Air-earth current density measurements at Lerwick; implications for seasonality in the global electric circuit

Atmospheric electricity measurements were made at Lerwick Observatory in the Shetland Isles (60°09′N, 1°08′W) during most of the 20th century. The Potential Gradient (PG) was measured from 1926 to 84 and the air-earth conduction current (Jc) was measured during the final decade of the PG measurements. Daily Jc values (1978–1984) observed at 15 UT are presented here for the first time, with independently-obtained PG measurements used to select valid data. The 15 UT Jc (1978–1984) spans 0.5–9.5 pA/m2, with median 2.5 pA/m2; the columnar resistance at Lerwick is estimated as 70 PΩm2. Smoke measurements confirm the low pollution properties of the site. Analysis of the monthly variation of Lerwick Jc data shows that winter (DJF) Jc is significantly greater than the summer (JJA) Jc by 20%. The Lerwick atmospheric electricity seasonality differs from the global lightning seasonality, but Jc has a similar seasonal phasing to that observed in Nimbostratus clouds globally, suggesting a role for non-thunderstorm rain clouds in the seasonality of the global circuit.

Introduction. Stochastic physics and climate modelling

Finite computing resources limit the spatial resolution of state-of-the-art global climate simulations to hundreds of kilometres. In neither the atmosphere nor the ocean are small-scale processes such as convection, clouds and ocean eddies properly represented. Climate simulations are known to depend, sometimes quite strongly, on the resulting bulk-formula representation of unresolved processes. Stochastic physics schemes within weather and climate models have the potential to represent the dynamical effects of unresolved scales in ways which conventional bulk-formula representations are incapable of so doing. The application of stochastic physics to climate modelling is a rapidly advancing, important and innovative topic. The latest research findings are gathered together in the Theme Issue for which this paper serves as the introduction.

How well do we understand and evaluate climate change feedback processes?

Processes in the climate system that can either amplify or dampen the climate response to an external perturbation are referred to as climate feedbacks. Climate sensitivity estimates depend critically on radiative feedbacks associated with water vapor, lapse rate, clouds, snow, and sea ice, and global estimates of these feedbacks differ among general circulation models. By reviewing recent observational, numerical, and theoretical studies, this paper shows that there has been progress since the Third Assessment Report of the Intergovernmental Panel on Climate Change in (i) the understanding of the physical mechanisms involved in these feedbacks, (ii) the interpretation of intermodel differences in global estimates of these feedbacks, and (iii) the development of methodologies of evaluation of these feedbacks (or of some components) using observations. This suggests that continuing developments in climate feedback research will progressively help make it possible to constrain the GCMs’ range of climate feedbacks and climate sensitivity through an ensemble of diagnostics based on physical understanding and observations.

Estimating surface CO2 fluxes from space-borne dry air mole fraction observations using an Ensemble Kalman filter

We have developed an ensemble Kalman Filter (EnKF) to estimate 8-day regional surface fluxes of CO2 from space-borne CO2 dry-air mole fraction observations (XCO2) and evaluate the approach using a series of synthetic experiments, in preparation for data from the NASA Orbiting Carbon Observatory (OCO). The 32-day duty cycle of OCO alternates every 16 days between nadir and glint measurements of backscattered solar radiation at short-wave infrared wavelengths. The EnKF uses an ensemble of states to represent the error covariances to estimate 8-day CO2 surface fluxes over 144 geographical regions. We use a 12×8-day lag window, recognising that XCO2 measurements include surface flux information from prior time windows. The observation operator that relates surface CO2 fluxes to atmospheric distributions of XCO2 includes: a) the GEOS-Chem transport model that relates surface fluxes to global 3-D distributions of CO2 concentrations, which are sampled at the time and location of OCO measurements that are cloud-free and have aerosol optical depths <0.3; and b) scene-dependent averaging kernels that relate the CO2 profiles to XCO2, accounting for differences between nadir and glint measurements, and the associated scene-dependent observation errors. We show that OCO XCO2 measurements significantly reduce the uncertainties of surface CO2 flux estimates. Glint measurements are generally better at constraining ocean CO2 flux estimates. Nadir XCO2 measurements over the terrestrial tropics are sparse throughout the year because of either clouds or smoke. Glint measurements provide the most effective constraint for estimating tropical terrestrial CO2 fluxes by accurately sampling fresh continental outflow over neighbouring oceans. We also present results from sensitivity experiments that investigate how flux estimates change with 1) bias and unbiased errors, 2) alternative duty cycles, 3) measurement density and correlations, 4) the spatial resolution of estimated flux estimates, and 5) reducing the length of the lag window and the size of the ensemble. At the revision stage of this manuscript, the OCO instrument failed to reach its orbit after it was launched on 24 February 2009. The EnKF formulation presented here is also applicable to GOSAT measurements of CO2 and CH4.

