Not exact matches
Morice, C. P., J. J. Kennedy, N. A. Rayner, and P. D. Jones, 2012: Quantifying uncertainties in
global and regional temperature change using an ensemble of
observational estimates: The HadCRUT4
dataset.
Morice, C. P., J. J. Kennedy, N. A. Rayner, and P. D. Jones (2012), Quantifying uncertainties in
global and regional temperature change using an ensemble of
observational estimates: The HadCRUT4
dataset, J. Geophys.
Figure 4:
Global annual mean surface air temperature for CMIP5 (thin red line) for greenhouse gas temperature influences (forcings) compared to the four
observational datasets (black lines).
Figure 3:
Global annual mean surface air temperature for CMIP3 (thin blue line) and CMIP5 (thin red line) for all natural external temperature influences (forcings) compared to the four
observational datasets (black lines).
«Evidence for climate change in the satellite cloud record» «Cloud feedback mechanisms and their representation in
global climate models» «A net decrease in the Earth's cloud, aerosol, and surface 340 nm reflectivity during the past 33 yr (1979 — 2011)» «New
observational evidence for a positive cloud feedback that amplifies the Atlantic Multidecadal Oscillation» «Impact of
dataset choice on calculations of the short - term cloud feedback»
The first set of simulations, referred to as
Global Atmosphere -
Global Ocean (GOGA) experiments, are forced with prescribed SST and sea ice concentrations from the
observational datasets of Hurrell et al. (2008) for 1979 — 2008, with different initial conditions for each ensemble member.
They are perhaps the largest uncertainty in our understanding of climate change, owing to disagreement among climate models and
observational datasets over what cloud changes have occurred during recent decades and will occur in response to
global warming2, 3.