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As an extension of Lee et al. (2013), solar cycle variation of carbon monoxide (CO) is analyzed with MLS observation, which covers more than thirteen years (2004–2017) including maximum of solar cycle 24. Being produced primarily by the carbon dioxide (CO2) photolysis in the lower thermosphere, the variations of the mesospheric CO concentration are largely driven by the solar cycle modulated ultraviolet (UV) variation. This solar signal extends down to the lower altitudes by the dynamical descent in the winter polar vortex, showing a time lag that is consistent with the average descent velocity. To characterize a global distribution of the solar impact, MLS CO is correlated with the SORCE measured total solar irradiance (TSI) and UV. As high as 0.8 in most of the polar mesosphere, the linear correlation coefficients between CO and UV/TSI are more robust than those found in the previous work. The photochemical contribution explains most (68%) of the total variance of CO while the dynamical contribution accounts for 21% of the total variance at upper mesosphere. The photochemistry driven CO anomaly signal is extended in the tropics by vertical mixing. The solar cycle signal in CO is further examined with the Whole Atmosphere Community Climate Model (WACCM) 3.5 simulation by implementing two different modeled Spectral Solar Irradiances (SSIs): SRPM 2012 and NRLSSI. The model simulations underestimate the mean CO amount and solar cycle variations of CO, by a factor of 3, compared to those obtained from MLS observation. Different inputs of the solar spectrum have small impacts on CO variation.

The recent extended, deep minimum of solar variability and the extended minima in the 19th and 20th centuries (1810–1830 and 1900–1920) are consistent with minima of the Centennial Gleissberg Cycle (CGC), a 90–100 year variation of the amplitude of the 11-year sunspot cycle observed on the Sun and at the Earth. The Earth’s climate response to these prolonged low solar radiation inputs involves heat transfer to the deep ocean causing a time lag longer than a decade. The spatial pattern of the climate response, which allows distinguishing the CGC forcing from other climate forcings, is dominated by the Pacific North American pattern (PNA). The CGC minima, sometimes coincidently in combination with volcanic forcing, are associated with severe weather extremes. Thus the 19th century CGC minimum, coexisted with volcanic eruptions, led to especially cold conditions in United States, Canada and Western Europe.

The Deterministic Annealing (DA) clustering method, which determines the cluster centers, their sizes, and probability with which data are associated with each cluster, is tested using artificial data and applied to atmospheric satellite data. It is also shown how the method can be advantageously used to characterize data outliers. The method is based on the optimization of a cost function that depends both on the averaged distance of data points to cluster centers and the Shannon entropy of the data. The cost function uses two independent parameters in a close analog to the Gibbs' thermodynamics (with the averaged distance similar to the internal energy) allowing a sufficient control of the formation of new clusters as “phase transitions” by changing the clustering parameter similar to the thermodynamical temperature. The satellite data used are a temperature–water vapor data set and the positions of deep convective clouds obtained from the measurements of the Atmospheric InfraRed Sounder (AIRS) on the Aqua satellite. The clustering of these data is demonstrated for the 2D case (at fixed pressure level) and for the 3D case at multiple pressure levels indicating potential applications to investigation of distributions of atmospheric profiles.

More than eight years (2004-2012) of carbon monoxide (CO) measurements from the Aura Microwave Limb Sounder (MLS) are analyzed. The mesospheric CO, largely produced by the carbon dioxide (CO2) photolysis in the lower thermosphere, is sensitive to the solar irradiance variability. The long-term variation of observed mesospheric MLS CO concentrations at high latitudes is likely driven by the solar-cycle modulated UV forcing. Despite of different CO abundances in the southern and northern hemispheric winter, the solar-cycle dependence appears to be similar. This solar signal is further carried down to the lower altitudes by the dynamical descent in the winter polar vortex. Aura MLS CO is compared with the Solar Radiation and Climate Experiment (SORCE) total solar irradiance (TSI) and also with the spectral irradiance in the far ultraviolet (FUV) region from the SORCE Solar-Stellar Irradiance Comparison Experiment (SOLSTICE). Significant positive correlation (up to 0.6) is found between CO and FUVTSI in a large part of the upper atmosphere. The distribution of this positive correlation in the mesosphere is consistent with the expectation of CO changes induced by the solar irradiance variations.

We use the 8-year long satellite temperature data (2002–2010) from Atmospheric InfraRed Sounder (AIRS) and Atmospheric Microwave Sounding Unit (AMSU) on the Aqua satellite to identify temperature trends in the troposphere and low stratosphere over the Nino 3.4 region of the Tropical Pacific Ocean in the most recent 11-year solar cycle. Employing more extended sea surface temperature (SST) data for five solar cycles (1950–2009) in this region we show that the satellite trends reflect a typical decrease of the sea surface temperature (SST) in the Nino 3.4 region in the declining phase of the solar cycle. The magnitude of the SST decrease depends on the solar cycle and ranges between 0.07 K/yr and 0.27 K/yr for the last five solar cycles.

The knowledge of the high intensity tails of probability distributions that determine the rate of occurrence of extreme events of solar energetic particles is a critical element in the evaluation of hazards for human and robotic space missions. Here instead of the standard approach based on fitting a selected distribution function to the observed data we investigate a different approach, which is based on a study of the scaling properties of the maximum particle flux in time intervals of increasing length. To find the tail of the probability distributions we apply the “Max-Spectrum” method (Stoev, S.A., Michailidis, G., 2006. On the estimation of the heavy-tail exponent in time series using the Max-Spectrum. Technical Report 447, Department of Statistics, University of Michigan) to 1973–1997 IMP-8 proton data and the 1987–2008 GOES data, which cover a wide range of proton energies. We find that both data sets indicate a power-law tail with the power exponents close to 0.6 at least in the energy range 9–60 MeV. The underlying probability distribution is consistent with the Frechet type (power-law behavior) extreme value distribution. Since the production of high fluxes of energetic particles is caused by fast Coronal Mass Ejections (CMEs) this heavy-tailed distribution also means that the Sun generates more fast CMEs than would be expected from a Poissonian-type process.

