Climate Change Detection and Attribution: Beyond Mean Temperature Signals

Author:

Hegerl Gabriele C.1,Karl Thomas R.2,Allen Myles3,Bindoff Nathaniel L.4,Gillett Nathan5,Karoly David6,Zhang Xuebin7,Zwiers Francis8

Affiliation:

1. Division of Earth and Ocean Sciences, Nicholas School for the Environment and Earth Sciences, Duke University, Durham, North Carolina

2. NOAA/National Climatic Data Center, Asheville, North Carolina

3. Climate Dynamics Group, Atmospheric, Oceanic and Planetary Physics, Department of Physics, University of Oxford, Oxford, United Kingdom

4. Antarctic Climate and Ecosystems Cooperative Research Centre, and CSIRO Marine Research, University of Tasmania, Hobart, Tasmania, Australia

5. Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, United Kingdom

6. School of Meteorology, University of Oklahoma, Norman, Oklahoma

7. Climate Monitoring and Data Interpretation Division, Meteorological Service of Canada, Downsview, Ontario, Canada

8. Canadian Centre for Climate Modelling and Analysis, Meteorological Service of Canada, Victoria, British Columbia, Canada

Abstract

Abstract A significant influence of anthropogenic forcing has been detected in global- and continental-scale surface temperature, temperature of the free atmosphere, and global ocean heat uptake. This paper reviews outstanding issues in the detection of climate change and attribution to causes. The detection of changes in variables other than temperature, on regional scales and in climate extremes, is important for evaluating model simulations of changes in societally relevant scales and variables. For example, sea level pressure changes are detectable but are significantly stronger in observations than the changes simulated in climate models, raising questions about simulated changes in climate dynamics. Application of detection and attribution methods to ocean data focusing not only on heat storage but also on the penetration of the anthropogenic signal into the ocean interior, and its effect on global water masses, helps to increase confidence in simulated large-scale changes in the ocean. To evaluate climate change signals with smaller spatial and temporal scales, improved and more densely sampled data are needed in both the atmosphere and ocean. Also, the problem of how model-simulated climate extremes can be compared to station-based observations needs to be addressed.

Publisher

American Meteorological Society

Subject

Atmospheric Science

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