Ions in the atmosphere

In Earth’s atmosphere, an ion is a cluster of molecules carrying an overall charge, known as a molecular cluster ion. Such cluster ions, with dimensions of approximately one nanometre, have usually been referred to as small ions, and their motion in air constitutes a small electric current. Large ions (or Langevin ions), by comparison, are physically larger (tens to hundreds of nm) and consequently electrically less mobile. Usage of the term “ion” to represent these molecular clusters originates from the early history of atmospheric electricity, which spans the discovery of the electron and the elucidation of the structure of matter. The distinction between large and small ions originates from distinguishing ions that could be accelerated by atmospheric electric fields (and therefore directly contribute to the conductivity of air), and those (the large ions) which were insufficiently electrically mobile to contribute to electrical conduction in air.

Cosmic ray measurements in the atmosphere at several latitudes in October, 2014

Cosmic ray fluxes in the atmosphere were recorded during balloon flights in October 2014 in northern Murmansk region, Apatity (Russia; 67o33’N, 33o24’E), in Antarctica (observatory Mirny; 66o33’S, 93o00’E), in Moscow (Russia; 55o45’N, 37o37’E), in Reading (United King-dom; 51o27’N, 0o 58’W), in Mitzpe-Ramon (Israel; 30o36’N, 34o48’E) and in Zaragoza (Spain; 41o9’N, 0o54’W). Two type of cosmic ray detectors were used, namely, (1) the standard ra-diosonde and its modification constructed at the Lebedev Physical Institute (Moscow, Russia) and (2) the device manufactured at the Reading University (Reading, United Kingdom). We compare and analyze obtained data and focus on the estimation of the cosmic ray latitudinal effect in the atmosphere.

The refreezing of melt ponds on Arctic sea ice

The presence of melt ponds on the surface of Arctic sea ice significantly reduces its albedo, inducing a positive feedback leading to sea ice thinning. While the role of melt ponds in enhancing the summer melt of sea ice is well known, their impact on suppressing winter freezing of sea ice has, hitherto, received less attention. Melt ponds freeze by forming an ice lid at the upper surface, which insulates them from the atmosphere and traps pond water between the underlying sea ice and the ice lid. The pond water is a store of latent heat, which is released during refreezing. Until a pond freezes completely, there can be minimal ice growth at the base of the underlying sea ice. In this work, we present a model of the refreezing of a melt pond that includes the heat and salt balances in the ice lid, trapped pond, and underlying sea ice. The model uses a two-stream radiation model to account for radiative scattering at phase boundaries. Simulations and related sensitivity studies suggest that trapped pond water may survive for over a month. We focus on the role that pond salinity has on delaying the refreezing process and retarding basal sea ice growth. We estimate that for a typical sea ice pond coverage in autumn, excluding the impact of trapped ponds in models overestimates ice growth by up to 265 million km3, an overestimate of 26%.

Small global-mean cooling due to volcanic radiative forcing

In both the observational record and atmosphere-ocean general circulation model (AOGCM) simulations of the last ∼∼ 150 years, short-lived negative radiative forcing due to volcanic aerosol, following explosive eruptions, causes sudden global-mean cooling of up to ∼∼ 0.3 K. This is about five times smaller than expected from the transient climate response parameter (TCRP, K of global-mean surface air temperature change per W m−2 of radiative forcing increase) evaluated under atmospheric CO2 concentration increasing at 1 % yr−1. Using the step model (Good et al. in Geophys Res Lett 38:L01703, 2011. doi:10.​1029/​2010GL045208), we confirm the previous finding (Held et al. in J Clim 23:2418–2427, 2010. doi:10.​1175/​2009JCLI3466.​1) that the main reason for the discrepancy is the damping of the response to short-lived forcing by the thermal inertia of the upper ocean. Although the step model includes this effect, it still overestimates the volcanic cooling simulated by AOGCMs by about 60 %. We show that this remaining discrepancy can be explained by the magnitude of the volcanic forcing, which may be smaller in AOGCMs (by 30 % for the HadCM3 AOGCM) than in off-line calculations that do not account for rapid cloud adjustment, and the climate sensitivity parameter, which may be smaller than for increasing CO2 (40 % smaller than for 4 × CO2 in HadCM3).

