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WorldClim

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WorldClim is a set of global climate data layers (gridded climate data) with a high spatial resolution. It provides public, free-of-charge access to climate variables used in a wide range of academic and research applications, particularly in ecological modelling and conservation biology.[1]

The dataset includes historical climate data (such as monthly average minimum, mean, and maximum temperature, and precipitation) as well as future climate projections derived from Global Climate Models (GCMs). One of its most widely used components is the set of 19 "bioclimatic variables," which are derived from the monthly temperature and precipitation values and are intended to represent more biologically meaningful aspects of the climate, such as seasonality and climatic extremes.[2]

History and Development

WorldClim was first developed by Robert J. Hijmans, Susan E. Cameron, Juan L. Parra, Peter G. Jones, and Andy Jarvis, with its first version (Version 1) described in a 2005 paper.[1] This initial version provided global climate surfaces at a resolution of 30 arc-seconds (approximately 1 km at the equator), which was a significant improvement in spatial detail at the time. The data was based on weather station records from 1950–2000.

In 2017, WorldClim Version 2 was released, as described by Fick and Hijmans.[2] This version provided updated climate surfaces for the 1970–2000 period, also at 30 arc-seconds resolution. It incorporated new data sources and improved interpolation methods to enhance accuracy.

The project also provides downscaled future climate data from the Coupled Model Intercomparison Project (CMIP), including projections from CMIP5 and CMIP6, under various Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs).

Data Description

WorldClim data is provided in a raster data format (such as GeoTIFF) and is available at several spatial resolutions: 30 seconds (~1 km), 2.5 minutes, 5 minutes, and 10 minutes.[3]

Climate Variables

The data is organized into several types of variables:

  • Monthly Variables: Average monthly values for minimum temperature (Tmin), maximum temperature (Tmax), mean temperature (Tmean), and precipitation (Prec).
  • Bioclimatic Variables: A set of 19 variables (often coded as BIO1–BIO19) that summarize temperature and precipitation patterns. These are often used directly in ecological models.
19 Bioclimatic Variables (BIO)
Variable Description
BIO1 Annual Mean Temperature
BIO2 Mean Diurnal Range (Mean of monthly (max temp - min temp))
BIO3 Isothermality (BIO2/BIO7) (×100)
BIO4 Temperature Seasonality (standard deviation ×100)
BIO5 Max Temperature of Warmest Month
BIO6 Min Temperature of Coldest Month
BIO7 Temperature Annual Range (BIO5-BIO6)
BIO8 Mean Temperature of Wettest Quarter
BIO9 Mean Temperature of Driest Quarter
BIO10 Mean Temperature of Warmest Quarter
BIO11 Mean Temperature of Coldest Quarter
BIO12 Annual Precipitation
BIO13 Precipitation of Wettest Month
BIO14 Precipitation of Driest Month
BIO15 Precipitation Seasonality (Coefficient of Variation)
BIO16 Precipitation of Wettest Quarter
BIO17 Precipitation of Driest Quarter
BIO18 Precipitation of Warmest Quarter
BIO19 Precipitation of Coldest Quarter

Temporal Coverage

  • Historical: Averages based on recent past periods (1970–2000 for Version 2.1).[2]
  • Future: Projections for future time periods (2021–2040, 2041–2060, etc.) based on GCM outputs.[3]

Applications and Significance

WorldClim is a foundational dataset in spatial ecology, conservation biology, and climate change research. Its notability stems from its widespread adoption as a standard data source in the following areas:

  • Species distribution modelling (SDM): Researchers use WorldClim's bioclimatic variables as environmental predictors to model the potential habitat of species under current and future climate conditions, supporting biodiversity conservation and ecosystem management (such as forest management).[4]
  • Climate Change Impact Studies: The data is used to analyze the vulnerability and adaptability of systems such as agriculture and forestry to climate change. This includes calculating metrics like "climate velocity," the speed at which climate zones are moving, to assess risks and potential migration trends in different regions.[5][6]
  • Agriculture and Forestry: Bibliometric analyses indicate that WorldClim is a key dataset for research into the impacts of climate change on agriculture and forestry.[7][8] It is used to assess crop suitability, forest productivity, carbon sink potential, and the impacts on practices such as conservation agriculture.[9]
  • Conservation Planning and Policy: The data provides a scientific basis for designing adaptive management strategies, selecting protected areas, and developing climate change adaptation frameworks.[10] It is also used in global and regional assessments to support food security goals and to coordinate mitigation and adaptation measures[11][12]

