Articles | Volume 13, issue 8
https://doi.org/10.5194/gmd-13-3627-2020
https://doi.org/10.5194/gmd-13-3627-2020
Methods for assessment of models
 | 
21 Aug 2020
Methods for assessment of models |  | 21 Aug 2020

Evaluating simulated climate patterns from the CMIP archives using satellite and reanalysis datasets using the Climate Model Assessment Tool (CMATv1)

John T. Fasullo

Related authors

An Overview of the E3SM version 2 Large Ensemble and Comparison to other E3SM and CESM Large Ensembles
John Fasullo, Jean-Christophe Golaz, Julie Caron, Nan Rosenbloom, Gerald Meehl, Warren Strand, Sasha Glanville, Samantha Stevenson, Maria Molina, Christine Shields, Chengzhu Zhang, James Benedict, and Tony Bartoletti
EGUsphere, https://doi.org/10.5194/egusphere-2023-2310,https://doi.org/10.5194/egusphere-2023-2310, 2023
Short summary
New model ensemble reveals how forcing uncertainty and model structure alter climate simulated across CMIP generations of the Community Earth System Model
Marika M. Holland, Cecile Hannay, John Fasullo, Alexandra Jahn, Jennifer E. Kay, Michael Mills, Isla R. Simpson, William Wieder, Peter Lawrence, Erik Kluzek, and David Bailey
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-125,https://doi.org/10.5194/gmd-2023-125, 2023
Revised manuscript under review for GMD
Short summary
Dependence of strategic solar climate intervention on background scenario and model physics
John T. Fasullo and Jadwiga H. Richter
Atmos. Chem. Phys., 23, 163–182, https://doi.org/10.5194/acp-23-163-2023,https://doi.org/10.5194/acp-23-163-2023, 2023
Short summary

Related subject area

Climate and Earth system modeling
A sub-grid parameterization scheme for topographic vertical motion in CAM5-SE
Yaqi Wang, Lanning Wang, Juan Feng, Zhenya Song, Qizhong Wu, and Huaqiong Cheng
Geosci. Model Dev., 16, 6857–6873, https://doi.org/10.5194/gmd-16-6857-2023,https://doi.org/10.5194/gmd-16-6857-2023, 2023
Short summary
Technology to aid the analysis of large-volume multi-institute climate model output at a central analysis facility (PRIMAVERA Data Management Tool V2.10)
Jon Seddon, Ag Stephens, Matthew S. Mizielinski, Pier Luigi Vidale, and Malcolm J. Roberts
Geosci. Model Dev., 16, 6689–6700, https://doi.org/10.5194/gmd-16-6689-2023,https://doi.org/10.5194/gmd-16-6689-2023, 2023
Short summary
A diffusion-based kernel density estimator (diffKDE, version 1) with optimal bandwidth approximation for the analysis of data in geoscience and ecological research
Maria-Theresia Pelz, Markus Schartau, Christopher J. Somes, Vanessa Lampe, and Thomas Slawig
Geosci. Model Dev., 16, 6609–6634, https://doi.org/10.5194/gmd-16-6609-2023,https://doi.org/10.5194/gmd-16-6609-2023, 2023
Short summary
Monte Carlo drift correction – quantifying the drift uncertainty of global climate models
Benjamin S. Grandey, Zhi Yang Koh, Dhrubajyoti Samanta, Benjamin P. Horton, Justin Dauwels, and Lock Yue Chew
Geosci. Model Dev., 16, 6593–6608, https://doi.org/10.5194/gmd-16-6593-2023,https://doi.org/10.5194/gmd-16-6593-2023, 2023
Short summary
Improvements in the Canadian Earth System Model (CanESM) through systematic model analysis: CanESM5.0 and CanESM5.1
Michael Sigmond, James Anstey, Vivek Arora, Ruth Digby, Nathan Gillett, Viatcheslav Kharin, William Merryfield, Catherine Reader, John Scinocca, Neil Swart, John Virgin, Carsten Abraham, Jason Cole, Nicolas Lambert, Woo-Sung Lee, Yongxiao Liang, Elizaveta Malinina, Landon Rieger, Knut von Salzen, Christian Seiler, Clint Seinen, Andrew Shao, Reinel Sospedra-Alfonso, Libo Wang, and Duo Yang
Geosci. Model Dev., 16, 6553–6591, https://doi.org/10.5194/gmd-16-6553-2023,https://doi.org/10.5194/gmd-16-6553-2023, 2023
Short summary

Cited articles

Adler, R., Sapiano, M., Huffman, G., Bolvin, D., Gu, G., Wang, J., and Schneider, U.: The new version 2.3 of the Global Precipitation Climatology Project (GPCP) monthly analysis product, University of Maryland, April, pp. 1072–1084, 2016. 
Adler, R. F., Gu, G., Huffman, G. J., Sapiano, M. R., and Wang, J. J.: GPCP and the Global Characteristics of Precipitation, in: Satellite Precipitation Measurement, pp. 677–697, Springer, Cham, 2020. 
Baker, N. C. and Taylor, P. C.: A framework for evaluating climate model performance metrics, J. Climate, 29, 1773–1782, https://doi.org/10.1175/JCLI-D-15-0114.1, 2016. 
Borovikov, A., Cullather, R., Kovach, R., Marshak, J., Vernieres, G., Vikhliaev, Y., and Li, Z.: GEOS-5 seasonal forecast system, Clim. Dynam., 53, 7335–7361, https://doi.org/10.1007/s00382-017-3835-2, 2019. 
Box, G. E. P.: Science and statistics, J. Am. Stat. Assoc., 71, 791–799, https://doi.org/10.1080/01621459.1976.10480949, 1976. 
Download
Short summary
The fidelity of climate model simulations included in the WCRP Coupled Model Intercomparison Project Versions 3 through 6 is evaluated using best estimates of fields considered by the community to be critical for climate change projections. The analysis benchmarks patterns of the mean state and variability (seasonal/interannual) both within and across model generations, highlighting progress and quantifying persisting biases across models.