Sensitivity of species climate envelope models to baseline climatology and effect on RCM-BASED future projections

Climate Envelope Models (CEMs) are predictive tools widely used in ecological research to estimate the distribution of species by combining observations of their occurrence/abundance with bioclimatic indicators. In this contribution, we show that the resulting projections are highly sensitive to the quality of the baseline climate data, an aspect often overlooked in model criticism. Using distributional data of European beech in northern Spain (Cantabria region), we analyse the discrepancies in model performance and future projections using three public high-resolution climate datasets: WorldClim (WC), the University of Barcelona Atlas (UAB) and a new regional climate grid developed by Cantabria University (UC). We considered the future climate scenarios from several regional climate models (RCMs) of the EU-funded project ENSEMBLES. We demonstrate that the quality of the baseline climate used to derive the present and future bioclimatic indices has a great impact on the stability of the estimated CEMs, although commonly used performance metrics (AUC, Cohen’s kappa) failed to detect this in the cross-validation experiments. WC models lead to unreliable future projections, whereas UAB models performed better but were outperformed by UC, demonstrating the paramount importance of reliable climate input data

Assessing the predictability of fire occurrence and area burned across phytoclimatic regions in Spain

Most fire protection agencies throughout the world have developed forest fire risk forecast systems, usually building upon existing fire danger indices and meteorological forecast data. In this context, the daily predictability of wildfires is of utmost importance in order to allow the fire protection agencies to issue timely fire hazard alerts. In this study, we address the predictability of daily fire occurrence using the components of the Canadian Fire Weather Index (FWI) System and related variables calculated from the latest ECMWF (European Centre for Medium Range Weather Forecasts) reanalysis, ERA-Interim. We develop daily fire occurrence models in peninsular Spain for the period 1990–2008 and, considering different minimum burned area thresholds for fire definition, assess their ability to reproduce the inter-annual fire frequency variability. We based the analysis on a phytoclimatic classification aiming the stratification of the territory into homogeneous units in terms of climatic and fuel type characteristics, allowing to test model performance under different climate/fuel conditions. We then extend the analysis in order to assess the predictability of monthly burned areas. The sensitivity of the models to the level of spatial aggregation of the data is also evaluated. Additionally, we investigate the gain in model performance with the inclusion of socioeconomic and land use/land cover (LULC) covariates in model formulation. Fire occurrence models have attained good performance in most of the phytoclimatic zones considered, being able to faithfully reproduce the inter-annual variability of fire frequency. Total area burned has exhibited some dependence on the meteorological drivers, although model performance was poor in most cases. We identified temperature and some FWI system components as the most important explanatory variables, highlighting the adequacy of the FWI system for fire occurrence prediction in the study area. The results were improved when using aggregated data across regions compared to when data were sampled at the grid-box level. The inclusion of socioeconomic and LULC covariates contributed marginally to the improvement of the models, and in most cases attained no relevant contribution to total explained variance – excepting northern Spain, where anthropogenic factors are known to be the major driver of fires. Models of monthly fire counts performed better in the case of fires larger than 0.1 ha, and for the rest of the thresholds (1, 10 and 100 ha) the daily occurrence models improved the predicted inter-annual variability, indicating the added value of daily models. Fire frequency predictions may provide a preferable basis for past fire history reconstruction, long-term monitoring and the assessment of future climate impacts on fire regimes across regions, posing several advantages over burned area as a response variable. Our results leave the door open to the development a more complex modelling framework based on daily data from numerical climate model outputs based on the FWI system.The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement 243888 (FUME Project)

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)

Robust projections of fire weather index in the Mediterranean using statistical downscaling

