Sentences with phrase «modes of variability from»

Not exact matches

Now scientists from Kyoto University and UC San Diego have discovered that this phenomenon occurred when the warming phase — «interdecadal variability mode» — of both the Pacific and Atlantic Oceans coincided.

While variability in the age of disease onset and incomplete pedigree information preclude us from drawing any definitive conclusions about the mode of inheritance, the available data is most consistent with an autosomal recessive pattern.

Patterns of variability that don't match the predicted fingerprints from the examined drivers (the «residuals») can be large — especially on short - time scales, and look in most cases like the modes of internal variability that we've been used to; ENSO / PDO, the North Atlantic multidecadal oscillation etc..

One key metric in this debate is the spatial pattern of cooling which may provide a «fingerprint» of the underlying climate change, whether that was externally forced (from solar or volcanic activity) or was part of an intrinsic mode of variability.

And given the inherent unpredictability of the internal modes of climate variability — as distinct from the external control imposed by the external drivers of climate, which themselves are also uncertain — such attribution statements will always be subject to uncertainty and therefore probabilistic.

The CO2 flux variability from the longest inversion correlates with the Southern Annular Mode (SAM), an index of the dominant mode of atmospheric variability in the Southern Ocean.

The NAO is the dominant mode of winter climate variability in the North Atlantic region ranging from central North America to Europe and much into Northern Asia.

They constructed a numerical network model from 4 observed ocean and climate indices — ENSO, PDO, the North Atlantic Oscillation (NAO) and the Pacific Northwest Anomaly (PNA)-- thus capturing most of the major modes of climate variability in the period 1900 — 2000.

A new study reconstructs a century - long South Atlantic Meridional Overturning Circulation index, from 1870 to present, finding it is highly correlated to the observational - based SAMOC time series and the Interdecadal Pacific Oscillation is the leading mode of variability.

The North Atlantic Oscillation (NAO), the dominant mode of atmospheric circulation variability over the North Atlantic / European sector, is a leading governor of wintertime climate fluctuations in Europe, the Mediterranean, parts of the Middle East and eastern North America over a wide range of time scales from intra-seasonal to multi-decadal (e.g., Hurrell 1995; Hurrell et al. 2003).

«The authors write that North Pacific Decadal Variability (NPDV) «is a key component in predictability studies of both regional and global climate change,»... they emphasize that given the links between both the PDO and the NPGO with global climate, the accurate characterization and the degree of predictability of these two modes in coupled climate models is an important «open question in climate dynamics» that needs to be addressed... report that model - derived «temporal and spatial statistics of the North Pacific Ocean modes exhibit significant discrepancies from observations in their twentieth - century climate... conclude that «for implications on future climate change, the coupled climate models show no consensus on projected future changes in frequency of either the first or second leading pattern of North Pacific SST anomalies,» and they say that «the lack of a consensus in changes in either mode also affects confidence in projected changes in the overlying atmospheric circulation.»»

Moreover, 370 years of tropical cyclone data from the Lesser Antilles (the eastern Caribbean island chain that bisects the main development region for landfalling U.S. hurricanes) show no long - term trend in either power or frequency but a 50 - to 70 - year wave pattern associated with the Atlantic Multidecadal Oscillation, a mode of natural climate variability.

Removing the influence of two major modes of natural internal variability (the Arctic Oscillation and Pacific Decadal Oscillation) from observations further improves attribution results, reducing the model - observation discrepancy in cold extremes.

From the paper: Over the whole globe, the dominant spatial mode of variability in OHC in the upper 300 m [as shown by the first empirical orthogonal function (EOF), which explains the most variance], occurs mainly in the tropical Pacific and has the structure of ENSO variability (Fig. 4, A and B).

The researchers from ETH Zurich found that the differences could be related to the modes of climate variability in the Pacific and Atlantic.

Some examples from energy balance model calculations indicate that: (1) solar variability has a near - global response, with the amplitude of response slightly larger over land; (2) volcanism has a proportionately larger amplitude of response over land than over ocean; and (3) the most oft - cited mode of internal variability, changes in the North Atlantic thermohaline circulation, has a hemispheric asymmetry in response.

These range from simple averaging of regional data and scaling of the resulting series so that its mean and standard deviation match those of the observed record over some period of overlap (Jones et al., 1998; Crowley and Lowery, 2000), to complex climate field reconstruction, where large - scale modes of spatial climate variability are linked to patterns of variability in the proxy network via a multivariate transfer function that explicitly provides estimates of the spatio - temporal changes in past temperatures, and from which large - scale average temperature changes are derived by averaging the climate estimates across the required region (Mann et al., 1998; Rutherford et al., 2003, 2005).

This is very different from standard climate modelling where no attempt is made to synchronise modes of internal variability with the real world.

My favorite quote from that paper is: «Because ENSO is the dominant mode of climate variability at interannual time scales, the lack of consistency in the model predictions of the response of ENSO to global warming currently limits our confidence in using these predictions to address adaptive societal concerns, such as regional impacts or extremes (Joseph and Nigam 2006; Power et al. 2006).»