Arctic 2050: better forecasts of near-future Arctic climate change
Lead Research Organisation:
UNIVERSITY OF EXETER
Department Name: Mathematics and Statistics
Abstract
Arctic climate has changed profoundly over the last four decades: the Arctic has warmed four times faster than the global average; the area covered by sea ice has dramatically shrunk; and the proportion of ice surviving more than one year is reduced. These trends are projected by state-of-the-art climate models to continue over the coming decades. However, the uncertainties associated with these projections are very large. CMIP6 models suggest that the Arctic warming experienced over the next 30 years could span the range of 1C to 6C. These uncertainties arise from imperfect models, the lack of perfect knowledge about future emissions, and internal climate variability. The overarching aim of Arctic 2050 is to systematically reduce these uncertainties, which would enable regional stakeholders and Arctic communities to develop robust plans for dealing with near-term climate change. Other research questions include: (i) how much of near-term Arctic climate change is unavoidable? (ii) relatedly, is the occurrence of an ice-free Arctic summer by mid-century inevitable as has been recently suggested? (iii) which observed features of the climate system give us predictability for the coming decades in the Arctic? and (iv) what is the best way to convey results to policymakers and regional stakeholders? To provide more precise near-term projections of Arctic climate change, the student will analyse comprehensive climate model output from the CMIP6 archive as well as leveraging the new generation of large ensembles. Throughout the project, the student will use novel approaches to constrain the climate projections, including advanced statistical techniques and a complementary process-based emergent constraint framework, as well as applying data visualisation strategies and working extensively with state-of-the-art climate model data. One approach for conveying results to policymakers and stakeholders will involve constructing climate storylines.
People |
ORCID iD |
| Chun (Anthony) Chan (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| NE/S007504/1 | 30/09/2019 | 30/11/2028 | |||
| 2859479 | Studentship | NE/S007504/1 | 30/09/2023 | 30/03/2027 | Chun (Anthony) Chan |