Detecting anthropogenic influence with a multi‐model ensemble

By N. Gillett, F. Zwiers, A. Weaver, G. Hegerl, Myles Allen, P. Stott. 2002

108 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Evolution, Behavior and Systematics, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/0667ad41905c49105977b6261626f93196b8fe03

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Reto Knutti
2. C. Tebaldi
3. Gab Abramowitz
4. L. Brunner
5. R. Lorenz
6. B. Sanderson
7. Qingyun Duan
8. A. Merrifield

In Global and Planetary Change

1. Reto Knutti
2. C. Tebaldi
3. Gab Abramowitz
4. R. Lorenz
5. B. Sanderson
6. L. Brunner
7. Qingyun Duan
8. A. Merrifield

Citations in the corpus

• The use of the multi-model ensemble in probabilistic climate projections (2007, background): “Improvements in the performance of a multi-model mean over single models were also found when detecting and attributing greenhouse gas warming and sulphate cooling patterns in the observed climate record (Gillett et al. 2002).”