The general modelling techniques used in
the atmospheric dynamical core, and the treatment of unresolved degrees of freedom are fairly standard as a general approach across many different applications of fluid dynamics.
«A study published in 2011 in Geophysical Research Letters on causes of the 2010 Russian heat wave deduced that it «was due to internal
atmospheric dynamical processes» — Paging Al Gore: Peer - reviewed Study: «It is unlikely that the warming attributable to increasing greenhouse gas concentrations contributed significantly to the magnitude of the [Russian] heat wave»
«Analysis of observations indicate that this heat wave was mainly due to internal
atmospheric dynamical processes that produced and maintained a strong and long - lived blocking event, and that similar atmospheric patterns have occurred with prior heat waves in this region.
Lower ice concentrations in 2011 relative to 2007 in late May indicate increased sensitivity of the arctic ice cover to
atmospheric dynamical forcing, with implications for ice transport during summer.
Contributions from the following topics (but not exclusively) are invited: • Solar irradiance and energetic particle impacts on the atmosphere • Upper
atmospheric dynamical variability and coupling between atmospheric layers • Solar variations and stratosphere - troposphere coupling • Solar influence on climate variability • Solar irradiance (spectral and total irradiance) variations
Not exact matches
Meteorologists have long used a similar technique to integrate
atmospheric and oceanic measurements with
dynamical models, allowing them to forecast the weather.
On the theoretical front, these discoveries have triggered extensive research on the formation,
dynamical evolution, interior dynamics, and
atmospheric characteristics of extrasolar habitable planets.
This was accomplished using a stochastic climate model based on the concept that ocean temperature variability is a slow
dynamical system, a red noise signal, generated by integrating stochastic
atmospheric forcing, or white noise71.
This year we received 14 June SIO submissions from
dynamical models, of which 3 were from ice - ocean models forced by
atmospheric reanalysis or other
atmospheric model output and 12 were from fully - coupled general circulation models.
Wang, B., et al., 2004: Design of a new
dynamical core for global
atmospheric models based on some efficient numerical methods.
In sensitivity experiments the influence of removed orography of Greenland on the Arctic flow patterns and cyclone tracks during winter have been determined using a global coupled model and a
dynamical downscaling with the regional
atmospheric model HIRHAM.
The lag between decreases in sea ice extent during late summer and changes in the mid-latitude
atmospheric circulation during other seasons (like autumn and winter, when the recent loss of sea ice is much smaller) have been demonstrated empirically, but have not been captured by existing
dynamical models.
It presents a significant reinterpretation of the region's recent climate change origins, showing that
atmospheric conditions have changed substantially over the last century, that these changes are not likely related to historical anthropogenic and natural radiative forcing, and that
dynamical mechanisms of interannual and multidecadal temperature variability can also apply to observed century - long trends.
Analysis of physical and
dynamical processes that initiate, maintain, and modulate
atmospheric mesoscale phenomena
Bailey, A., J. Nusbaumer, and D. Noone, 2015: Precipitation efficiency derived from isotope ratios in water vapor distinguishes
dynamical and microphysical influences on subtropical
atmospheric constituents.
This mode of intra-annual tropical
atmospheric variability is strongly associated with California rainfall events when its active phase shifts eastward, as is currently being suggested by
dynamical model forecasts.
In turn, temperature change affects
atmospheric water vapor as well as the more
dynamical components of equator - to - pole insolation and of temperature gradients that vary on timescales of decades to hundreds of years.
Lukovich et al. (Centre for Earth Observation Science, U. of Manitoba); 4.6; Heuristic - Dynamics Investigation of
dynamical atmospheric contributions in spring to sea ice conditions in fall, based on comparison of 2011 and 2007 stratospheric and surface winds and sea level pressure (SLP) in April and May suggests regional differences in sea ice extent in fall, in a manner consistent with recent studies highlighting the importance of coastal geometry in seasonal interpretations of sea ice cover (Eisenman, 2010).
It is emergent behviour in a complex
dynamical system characterised by changes in ocean and
atmospheric circulation and consequential changes in cloud radiative forcing.
