Abstract
Compound climate extremes are receiving increasing attention because of their disproportionate impacts on humans and ecosystems. However, risks assessments generally focus on univariate statistics. We analyze the co-occurrence of hot and dry summers and show that these are correlated, inducing a much higher frequency of concurrent hot and dry summers than what would be assumed from the independent combination of the univariate statistics. Our results demonstrate how the dependence structure between variables affects the occurrence frequency of multivariate extremes. Assessments based on univariate statistics can thus strongly underestimate risks associated with given extremes, if impacts depend on multiple (dependent) variables. We conclude that a multivariate perspective is necessary to appropriately assess changes in climate extremes and their impacts and to design adaptation strategies.
| Original language | English |
|---|---|
| Article number | e1700263 |
| Journal | Science advances |
| Volume | 3 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 1 Jun 2017 |
| Externally published | Yes |
Funding
We thank the World Climate Research Programme’s Working Group on Coupled Modeling, which is responsible for CMIP, and we thank the climate modeling groups (listed in table S1 of this paper) for producing and making available their model output. For CMIP, the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. Funding: We acknowledge partial funding from the European Research Council DROUGHT-HEAT project funded by the European Community’s Seventh Framework Programme (grant agreement FP7-IDEAS-ERC-617518).
| Funders | Funder number |
|---|---|
| Seventh Framework Programme | 617518 |
| European Research Council | |
| Seventh Framework Programme |