4 why
There is no much fluctuations in the
highest and lowest temperature?
Answers
Answer:
Explanation:
Temperature fluctuations in a
changing climate: an ensemblebased experimental approach
Miklós Vincze1,2, Ion Dan Borcia3 & Uwe Harlander3
There is an ongoing debate in the literature about whether the present global warming is increasing
local and global temperature variability. The central methodological issues of this debate relate to the
proper treatment of normalised temperature anomalies and trends in the studied time series which
may be difficult to separate from time-evolving fluctuations. Some argue that temperature variability is
indeed increasing globally, whereas others conclude it is decreasing or remains practically unchanged.
Meanwhile, a consensus appears to emerge that local variability in certain regions (e.g. Western Europe
and North America) has indeed been increasing in the past 40 years. Here we investigate the nature of
connections between external forcing and climate variability conceptually by using a laboratory-scale
minimal model of mid-latitude atmospheric thermal convection subject to continuously decreasing
‘equator-to-pole’ temperature contrast ΔT, mimicking climate change. The analysis of temperature
records from an ensemble of experimental runs (‘realisations’) all driven by identical time-dependent
external forcing reveals that the collective variability of the ensemble and that of individual realisations
may be markedly different – a property to be considered when interpreting climate records.
To quantify connections between climate change and the temporal variability of a climate index the typical
procedure researchers follow is comparing its recently observed fluctuations to those from a base period1–9
.
This approach is inherently built on the naïve assumption of ergodicity, a property that does not apply to
far-from-equilibrium processes. In ‘climate-like’ nonlinear, evolving systems the only way to acquire appropriate
expectation values– as ‘climate is what you expect, weather is what you get’10– would be ensemble averaging over
a multitude of parallel realisations of the system’s response to the same time-dependent forcing, all obeying the
same physical laws and differing only in their initial conditions. It is to be emphasized that differences between
the ensemble members represent an inherent property of the problem, internal variability, and cannot only be
associated with ‘measurement errors’. The ensemble average of the paths of such parallel realisations in the space
of essential variables would then trace out a time-evolving, so-called snapshot- or pullback- chaotic attractor11, 12.
It seems quite appropriate to adapt this approach to the description of any highly nonlinear chaos-like process,
like e.g. turbulence.
The concept’s applicability in climatology has been demonstrated in numerical models ranging from minimal models12–14 to intermediate complexity GCMs15, concluding that the snapshot attractor framework provides the only self-consistent definition of ‘climate’ from the dynamical systems point of view. Obviously, for the
actual Earth system only a single observable realisation exists but experiments in a laboratory characterised by
‘climate-like’ externally forced dynamics can be repeated multiple times and thus provide a real world test-bed for
this approach, whose evaluation has so far been limited to numerical investigations.
The tabletop-size rotating, differentially heated annular wave tank we use for this purpose is a widely studied
experimental minimal model of the mid-latitude Earth system16–19 (Fig. 1a, Methods). It captures the two essential components of large-scale atmospheric circulation: lateral (‘meridional’) temperature difference and rotation.