Regional
climate model bias correction improved the estimates on changes to future mean runoff
Regional
climate model bias correction improved the estimates on changes to future mean runoff
Firstly,
climate model biases are still substantial, and may well be systemically related to the use of deterministic bulk - formula closure - this is an area where a much better basic understanding is needed.
Not exact matches
«Being based on
climate records, this approach avoids any
biases that might affect the sophisticated computer
models that are commonly used for understanding global warming.»
A warm
bias in sea surface temperature in most global
climate models is due to a misrepresentation of the coastal separation position of the Gulf Stream, which extends too far north of Cape Hatteras, North Carolina.
Using 19
climate models, a team of researchers led by Professor Minghua Zhang of the School of Marine and Atmospheric Sciences at Stony Brook University, discovered persistent dry and warm
biases of simulated
climate over the region of the Southern Great Plain in the central U.S. that was caused by poor
modeling of atmospheric convective systems — the vertical transport of heat and moisture in the atmosphere.
A main point in conducting the experiments was to show that
climate models contain a
bias that could be corrected.
The
models are all over the place, and most
climate models yield patterns with some
bias either in geographical character, amplitude or time scales.
In fact, it increasingly appears that, if there is any systemic
bias in the
climate models, it's that they understate the gravity of the situation.
In order to do
model versus proxy comparisons, it is necessary to understand how proxies filter and reflect the
climate system, and to account of those processes and any potential
biases.
Large warm
bias can hinder
models» fidelity of
climate simulations and their future projections.
Researchers proposed a framework that holds promise for addressing several challenges and
biases in
climate models related to cloud size, interactions among clouds, and evolution of clouds over time.
Scientists at Lawrence Livermore National Laboratory within the Atmospheric, Earth, and Energy Division, along with collaborators from the U.K. Met Office and other
modeling centers around the world, organized an international multi-
model intercomparison project, name CAUSES (Clouds Above the United States and Errors at the Surface), to identify possible causes for the large warm surface air temperature
bias seen in many weather forecast and
climate model simulations.
«By comparing the response of clouds and water vapor to ENSO forcing in nature with that in AMIP simulations by some leading
climate models, an earlier evaluation of tropical cloud and water vapor feedbacks has revealed two common
biases in the
models: (1) an underestimate of the strength of the negative cloud albedo feedback and (2) an overestimate of the positive feedback from the greenhouse effect of water vapor.
«Given the current uncertainties in both the reconstruction and
model sensitivity, however, this
model - data discrepancy could be attributed to either the seasonal
bias in the SST reconstructions or the
model bias in regional and seasonal
climate sensitivity.
Crichton seems unaware that the discussion of
climate model validation is a common feature of publications utilizing these
models and
model errors and
biases are often explicitly quantified and described.
Hence the
biases in
climate models in reproducing the basic conditions ripe for tornadoes is not as well reproduced as one would like, and how one «corrects» for those
biases (downscales) is subject to problems.
Several other groups have evaluated the impact of coupling specific
models of carbon to
climate models but clear results are difficult to obtain because of inevitable
biases in both the terrestrial and atmospheric modules (e.g., Delire et al., 2003).
Others include, the role of the Sun (being the main heat source), the vast oceans which cover over 70 % of the Earth's surface (and the natural factors which determine the storage and release of CO2 back into the atmosphere), water - vapour being the dominant greenhouse gas comprising 98 % of the atmosphere, the important role of low - level clouds which is thought to be a major factor in determining the natural variation of
climate temperatures (P.S. Significantly, computer -
models are unable to replicate cloud - formation and coverage — which again — injects
bias into
model).
Of course, nobody is going to point out that the «
bias» is against a bunch of worthless rubbish
climate models.
Stick to your
climate fiction and religious affinity, but do nt bother us with your patological convictions based on
biased climate computer
models which is spurious and swamped with wishful thinking.
IN this case, Judith's explains her own «
bias» (what could be fairer that that) thusly: «my reasoning is weighted heavily in favor of observational evidence and understanding of natural internal variability of the
climate system, whereas the IPCC's reasoning is weighted heavily in favor of
climate model simulations and external forcing of
climate change.»
This Nature
Climate Change paper concluded, based purely on simulations by the GISS - E2 - R climate model, that estimates of the transient climate response (TCR) and equilibrium climate sensitivity (ECS) based on observations over the historical period (~ 1850 to recent times) were bias
Climate Change paper concluded, based purely on simulations by the GISS - E2 - R
climate model, that estimates of the transient climate response (TCR) and equilibrium climate sensitivity (ECS) based on observations over the historical period (~ 1850 to recent times) were bias
climate model, that estimates of the transient
climate response (TCR) and equilibrium climate sensitivity (ECS) based on observations over the historical period (~ 1850 to recent times) were bias
climate response (TCR) and equilibrium
climate sensitivity (ECS) based on observations over the historical period (~ 1850 to recent times) were bias
climate sensitivity (ECS) based on observations over the historical period (~ 1850 to recent times) were
biased low.
