From this experiment we hoped to achieve a better understanding of the range of
uncertainty in climate models due to the parameters in the sulphur cycle.
Not exact matches
Much of the
uncertainty in projections of global
climate change is
due to the complexity of clouds, aerosols, and cloud - aerosol interactions, and the difficulty of incorporating this information into
climate models.
Due to the complexity of physical processes,
climate models have
uncertainties in global temperature prediction.
Furthermore, all approaches that use the
climate's time evolution attempt to account for
uncertainty due to internal
climate variability, either by bootstrapping (Andronova and Schlesinger, 2001), by using a noise
model in fingerprint studies whose results are used (Frame et al., 2005) or directly (Forest et al., 2002, 2006).
So, of course there are
uncertainties in the findings, as
in any attribution and detection result, there is a remaining chance that the observed change is
due to internal
climate variability (5 - ish %) particularly if the
models would underestimate that variability.
In addition, there are numerous uncertainties in the climate models themselves, due to the challenge of numerically simulating all relevant aspects of the climate system over long timescales of decades to centurie
In addition, there are numerous
uncertainties in the climate models themselves, due to the challenge of numerically simulating all relevant aspects of the climate system over long timescales of decades to centurie
in the
climate models themselves,
due to the challenge of numerically simulating all relevant aspects of the
climate system over long timescales of decades to centuries.
Lyman and colleagues combined different ocean monitoring groups» data sets, taking into account different sources of bias and
uncertainty —
due to researchers using different instruments, the lack of instrument coverage
in the ocean, and different ways of analyzing data used among research groups — and put forth a warming rate estimate for the upper ocean that it is more useful
in climate models.
These NAO «book - ends» provide an estimate of the 5 — 95 % range of
uncertainty in projected trends
due to internal variability of the NAO based on observations superimposed upon
model estimates of human - induced
climate change.
The aim of this project is to examine much more exhaustively than has previously been possible the
climate uncertainties due to
uncertainties in how to
model land surface effects.
If one uses the historical record of warming to help tune your
climate model, you are assuming that 100 % of warming is
due to the forcing we know about (with a great deal of
uncertainty in the case of aerosols).
Uncertainties in forcings and
in climate models» temperature responses to individual forcings and difficulty
in distinguishing the patterns of temperature response
due to GHGs and other anthropogenic forcings prevent a more precise quantification of the temperature changes attributable to GHGs.