The effect of climate change on wildfires constitutes a serious concern in fire-prone regions with complex fire behavior such as the Mediterranean. The coarse resolution of future climate projections produced by General Circulation Models (GCMs) prevents their direct use in local climate change studies. Statistical downscaling techniques bridge this gap using empirical models that link the synoptic-scale variables from GCMs to the local variables of interest (using e.g. data from meteorological stations). In this paper, we investigate the application of statistical downscaling methods in the context of wildfire research, focusing in the Canadian Fire Weather Index (FWI), one of the most popular fire danger indices. We target on the Iberian Peninsula and Greece and use historical observations of the FWI meteorological drivers (temperature, humidity, wind and precipitation) in several local stations. In particular, we analyze the performance of the analog method, which is a convenient first choice for this problem since it guarantees physical and spatial consistency of the downscaled variables, regardless of their different statistical properties. First we validate the method in perfect model conditions using ERA-Interim reanalysis data. Overall, not all variables are downscaled with the same accuracy, with the poorest results (with spatially averaged daily correlations below 0.5) obtained for wind, followed by precipitation. Consequently, those FWI components mostly relying on those parameters exhibit the poorest results. However, those deficiencies are compensated in the resulting FWI values due to the overall high performance of temperature and relative humidity. Then, we check the suitability of the method to downscale control projections (20C3M scenario) from a single GCM (the ECHAM5 model) and compute the downscaled future fire danger projections for the transient A1B scenario. In order to detect problems due to non-stationarities related to climate change, we compare the results with those obtained with a Regional Climate Model (RCM) driven by the same GCM. Although both statistical and dynamical projections exhibit a similar pattern of risk increment in the first half of the 21st century, they diverge during the second half of the century. As a conclusion, we advocate caution in the use of projections for this last period, regardless of the regionalization technique applied. © 2013 Springer Science+Business Media Dordrecht.This work was partly funded by European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreements 243888 (FUME Project) and from Spanish Ministry MICINN under grant EXTREMBLES (CGL2010-21869).Peer Reviewe

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

Regional assessment of the Jenkinson-Collison weather types classification and observational uncertainty based on different reanalyses over the Mediterranean region

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]El algoritmo de clasificación en tipos de tiempo de Jenkinson y Collison (JC-WT, Jenkinson and Collison 1977) es una técnica de agrupamiento usada para clasificar la circulación atmosférica en un número reducido de patrones de presión a nivel del mar. Esta metodología se basa en el cálculo de 6 parámetros intermedios relacionados con las características del flujo del viento. Este método ha tenido numerosas aplicaciones, siendo una de ellas la caracterización objetiva de la circulación atmosférica tanto a nivel global como regional, esencial para la evaluación de modelos climáticos y para su aplicabilidad en regionalización dinámica y estadística. La primera definición del método JC-WT centraba el estudio sobre las Islas Británicas pero puede ser, en principio, aplicado en latitudes medias-altas (Jones et al., 2013). El presente estudio examina la aplicabilidad la metodología JC-WT sobre la región Mediterránea y explora las diferencias entre cinco reanálisis a la hora de representar las características de los 27 JC-WT (sus frecuencias relativas y las probabilidades de transición entre tipos). Los resultados muestran diferencias importantes entre los distintos catálogos, sobre todo en verano. Además, se analizan estas diferencias entre reanálisis a nivel de los 6 parámetros intermedios de JC-WT con el fin de arrojar luz sobre la naturaleza sinóptica de las mismas. Estas discrepancias pueden comprometer la robustez de los estudios relacionados con la evaluación de modelos basada en procesos para esta región y desaconsejan el uso de un único reanálisis como referencia.[EN]The Jenkinson-Collison Weather Type (JC-WT; Jenkinson and Collison, 1977) classification is a clustering method used to classify the regional atmospheric circulation into a reduced number of typical recurrent sea-level pressure patterns. This methodology is a function of six parameters related to wind-flow characteristics. Originally developed for the British Isles, the method since then has seen many applications. One of its applications is serving for an objective characterization of either global or regional atmospheric circulation, a key feature for the assessment of climate models and their suitability for driving dynamical and statistical modeling experiments. Encouraged by the estimate that the JC-WT approach can in principle be applied to any mid-to-high latitude region (Jones et al, 2013), this study assesses the general application of JC-WT over the Mediterranean region, extending from the Iberian Peninsula in the west to the Levant in the east. We also explore to what extent the JC-WT features (such as frequencies of the 27 weather types and transition probabilities between pairs of types) obtained from five distinct reanalysis products agree with each other. Our results unveil important discrepancies among reanalyses, accentuated in summer. We furtherly explore these discrepancies deepening on the JC-WT base parameters in order to shed some light on the synoptic nature of these inconsistencies, that may compromise the robustness of circulation-based model assessments relying on a single reanalysis in these regions.The authors acknowledge funding from the R+D+i projects CORDyS (PID2020- 116595RB-I00) and ATLAS (PID2019-111481RB-I00), funded by MCIN/AEI/10.13039/501100011033. J.A.F. acknowledge funding from grant PRE2020-094728 funded by MCIN/AEI/10.13039/501100011033

