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

Cofiño, Antonio S.; Bedia, Joaquín; Iturbide, Maialen; Vega, María J. Sánchez de; Herrera, Sixto; Fernández, Jesús Fernández; Frías, M. D.; Manzanas, Rodrigo; Gutiérrez, José Manuel

doi:10.1016/j.cliser.2017.07.001

Cited by 31 publications

(28 citation statements)

References 11 publications

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“…The daily variables produced by the RCM‐GCM chains were first bias‐corrected for 1989–2005 using the evaluation runs as a reference and following the “Empirical Quantile Mapping” methodology (Cofiño et al ., ). This correction was subsequently applied to the future period (2041–2070), thus obtaining future bias corrected data.…”

Section: Methodsmentioning

confidence: 97%

Climate change projections for olive yields in the Mediterranean Basin

Fraga

Pinto

Viola

et al. 2019

Intl Journal of Climatology

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The olive tree is one of the most important crops in the Mediterranean basin. Given the strong climatic influence on olive trees, it becomes imperative to assess climate change impacts on this crop. Herein, these impacts were innovatively assessed, based on an ensemble of state‐of‐the‐art climate models, future scenarios and dynamic crop models. The recent‐past (1989–2005) and future (2041–2070, RCP4.5 and RCP8.5) olive growing season length (GSL), yield, growing season temperature (GST) and precipitation (GSP), potential (ETP) and actual (ETA) evapotranspiration, water demand (WD) and water productivity (WP) were assessed over Southern Europe. Crop models were fed with an ensemble of EURO‐CORDEX regional climate model data, along with soil and terrain data. For the recent‐past, important differences between western and eastern olive growing areas are found. GSL presents a strong latitudinal gradient, with higher/lower values at lower/higher latitudes. Yields are lower in inner south Iberia and higher in Italy and Greece, which is corroborated by historical data. Southern Iberia shows higher GST and lower GSP, which contributes to a higher ETP, lower ETA and, consequently, stronger WD. Regarding WP, the recent‐past values show similar ranges across Europe. Future projections point to a general increase in GSL along with an increase in GST up to 3°C. GSP is projected to decrease in Western Europe, leading to enhanced WD and consequently a yield decrease (down to −45%). Over eastern European, GSP is projected to slightly increase, leading to lower WD and to a small yield increase (up to +15%). WP will remain mostly unchanged. We conclude that climate change may negatively impact the viability of olive orchards in southern Iberia and some parts of Italy. Thus, adequate and timely planning of suitable adaptation measures are needed to ensure the sustainability of the olive sector.

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Section: Methodsmentioning

confidence: 97%

Climate change projections for olive yields in the Mediterranean Basin

Fraga

Pinto

Viola

et al. 2019

Intl Journal of Climatology

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“…However, there are not implemented dependencies between temperature and relative humidity yet. This BC method correction is implemented for several variables as part of the R package downscaleR (Bedia, et al, 2017), included in the R bundle climate4R (Cofiño, et al, 2018;Iturbide, et al, 2019). We correct dew point temperature following the same procedure as for daily mean temperature, thus, dependencies with other variables are not 30 considered.…”

Section: Isimip Bias Correctionmentioning

confidence: 99%

“…The code used for ISIMIP is an open source, R package climate4R (Cofiño, et al, 2018;Iturbide, et al, 2019) available from a GitHub repository (https://github.com/SantanderMetGroup/downscaleR). The quantile mapping code is also an R package that can be obtained from the authors upon request.…”

Section: Code Availability 20mentioning

confidence: 99%

“…In the last years the amount of available climate projection data has largely increased thanks to the development of intercomparison projects (Coupled Model Intercomparison Projects CMIP, Taylor, et al, 2011; Inter-Sectoral Impact Model 5 Intercomparison Project ISIMIP, Warszawski, et al, 2014) and other initiatives (CORDEX, Giorgi, et al, 2009;Jones, et al, 2011;CORDEX-Adjust). Due to this, there have been many efforts towards the distillation of climate data into usable climate information (Hewitson, et al, 2014;Fernández, et al, 2018). This is largely hampered by the large envelope of uncertainty, which grows in the subsequent steps in the production of climate information, the so-called "uncertainty cascade" (Wilby & Dessai, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Climate projections of a multi-variate heat stress index: the role of downscaling and bias correction

Casanueva¹,

Kotlarski²,

Herrera³

et al. 2019

Preprint

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Abstract. Along with the higher demand of bias-corrected data for climate impact studies, the number of available data sets has largely increased in the recent years. For instance, the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) constitutes a framework for consistently projecting the impacts of climate change across affected sectors and spatial scales. These data are very attractive for any impact application since they offer worldwide bias-corrected data based on Global Climate Models (GCMs). Complementary, the CORDEX initiative has incorporated experiments based on regionally-downscaled bias-corrected data by means of debiasing and quantile mapping (QM) methods. In light of this situation, it is challenging to distil the most accurate and useful information for climate services, but at the same time it creates a perfect framework for intercomparison and sensitivity analyses. In the present study, the trend-preserving ISIMIP method and empirical QM are applied to climate model simulations that were carried out at different spatial resolutions (CMIP5 GCM and EURO-CORDEX Regional Climate Models (RCMs), at approximately 150 km, 50 km and 12 km horizontal resolution, respectively) in order to assess the role of downscaling and bias correction in a multi-variate framework. The analysis is carried out for the wet bulb globe temperature (WBGT), a heat stress index that is commonly used in the context of working people and labour productivity. WBGT for shaded conditions depends on air temperature and dew point temperature, which in this work are individually bias-corrected prior to the index calculation. Our results show that the added value of RCMs with respect to the driving GCM is limited after bias correction. The two bias correction methods are able to adjust the central part of the WBGT distribution, but some added value of QM is found in WBGT percentiles and in the intervariable relationships. The evaluation in present climate of such multivariate indices should be performed with caution since biases in the individual variables might compensate, thus leading to better performance for the wrong reason. Climate change projections of WBGT reveal a larger increase of summer mean heat stress for the GCM than for the RCMs, related to the well-known reduced summer warming of the EURO-CORDEX RCMs. These differences are lowered after QM, since this bias correction method modifies the change signals and brings the results for GCM and RCMs closer to each other. We also highlight the need of large ensembles of simulations to assess the feasibility of the derived projections.

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“…The climate4R bundle is a set of R packages specifically designed to ease climate data access, analysis and processing in a straightforward manner, tailored to the needs of the impacts and vulnerability assessment community. Further details and references to worked examples and tutorials can be found for instance in Cofino et al (2017), and Frías et al (2018). With this regard, mopa was developed as part of the climate4R ecosystem, so that typical climate data operations for SDM applications and conversion features to the data type handled by mopa are provided.…”

Section: Mopa and The Climate4r Bundle For Climate Data Accessmentioning

confidence: 99%

Tackling Uncertainties of Species Distribution Model Projections with Package mopa

Iturbide¹,

Bedia²,

Gutiérrez³

2018

The R Journal

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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.

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

Cited by 31 publications

References 11 publications

Climate change projections for olive yields in the Mediterranean Basin

Climate change projections for olive yields in the Mediterranean Basin

Climate projections of a multi-variate heat stress index: the role of downscaling and bias correction

Tackling Uncertainties of Species Distribution Model Projections with Package mopa

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