Responses of midlatitude blocks and wave amplitude to changes in the meridional temperature gradient in an idealized dry GCM

This paper examines the physical processes controlling how synoptic midlatitude temperature variability near the surface changes with climate. Because synoptic temperature variability is primarily generated by advection, it can be related to mean potential temperature gradients and mixing lengths near the surface. Scaling arguments show that the reduction of meridional potential temperature gradients that accompanies polar amplification of global warming leads to a reduction of the synoptic temperature variance near the surface. This is confirmed in simulations of a wide range of climates with an idealized GCM. In comprehensive climate simulations (CMIP5), Arctic amplification of global warming similarly entails a large-scale reduction of the near-surface temperature variance in Northern Hemisphere midlatitudes, especially in winter. The probability density functions of synoptic near-surface temperature variations in midlatitudes are statistically indistinguishable from Gaussian, both in reanalysis data and in a range of climates simulated with idealized and comprehensive GCMs. This indicates that changes in mean values and variances suffice to account for changes even in extreme synoptic temperature variations. Taken together, the results indicate that Arctic amplification of global warming leads to even less frequent cold outbreaks in Northern Hemisphere winter than a shift toward a warmer mean climate implies by itself.

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“…3c). This is consistent with what is seen in other idealized GCM simulations (Schneider and Walker 2008;Hassanzadeh et al 2014). …”

Section: Idealized Gcm Simulationssupporting

confidence: 93%

“…We did not examine specifically how the frequency of blocking episodes changes with climate (cf. Liu et al 2012;Hassanzadeh et al 2014). However, our analyses suggest they do not modify synoptic potential temperature variations and/or their departures from Gaussian statistics substantially.…”

Section: Summary and Discussionmentioning

confidence: 60%

Physics of Changes in Synoptic Midlatitude Temperature Variability

2015

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“…Results suggest that the observed blocking-NAM relationship is a correlation which does not imply that the mean-state of the negative phase of NAM causes more blocking. These findings have important implications for the ongoing debate on the linkage between Arctic Amplification and the midlatitude weather extremes (see, e.g., Hassanzadeh et al 2014;Barnes and Screen 2015).…”

Section: Applications Of the Lrf And Efmmentioning

confidence: 82%

The Linear Response Function of an Idealized Atmosphere. Part I: Construction Using Green’s Functions and Applications

Hassanzadeh

Kuang

2016

Journal of the Atmospheric Sciences

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A linear response function (LRF) determines the mean-response of a nonlinear climate system to weak imposed forcings, and an eddy flux matrix (EFM) determines the eddy momentum and heat flux responses to mean-flow changes. Neither LRF nor EFM can be calculated from first principles due to the lack of a complete theory for turbulent eddies. Here the LRF and EFM for an idealized dry atmosphere are computed by applying numerous localized weak forcings, one at a time, to a GCM with Held-Suarez physics and calculating the mean-responses. The LRF and EFM for zonally-averaged responses are then constructed using these forcings and responses through matrix inversion. Tests demonstrate that LRF and EFM are fairly accurate. Spectral analysis of the LRF shows that the most excitable dynamical mode, the neutral vector, strongly resembles the model's Annular Mode. The framework described here can be employed to compute the LRF/EFM for zonally-asymmetric responses and more complex GCMs. The potential applications of the LRF/EFM constructed here are i) forcing a specified mean-flow for hypothesis-testing, ii) isolating/quantifying the eddyfeedbacks in complex eddy-mean flow interaction problems, and iii) evaluating/improving more generallyapplicable methods currently used to construct LRFs or diagnose eddy-feedbacks in comprehensive GCMs or observations. As an example for iii, in Part 2, the LRF is also computed using the fluctuation-dissipation theorem (FDT), and the previously-calculated LRF is exploited to investigate why FDT performs poorly in some cases. It is shown that dimension-reduction using leading EOFs, which is commonly used to construct LRFs from the FDT, can significantly degrade the accuracy due to the non-normality of the operator.

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“…Dipolar configurations of blocking and surface cyclones are often associated with near-surface wind extremes 5 , and blocking can act as a precursor to heavy precipitation events downstream 2 . However, potential future changes of the frequency and location of blocking and related weather extremes are still largely uncertain 8,9,10 . Furthermore, climate models typically exhibit biases in the representation of blocking 18 and weather prediction models sometimes fail in accurately forecasting blocking onset, maintenance or decay 19 .…”

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confidence: 99%