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)

Climadjust: an operational service to adjust biases in climate projections

Ponencia presentada en: XII Congreso de la Asociación Española de Climatología celebrado en Santiago de Compostela entre el 19 y el 21 de octubre de 2022.[ES]Las proyecciones climáticas de los modelos climáticos globales y regionales suelen presentar sesgos respecto a las observaciones. Habitualmente, ajustar estos sesgos es el primer paso para obtener información climática accionable, que pueda utilizarse en los estudios de impacto. Sin embargo, este proceso de ajuste de los sesgos es altamente técnico y demanda recursos especializados en términos de infraestructuras de computación y conocimientos científico-técnicos. Climadjust (https://climadjust.com/) es un servicio web desarrollado con la financiación y apoyo del Servicio de Cambio Climático de Copernicus (C3S), que implementa un servicio de ajuste de sesgos fácil de usar para los usuarios. El servicio ha sido desarrollado por Predictia -empresa centrada en el desarrollo de servicios climáticos- en colaboración con el Instituto de Física de Cantabria (IFCA-CSIC-UC). Climadjust proporciona recursos escalables en la nube para obtener proyecciones climáticas ajustadas para un área de interés, para datos de CMIP y CORDEX. En este proceso, los usuarios pueden (i) cargar sus propios datos de observaciones de referencia para ajustar las proyecciones climáticas, o elegir datos provenientes de ERA5-Land o WFDE-5, (ii) elegir entre seis técnicas de ajuste de sesgo de última generación, y (iii) validar los resultados a través del marco estándar desarrollado en la acción europea COST VALUE. El resultado es un archivo netCDF validado, listo para ser utilizado por los usuarios.[EN]Climate projections obtained from global and regional climate models usually exhibit biases: systematic deviations from observations. Adjusting these biases is typically the first step towards obtaining actionable climate information to be used in impact studies. However, this bias adjustment process is highly technical and resourcedemanding, in terms of data and computing infrastructures, and technical knowledge. Climadjust (https://climadjust.com/) is a web service developed with the support of the Copernicus Climate Change Service (C3S), that implements user-friendly bias adjustment for climate projections. The service was developed by Predictia —a company with a strong focus on climate services development and climate modelling— in collaboration with the Spanish Research Council (CSIC). Climadjust provides scalable cloud resources to compute bias-adjusted climate projections from the ensembles of CMIP and CORDEX datasets over customised areas of interest. In this process, the users are able to (i) upload their own dataset of observations to adjust the climate projections, or choose among reference datasets such as ERA5-Land or WFDE-5, (ii) choose among six state-of-the-art Bias Adjustment techniques, and (iii) validate the results through the standard framework developed in the European VALUE COST Action. The output is a validated netCDF file, ready to be used by the climate impact modellers

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)