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Register a new userHigh solar cycle spectral variations inconsistent with stratospheric ozone observations
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Some of the natural variability in climate is understood to come from changes in the Sun. A key route whereby the Sun may influence surface climate is initiated in the tropical stratosphere by the absorption of solar ultraviolet (UV) radiation by ozone, leading to a modification of the temperature and wind structures and consequently to the surface through changes in wave propagation and circulation. While changes in total, spectrally-integrated, solar irradiance lead to small variations in global mean surface temperature, the `top-down UV effect preferentially influences on regional scales at mid-to-high latitudes with, in particular, a solar signal noted in the North Atlantic Oscillation (NAO). The amplitude of the UV variability is fundamental in determining the magnitude of the climate response but understanding of the UV variations has been challenged recently by measurements from the SOlar Radiation and Climate Experiment (SORCE) satellite, which show UV solar cycle changes up to 10 times larger than previously thought. Indeed, climate models using these larger UV variations show a much greater response, similar to NAO observations. Here we present estimates of the ozone solar cycle response using a chemistry-climate model (CCM) in which the effects of transport are constrained by observations. Thus the photolytic response to different spectral solar irradiance (SSI) datasets can be isolated. Comparison of the results with the solar signal in ozone extracted from observational datasets yields significantly discriminable responses. According to our evaluation the SORCE UV dataset is not consistent with the observed ozone response whereas the smaller variations suggested by earlier satellite datasets, and by UV data from empirical solar models, are in closer agreement with the measured stratospheric variations. Determining the most appropriate SSI variability to apply in models...
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Using the non-linear mean-field dynamo models we calculate the magnetic cycle parameters, like the dynamo cycle period, the amplitude of the total magnetic energy, and the Poynting flux luminosity from the surface for the solar analogs with rotation periods of range from 1 to 30 days. We do simulations both for the kinematic and non-kinematic dynamo models. The kinematic dynamo models, which take into account the non-linear $alpha$-effect and the loss of the magnetic flux due to magnetic buoyancy, show a decrease of the magnetic cycle with the decrease of the stellar rotation period. The stars with a rotational period of less than 10 days show the non-stationary long-term variations of the magnetic activity. The non-kinematic dynamo models take into account the magnetic field feedback on the large-scale flow and heat transport inside the convection zone. They show the non-monotonic variation of the dynamo period with the rotation rate. The models for the rotational periods fewer than 10 days show the non-stationary evolution with a slight increase in the primary dynamo period with the increase of the rotation rate. The non-kinematic models show the growth of the dynamo generated magnetic flux with the increase of the rotation rate. There is a dynamo saturation for the star rotating with a period of two days and less. The saturation of the magnetic activity parameters is accompanied by depression of the differential rotation.
Using full-disk observations obtained with the Michelson Doppler Imager (MDI) on board the Solar and Heliospheric Observatory (SOHO) spacecraft, we present variations of the solar acoustic mode frequencies caused by the solar activity cycle. High-degree (100 < l < 900) solar acoustic modes were analyzed using global helioseismology analysis techniques over most of solar cycle 23. We followed the methodology described in details in Korzennik, Rabello-Soares and Schou (2004) to infer unbiased estimates of high-degree mode parameters (see also Rabello-Soares, Korzennik and Schou, 2006). We have removed most of the known instrumental and observational effects that affect specifically high-degree modes. We show that the high-degree changes are in good agreement with the medium-degree results, except for years when the instrument was highly defocused. We analyzed and discuss the effect of defocusing on high degree estimation. Our results for high-degree modes confirm that the frequency shift scaled by the relative mode inertia is a function of frequency and it is independent of degree.
Extreme weather events are simultaneously the least likely and the most impactful features of the climate system, increasingly so as climate change proceeds. Extreme events are multi-faceted, highly variable processes which can be characterized in many ways: return time, worst-case severity, and predictability are all sought-after quantities for various kinds of rare events. A unifying framework is needed to define and calculate the most important quantities of interest for the purposes of near-term forecasting, long-term risk assessment, and benchmarking of reduced-order models. Here we use Transition Path Theory (TPT) for a comprehensive analysis of sudden stratospheric warming (SSW) events in a highly idealized wave-mean flow interaction system with stochastic forcing. TPT links together probabilities, dynamical behavior, and other risk metrics associated with rare events that represents their full statistical variability. At face value, fulfilling this promise demands extensive direct simulation to generate the rare event many times. Instead, we implement a highly parallel computational method that launches a large ensemble of short simulations, estimating long-timescale rare event statistics from short-term tendencies. We specifically investigate properties of SSW events including passage time distributions and large anomalies in vortex strength and heat flux. We visualize high-dimensional probability densities and currents, obtaining a nuanced picture of critical altitude-dependent interactions between waves and the mean flow that fuel SSW events. We find that TPT more faithfully captures the statistical variability between events as compared to the more conventional minimum action method.
Using meteor wind data from the Super Dual Auroral Radar Network (SuperDARN) in the Northern Hemisphere, we (1) demonstrate that the migrating (Sun-synchronous) tides can be separated from the nonmigrating components in the mesosphere and lower thermosphere (MLT) region and (2) use this to determine the response of the different components of the semidiurnal tide (SDT) to sudden stratospheric warming (SSW) conditions. The radars span a limited range of latitudes around 60$^{circ}$ N and are located over nearly 180$^{circ}$ of longitude. The migrating tide is extracted from the nonmigrating components observed in the meridional wind recorded from meteor ablation drift velocities around 95-km altitude, and a 20-year climatology of the different components is presented. The well-documented late summer and wintertime maxima in the semidiurnal winds are shown to be due primarily to the migrating SDT, whereas during late autumn and spring the nonmigrating components are at least as strong as the migrating SDT. The robust behavior of the SDT components during SSWs is then examined by compositing 13 SSW events associated with an elevated stratopause recorded between 1995 and 2013. The migrating SDT is seen to reduce in amplitude immediately after SSW onset and then return anomalously strongly around 10-17 days after the SSW onset. We conclude that changes in the underlying wind direction play a role in modulating the tidal amplitude during the evolution of SSWs and that the enhancement in the midlatitude migrating SDT (previously reported in modeling studies) is observed in the MLT at least up to 60$^{circ}$ N.
A Lagrangian column model has been developed to simulate the mean (monthly and annual) three-dimensional structure in ozone and nitrogen oxides concentrations in the boundary layer within and immediately around an urban area. Short time-scale photochemical processes of ozone, as well as emissions and deposition to the ground are simulated. The results show that the average surface ozone concentration in the urban area is lower than the surrounding rural areas by typically 50%. Model results are compared with observations.
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