A high-resolution downscaled CMIP5 projections dataset of essential surface climate variables over the globe coherent with the ERA5 reanalysis for climate change impact assessments

Noël, T.; Loukos, Harilaos; Defrance, Dimitri; Vrac, Mathieu; Levavasseur, Guillaume

doi:10.1016/j.dib.2021.106900

Cited by 14 publications

(5 citation statements)

References 16 publications

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“…and ACCESS1-3 (1.9 • × 1.2 • lat-lon res. ), were chosen based on different recommendations and results [49][50][51][52]. Notwithstanding, we generally found several arguments that make the best choice a difficult one.…”

Section: Statistical Downscaling Of Gcmsmentioning

confidence: 99%

Future Projection of Precipitation Changes in the Júcar and Segura River Basins (Iberian Peninsula) by CMIP5 GCMs Local Downscaling

et al. 2021

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The basins of the Júcar and Segura rivers, on the Mediterranean coast of the Iberian Peninsula, present a special water problem and are of particular interest regarding climate change. These basins are very vulnerable to a possible scenario of decreasing water resources. Recent studies on historic rainfall since 1955 have indicated an ongoing loss of precipitation in their headwaters, especially in the case of the Júcar river. The aim of the present study is to perform climate projections for the precipitation variable for several future periods (2021–2040, 2051–2070, 2081–2100) and emission scenarios (RCPs 4.5, 8.5) within the Júcar and Segura River Basin authorities. For this purpose, a set of CMIP5 global models have been used, as well as the CDRD-HR-EIP-1955-2016 database, as a source of local observed information. This database comprises nearly 900 precipitation series in both basins and has been used in recent studies to determine historic trends of change in these basins. A statistical downscaling of the global models for all available observed series has been applied using the LARS-WG method. The results, although variable according to the CMIP5 model used, show the continuation of the patterns of precipitation change in the future, as already observed in the historical series. The results also predict a clear reduction in precipitation in the long term. However, torrential rainfall tends to increase in the coastal areas in relation to that observed in the short-term predictions. These results, due to their high spatial resolution, are of great interest for their use in small-scale hydrological and spatial planning (regional and local), which is one of the current challenges of climate modeling.

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Section: Statistical Downscaling Of Gcmsmentioning

confidence: 99%

Future Projection of Precipitation Changes in the Júcar and Segura River Basins (Iberian Peninsula) by CMIP5 GCMs Local Downscaling

et al. 2021

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“…According to the Intergovernmental Panel on Climate Change (IPCC), future scenarios indicate an increase in mean annual precipitation and temperature worldwide, with temperatures expected to rise by 2 to 3 • C by the mid-21st century and 3 to 5 • C toward its end (Hood, 2019;Noél et al, 2021). Consistent with these global projections, our analysis for Ecuador forecasts mean seasonal temperature increases ranging from 2.3 to 6 • C across different climatic regions by the latter half of the century, relative to historical baselines.…”

Section: Discussionmentioning

confidence: 99%

“…Daily air temperature data was sourced from the ERA5 reanalysis (Buontempo et al, 2020;Noél et al, 2021), renowned for its intricate portrayal of atmospheric conditions. The ERA5 dataset, with higher spatial resolution of 0.25 • latitude by 0.25 • longitude, spans from 1980 to 2004.…”

Section: Model Bias Correction and Validation Observational Datamentioning

confidence: 99%

Climate variability and change in Ecuador: dynamic downscaling of regional projections with RegCM4 and HadGEM2-ES for informed adaptation strategies

Portalanza,

Torres,

Rosso

et al. 2024

Front. Clim.