It involves
dynamical complexity as the underlying mode of climate variability — and persistent modes of ocean and
atmospheric variability including ENSO and the PDO.
Nine contributions stemmed from fully - coupled
dynamical models, and five from ocean - sea ice models forced by
atmospheric reanalyses or
atmospheric model output.
Dynamical effects arising from changes in
atmospheric pressure also play a role in distributing meltwater, as do geostrophic ocean currents that flow along the lines where pressure gradients are counterbalanced by the Coriolis effect associated with the Earth's rotation.
For the July report, we received 14 June SIO submissions from
dynamical models: 5 from ice - ocean models forced by
atmospheric reanalysis or other
atmospheric model output (in green in Figure 3) and 9 from fully coupled general circulation models (in blue in Figure 3).
«Our climate simulations, using a simplified three - dimensional climate model to solve the fundamental equations for conservation of water,
atmospheric mass, energy, momentum and the ideal gas law, but stripped to basic radiative, convective and
dynamical processes, finds upturns in climate sensitivity at the same forcings as found with a more complex global climate model»
This forcing has a particularly strong and direct impact on the surface energy cycle, but interacts with many aspects of the surface and column - integrated water and energy cycles through
dynamical convergence, leading to large diurnal fluctuations in the
atmospheric reservoir of water vapor and total dry energy.
Wang et al. (2012b) force the
dynamical core of an
atmospheric general circulation model with warming in the tropical troposphere that mimics the effects of climate change there.
Our climate simulations, using a simplified three - dimensional climate model to solve the fundamental equations for conservation of water,
atmospheric mass, energy, momentum and the ideal gas law, but stripped to basic radiative, convective and
dynamical processes, finds upturns in climate sensitivity at the same forcings as found with a more complex global climate model [66].
(SW - CLR is related to the distribution of
atmospheric water vapour and aerosol which has a close link to the model
dynamical processes).
Modes or patterns of climate variability - Natural variability of the climate system, in particular on seasonal and longer time scales, predominantly occurs with preferred spatial patterns and time scales, through the
dynamical characteristics of the
atmospheric circulation and through interactions with the land and ocean surfaces.
Multi-decadal regime shift — chaotic — unpredictable — involving abrupt shifts in ocean and
atmospheric circulation — show the
dynamical mechanism at the core of climate on a global scale.
The
dynamical core, physical parameterizations, and basic simulation characteristics of the
atmospheric component AM3 of the GFDL global coupled model CM3.
The first three principal components are statistically separable and can be meaningfully related to important
dynamical features of high - latitude Southern Hemisphere
atmospheric circulation, as defined independently by extrapolar instrumental data
After looking at the various elements of the climate models, they judged that there was little to do with the
dynamical core of the
atmospheric model (that it was quite mature and performing quite well), although there were issues with the parameterizations of convection and the
atmospheric boundary layer.
Reality check, my statement about no standardized tests to evaluate codes for
dynamical cores in
atmospheric models has been misinterpreted.
I refer you to a paper by Leonard Smith, who is somewhat of a guru in the field
dynamical systems, their simulation, and applications to
atmospheric models http://www2.maths.ox.ac.uk/~lenny/PNAS.ps
A baroclinic instability test case for
atmospheric model
dynamical cores.
Currently, there are several EMICs in operation such as: two - dimensional, zonally averaged ocean models coupled to a simple
atmospheric module (e.g., Stocker et al., 1992; Marchal et al., 1998) or geostrophic two - dimensional (e.g., Gallee et al., 1991) or statistical -
dynamical (e.g., Petoukhov et al., 2000)
atmospheric modules; three - dimensional models with a statistical -
dynamical atmospheric and oceanic modules (Petoukhov et al., 1998; Handorf et al., 1999); reduced - form comprehensive models (e.g., Opsteegh et al., 1998) and those that involve an energy - moisture balance model coupled to an OGCM and a sea - ice model (e.g., Fanning and Weaver, 1996).
Please keep unrelated questions on other issues (such as forced
atmospheric models or full climate models) off of this thread so that this manuscript can be used to illuminate the serious and unresolvable problems with numerical approximations of the unforced
dynamical systems.