A high-resolution model of the external and induced magnetic field at the Earth’s surface in the northern hemisphere

We describe a method of producing high-resolution models of the Earth’s combined external and induced magnetic field (EIMF) using the method of Empirical Orthogonal Functions (EOFs) applied to the SuperMAG archive of ground-based magnetometer data. EOFs partition the variance of a system into independent modes, allowing us to extract the spatiotemporal patterns of greatest dynamical importance without applying the a priori assumptions of other methods (such as spherical harmonic analysis, parameterised averaging, or multi-variate regression). We develop an approach based on that of Beckers and Rixen [2003] and use the EOF modes to infill missing data in a self-consistent manner. Applying our method to a north polar case study spanning February 2001 (chosen for its proximity to solar maximum and good data coverage), we demonstrate that 41.7% and 9.4% of variance is explained by the leading two modes, respectively describing the temporal variations of the Disturbance Polar types 2 and 1 (DP2 and DP1) patterns. A further 14.1% of variance is explained by four modes that describe separate aspects of the motion of the DP1 and DP2 systems. Thus, collectively over 65% of variance is described by the leading 6 modes and is attributable to DP1 and DP2. This attribution is based on inspection of the spatial morphology of the modes, and analysis of the temporal variation of the mode amplitudes with respect to solar wind measures and substorm occurrence. This study is primarily a demonstration of the technique and a prelude to a model spanning the full solar cycle

Interplanetary magnetic field control of polar ionospheric equivalent current system modes

We analyse the response of different ionospheric equivalent current modes to variations in the interplanetary magnetic field (IMF) components By and Bz. Each mode comprises a fixed spatial pattern whose amplitude varies in time, identified by a month‐by‐month empirical orthogonal function separation of surface measured magnetic field variance. Here we focus on four sets of modes that have been previously identified as DPY, DP2, NBZ and DP1. We derive the cross‐correlation function of each mode set with either IMF By or Bz for lags ranging from ‐10 to +600 mins with respect to the IMF state at the bow shock nose. For all four sets of modes, the average correlation can be reproduced by a sum of up to three linear responses to the IMF component, each centered on a different lag. These are interpreted as the statistical ionospheric responses to magnetopause merging (15‐20 mins lag) and magnetotail reconnection (60 mins lag), and to IMF persistence. Of the mode sets, NBZ and DPY are the most predictable from a given IMF component, with DP1 (the substorm component) the least predictable. The proportion of mode variability explained by the IMF increases for the longer lags, thought to indicate conductivity feedbacks from substorms. In summary, we confirm the postulated physical basis of these modes and quantify their multiple reconfiguration timescales

An empirical orthogonal function reanalysis of the northern polar external and induced magnetic field during solar cycle 23

We apply the method of data-interpolating Empirical Orthogonal Functions (EOFs) to ground-based magnetic vector data from the SuperMAG archive to produce a series of month-length reanalyses of the surface external and induced magnetic field (SEIMF) in 110,000 km2 equal-area bins over the entire northern polar region at 5-minute cadence over solar cycle 23, from 1997.0 to 2009.0. Each EOF re-analysis also decomposes the measured SEIMF variation into a hierarchy of spatio-temporal patterns which are ordered by their contribution to the monthly magnetic field variance. We find that the leading EOF patterns can each be (subjectively) interpreted as well-known SEIMF systems or their equivalent current systems. The relationship of the equivalent currents to the true current flow is not investigated. We track the leading SEIMF or equivalent current systems of similar type by inter-monthly spatial correlation, and apply graph theory to (objectively) group their appearance and relative importance throughout a solar cycle, revealing seasonal and solar cycle variation. In this way, we identify the spatiotemporal patterns which maximally contribute to SEIMF variability over a solar cycle. We propose this combination of EOF and graph theory as a powerful method for objectively defining and investigating the structure and variability of the SEIMF or their equivalent ionospheric currents for use in both geomagnetism and space weather applications. It is demonstrated here on solar cycle 23, but is extendable to any epoch with sufficient data coverage

Data‐driven basis functions for SuperDARN ionospheric plasma flow characterisation and prediction

The archive of plasma velocity measurements from the Super Dual Auroral Radar Network (SuperDARN) provides a rich dataset for investigation of magnetosphere-ionosphere-thermosphere coupling. However, systematic gaps in this archive exist in space, time, and radar look-direction. These gaps are generally infilled using climatological averages, spatially smoothed models, or a priori relationships determined from solar wind drivers. We describe a new technique for infilling the data gaps in the SuperDARN archive which requires no external information and is based solely on the SuperDARN measurements. We also avoid the use of climatological averaging or spatial smoothing when computing the infill. In this regard, our approach captures the true variability in the SuperDARN measurements. Our technique is based on data-interpolating Empirical Orthogonal Function (EOF) analysis. This method discovers from the SuperDARN data a series of dynamical modes of plasma velocity variation. We compute the modes of a sample month of northern hemisphere winter data, and investigate these in terms of solar wind driving. We find that the By component of the Interplanetary Magnetic Field (IMF) dominates the variability of the plasma velocity. The IMF Bz component is the dominant driver for the background mean field, and a series of non-leading modes which describe the two-cell convection variability, and the substorm. We recommend our new technique for reanalysis investigations of polar-scale plasma drift phenomena, particularly those with rapid temporal fluctuations and an indirect relationship to the solar wind