The spectrum of velocity and magnetic fields in the solar wind is self-similar (power-law type) in the frequency range greater than >1/day indicating well-mixed turbulence. But it loses self-similarity for lower frequencies indicating the presence of large-scale patterns, which are intermittently generated inside the Sun and propagate from the Sun to the Earth. Here we discuss the spatia–temporal characteristics and origin of the 1.3-year quasi-periodic pattern found inside the Sun by helioseismic methods and detected in the solar wind. To identify and characterize this pattern on the Sun we use time series of solar magnetic Carrington maps generated at the Wilcox Solar Observatory and independent component data analysis. This analysis shows the latitudinal distribution of the pattern, its variable frequency and intermittent appearance.

The high-energy solar proton data obtained from instruments onboard the IMP-8 spacecraft are used to investigate statistical distributions of event fluences, event durations, and time intervals between adjacent events. The results show that: (1) the event fluences can be approximately fit to a log-normal distribution and (2) the distributions of event durations and time intervals between the events can be represented by Poisson distributions. A virtual data set was generated using these distributions to extend the Solar Probe method [Feynman, J., Tylka, A.J., Reames, D.B., Gabriel, S.B. Near-sun energetic particle environment of Solar Probe-Phase1 final report, JPL Report, 2000] of obtaining the mission-integrated fluences to longer missions. By using the virtual data set generated in this study, we are able to obtain the smooth distribution of mission fluences for missions of any duration, which is not possible when only the available data sets was used.

We discuss effects of solar variability on the Earth’s large-scale climate patterns. These patterns are naturally excited as deviations (anomalies) from the mean state of the Earth’s atmosphere-ocean system. We consider in detail an example of such a pattern, the North Annular Mode (NAM), a climate anomaly with two states corresponding to higher pressure at high latitudes with a band of lower pressure at lower latitudes and the other way round. We discuss a mechanism by which solar variability can influence this pattern and formulate an updated general conjecture of how external influences on Earth’s dynamics can affect climate patterns.

We show that the interaction between planetary waves and the stratospheric zonal mean flow results in bi-modal (direct-reverse flow) or unimodal state depending on wave number of the waves. First we demonstrate this using a simple non-linear dynamic system of the wave-flow interaction, which has two stable equilibrium states and one unstable state (attractors) in its phase space. Then we compare this model dynamics with the stratosphere dynamics using the same dynamical variables and a similar parameter range in the National Centers for Environmental Prediction (NCEP) Reanalysis data. This comparison supports the tendency for the states of planetary wave–zonal mean flow in the upper stratosphere to be bi-modal for wave number 2 and unimodal for wave number 1.

We present a simple dynamic model of solar variability influence on climate, which is truncated from the stratospheric wave-zonal flow interaction dynamics over a β-plane. The model consists of three ordinary differential equations controlled by two parameters: the initial amplitude of planetary waves and the vertical gradient of the zonal wind. The changes associated with the solar UV variability, as well as with seasonal variations, are introduced as periodic modulations of the zonal wind gradient. Influence of the Quasi-Biennial Oscillation is included as a periodic change of the width of the latitudinal extent of the β-plane. The major climate response to these changes is seen through modulation of the number of cold and warm winters.

The development of new technologies and the miniaturization of sensors bring new requirements for our ability to predict and forecast hazardous space weather conditions.

Manned spacecraft will require a much improved ability to forecast solar particle events. The lead time required will depend on the use to which the forecast is put. Here we discuss problems of forecasting with the lead times of hours to weeks. Such forecasts are needed for scheduling and carrying out activities. Our present capabilities with these lead times is extremely limited. To improve our capability we must develop an ability to predict fast coronal mass ejections (CMEs). It is not sufficient to observe that a CME has already taken place since by that time it is already too late to make predictions with these lead times. Both to learn how to predict CMEs and to carry out forecasts on time scales of several days to weeks, observations of the other side of the Sun are required. We describe a low-cost space mission of this type that would further the development of an hours-to-weeks forecast capability.

The Uranus and Neptune magnetic fields discovered by Voyager 2 can be explained by a dynamo acting in a thin conductive convective shell existing at the bottom of the icy oceans of the planets. The main helicity and differential rotation are the source for the dynamo which effectively excites nonaxisymmetric modes of the mean magnetic field. Estimates of the magnetic field amplitude in the nonlinear regime and of the inclination between the magnetic moment and the rotation axis are given.

Mars has a well-conducting core composed of a mixture of iron and sulphur. It is probably liquid and differentially rotating. The main problem is whether the core is stably stratified or convective. About 10–15% of sulphur is enough to drive a compositional convection over a great part of Martian evolution if an inner solid solid core was formed. Convection in a liquid rotating stratified core results in a mean helicity, which, together with differential rotation and turbulent magnetic diffusivity, is a source for mean field dynamo. The magnitude of the magnetic field generated can explain the origin of magnetization of the shergottie, nakhlite and chassignite meteorites. However, the dynamo probably does not work at present, in accordance with a weak large-scale magnetic field on the Martian surface estimated from the Mars-2, -3, -5, and Phobos mission results. This field is interpreted as an external product of the magnetized mantle having a slightly elliptical form. Some other possible explanations for the outer magnetic field are discussed.

We propose and analyze a possible mechanism of amplification and variations in alignment of magnetic fields in astrophysical jets. We associate these processes with combined action of helical turbulent plasma motions and large-scale velocity within a jet which stretch and distort a seed magnetic field.