128
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      CLIMBra - Climate Change Dataset for Brazil

      data-paper

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          General Circulation and Earth System Models are the most advanced tools for investigating climate responses to future scenarios of greenhouse gas emissions, playing the role of projecting the climate throughout the century. Nevertheless, climate projections are model-dependent and may show systematic biases, requiring a bias correction for any further application. Here, we provide a dataset based on an ensemble of 19 bias-corrected CMIP6 climate models projections for the Brazilian territory based on the SSP2-4.5 and SSP5-8.5 scenarios. We used the Quantile Delta Mapping approach to bias-correct daily time-series of precipitation, maximum and minimum temperature, solar net radiation, near-surface wind speed, and relative humidity. The bias-corrected dataset is available for both historical (1980–2013) and future (2015–2100) simulations at a 0.25° × 0.25° spatial resolution. Besides the gridded product, we provide area-averaged projections for 735 catchments included in the Catchments Attributes for Brazil (CABra) dataset. The dataset provides important variables commonly used in environmental and hydroclimatological studies, paving the way for the development of high-quality research on climate change impacts in Brazil.

          Related collections

          Most cited references78

          • Record: found
          • Abstract: found
          • Article: not found
          Is Open Access

          The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6

          Projections of future climate change play a fundamental role in improving understanding of the climate system as well as characterizing societal risks and response options. The Scenario Model Intercomparison Project (ScenarioMIP) is the primary activity within Phase 6 of the Coupled Model Intercomparison Project (CMIP6) that will provide multi-model climate projections based on alternative scenarios of future emissions and land use changes produced with integrated assessment models. In this paper, we describe ScenarioMIP's objectives, experimental design, and its relation to other activities within CMIP6. The ScenarioMIP design is one component of a larger scenario process that aims to facilitate a wide range of integrated studies across the climate science, integrated assessment modeling, and impacts, adaptation, and vulnerability communities, and will form an important part of the evidence base in the forthcoming Intergovernmental Panel on Climate Change (IPCC) assessments. At the same time, it will provide the basis for investigating a number of targeted science and policy questions that are especially relevant to scenario-based analysis, including the role of specific forcings such as land use and aerosols, the effect of a peak and decline in forcing, the consequences of scenarios that limit warming to below 2 °C, the relative contributions to uncertainty from scenarios, climate models, and internal variability, and long-term climate system outcomes beyond the 21st century. To serve this wide range of scientific communities and address these questions, a design has been identified consisting of eight alternative 21st century scenarios plus one large initial condition ensemble and a set of long-term extensions, divided into two tiers defined by relative priority. Some of these scenarios will also provide a basis for variants planned to be run in other CMIP6-Endorsed MIPs to investigate questions related to specific forcings. Harmonized, spatially explicit emissions and land use scenarios generated with integrated assessment models will be provided to participating climate modeling groups by late 2016, with the climate model simulations run within the 2017–2018 time frame, and output from the climate model projections made available and analyses performed over the 2018–2020 period.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Performance metrics for climate models

              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found
              Is Open Access

              Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble

              EURO-CORDEX is an international climate downscaling initiative that aims to provide high-resolution climate scenarios for Europe. Here an evaluation of the ERA-Interim-driven EURO-CORDEX regional climate model (RCM) ensemble is presented. The study documents the performance of the individual models in representing the basic spatiotemporal patterns of the European climate for the period 1989–2008. Model evaluation focuses on near-surface air temperature and precipitation, and uses the E-OBS data set as observational reference. The ensemble consists of 17 simulations carried out by seven different models at grid resolutions of 12 km (nine experiments) and 50 km (eight experiments). Several performance metrics computed from monthly and seasonal mean values are used to assess model performance over eight subdomains of the European continent. Results are compared to those for the ERA40-driven ENSEMBLES simulations. The analysis confirms the ability of RCMs to capture the basic features of the European climate, including its variability in space and time. But it also identifies nonnegligible deficiencies of the simulations for selected metrics, regions and seasons. Seasonally and regionally averaged temperature biases are mostly smaller than 1.5 °C, while precipitation biases are typically located in the ±40% range. Some bias characteristics, such as a predominant cold and wet bias in most seasons and over most parts of Europe and a warm and dry summer bias over southern and southeastern Europe reflect common model biases. For seasonal mean quantities averaged over large European subdomains, no clear benefit of an increased spatial resolution (12 vs. 50 km) can be identified. The bias ranges of the EURO-CORDEX ensemble mostly correspond to those of the ENSEMBLES simulations, but some improvements in model performance can be identified (e.g., a less pronounced southern European warm summer bias). The temperature bias spread across different configurations of one individual model can be of a similar magnitude as the spread across different models, demonstrating a strong influence of the specific choices in physical parameterizations and experimental setup on model performance. Based on a number of simply reproducible metrics, the present study quantifies the currently achievable accuracy of RCMs used for regional climate simulations over Europe and provides a quality standard for future model developments.
                Bookmark

                Author and article information

                Contributors
                asballarin@gmail.com , andre.ballarin@usp.br
                Journal
                Sci Data
                Sci Data
                Scientific Data
                Nature Publishing Group UK (London )
                2052-4463
                20 January 2023
                20 January 2023
                2023
                : 10
                : 47
                Affiliations
                [1 ]GRID grid.11899.38, ISNI 0000 0004 1937 0722, Department of Hydraulics and Sanitation, São Carlos School of Engineering, , University of São Paulo, ; CxP. 359, São Carlos, São Paulo, 13566-590 Brazil
                [2 ]GRID grid.412352.3, ISNI 0000 0001 2163 5978, Faculty of Engineering, Architecture and Urbanism, and Geography, , Federal University of Mato Grosso Do Sul, ; CxP 549, Campo Grande, Mato Grosso Do Sul 79070-900 Brazil
                Author information
                http://orcid.org/0000-0001-6997-8662
                http://orcid.org/0000-0001-9932-431X
                http://orcid.org/0000-0002-3822-4865
                Article
                1956
                10.1038/s41597-023-01956-z
                9860025
                36670117
                a4d5da87-70b5-4dcf-a9f2-a00f73a014d1
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 15 September 2022
                : 10 January 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001807, Fundação de Amparo à Pesquisa do Estado de São Paulo (São Paulo Research Foundation);
                Award ID: 2020/08140-0
                Award ID: 2022/06017-1
                Award ID: 2015/03806–1
                Award ID: 2015/03806–1
                Award ID: 2019/24292-7
                Award ID: 2021/14016-2
                Award ID: 2015/03806–1
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100003593, Ministry of Science, Technology and Innovation | Conselho Nacional de Desenvolvimento Científico e Tecnológico (National Council for Scientific and Technological Development);
                Funded by: FundRef https://doi.org/10.13039/501100002322, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brazilian Federal Agency for the Support and Evaluation of Graduate Education);
                Award ID: Finance Code 001
                Award Recipient :
                Categories
                Data Descriptor
                Custom metadata
                © The Author(s) 2023

                projection and prediction,hydrology,climate-change impacts

                Comments

                Comment on this article