Decadal predictions with the HiGEM high resolution global coupled climate model: description and basic evaluation

This paper describes the development and basic evaluation of decadal predictions produced using the HiGEM coupled climate model. HiGEM is a higher resolution version of the HadGEM1 Met Office Unified Model. The horizontal resolution in HiGEM has been increased to 1.25◦ × 0.83◦ in longitude and latitude for the atmosphere, and 1/3◦ × 1/3◦ globally for the ocean. The HiGEM decadal predictions are initialised using an anomaly assimilation scheme that relaxes anomalies of ocean temperature and salinity to observed anomalies. 10 year hindcasts are produced for 10 start dates (1960, 1965,..., 2000, 2005). To determine the relative contributions to prediction skill from initial conditions and external forcing, the HiGEM decadal predictions are compared to uninitialised HiGEM transient experiments. The HiGEM decadal predictions have substantial skill for predictions of annual mean surface air temperature and 100 m upper ocean temperature. For lead times up to 10 years, anomaly correlations (ACC) over large areas of the North Atlantic Ocean, the Western Pacific Ocean and the Indian Ocean exceed values of 0.6. Initialisation of the HiGEM decadal predictions significantly increases skill over regions of the Atlantic Ocean,the Maritime Continent and regions of the subtropical North and South Pacific Ocean. In particular, HiGEM produces skillful predictions of the North Atlantic subpolar gyre for up to 4 years lead time (with ACC > 0.7), which are significantly larger than the uninitialised HiGEM transient experiments.

Fathoming solar effects in Neptune’s atmosphere

Long-duration observations of Neptune’s brightness in two visible wavelengths provide a disk-averaged estimate of its atmospheric aerosol. Brightness variations were previously associated with the 11-year solar cycle, through solar-modulated mechanisms linked with either ultra-violet (UV) or galactic cosmic ray (GCR) effects on atmospheric particles. Here we use a recently extended brightness dataset (1972-2014), with physically realistic modelling to show that rather than alternatives, UV and GCR are likely to be modulating Neptune’s atmosphere in combination. The importance of GCR is further supported by the response of Neptune's atmosphere to an intermittent 1.5 to 1.9 year periodicity, which occurred preferentially in GCR (not UV) during the mid-1980s. This periodicity was detected both at Earth, and in GCR measured by Voyager 2, then near Neptune. A similar coincident variability in Neptune’s brightness suggests nucleation onto GCR ions. Both GCR and UV mechanisms may occur more rapidly than the subsequent atmospheric particle transport.

An idealized study of coupled atmosphere-ocean 4D-Var in the presence of model error

Atmosphere only and ocean only variational data assimilation (DA) schemes are able to use window lengths that are optimal for the error growth rate, non-linearity and observation density of the respective systems. Typical window lengths are 6-12 hours for the atmosphere and 2-10 days for the ocean. However, in the implementation of coupled DA schemes it has been necessary to match the window length of the ocean to that of the atmosphere, which may potentially sacrifice the accuracy of the ocean analysis in order to provide a more balanced coupled state. This paper investigates how extending the window length in the presence of model error affects both the analysis of the coupled state and the initialized forecast when using coupled DA with differing degrees of coupling. Results are illustrated using an idealized single column model of the coupled atmosphere-ocean system. It is found that the analysis error from an uncoupled DA scheme can be smaller than that from a coupled analysis at the initial time, due to faster error growth in the coupled system. However, this does not necessarily lead to a more accurate forecast due to imbalances in the coupled state. Instead coupled DA is more able to update the initial state to reduce the impact of the model error on the accuracy of the forecast. The effect of model error is potentially most detrimental in the weakly coupled formulation due to the inconsistency between the coupled model used in the outer loop and uncoupled models used in the inner loop.