Technical Note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studies

Statistical downscaling is a commonly used technique for translating large-scale climate model output to a scale appropriate for assessing impacts. To ensure downscaled meteorology can be used in climate impact studies, downscaling must correct biases in the large-scale signal. A simple and generally effective method for accommodating systematic biases in large-scale model output is quantile mapping, which has been applied to many variables and shown to reduce biases on average, even in the presence of non-stationarity. Quantile-mapping bias correction has been applied at spatial scales ranging from hundreds of kilometers to individual points, such as weather station locations. Since water resources and other models used to simulate climate impacts are sensitive to biases in input meteorology, there is a motivation to apply bias correction at a scale fine enough that the downscaled data closely resemble historically observed data, though past work has identified undesirable consequences to applying quantile mapping at too fine a scale. This study explores the role of the spatial scale at which the quantile-mapping bias correction is applied, in the context of estimating high and low daily streamflows across the western United States. We vary the spatial scale at which quantile-mapping bias correction is performed from 2° ( ∼  200 km) to 1∕8° ( ∼  12 km) within a statistical downscaling procedure, and use the downscaled daily precipitation and temperature to drive a hydrology model. We find that little additional benefit is obtained, and some skill is degraded, when using quantile mapping at scales finer than approximately 0.5° ( ∼  50 km). This can provide guidance to those applying the quantile-mapping bias correction method for hydrologic impacts analysis

Downscaled climate change scenarios for Central America

The intersectoral workshop held in December 2016 among the Ibero-American networks on water, climate change and meteorology, identified the need of downscaled climate change scenarios for Central America. Such scenarios would be developed by National Meteorological and Hydrological Services in the region, based on a common methodology, allowing the assessment of climate change impacts on water resources and extreme hydro-meteorological events. This project was supported by the International and Ibero-American Foundation for Administration and Public Policies of Spain in the framework of the EUROCLIMA+ programme. One final outcome of the project has been a freely accessible web viewer, installed on the Centro Clima webpage (https://centroclima.org/escenarios-cambio-climatico/, last access: 26 September 2022), managed by the Regional Committee on Hydraulic Resources of the Central American Integration System, where all information generated during the project is available for consultation and data downloading by the different sectors of users. A key element in this project has been to integrate many downscaled projections based on different methods (dynamical and statistical), totalizing 45 different projections, and aiming at estimating the uncertainty coming from different sources in the best possible way. Another essential element has been the strong involvement of the different user sectors through national workshops, first, at the beginning of the project for the identification and definition of viewer features, and then for the presentation of results and planning of its use by prioritized sectors. In a second phase of the project, a regional working group made up of experts from the participating National Meteorological and Hydrological Services will be in charge of viewer maintenance and upgrade, including new sectoral parameters, developed in collaboration with interested users, and computation and addition of new downscaled projections from CMIP6 in collaboration with the State Meteorological Agency of Spain.</p

Information sources to support ADB climate risk assessments and management

This technical note provides information that supports climate risk assessment experts undertaking early stages of project development in Asia and the Pacific region. The Asia and Pacific region is vulnerable to extreme temperatures, flooding by heavy rainfall, sea level rise, coastal erosion, and damage by tropical cyclones. This technical note provides information that supports climate risk assessment experts undertaking early stages of project development in the region. The information is grouped into four major categories: inventories of national emissions, climate risks, vulnerability, and impacts; historic weather, climate, and environmental change; regional climate change projections; and climate change impacts and adaptation. The note also identifies opportunities for capacity development in key skills such as geospatial analysis, data testing and post-processing, regional climate downscaling, and impact assessment

A climate service for ecologists: sharing pre-processed EURO-CORDEX regional climate scenario data using the eLTER information system

