Regional Climate Model Evaluation System powered by Apache Open Climate Workbench v1.3.0: an enabling tool for facilitating regional climate studies

Lee, Huikyo; Goodman, A.; McGibbney, L. J.; Waliser, Duane E.; Kim, Jinwon; Loikith, Paul C.; Massoud, Elias

doi:10.5194/gmd-2018-113

Cited by 5 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The inclusion of this simple diagnostic in the Earth System Model eValuation Tool—ESMValTool (Eyring et al ., ) and Regional Climate Model Evaluation System—RCMES (Lee et al ., ) will be very useful for intercomparison of the rainfall variability over different TRB regions in the CMIP6 and CORDEX models. Practical application of the TRB indices can be very wide and includes (a) describing precipitation climatologies in the tropics and sub‐tropics, (b) estimating observational uncertainties, (c) evaluating global and regional climate models and (d) assessing projected changes in tropical precipitation.…”

Section: Discussionmentioning

confidence: 99%

A simple set of indices describing the Tropical Rain Belt over central and southern Africa

Nikulin

2019

Atmospheric Science Letters

View full text Add to dashboard Cite

Understanding the variability of the Tropical Rain Belt (TRB) in Africa is a challenging task due to complex interactions among global, regional and local drivers of rainfall. A simple method that provides a set of three indices describing large‐scale spatial–temporal characteristics of the TRB over central and southern Africa is introduced. The method is based on approximation of a meridional cross‐section of monthly mean precipitation by the Gaussian function. The three parameters of the Gaussian function correspond to the three TRB indices, namely: intensity, location and width. It is shown how the TRB indices can be used for describing the climatology of the TRB, for estimating observational uncertainties, and for evaluating the ERA‐Interim and ERA5 reanalyses against observations. Simplicity and flexibility of the method allow using the TRB indices for a wide range of applications, including evaluation of climate models and assessment of projected climate change in tropical rainfall.

show abstract

Section: Discussionmentioning

confidence: 99%

A simple set of indices describing the Tropical Rain Belt over central and southern Africa

Nikulin

2019

Atmospheric Science Letters

View full text Add to dashboard Cite

show abstract

“…This study employed RCMES (Regional Climate Model Evaluation System), an open-source Python-based program (Lee et al, 2018), as the primary tool for processing, analyzing, and visualizing the data. Adjustments were made to match it to the specific characteristics of the data and the study domain.…”

Section: Data Processingmentioning

confidence: 99%

A Comparison of the Performance of the Weighted Ensembles Means in CORDEX-SEA Precipitation Simulations

Aminoto,

Faqih,

Perdinan

et al. 2024

Agromet

View full text Add to dashboard Cite

Numerous studies stated that the performance of ensemble mean derived from multiple climate models generally surpassed the individual member model, and applying weighting factors potentially increase the ensemble mean of performance. This study aims to assess the performance of unweighted and weighted ensemble means of 9-modelled precipitation datasets in the CORDEX-SEA multi-model simulations for 1981-2005. The 9 datasets included: CNRM_a, ECE_b, GFDL_b, IPSL_b, HadGEM2_a, HadGEM2_c, HadGEM2_d, MPI_c, and NorESM1_d. The weighting factors were derived from the models' skill scores measured using five statistical-based metrics, namely Taylor, Pierce (SS), Tian skill score (Tian), Climate prediction index (CPI), and Performance and Independence (PI). The ERA5 and GPCP precipitation datasets were used as the references for comparison. Then, reliable metrics will be used to determine the weighting factor. The results found that three metrics namely Taylor, SS, and Tian were more reliable than the other two metrics (CPI and PI). Spatially, the weighted ensemble mean based on a random method was superior to other ensemble mean methods and individual models. We found that the CNRM_a and GFDL_b models were spatially performed best. In contrast, most the ensemble means was temporally less performed compared to the individual model. Our findings suggested that by removal of low performance models will significantly influence on the overall ensemble model performance. Further, the research may provide valuable considerations of climate models selection for climate projection assessments, especially in the Southeast Asia region.

