DKRZ MPI-ESM1.2-LR model output prepared for CMIP6 ScenarioMIP

Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Milinski, Sebastian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Früh, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, Jörg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; Müller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich

doi:10.22033/esgf/cmip6.15349

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The solar radiation data for driving Carnegie-Ames-Stanford approach (CASA) are acquired from the Global Land Surface Satellite Downward Shortwave Radiation data set (GLASS DSR), which are available at https:// www.geodata.cn/thematicView/GLASS.html. The future meteorological conditions were acquired from 16 Earth system models (ESMs) which simulated four different future climate scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) as part of CMIP6, which are available in Boucher et al (2019), Dix et al (2019), EC-Earth (2019), Good et al (2019), Lovato et al (2021; Schupfner et al (2021), Schupfner et al (2019), Seferian (2019), Swart et al (2019), Tachiiri et al (2019), Voldoire (2019aVoldoire ( , 2019b, Volodin et al (2019aVolodin et al ( , 2019b, Xin et al (2019), andZiehn et al (2019). The RF was built according to scikit-learn 1.3.0 (Pedregosa et al, 2011), available at https:// scikit-learn.org/stable/.…”

Section: Data Availability Statementmentioning

confidence: 99%

Natural Vegetation Succession Under Climate Change and the Combined Effects on Net Primary Productivity

Zhang,

Hao,

Zhao

et al. 2023

Earth's Future

View full text Add to dashboard Cite

Climate change and the resulting natural vegetation succession can alter vegetation productivity. However, the mechanisms underlying future productivity changes under the two influences remain unclear. Here, we used the comprehensive sequence classification system to simulate changes in global potential natural vegetation under different climate scenarios (SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5), and combined the Carnegie–Ames–Stanford Approach model with random forest to assess the response of net primary productivity (NPP) to climate change and vegetation succession from 2020 to 2100. Except for SSP126, terrestrial NPP in 2100 decreased by 0.86, 2.39, and 2.54 Pg C·a−1 versus 2020 under SSP2‐4.5, SSP5‐8.5, and SSP3‐7.0, respectively. Forest was the primary contributor to terrestrial NPP changes. The total forest area was projected to increase under all scenarios, with SSP2‐4.5 showing the largest increase (358.57 × 104 km2). However, expanding forest regions exhibited a relatively low mean NPP, while stable regions demonstrated a declining pattern. Consequently, forest NPP increased under SSP1‐2.6 but decreased by 4.03, 3.43, and 0.82 Pg C·a−1 in 2100 versus 2020 under SSP5‐8.5, SSP3‐7.0, and SSP2‐4.5, respectively. In comparison, grassland and desert exerted minor influence on terrestrial NPP changes, their total NPP decreased only under the SSP1‐2.6 scenario. The grassland area decreased, but the mean NPP increased, whereas the desert area expanded, resulting in consistent changes in both total and mean NPP. Our results analyzed the effects of climate change and vegetation distribution under its influence on the change of NPP, which can deepen our understanding of their relationship.

show abstract

Section: Data Availability Statementmentioning

confidence: 99%

Natural Vegetation Succession Under Climate Change and the Combined Effects on Net Primary Productivity

Zhang,

Hao,

Zhao

et al. 2023

Earth's Future

View full text Add to dashboard Cite

show abstract

An ensemble of bias-adjusted CMIP6 climate simulations based on a high-resolution North American reanalysis

Lavoie,

Bourgault,

Smith

et al. 2024

Sci Data

View full text Add to dashboard Cite

ESPO-G6-R2 v1.0 is a set of statistically downscaled and bias-adjusted climate simulations based on the Coupled Model Intercomparison Project 6 (CMIP6) models. The dataset is composed of daily timeseries of three variables: daily maximum temperature, daily minimum temperature and daily precipitation. Data are available from 1950 to 2100 over North America. The simulation ensemble is comprised of 14 models driven by two emissions scenarios (SSP2-4.5 and SSP3-7.0). In this paper, we describe the workflow used for the bias-adjustment, which relies on the detrended quantile mapping method and the Regional Deterministic Reforecast System (RDRS) v2.1 reference dataset. Using the framework defined in the VALUE project, we show the improvements made by the bias-adjustment on marginal, temporal and multivariate aspects of the data. We also verify that the bias-adjusted climate data have similar climate change signal to the original climate model simulations. Finally, we provide guidance to users on how to use this dataset.

show abstract

DKRZ MPI-ESM1.2-LR model output prepared for CMIP6 ScenarioMIP

Cited by 2 publications

References 0 publications

Natural Vegetation Succession Under Climate Change and the Combined Effects on Net Primary Productivity

Natural Vegetation Succession Under Climate Change and the Combined Effects on Net Primary Productivity

An ensemble of bias-adjusted CMIP6 climate simulations based on a high-resolution North American reanalysis

Contact Info

Product

Resources

About