See Also

References

  1. 1.0 1.1 Hijmans, R. J.; Cameron, S. E.; Parra, J. L.; Jones, P. G.; Jarvis, A. (2005). "Very high resolution interpolated climate surfaces for global land areas". International Journal of Climatology. 25 (15): 1965–1978. Bibcode:2005IJCli..25.1965H. doi:10.1002/joc.1276.
  2. 2.0 2.1 2.2 Fick, S. E.; Hijmans, R. J. (2017). "WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas". International Journal of Climatology. 37 (12): 4302–4315. Bibcode:2017IJCli..37.4302F. doi:10.1002/joc.5086.
  3. 3.0 3.1 "WorldClim 2.1 climate data". WorldClim. Retrieved 2025-11-11.
  4. Vacek, Z.; Vacek, S.; Cukor, J. (2023). "European forests under global climate change: Review of tree growth processes, crises and management strategies". Journal of Environmental Management. 332. Bibcode:2023JEnvM.33217353V. doi:10.1016/j.jenvman.2023.117353. PMID 36716544 Check |pmid= value (help). Unknown parameter |article-number= ignored (help)
  5. Shumilo, L.; Skakun, S. (2024). "Optical Flow of Temperature Reveals Climate Change Patterns for Agriculture and Forestry". Remote Sensing Applications: Society and Environment. 34. Bibcode:2024RSASE..3401198S. doi:10.1016/j.rsase.2024.101198. Unknown parameter |article-number= ignored (help)
  6. Tian, X.; Sohnen, B.; Kim, J.; Ohrel, S.; Cole, J. (2016). "Global climate change impacts on forests and markets". Environmental Research Letters. 11 (3): 035011. Bibcode:2016ERL....11c5011T. doi:10.1088/1748-9326/11/3/035011.
  7. Román-Vázquez, J.; Carbonell-Bojollo, R.; Veroz-González, Ó.; Da Silva Piletti, L.; Márquez-García, F.; Cabeza-Ramírez, L.; González-Sánchez, E. (2025). "Global Trends in Conservation Agriculture and Climate Change Research: A Bibliometric Analysis". Agronomy. 15 (1): 249. Bibcode:2025Agron..15..249R. doi:10.3390/agronomy15010249.
  8. Aleixandre-Benavent, R.; Aleixandré-Tudo, J.; Castelló-Cogollos, L.; Aleixandre, J. (2017). "Trends in scientific research on climate change in agriculture and forestry subject areas (2005–2014)". Journal of Cleaner Production. 147: 406–418. Bibcode:2017JCPro.147..406A. doi:10.1016/j.jclepro.2017.01.112.
  9. Su, Y.; Gabrielle, B.; Makowski, D. (2021). "The impact of climate change on the productivity of conservation agriculture". Nature Climate Change. 11 (7): 628–633. Bibcode:2021NatCC..11..628S. doi:10.1038/s41558-021-01075-w.
  10. Vizinho, A.; Avelar, D.; Branquinho, C.; Lourenço, C.; Carvalho, S.; Nunes, A.; Sucena-Paiva, L.; Oliveira, H.; Fonseca, A.; Santos, F.; Roxo, M.; Penha-Lopes, G. (2021). "Framework for Climate Change Adaptation of Agriculture and Forestry in Mediterranean Climate Regions". Land. 10 (2): 161. Bibcode:2021Land...10..161V. doi:10.3390/land10020161.
  11. Loboguerrero, A.; Campbell, B.; Cooper, P.; Hansen, J.; Rosenstock, T.; Wollenberg, E. (2019). "Food and Earth Systems: Priorities for Climate Change Adaptation and Mitigation for Agriculture and Food Systems". Sustainability. 11 (5): 1372. Bibcode:2019Sust...11.1372L. doi:10.3390/su11051372.
  12. Kongsager, R.; Locatelli, B.; Chazarin, F. (2016). "Addressing Climate Change Mitigation and Adaptation Together: A Global Assessment of Agriculture and Forestry Projects". Environmental Management. 57 (2): 271–282. Bibcode:2016EnMan..57..271K. doi:10.1007/s00267-015-0605-y. PMID 26306792.

Category:Scientific databases


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