This study addresses the challenge by undertaking a formal detection and attribution analysis of SCE changes based on several
observational datasets with different structural characteristics, in order to account for the substantial observational uncertainty.
Novel approaches combining
observational datasets with models to improve constraints were particularly well represented....
Not exact matches
This session examined the biogeochemical processes that are likely to affect the evolution of the Earth system over the coming decades,
with a focus on the dynamics of marine and terrestrial ecosystems and the development of improved understanding through (a) fieldwork and laboratory experiments, (b) development of new
observational datasets, both modern and palaeo, and (c) simulations using numerical models.
Then, the function is illustrated on a real
dataset from a nationwide prospective
observational cohort including patients
with inflammatory bowel disease.
Using the SFZ 2008 tar file archive data in combination
with the deep - ocean diagnostic model and control - run data used in SFZ 2008, and a deep - ocean diagnostic
observational trend calculated from the Levitus et al 2005
dataset, I can produce broadly similar climate parameter PDFs to those in the Forest 2006 main results (Figure 2: GSOLSV, κsfc = 16, uniform prior),
with a peak climate sensitivity around S = 3.
b) when used
with the HadCM2 - derived surface control data covariance matrix from the SFZ 2008 data, which I have largely been able to agree to raw data from the HadCM2 AOGCM control run (which data Dr Forest has confirmed was used for the Forest 2006 main results), the CSF 2005 surface model and
observational data produces, irrespective of which upper air and deep - ocean
dataset is used, a strongly peaked PDF for climate sensitivity, centred close to S = 1, not S = 3 as per Forest 2006.
and later: «
With the exception of one SR case (RSS TLT) out of 18, none of the directly - measured observational datasets is consistent with the — best estimate ‖ of the IPCC AR4 [12] model - m
With the exception of one SR case (RSS TLT) out of 18, none of the directly - measured
observational datasets is consistent
with the — best estimate ‖ of the IPCC AR4 [12] model - m
with the — best estimate ‖ of the IPCC AR4 [12] model - mean.
The simulations were evaluated using the spline - interpolated
dataset ANUSPLIN, a daily
observational gridded surface temperature and precipitation product
with a nominal resolution of approximately 10 km.
Which is pretty much exactly what I wrote in my original response
with a few additional details about reconciling the differences between
observational datasets.
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.
Thorne et al. (2007) suggested that the absence of the mid-tropospheric warming might be attributable to uncertainties in the observed record: however, Douglass et al. (2007) responded
with a detailed statistical analysis demonstrating that the absence of the projected degree of warming is significant in all
observational datasets.
The first panel shows the raw «spaghetti» projections,
with different
observational datasets in black and the different emission scenarios (RCPs) shown in colours.