Articles | Volume 11, issue 10
Geosci. Model Dev., 11, 4139–4153, 2018
https://doi.org/10.5194/gmd-11-4139-2018
Geosci. Model Dev., 11, 4139–4153, 2018
https://doi.org/10.5194/gmd-11-4139-2018
Model evaluation paper
12 Oct 2018
Model evaluation paper | 12 Oct 2018

Global hydro-climatic biomes identified via multitask learning

Christina Papagiannopoulou et al.

Related authors

A non-linear Granger-causality framework to investigate climate–vegetation dynamics
Christina Papagiannopoulou, Diego G. Miralles, Stijn Decubber, Matthias Demuzere, Niko E. C. Verhoest, Wouter A. Dorigo, and Willem Waegeman
Geosci. Model Dev., 10, 1945–1960, https://doi.org/10.5194/gmd-10-1945-2017,https://doi.org/10.5194/gmd-10-1945-2017, 2017
Short summary

Related subject area

Earth and space science informatics
Twenty-five years of the IPCC Data Distribution Centre at the DKRZ and the Reference Data Archive for CMIP data
Martina Stockhause and Michael Lautenschlager
Geosci. Model Dev., 15, 6047–6058, https://doi.org/10.5194/gmd-15-6047-2022,https://doi.org/10.5194/gmd-15-6047-2022, 2022
Short summary
Effectiveness and computational efficiency of absorbing boundary conditions for full-waveform inversion
Daiane Iglesia Dolci, Felipe A. G. Silva, Pedro S. Peixoto, and Ernani V. Volpe
Geosci. Model Dev., 15, 5857–5881, https://doi.org/10.5194/gmd-15-5857-2022,https://doi.org/10.5194/gmd-15-5857-2022, 2022
Short summary
LAND-SUITE V1.0: a suite of tools for statistically based landslide susceptibility zonation
Mauro Rossi, Txomin Bornaetxea, and Paola Reichenbach
Geosci. Model Dev., 15, 5651–5666, https://doi.org/10.5194/gmd-15-5651-2022,https://doi.org/10.5194/gmd-15-5651-2022, 2022
Short summary
Towards physics-inspired data-driven weather forecasting: integrating data assimilation with a deep spatial-transformer-based U-NET in a case study with ERA5
Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh, Eviatar Bach, and Karthik Kashinath
Geosci. Model Dev., 15, 2221–2237, https://doi.org/10.5194/gmd-15-2221-2022,https://doi.org/10.5194/gmd-15-2221-2022, 2022
Short summary
Fast infrared radiative transfer calculations using graphics processing units: JURASSIC-GPU v2.0
Paul F. Baumeister and Lars Hoffmann
Geosci. Model Dev., 15, 1855–1874, https://doi.org/10.5194/gmd-15-1855-2022,https://doi.org/10.5194/gmd-15-1855-2022, 2022
Short summary

Cited articles

Ando, R. K. and Zhang, T.: A framework for learning predictive structures from multiple tasks and unlabeled data, J. Mach. Learn. Res., 6, 1817–1853, 2005.
Baker, B., Diaz, H., Hargrove, W., and Hoffman, F.: Use of the Köppen–Trewartha climate classification to evaluate climatic refugia in statistically derived ecoregions for the People's Republic of China, Climatic Change, 98, 113, https://doi.org/10.1007/s10584-009-9622-2, 2009.
Bartholomé, E. and Belward, A. S.: GLC2000: a new approach to global land cover mapping from Earth observation data, Int. J. Remote Sens., 26, 1959–1977, 2005.
Barzilai, A. and Crammer, K.: Convex multi-task learning by clustering, in: Artificial Intelligence and Statistics, San Diego, California, USA, 9–12 May 2015, 65–73, 2015.
Baxter, J.: A Bayesian/information theoretic model of learning to learn via multiple task sampling, Mach. Learn., 28, 7–39, 1997.
Download
Short summary
Common global land cover and climate classifications are based on vegetation–climatic characteristics derived from observational data, ignoring the interaction between the local climate and biome. Here, we model the interplay between vegetation and local climate by discovering spatial relationships among different locations. The resulting global hydro-climatic biomes correspond to regions of coherent climate–vegetation interactions that agree well with traditional global land cover maps.