Spatial variation in the responses of the surface external and induced magnetic field to the solar wind

We analyse the spatial variation in the response of the surface geomagnetic field (or the equivalent ionospheric current) to variations in the solar wind. Specifically, we regress a reanalysis of surface external and induced magnetic field (SEIMF) variations onto measurements of the solar wind. The regression is performed in monthly sets, independently for 559 regularly‐spaced locations covering the entire northern polar region above 50° magnetic latitude. At each location, we find the lag applied to the solar wind data that maximises the correlation with the SEIMF. The resulting spatial maps of these independent lags and regression coefficients provide a model of the localised SEIMF response to variations in the solar wind, which we call ‘Spatial Information from Distributed Exogenous Regression’ (SPIDER). We find that the lag and regression coefficients vary systematically with ionospheric region, season, and solar wind driver. In the polar cap region the SEIMF is best described by the By component of the interplanetary magnetic field (50–75% of total variance explained) at a lag ∼20–25 min. Conversely, in the auroral zone the SEIMF is best described by the solar wind ϵ function (60–80% of total variance explained), with a lag that varies with season and magnetic local time (MLT), from ∼15–20 min for dayside and afternoon MLT (except in Oct‐Dec) to typically 30–40 min for nightside and morning MLT, and even longer (60–65 min) around midnight MLT

How dо pesticides get into honey?

Honey is nature’s sweetest gift. But did you know that honey may contain pesticides? Farmers use pesticides to kill pests that harm their crops. But pesticides also hurt honey bees and other beneficial insects. Furthermore, when bees collect nectar from flowers which received pesticide treatments, these chemicals make their way into the honey. In the past, scientists found neonicotinoids (a class of pesticides) in about half of the honey samples collected in the United Kingdom. Since 2014, the European Union banned neonicotinoids in flowering crops that bees visit. We wanted to know how effective this policy was. Does UK honey still contain neonicotinoids? Here, we collected and tested honey samples from beekeepers across the UK. We found that about a fifth of all honey contained neonicotinoids. These chemicals are not at dangerous levels for human health but may harm the bees in the long run

Neonicotinoid residues in UK honey despite European Union moratorium

Due to concerns over negative impacts on insect pollinators, the European Union has implemented a moratorium on the use of three neonicotinoid pesticide seed dressings for mass-flowering crops. We assessed the effectiveness of this policy in reducing the exposure risk to honeybees by collecting 130 samples of honey from bee keepers across the UK before (2014: N = 21) and after the moratorium was in effect (2015: N = 109). Neonicotinoids were present in about half of the honey samples taken before the moratorium, and they were present in over a fifth of honey samples following the moratorium. Clothianidin was the most frequently detected neonicotinoid. Neonicotinoid concentrations declined from May to September in the year following the ban. However, the majority of post-moratorium neonicotinoid residues were from honey harvested early in the year, coinciding with oilseed rape flowering. Neonicotinoid concentrations were correlated with the area of oilseed rape surrounding the hive location. These results suggest mass flowering crops may contain neonicotinoid residues where they have been grown on soils contaminated by previously seed treated crops. This may include winter seed treatments applied to cereals that are currently exempt from EU restrictions. Although concentrations of neonicotinoids were low (<2.0 ng g-1), and posed no risk to human health, they may represent a continued risk to honeybees through long-term chronic exposure

Timescales of Birkeland Currents driven by the IMF

We obtain current densities from the Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE), alongside By and Bz from the Interplanetary Magnetic Field (IMF) for March 2010. For each AMPERE spatial coordinate, we cross‐correlate current density with By and Bz, finding the maximum correlation for lags up to 360 min. The patterns of maximum correlation contain large‐scale structures consistent with the literature. For the correlation with By, the lags on the dayside are 10 min at high latitudes but up to 240 min at lower latitudes. Lags on the nightside are 90–150 min. For Bz, the shortest lags on the dayside are 10–20 min; on the equatorward edge of the current oval, 60–90 min; and on the nightside, predominantly 90–150 min. This novel approach enables us to see statistically the timescales on which information is electrodynamically communicated to the ionosphere after magnetic field lines reconnect on the dayside and nightside