30 October 2015

Identifying the Human Fingerprint in Observed Cloud Trends


How much the planet warms due to increasing greenhouse gases is critically dependent on how clouds respond.

  • Determine the fingerprint of anthropogenic climate change is detectible in the nearly 30-year ISCCP and PATMOS-x satellite cloud datasets
  • Use climate models to determine when one should expect such responses (the “signal”) to become distinguishable from the “noise” arising from unforced climate variability.
  • Following the technique developed in Marvel & Bonfils (2013), we 1) define indicators of cloud amount C(t), latitude D(t), and height H(t) of five extrema in the zonally averaged total cloud fraction field; and 2) derive the multivariate “fingerprint” that characterizes their coherent response to external forcings.
  • We estimate the time at which a signal of externally forced cloud change emerges from background noise in models and whether the anthropogenic signal is present in observations.

The strength of the forced signal in the PATMOS-x dataset is not compatible with internal climate variability, but is compatible with GCM simulations including anthropogenic forcings.

Kate Marvel
NASA Goddard Institute for Space Studies (GISS)
Marvel, K, M Zelinka, SA Klein, C Bonfils, P Caldwell, C Doutriaux, BD Santer, and KE Taylor.  2015.  "External Influences on Modeled and Observed Cloud Trends."  Journal of Climate 28: 4820-4840, doi:10.1175/JCLI-D-14-00734.1.

CMIP5 data processing was enabled by the CDAT analysis package. The EOF analysis was performed using the eofs software package available from http://ajdawson.github.io/eofs/. This work was supported by the Regional and Global Climate Modeling Program of the U.S. Department of Energy (DOE) Office of Science and was performed under the auspices of the DOE Lawrence Livermore National Laboratory (Contract DE-AC52-07NA27344). KM was supported by a Laboratory Directed Research and Development award (13-ERD-032). CB was supported by the DOE/OBER Early Career Research Program Award SCW1295. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table A1 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.