Performance of the CMIP6 global climate models over the Iberian Peninsula and relationships with the simulated climate system complexity

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]En el presente estudio se muestra una evaluación del rendimiento de las diferentes configuraciones de los modelos climáticos globales que han aportado experimentos históricos a CMIP6 (Coupled Model Intercomparison Project 6) para la Península Ibérica (IB), utilizando un dominio similar al aplicado en iniciativas de downscalling anteriores. La evaluación se basa en los patrones típicos de circulación atmosférica regional definidos por Jenkinson y Collison (1977), que se sabe que están vinculados con un gran número de variables de la física y la química atmosféricas. Los resultados se comparan con los obtenidos de la generación anterior de los modelos (CMIP5) y con los obtenidos de un análisis hemisférico (Brands 2022a), para comprobar 1) si los modelos han mejorado con el tiempo y 2) si los resultados específicos concuerdan con los obtenidos en un dominio más grande, lo que los hace menos propensos a la propagación de errores durante períodos de tiempo no observados.[EN]performance assessment of the global climate model configurations contributing historical experiments to CMIP6 is provided for the Iberian Peninsula (IB), using a spatial domain similar to that applied in previous downscaling initiatives. The evaluation is based on typical recurrent regional atmospheric circulation patterns as defined by Jenkinson & Collison (1977), which are well known to be linked with a large number of variables from atmospheric physics and chemistry. Results are compared to those obtained from the previous model generation (CMIP5), and to those retrieved from a hemispheric-wide analysis (Brands 2022) in order to see 1) if the models have improved over time, and 2) whether the region-specific findings agree with those obtained on a larger domain, thereby making them less prone to error propagation during unobserved time periods. It is found that the model version changes from CMIP5 and 6 lead to slight improvements, mainly associated with an increase in horizontal model resolution, but that the selection of the right model family is more important to obtain good model performance

Projecting wildfire occurrence at regional scale from Land Use/Cover and climate change scenarios

LUC4FIRE: Project funded by the Spanish Ministry of Science, Innovation and Universities (CSO2015-73407-JIN)

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

Los incendios forestales en España ante al cambio climático

1. Resultados clave 1. Los incendios resultan de interacciones entre el clima y otros motores socioeconómicos que afectan al territorio. Todos cuentan, y todos han de ser tenidos en consideración al evaluar el futuro riesgo de incendio. 2. Los incendios se reparten por todo el territorio. Gestionar y conservar los ecosistemas terrestres españoles no puede ignorar el papel del fuego. La gestión debe ir más allá que la mera prevención, e integrar el conocimiento de la ecología de los ecosistemas en relación con el fuego. 3. El clima futuro y otros cambios socioeconómicos y globales ocasionarán situaciones extremadamente adversas, que se extenderán por más zonas del país de manera simultánea y con mayor frecuencia. 4. La planificación futura del riesgo no puede hacerse usando solo el pasado. Hasta mediados de este siglo, el cambio climático esperable es poco variable, con lo que las proyecciones futuras pueden usarse como marco de cálculo del peligro futuro. 5. Hay que gestionar el territorio con objetivos diferenciados de los bienes y servicios que se pretenden proteger, incluyendo la disminución de la peligrosidad del territorio, así como de las pérdidas y costes, al tiempo que se mantiene o aumenta la prestación de los servicios y su resiliencia. 6. El fuego puede tener cabida en una planificación por objetivos, aunque solo sea para reducir gastos, pues no será posible detener todos los incendios en los plazos establecidos en los programas de gestión y lucha contra el fuego. 7. Los incendios no se distribuyen homogéneamente, sino que hay zonas particularmente peligrosas. Hay que planificar minimizando el riego a la población, con especial consideración a la interfaz urbano-forestal. 8. Anticiparse al riesgo y a los impactos requiere conocer las vulnerabilidades y muchas de éstas están muy ligadas a la recurrencia de incendios. Se necesitan bases de incendios espacialmente explícitas para poder anticiparse a los impactos más indeseados