show abstract

“…Obs4MIPs as an enabling mechanism continues to be the primary objective however it is now evident that there is added value beyond its original intent. In addition to providing data for researchers, obs4MIPs will be a critical link in support of current community efforts to develop routine and systematic evaluation [e.g., Gleckler et al, 2016;Eyring et al, 2016a,b, Righi et al 2019Phillips et al, 2014;Lee et al 2018;Collier et al, 2018]. With the rapid growth in the number of experiments, models and output volumes, these developing evaluation tools promise to produce a first high-level set of evaluation and characterization summaries, well ahead of the more in-depth analyses expected to come from the climate research community.…”

Section: F) Intersection With Cmip6 Model Evaluation Activitiesmentioning

confidence: 99%

Observations for Model Intercomparison Project (Obs4MIPs): Status for CMIP6

Waliser¹,

Gleckler²,

Ferraro³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. The Observations for Model Intercomparison Projects (Obs4MIPs) was initiated in 2010 to facilitate the use of observations in climate model evaluation and research, with a particular target being the Coupled Model Intercomparison Project (CMIP), a major initiative of the World Climate Research Programme (WCRP). To this end, Obs4MIPs: 1) targets observed variables that can be compared to CMIP model variables, 2) utilizes dataset formatting specifications and metadata requirements closely aligned with CMIP model output, 3) provides brief technical documentation for each dataset, designed for non-experts and tailored towards relevance for model evaluation, including information on uncertainty, dataset merits and limitations, and 4) disseminates the data through the Earth System Grid Federation (ESGF) platforms, making the observations searchable and accessible via the same portals as the model output. Taken together, these characteristics of the organization and structure of obs4MIPs should entice a more diverse community of researchers to engage in the comparison of model output with observations and to contribute to a more comprehensive evaluation of the climate models. At present, the number of obs4MIPs datasets has grown to about 80, many undergoing updates, with another 20 or so in preparation, and more than 100 proposed and under consideration. Current global satellite-based datasets include, but are not limited to, humidity and temperature profiles; a wide range of cloud and aerosol observations; ocean surface wind, temperature, height, and sea ice fraction; surface and top of atmosphere longwave and shortwave radiation; along with ozone (O3), methane (CH4) and carbon dioxide (CO2) products. Proposed products expected for inclusion for CMIP6 analysis include, but are not limited to, alternative products for the above quantities, along with additional products for ocean surface flux and chlorophyll products, a number of vegetation products (e.g. FAPAR, LAI, burnt area fraction), ice sheet mass and height, carbon monoxide (CO) and nitrogen dioxide (NO2). While most obs4MIPs datasets are delivered as monthly and global, greater emphasis is being places on products with higher time resolution (e.g. daily) and/or regional products. Along with an increasing number of datasets, obs4MIPs has implemented a number of capability upgrades including: 1) an updated obs4MIPs data specifications document that provides for additional search facets and generally improves congruence with CMIP6 specifications for model datasets, 2) a set of six easily understood indicators that help guide users as to a dataset’s maturity and suitability for application, and 3) an option to supply supplemental information about a dataset beyond what can be found in the standard metadata. With the maturation of the obs4MIPs framework, the dataset inclusion process, and the dataset formatting guidelines and resources, the scope of the observations being considered is expected to grow to include gridded in-situ datasets as well as datasets with a regional focus, and the ultimate intent is to judiciously expand this scope to any observation dataset that has applicability for evaluation of the types of Earth System models used in CMIP.

show abstract

Regional Climate Model Evaluation System powered by Apache Open Climate Workbench v1.3.0: an enabling tool for facilitating regional climate studies

Cited by 5 publications

References 8 publications

A simple set of indices describing the Tropical Rain Belt over central and southern Africa

A simple set of indices describing the Tropical Rain Belt over central and southern Africa

A Comparison of the Performance of the Weighted Ensembles Means in CORDEX-SEA Precipitation Simulations

Observations for Model Intercomparison Project (Obs4MIPs): Status for CMIP6

Contact Info

Product

Resources

About