eLTER was a “Horizon 2020” project with the aim of advancing the development of long-term ecosystem research infrastructure in Europe. This paper describes how eLTER Information System infrastructure has been expanded by a climate service data product providing access to specifically pre-processed regional climate change scenario data from a state-of-the-art regional climate model ensemble of the Coordinated Regional Downscaling Experiment (CORDEX) for 702 registered ecological research sites across Europe. This tailored, expandable, easily accessible dataset follows FAIR principles and allows researchers to describe the climate at these sites, explore future projections for different climate change scenarios and make regional climate change assessments and impact studies. The data for each site are available for download from the EUDAT collaborative data infrastructure B2SHARE service and can be easily accessed and visualised through the Dynamic Ecological Information Management System – Site and Dataset Registry (DEIMS-SDR), a web-based information management system which shares detailed information and metadata on ecological research sites around the globe. This paper describes these data and how they can be accessed by users through the extended eLTER Information System architecture. The data and supporting information are available from B2SHARE. Each individual site (702 sites are available) dataset has its own DOI. To aid data discovery, a persistent B2SHARE lookup table has been created which matches the DOIs of the individual B2SHARE record with each DEIMS site ID. This lookup table is available at https://doi.org/10.23728/b2share.bf41278d91b445bda4505d5b1eaac26c (eLTER EURO-CORDEX Climate Service, 2020)

Hydrologic modelling

Advances in computational tools and modeling techniques combined with enhanced process knowledge have, in recent decades, facilitated a rapid progress in hydrologic modeling. From the use of traditional lumped models, the hydrologic science has moved to the much more complex, fully distributed models that exude an enhanced knowledge of hydrologic processes. Despite this progress, uncertainties in hydrologic predictions remain. The Indian contribution to hydrologic science literature in the recent years has been significant, covering areas of surface water, groundwater, climate change impacts and quantification of uncertainties. Future scientific efforts in hydrologic science in India are expected to involve better, more robust observation techniques and datasets, deeper process-knowledge at a range of spatio-temporal scales, understanding links between hydrologic and other natural and human systems and integrated solutions using multidisciplinary approaches

Evaluation of the EURO-CORDEX Regional Climate Models Over the Iberian Peninsula: Observational Uncertainty Analysis

ABSTRACT: This work evaluates the daily precipitation and mean temperature of eight CORDEX-EUR11 ERA-Interim-driven simulations of EURO-CORDEX over the Iberian Peninsula (IP) for the period 1989-2008. To this aim, three observational data sets (Iberia01, E-OBS-v19e, and MESAN-0.11) were considered as reference and compared with the models by means of several indices reflecting the mean and extreme regimes over the IP. For precipitation the Lamb weather types were considered to identify synoptic conditions related with higher observational uncertainty. RCMs are able to reproduce the spatial pattern and the variability observed in the IP. However, there is a higher agreement between models and observations for mean temperature than for precipitation, decreasing when extremes are analyzed. For the observational uncertainty analysis, also extreme daily temperatures were considered to obtain a wider picture of this topic. A higher dependence on the observational data set has been found for precipitation than for temperature. This uncertainty is particularly significant when the 50-year return value is considered for which the observational uncertainty doubles the model uncertainty. Only the wet-day frequency presents values lower than 0.5 for all seasons, with most of the rest of values reflecting a similar contribution of both components to the uncertainty. In the case of temperatures, the main contribution of the observations has been found when the lower (MAE01) and upper (MAE99) extremes are considered, with values lower than 0.5. For precipitation the observational uncertainty increases when synoptic patterns affecting the Mediterranean Basin are considered, reflecting the difficulty to properly capture the Mediterranean precipitation regimes.This work was partially funded by the Spanish Government R&D Programme (Exp. CGL2010-21869 and CGL2010-22158-C02) and the European Comission (INDECIS: H2020-690462). Pedro M. M. Soares and Rita M. Cardoso wish to acknowledge the SOLAR (PTDC/GEOMET/7078/2014) Project and the funding by the Instituto Dom Luiz (Project FCT UID/GEO/50019/2019)

Added value of EURO-CORDEX high-resolution downscaling over the Iberian Peninsula revisited - Part 1: Precipitation

