The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015
Open accessAbstract
We present TerraClimate, a dataset of high-spatial resolution (1/24°, ~4-km) monthly climate and climatic water balance for global terrestrial surfaces from 1958–2015. TerraClimate uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser resolution time varying (i.e., monthly) data from other sources to produce a monthly dataset of precipitation, maximum and minimum temperature, wind speed, vapor pressure, and solar radiation. TerraClimate additionally produces monthly surface water balance datasets using a water balance model that incorporates reference evapotranspiration, precipitation, temperature, and interpolated plant extractable soil water capacity. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time varying climate and climatic water balance data. We validated spatiotemporal aspects of TerraClimate using annual temperature, precipitation, and calculated reference evapotranspiration from station data, as well as annual runoff from streamflow gauges. TerraClimate datasets showed noted improvement in overall mean absolute error and increased spatial realism relative to coarser resolution gridded datasets.
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Cites
Crop evapotranspiration : guidelines for computing crop water requirements
Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998) · FAO eBooks · Cited by 20,520
Very high resolution interpolated climate surfaces for global land areas
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., & Jarvis, A. (2005) · International Journal of Climatology · doi:10.1002/joc.1276 · Cited by 19,825
WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas
Fick, S. E., & Hijmans, R. J. (2017) · International Journal of Climatology · doi:10.1002/joc.5086 · Cited by 16,088
Updated high‐resolution grids of monthly climatic observations – the CRU TS3 .10 Dataset
Harris, I., Jones, P. D., Osborn, T. J., & Lister, D. H. (2014) · International Journal of Climatology · doi:10.1002/joc.3711 · Cited by 6,094
The JRA-55 Reanalysis: General Specifications and Basic Characteristics
KOBAYASHI, S., OTA, Y., HARADA, Y., EBITA, A., MORIYA, M., ONODA, H., ONOGI, K., KAMAHORI, H., KOBAYASHI, C., ENDO, H., et al. (2015) · Journal of the Meteorological Society of Japan. Ser. II · doi:10.2151/jmsj.2015-001 · Cited by 4,760
Climate Trends and Global Crop Production Since 1980
Lobell, D. B., Schlenker, W., & Costa-Roberts, J. (2011) · Science · doi:10.1126/science.1204531 · Cited by 4,546
Climatologies at high resolution for the earth’s land surface areas
Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2017) · Scientific Data · doi:10.1038/sdata.2017.122 · Cited by 4,171
Climatologies at high resolution for the earth’s land surface areas
Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2017) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-139381 · Cited by 3,598
Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor, G. H., Curtis, J., & Pasteris, P. P. (2008) · International Journal of Climatology · doi:10.1002/joc.1688 · Cited by 3,100
Impact of anthropogenic climate change on wildfire across western US forests
Abatzoglou, J. T., & Williams, A. P. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1607171113 · Cited by 3,073
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Beerling, D. J., Kantzas, E. P., Lomas, M. R., Wade, P., Eufrasio, R. M., Renforth, P., Sarkar, B., Andrews, M. G., James, R. H., Pearce, C. R., et al. (2020) · Nature · doi:10.1038/s41586-020-2448-9 · Cited by 700
Hartmann, H., Bastos, A., Das, A. J., Esquivel-Muelbert, A., Hammond, W. M., Martínez-Vilalta, J., McDowell, N. G., Powers, J. S., Pugh, T. A. M., Ruthrof, K. X., et al. (2022) · Annual Review of Plant Biology · doi:10.1146/annurev-arplant-102820-012804 · Cited by 574
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Sillero, N., Arenas-Castro, S., Enriquez‐Urzelai, U., Vale, C. G., Sousa-Guedes, D., Martínez-Freiría, F., Real, R., & Barbosa, A. M. (2021) · Ecological Modelling · doi:10.1016/j.ecolmodel.2021.109671 · Cited by 456
A new global database of meteorological drought events from 1951 to 2016
Spinoni, J., Barbosa, P., De Jager, A., McCormick, N., Naumann, G., Vogt, J. V., Magni, D., Masante, D., & Mazzeschi, M. (2019) · Journal of Hydrology: Regional Studies · doi:10.1016/j.ejrh.2019.100593 · Cited by 452
Metadata
- Authors
- Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., & Hegewisch, K. C.
- Venue
- Scientific Data
- Published
- 2018-01-09
- Volume
- 5
- Issue
- 1
- Pages
- 170191-170191
- ISSN
- 2052-4463 (ISSN-L)
- OpenAlex
- W2784327149
- Cited by
- 3,421
- References
- 45
- Type
- data paper
- Language
- en
- Open access
- gold
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- Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., & Hegewisch, K. C. (2018). TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Scientific Data. https://doi.org/10.1038/sdata.2017.191
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