15 June 2015

Identifying the Human Fingerprint in Observed Cloud Trends

Signal-to-noise ratio of the multivariate anthropogenic fingerprint. Values of S/N that lie outside the gray noise envelope are incompatible with internal variability (anthro signal is detected). If the observed S/N (circle for ISCCP and square for PATMOS-x) lies within the 95% distributions estimated from forced models (horiz. lines), it can reasonably be attributed to external forcing.

How much the planet warms due to increasing greenhouse gases is critically dependent on how clouds respond.  Climate models robustly predict several changes in gross cloud properties under global warming: a reduction in low-level clouds at low and mid-latitudes, an increase in high cloud altitude at all latitudes, and a poleward shift of clouds in both hemispheres. It is natural to ask whether any or all of these cloud responses to anthropogenic forcing are apparent in long-term satellite cloud records, and – more fundamentally – when one should expect such responses (the “signal”) to become distinguishable from the “noise” arising from unforced climate variability. 


In this study DOE funded researchers performed the first formal detection and attribution analysis on cloud trends in order to investigate whether one can detect the fingerprint of anthropogenic climate change in the nearly 30-year ISCCP and PATMOS-x satellite cloud datasets. They defined indices that measure the latitude, total cloud amount, and altitude of high clouds at the five extrema in the zonally averaged total cloud fraction field, and used them to derive a multivariate “fingerprint” that characterizes their coherent response to external forcing. The researchers estimated the time at which a signal of externally forced cloud change emerges from background noise in models, and whether the anthropogenic fingerprint is becoming more apparent in the observed cloud record.


The authors demonstrate that employing a multivariate approach allows one to detect the anthropogenic signal much more quickly than if one tracks cloud properties in isolation.  Given perfect satellite cloud observations beginning in 1983, the models indicate that a detectable multivariate signal should have emerged by 2010. In contrast, the signal of poleward cloud shifts (taken in isolation) does not emerge from the noise until 2063, on average.  Despite significant observational uncertainties, they found that the multivariate signal of externally forced change is present in both ISCCP and PATMOS-x.  In PATMOS-x, the signal is detectable and attributable to external forcing at the 95% confidence level. That is, the strength of the forced signal in the PATMOS-x dataset is not compatible with internal climate variability (as determined from the unforced control runs), but is compatible with GCM simulations including anthropogenic forcings (as determined from splicing together historical and RCP8.5 experiments). 

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.  https://doi.org/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 DEAC52-07NA27344). 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.