ABSTRACT: Over the years, higher-resolution regional climate model simulations have emerged owing to the large increase in computational resources. The 12 km resolution from the Coordinated Regional Climate Downscaling Experiment for the European domain (EURO-CORDEX) is a reference, which includes a larger multi-model ensemble at a continental scale while spanning at least a 130-year period. These simulations are computationally demanding but do not always reveal added value. In this study, a recently developed regular gridded dataset and a new metric for added value quantification, the distribution added value (DAV), are used to assess the precipitation of all available EURO-CORDEX hindcast (1989-2008) and historical (1971-2005) simulations. This approach enables a direct comparison between the higher-resolution regional model runs against their forcing global model or ERA-Interim reanalysis with respect to their probability density functions. This assessment is performed for the Iberian Peninsula. Overall, important gains are found for most cases, particularly in precipitation extremes. Most hindcast models reveal gains above 15 %, namely for wintertime, while for precipitation extremes values above 20% are reached for the summer and autumn. As for the historical models, although most pairs display gains, regional models forced by two general circulation models (GCMs) reveal losses, sometimes around -5% or lower, for the entire year. However, the spatialization of the DAV is clear in terms of added value for precipitation, particularly for precipitation extremes with gains well above 100 %.Financial support. João António Martins Careto is supported by the Portuguese Foundation for Science and Technology (FCT) with the doctoral grant SFRH/BD/139227/2018 financed by national funds from the MCTES within the Faculty of Sciences, University of Lisbon. Pedro Miguel Matos Soares would like to acknowledge the financial support of FCT through project UIDB/50019/2020 (IDL and EEA-Financial Mechanism 2014–2021) and the Portuguese Environment Agency through Pre-defined Project-2 National Roadmap for Adaptation XXI (PDP-2). Rita Margarida Cardoso is supported by the FCT under the project LEADING (PTDC/CTA-MET/28914/2017). This work was also supported by project FCT UIDB/50019/2020 – Instituto Dom Luiz (IDL)

Redes neuronales de convolución profundas para la regionalización estadística de proyecciones de cambio climático

RESUMEN Las proyecciones climáticas a escala local y/o regional son muy demandadas por diversos sectores socioeconómicos para elaborar sus planes de adaptación y mitigación al cambio climático. Sin embargo, los modelos climáticos globales actuales presentan una resolución espacial muy baja, lo que dificulta enormemente la elaboración de este tipo de estudios. Una manera de aumentar esta resolución es establecer relaciones estadísticas entre la variable local de interés (por ejemplo la temperatura y/o precipitación en una localidad dada) y un conjunto de variables de larga escala (por ejemplo, geopotencial y/o vientos en distintos niveles verticales) dadas por los modelos climáticos. En particular, en esta Tesis se explora la idoneidad de las redes neuronales de convolución (CNN) como método de downscaling estadístico para generar proyecciones de cambio climático a alta resolución sobre Europa. Para ello se evalúa primero la capacidad de estos modelos para reproducir la variabilidad local de precipitación y de temperatura en un período histórico reciente, comparándolas contra otros métodos estadísticos de referencia. A continuación, se analiza la idoneidad de estos modelos para regionalizar las proyecciones climáticas en el futuro (hasta el año 2100). Además, se desarrollan diversos estudios de interpretabilidad sobre redes neuronales para ganar confianza y conocimiento sobre el uso de este tipo de técnicas para aplicaciones climáticas, puesto que a menudo son rechazadas por ser consideradas “cajas negras”.ABSTRACT Regional climate projections are very demanded by different socioeconomics sectors to elaborate their adaptation and mitigation plans to climate change. Nevertheless, the state-of-the-art Global Glimate Models (GCMs) present very coarse spatial resolutions what limits their use in most of practical applications and impact studies. One way to increase this limited spatial resolution is to establish empirical/statistical functions which link the local variable of interest (e.g. temperature and/or precipitation at a given site) with a set of large-scale atmospheric variables (e.g. geopotential and/or winds at different vertical levels), which are typically well-reproduced by GCMs. In this context, this Thesis explores the suitability of deep learning, and in particular modern Convolutional Neural Networks (CNNs), as statistical downscaling techniques to produce regional climate change projections over Europe. To achieve this ambitious goal, the capacity of CNNs to reproduce the local variability of precipitation and temperature fields in present climate conditions is first assessed by comparing their performance with that from a set of traditional, benchmark statistical methods. Subsequently, their suitability to produce plausible future (up to 2100) high-resolution scenarios is put to the test by comparing their projected signals of change with those given by a set of state-of-the-art GCMs from CMIP5 and Regional Climate Models (RCMs) from the flagship EURO-CORDEX initiative. Also, a variety of interpretability techniques are also carried out to gain confidence and knowledge on the use of CNNs for climate applications, which have typically discarded until now for being considered as "black-boxes"