Space Weather: Physics,Effects and Predictability

The time varying conditions in the near-Earth space environment that may affect space-borne or ground-based technological systems and may endanger human health or life are referred to as space weather. Space weather effects arise from the dynamic and highly variable conditions in the geospace environment starting from explosive events on the Sun (solar flares), Coronal Mass Ejections near the Sun in the interplanetary medium, and various energetic effects in the magnetosphere–ionosphere–atmosphere system. As the utilization of space has become part of our everyday lives, and as our lives have become increasingly dependent on technological systems vulnerable to the space weather influences, the understanding and prediction of hazards posed by these active solar events have grown in importance. In this paper, we review the processes of the Sun–Earth interactions, the dynamic conditions within the magnetosphere, and the predictability of space weather effects on radio waves, satellites and ground-based technological systems today.     

Fields as a framework for integrating GIS and environmental process models. Part 1: Representing spatial continuity

Linking a GIS to a spatially distributed, physically-based environmental model offers many advantages. However, the implementation of such linkages is generally problematic. Many problems arise because the relationship between the reality being represented by the mathematical model and the data model used to organize the spatial data in the GIS has not been rigorously defined. In particular, while many environmental models are based on theories that assume continuity and incorporate physical fields as independent variables, current GISs can only represent continuous phenomena in a variety of discrete data models. This paper outlines a strategy in which field variables are used to enable modellers to work directly with the spatial data as spatially continuous phenomena. This allows the manner in which the spatial data has been discretized and the ways in which it can be manipulated to be treated independently from the conceptual modelling of physical processes. Modellers can express their spatial data needs as representations of reality, rather than as elements of a GIS database, and a GIS-independent language for model development may result. By providing a formal linkage between the various models of spatial phenomena, a mechanism is created for the explicit expression of transformation rules between the different spatial data models stored and manipulated by a GIS.     

Paleoclimatic implications of glacial and postglacial refugia for Pinus pumila in western Beringia

Palynological results from Julietta Lake currently provide the most direct evidence to support the existence of a glacial refugium for Pinus pumila in mountains of southwestern Beringia. Both percentages and accumulation rates indicate the evergreen shrub survived until at least ∼ 19,000 ¹⁴C yr BP in the Upper Kolyma region. Percentage data suggest numbers dwindled into the late glaciation, whereas pollen accumulation rates point towards a more rapid demise shortly after ∼ 19,000 ¹⁴C yr BP. Pinus pumila did not re-establish in any great numbers until ∼ 8100 ¹⁴C yr BP, despite the local presence ∼ 9800 ¹⁴C yr BP of Larixdahurica, which shares similar summer temperature requirements. The postglacial thermal maximum (in Beringia ∼ 11,000-9000 ¹⁴C yr BP) provided Pinus pumila shrubs with equally harsh albeit different conditions for survival than those present during the LGM. Regional records indicate that in this time of maximum warmth Pinus pumila likely sheltered in a second, lower-elevation refugium. Paleoclimatic models and modern ecology suggest that shifts in the nature of seasonal transitions and not only seasonal extremes have played important roles in the history of Pinus pumila over the last ∼ 21,000 ¹⁴C yr BP.     

Stratospheric methane and nitrogen dioxide from infrared spectra

Values of the mixing ratio by volume of stratospheric NO₂ and CH₄ deduced from infrared spectra taken by means of balloon-borne spectrometers are presented. Possible evidence for the presence of formaldehyde in the stratosphere is also given.     

Variation in Coronal Activity from Solar Cycle 24 Minimum to Maximum Using Three-Dimensional Reconstructions of the Coronal Electron Density from STEREO/COR1

Three-dimensional electron density distributions in the solar corona are reconstructed for 100 Carrington rotations (CR 2054?–?2153) during 2007/03?–?2014/08 using the spherically symmetric method from polarized white-light observations with the inner coronagraph (COR1) onboard the twin Solar Terrestrial Relations Observatory (STEREO). These three-dimensional electron density distributions are validated by comparison with similar density models derived using other methods such as tomography and a magnetohydrodynamics (MHD) model as well as using data from the Solar and Heliospheric Observatory (SOHO)/Large Angle and Spectrometric Coronagraph (LASCO)-C2. Uncertainties in the estimated total mass of the global corona are analyzed based on differences between the density distributions for COR1-A and -B. Long-term variations of coronal activity in terms of the global and hemispheric average electron densities (equivalent to the total coronal mass) reveal a hemispheric asymmetry during the rising phase of Solar Cycle 24, with the northern hemisphere leading the southern hemisphere by a phase shift of 7?–?9 months. Using 14 CR (\(\approx13\)-month) running averages, the amplitudes of the variation in average electron density between Cycle 24 maximum and Cycle 23/24 minimum (called the modulation factors) are found to be in the range of 1.6?–?4.3. These modulation factors are latitudinally dependent, being largest in polar regions and smallest in the equatorial region. These modulation factors also show a hemispheric asymmetry: they are somewhat larger in the southern hemisphere. The wavelet analysis shows that the short-term quasi-periodic oscillations during the rising and maximum phases of Cycle 24 have a dominant period of 7?–?8 months. In addition, it is found that the radial distribution of the mean electron density for streamers at Cycle 24 maximum is only slightly larger (by \(\approx30\%\)) than at cycle minimum.