Exploitation of Geostationary Earth Radiation Budget data using simulations from a numerical weather prediction model: Methodology and data validation

We describe a new methodology for comparing satellite radiation budget data with a numerical weather prediction (NWP) model. This is applied to data from the Geostationary Earth Radiation Budget (GERB) instrument on Meteosat-8. The methodology brings together, in near-real time, GERB broadband shortwave and longwave fluxes with simulations based on analyses produced by the Met Office global NWP model. Results for the period May 2003 to February 2005 illustrate the progressive improvements in the data products as various initial problems were resolved. In most areas the comparisons reveal systematic errors in the model's representation of surface properties and clouds, which are discussed elsewhere. However, for clear-sky regions over the oceans the model simulations are believed to be sufficiently accurate to allow the quality of the GERB fluxes themselves to be assessed and any changes in time of the performance of the instrument to be identified. Using model and radiosonde profiles of temperature and humidity as input to a single-column version of the model's radiation code, we conduct sensitivity experiments which provide estimates of the expected model errors over the ocean of about ±5–10 W m−2 in clear-sky outgoing longwave radiation (OLR) and ±0.01 in clear-sky albedo. For the more recent data the differences between the observed and modeled OLR and albedo are well within these error estimates. The close agreement between the observed and modeled values, particularly for the most recent period, illustrates the value of the methodology. It also contributes to the validation of the GERB products and increases confidence in the quality of the data, prior to their release.

The Geostationary Earth Radiation Budget Project

This paper reports on a new satellite sensor, the Geostationary Earth Radiation Budget (GERB) experiment. GERB is designed to make the first measurements of the Earth's radiation budget from geostationary orbit. Measurements at high absolute accuracy of the reflected sunlight from the Earth, and the thermal radiation emitted by the Earth are made every 15 min, with a spatial resolution at the subsatellite point of 44.6 km (north–south) by 39.3 km (east–west). With knowledge of the incoming solar constant, this gives the primary forcing and response components of the top-of-atmosphere radiation. The first GERB instrument is an instrument of opportunity on Meteosat-8, a new spin-stabilized spacecraft platform also carrying the Spinning Enhanced Visible and Infrared (SEVIRI) sensor, which is currently positioned over the equator at 3.5°W. This overview of the project includes a description of the instrument design and its preflight and in-flight calibration. An evaluation of the instrument performance after its first year in orbit, including comparisons with data from the Clouds and the Earth's Radiant Energy System (CERES) satellite sensors and with output from numerical models, are also presented. After a brief summary of the data processing system and data products, some of the scientific studies that are being undertaken using these early data are described. This marks the beginning of a decade or more of observations from GERB, as subsequent models will fly on each of the four Meteosat Second Generation satellites.

A lightweight balloon-carried cloud charge sensor

Despite the importance of microphysical cloud processes on the climate system, some topics are under-explored. For example, few measurements of droplet charges in nonthunderstorm clouds exist. Balloon carried charge sensors can be used to provide new measurements. A charge sensor is described for use with meteorological balloons, which has been tested over a range of atmospheric temperatures from -60 to 20 degrees C, in cloudy and clear air. The rapid time response of the sensor (to >10 V s(-1)) permits charge densities from 100 fC m(-3) to 1 nC m(-3) to be determined, which is sufficient for it to act as a cloud edge charge detector at weakly charged horizontal cloud boundaries.