These systems likely contribute to an observed regional trend of increasing extreme rainfall, and poor prediction of them likely contributes to a warm, dry
bias in
climate models downstream of the Sierras de Córdoba in a key agricultural region.
Instead of involving a choice of whether to keep or discard an observation based upon a prior expectation, we hypothesize that this selection
bias involves the «survival» of
climate models from generation to generation, based upon their warming rate.
Levine, R.C., Turner, A.G., Marathayil, D. and Martin, G.M. (accepted Dec 2012), The role of northern Arabian Sea surface temperature
biases in CMIP5
model simulations and future predictions of Indian summer monsoon rainfall, in press,
Climate Dynamics., DOI 10.1007 / s00382 -012-1656-x link
For this purpose, we instructed them to indicate their level of agreement or disagreement with statements such as «the scientists who did the study were
biased,» «computer
models like those relied on in the study are not a reliable basis for predicting the impact of CO2 on the
climate,» and «more studies must be done before policymakers rely on the findings» of the study etc..
I show that cloud error is highly correlated among CMIP5
climate models, which implies that the (+ / --RRB- 4 W / m ^ 2 reflects a theory -
bias.
To use just one example, consider
climate models and the
bias in experimental design.
In fact, it increasingly appears that, if there is any systemic
bias in the
climate models, it's that they understate the gravity of the situation.
My impression from outside is that the statistical analyses are weak, the
climate models are simplistic and overinfluenced by selection and publication
biases, the theoretic underpinning is extraordinarily shakey and the belief engine is overrevved with the popularity of certain «star performers» and the Romantic desire for a Paradise Lost that never existed.
The author's points on non-linearity and time delays are actually more relevant to the discussion in other presentations when I talked about whether the
climate models that show high future sensitivities to CO2 are consistent with past history, particularly if warming in the surface temperature record is exaggerated by urban
biases.
I note that 1) they can't easily go back and re-run all the
climate model simulations with more accurate forcings, and 2) if they did, the
climate models would be
biased much too high relative to measured temperatures over the last decade, effectively proving that the
modeled climate sensitivities are too high.
Psychologists studying
climate communication make two additional (and related) points about why the warming - snow link is going to be exceedingly difficult for much of the public to accept: 1) people's confirmation
biases lead them to pay skewed attention to weather events, in such a way as to confirm their preexisting beliefs about
climate change (see p. 4 of this report); 2) people have mental
models of «global warming» that tend to rule out wintry impacts.
Consequently, Callendar's error or
bias has been a basis for accumulating other errors and
biases in modern - day
climate models.
«You can't fix the
climate model simulation via «
bias removal» — you should fix what is wrong with the
model physics,» he said in an email.
A consolidated estimate of ocean surface fluxes based on multiple reanalyses also helps understand
biases in ENSO predictions and simulations from
climate models.
His
climate investigations are conducted in the limited spare time available to a parent, and are currently focussed in two areas; coverage
bias in the instrumental temperature record, and simple response - function
climate models.
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.
By 2015 and especially 2020, it will be obvious to anyone with a brain that the Alarmists have got it wrong, as the
climate continues not to play along with their simplistic,
biased computer
models.
Research interests: Physics - dynamics interaction and multi-scale physics in
climate models, double ITCZ
bias dynamics, coupled ITCZ shift dynamics.
``... since uncertainty is a structural component of
climate and hydrological systems, Anagnostopoulos et al. (2010) found that large uncertainties and poor skill were shown by GCM predictions without
bias correction... it can not be addressed through increased
model complexity....
(3) Satellite temperature data says that
climate models are warming too fast in the troposphere, therefore
model projections of precipitation change are systematically
biased.
«When initialized with states close to the observations,
models «drift» towards their imperfect climatology (an estimate of the mean
climate), leading to
biases in the simulations that depend on the forecast time.
A recent study by Cowtan et al. (paper here) suggests that accounting for these
biases between the global temperature record and those taken from
climate models reduces the divergence in trend between
models and observations since 1975 by over a third.
I have been worrying that even common properties of all present
climate models and
models than can be developed in near future may common
bias towards such stability that is not necessarily true for the real Earth system.
Second, results from
climate model simulations are not evidence of anything but the
biases, beliefs, and mistakes of the programmers.
And in that sense, a
climate model is nothing more or less than a conceptual
model, necessary to give a frame of reference to the state of the real world, which can never be generated by observations alone (which are incomplete, may be inconsistent, contradicting each other,
biased, non-representative,...).
Could unrecognized systemic
bias from excluded or unrecognized physics be causing the major disconnect between observations of
climate sensitivity and projections from global
climate models?
Most of the theoretical claims embodied in the
climate models are based on empirical evidence:
biases, beliefs and mistakes are only a subset of those.