Articles | Volume 12, issue 11
https://doi.org/10.5194/gmd-12-4661-2019
https://doi.org/10.5194/gmd-12-4661-2019
Development and technical paper
 | 
07 Nov 2019
Development and technical paper |  | 07 Nov 2019

GlobSim (v1.0): deriving meteorological time series for point locations from multiple global reanalyses

Bin Cao, Xiaojing Quan, Nicholas Brown, Emilie Stewart-Jones, and Stephan Gruber

Related authors

Brief communication: Improving ERA5-Land soil temperature in permafrost regions using an optimized multi-layer snow scheme
Bin Cao, Gabriele Arduini, and Ervin Zsoter
The Cryosphere, 16, 2701–2708, https://doi.org/10.5194/tc-16-2701-2022,https://doi.org/10.5194/tc-16-2701-2022, 2022
Short summary
The ERA5-Land soil temperature bias in permafrost regions
Bin Cao, Stephan Gruber, Donghai Zheng, and Xin Li
The Cryosphere, 14, 2581–2595, https://doi.org/10.5194/tc-14-2581-2020,https://doi.org/10.5194/tc-14-2581-2020, 2020
Short summary
Brief communication: Evaluation and inter-comparisons of Qinghai–Tibet Plateau permafrost maps based on a new inventory of field evidence
Bin Cao, Tingjun Zhang, Qingbai Wu, Yu Sheng, Lin Zhao, and Defu Zou
The Cryosphere, 13, 511–519, https://doi.org/10.5194/tc-13-511-2019,https://doi.org/10.5194/tc-13-511-2019, 2019
Short summary
REDCAPP (v1.0): parameterizing valley inversions in air temperature data downscaled from reanalyses
Bin Cao, Stephan Gruber, and Tingjun Zhang
Geosci. Model Dev., 10, 2905–2923, https://doi.org/10.5194/gmd-10-2905-2017,https://doi.org/10.5194/gmd-10-2905-2017, 2017
Short summary

Related subject area

Climate and Earth system modeling
Hydrological modelling on atmospheric grids: using graphs of sub-grid elements to transport energy and water
Jan Polcher, Anthony Schrapffer, Eliott Dupont, Lucia Rinchiuso, Xudong Zhou, Olivier Boucher, Emmanuel Mouche, Catherine Ottlé, and Jérôme Servonnat
Geosci. Model Dev., 16, 2583–2606, https://doi.org/10.5194/gmd-16-2583-2023,https://doi.org/10.5194/gmd-16-2583-2023, 2023
Short summary
The sea level simulator v1.0: a model for integration of mean sea level change and sea level extremes into a joint probabilistic framework
Magnus Hieronymus
Geosci. Model Dev., 16, 2343–2354, https://doi.org/10.5194/gmd-16-2343-2023,https://doi.org/10.5194/gmd-16-2343-2023, 2023
Short summary
Structural k-means (S k-means) and clustering uncertainty evaluation framework (CUEF) for mining climate data
Quang-Van Doan, Toshiyuki Amagasa, Thanh-Ha Pham, Takuto Sato, Fei Chen, and Hiroyuki Kusaka
Geosci. Model Dev., 16, 2215–2233, https://doi.org/10.5194/gmd-16-2215-2023,https://doi.org/10.5194/gmd-16-2215-2023, 2023
Short summary
The emergence of the Gulf Stream and interior western boundary as key regions to constrain the future North Atlantic carbon uptake
Nadine Goris, Klaus Johannsen, and Jerry Tjiputra
Geosci. Model Dev., 16, 2095–2117, https://doi.org/10.5194/gmd-16-2095-2023,https://doi.org/10.5194/gmd-16-2095-2023, 2023
Short summary
Evaluating wind profiles in a numerical weather prediction model with Doppler lidar
Pyry Pentikäinen, Ewan J. O'Connor, and Pablo Ortiz-Amezcua
Geosci. Model Dev., 16, 2077–2094, https://doi.org/10.5194/gmd-16-2077-2023,https://doi.org/10.5194/gmd-16-2077-2023, 2023
Short summary

Cited articles

Albergel, C., Dutra, E., Munier, S., Calvet, J.-C., Munoz-Sabater, J., de Rosnay, P., and Balsamo, G.: ERA-5 and ERA-Interim driven ISBA land surface model simulations: which one performs better?, Hydrol. Earth Syst. Sci., 22, 3515–3532, https://doi.org/10.5194/hess-22-3515-2018, 2018. a, b, c
Arsenault, K. R., Kumar, S. V., Geiger, J. V., Wang, S., Kemp, E., Mocko, D. M., Beaudoing, H. K., Getirana, A., Navari, M., Li, B., Jacob, J., Wegiel, J., and Peters-Lidard, C. D.: The Land surface Data Toolkit (LDT v7.2) – a data fusion environment for land data assimilation systems, Geosci. Model Dev., 11, 3605–3621, https://doi.org/10.5194/gmd-11-3605-2018, 2018. a
Beck, H. E., Pan, M., Roy, T., Weedon, G. P., Pappenberger, F., van Dijk, A. I. J. M., Huffman, G. J., Adler, R. F., and Wood, E. F.: Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS, Hydrol. Earth Syst. Sci., 23, 207–224, https://doi.org/10.5194/hess-23-207-2019, 2019. a
Benestad, R. E., Hanssen-Bauer, I., and Chen, D.: Empirical-Statistical Downscaling, World Scientific, https://doi.org/10.1142/6908, 2008. a
Bieniek, P. A., Bhatt, U. S., Walsh, J. E., Rupp, T. S., Zhang, J., Krieger, J. R., and Lader, R.: Dynamical downscaling of ERA-Interim temperature and precipitation for Alaska, J. Appl. Meteorol. Climatol., 55, 635–654, https://doi.org/10.1175/JAMC-D-15-0153.1, 2016. a
Download
Short summary
GlobSim is a tool for simulating land-surface processes and phenomena at point locations globally, even where no site-specific meteorological observations exist. This is important because simulation can add insight to the analysis of observations or help in anticipating climate-change impacts and because site-specific simulation can help in model evaluation.