Understanding global climate change scenarios through bioclimate stratification
Bibliographic Data
| ID | 15545241 |
|---|---|
| Authors | Andreas Diomedes Soteriades (0000-0002-5992-9602, University of Edinburgh), Dave Murray-Rust (0000-0001-6098-7861, University of Edinburgh), Antonio Trabucco (0000-0002-0743-3680, CMCC Foundation - Euro-Mediterranean Center on Climate Change), Patrick Metzger (0000-0002-5119-5894, University of Edinburgh, corresponding author) |
| Year | 2017 |
| Volume | 12 |
| Issue | 8 |
| Pages | 084002-084002 |
| Publication date | 2017-07-21 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environmental Research Letters (JOURNAL) |
| Journal identifiers | ISSN: 1748-9326 • E-ISSN: 1748-9326 |
| Publisher | IOP Publishing (PUBLISHER • GB) |
| DOI | 10.1088/1748-9326/aa7689 |
| OpenAlex | W2724177090 |
| Language | EN |
| Citations received | 2 |
| References cited | 59 |
Despite progress in impact modelling, communicating and understanding the implications of climatic change projections is challenging due to inherent complexity and a cascade of uncertainty. In this letter, we present an alternative representation of global climate change projections based on shifts in 125 multivariate strata characterized by relatively homogeneous climate. These strata form climate analogues that help in the interpretation of climate change impacts. A Random Forests classifier was calculated and applied to 63 Coupled Model Intercomparison Project Phase 5 climate scenarios at 5 arcmin resolution. Results demonstrate how shifting bioclimate strata can summarize future environmental changes and form a middle ground, conveniently integrating current knowledge of climate change impact with the interpretation advantages of categorical data but with a level of detail that resembles a continuous surface at global and regional scales. Both the agreement in major change and differences between climate change projections are visually combined, facilitating the interpretation of complex uncertainty. By making the data and the classifier available we provide a climate service that helps facilitate communication and provide new insight into the consequences of climate change
Categorical variable · Climate change · Climate change scenario · Climate model · Climatology · Environmental resource management · Global change · Homogeneous · Machine learning · Multivariate statistics · Climate change impacts on agriculture · Climate variability and models · Computer Science · Environmental Science · Mathematics · Species Distribution and Climate Change · Geology
Very high resolution interpolated climate surfaces for global land areas
The Weka data mining software
Random Forests for Classification in Ecology
Climate change threats to plant diversity in Europe
Ecosystem Service Supply and Vulnerability to Global Change in Europe
The representative concentration pathways
The carbon balance of terrestrial ecosystems in China
Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model
Random Forests
Observed and Projected Future Shifts of Climatic Zones in Europe and Their Use to Visualize Climate Change Information
Historic and future increase in the global land area affected by monthly heat extremes
Coupled impacts of climate and land use change across a river–lake continuum
Global temperature evolution 1979–2010
Combining biodiversity modeling with political and economic development scenarios for 25 EU countries
Future cereal production in China
| Unique citing works | 2 |
|---|---|
| Citations per year | 0,29 |
| Citation span | 2019 - 2021 (3) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 2 |