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Ecuador, a country with distinct coastal (CO), highland (HL), and Amazon (AM) regions that are characterized by unique climatic, ecological, and socio-economic features is highly vulnerable to climate change. This study focuses on these three regions, highlighting their individual importance in the broader context of Ecuador's climate vulnerability. Utilizing dynamically downscaled data from the Regional Climate Model (RCM), we generated precipitation and air temperature projections for the period 2070–2099 under three different climate change scenarios. We indicate projected temperature increases across all three regions: mean temperature increases for the CO, HL and AM regions are of 1.35, 1.55, and 1.21°C, respectively. Each year, the largest temperature increases are predicted for the third quarter (June–August), with the smallest increases predicted for the last quarter (December–February). Precipitation patterns show varied changes, with CO exhibiting a positive mean daily change, in contrast to a mean negative change in the AM region. These region-specific projections underscore the differential impacts of climate change within Ecuador and highlight the necessity for tailored adaptation measures. The study's novel approach, focusing on distinct regional impacts within a single nation, offers valuable insights for policymakers, aiding in the development of effective, region-specific climate change mitigation and adaptation strategies. This targeted approach is crucial to address unique challenges faced by different regions, thereby supporting national resilience strategies.

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“…The QM method was shown to have the potential to reduce biases in palaeoclimate simulations (Beyer et al 2020). The CDF-t method was previously applied as a downscaling technique (Michelangeli et al 2009, Vigaud et al 2013, Noël et al 2021 and as a bias correction methodology (Luu et al 2018, Mesta andKentel 2022) to correct regional climate model outputs, used as an input for impact models. However, this methodology has not yet been tested for past time periods with available paleoenvironmental data synthesis, which is the goal of the present study.…”

Section: Introductionmentioning

confidence: 99%

Improving biome and climate modelling for a set of past climate conditions: evaluating bias correction using the CDF-t approach

Zapolska

Vrac

Quiquet

et al. 2023

Environ. Res.: Climate

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Climate model simulations are inherently biased. It is a notably difficult problem when dealing with climate impact assessments and model-data integration. This is especially true when looking at derived quantities such as biomes, where not only climate but also vegetation dynamics biases come into play. To overcome such difficulties, we evaluate the performance of an existing methodology to correct climate model outputs, applied here for the first time to long past climate conditions. The proposed methodology relies on the “Cumulative Distribution Function - transform” (CDF-t) technique, which allows to account for climate change within the bias-correction procedure. The results are evaluated in two independent ways: i- using forward modelling, so that model results are directly comparable to reconstructed vegetation distribution; ii- using climatic reconstructions based on an inverse modelling approach. The modelling is performed using the intermediate complexity model iLOVECLIM in the standard global and interactively downscaled over the Europe version. The combined effects of dynamical downscaling and bias correction resulted in significantly stronger agreement between the simulated results and pollen-based biome reconstructions (BIOME6000) for the pre-industrial (0.18 versus 0.44) and mid-Holocene (0.31 versus 0.40). Higher correlation is also observed between statistically modelled global gridded potential natural distribution and modelled biomes (0.36 versus 0.41). Similarly, we find higher correlation between the reconstructed and the modelled temperatures for the mid-Holocene (0.02 versus 0.21). No significant difference is found for the Last Glacial Maximum when using temperature reconstructions, due to the low number of data points available. Our findings show that the application of the CDF-t method on simulated climate variables enables us to simulate palaeoclimate and vegetation distribution in better agreement with independent reconstructions.

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A high-resolution downscaled CMIP5 projections dataset of essential surface climate variables over the globe coherent with the ERA5 reanalysis for climate change impact assessments

Cited by 14 publications

References 16 publications

Future Projection of Precipitation Changes in the Júcar and Segura River Basins (Iberian Peninsula) by CMIP5 GCMs Local Downscaling

Future Projection of Precipitation Changes in the Júcar and Segura River Basins (Iberian Peninsula) by CMIP5 GCMs Local Downscaling

Climate variability and change in Ecuador: dynamic downscaling of regional projections with RegCM4 and HadGEM2-ES for informed adaptation strategies

Improving biome and climate modelling for a set of past climate conditions: evaluating bias correction using the CDF-t approach

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