Evidence for global circuit current flow through water droplet layers

Abstract Foggy air and clear air have appreciably different electrical conductivities. The conductivity gradient at horizontal droplet boundaries causes droplet charging, as a result of vertical current flow in the global atmospheric electrical circuit. The charging is poorly known, as both the current flow through atmospheric water droplet layers and the air conductivity are poorly characterised experimentally. Surface measurements during three days of continuous fog using new instrument techniques show that a shallow (of order 100 m deep) fog layer still permits the vertical conduction current to pass. Further, the conductivity in the fog is estimated to be approximately 20% lower than in clear air. Assuming a fog transition thickness of one metre, this implies a vertical conductivity gradient of order 10 fS m−2 at the boundary. The actual vertical conductivity gradient at a cloud boundary would probably be greater, due to the presence of larger droplets in clouds compared to fog, and cleaner, more conductive clear air aloft.

Testing the influence of small crystals on ice size spectra using Doppler lidar observations

Analysis of the vertical velocity of ice crystals observed with a 1.5micron Doppler lidar from a continuous sample of stratiform ice clouds over 17 months show that the distribution of Doppler velocity varies strongly with temperature, with mean velocities of 0.2m/s at -40C, increasing to 0.6m/s at -10C due to particle growth and broadening of the size spectrum. We examine the likely influence of crystals smaller than 60microns by forward modelling their effect on the area-weighted fall speed, and comparing the results to the lidar observations. The comparison strongly suggests that the concentration of small crystals in most clouds is much lower than measured in-situ by some cloud droplet probes. We argue that the discrepancy is likely due to shattering of large crystals on the probe inlet, and that numerous small particles should not be included in numerical weather and climate model parameterizations.

Relative humidity changes in a warmer climate

Key climate feedbacks due to water vapor and clouds rest largely on how relative humidity R changes in a warmer climate, yet this has not been extensively analyzed in models. General circulation models (GCMs) from the CMIP3 archive and several higher resolution atmospheric GCMs examined here generally predict a characteristic pattern of R trend with global temperature that has been reported previously in individual models, including increase around the tropopause, decrease in the tropical upper troposphere, and decrease in midlatitudes. This pattern is very similar to that previously reported for cloud cover in the same GCMs, confirming the role of R in controlling changes in simulated cloud. Comparing different models, the trend in each part of the troposphere is approximately proportional to the upward and/or poleward gradient of R in the present climate. While this suggests that the changes simply reflect a shift of the R pattern upward with the tropopause and poleward with the zonal jets, the drying trend in the subtropics is roughly three times too large to be attributable to shifts of subtropical features, and the subtropical R minima deepen in most models. R trends are correlated with horizontal model resolution, especially outside the tropics, where they show signs of convergence and latitudinal gradients become close to available observations for GCM resolutions near T85 and higher. We argue that much of the systematic change in R can be explained by the local specific humidity having been set (by condensation) in remote regions with different temperature changes, hence the gradients and trends each depend on a model’s ability to resolve moisture transport. Finally, subtropical drying trends predicted from the warming alone fall well short of those observed in recent decades. While this discrepancy supports previous reports of GCMs underestimating Hadley Cell expansion, our results imply that shifts alone are not a sufficient interpretation of changes.

Atmosphere feedbacks during ENSO in a coupled GCM with a modified atmospheric convection scheme

The too diverse representation of ENSO in a coupled GCM limits one’s ability to describe future change of its properties. Several studies pointed to the key role of atmosphere feedbacks in contributing to this diversity. These feedbacks are analyzed here in two simulations of a coupled GCM that differ only by the parameterization of deep atmospheric convection and the associated clouds. Using the Kerry–Emanuel (KE) scheme in the L’Institut Pierre-Simon Laplace Coupled Model, version 4 (IPSL CM4; KE simulation), ENSO has about the right amplitude, whereas it is almost suppressed when using the Tiedke (TI) scheme. Quantifying both the dynamical Bjerknes feedback and the heat flux feedback in KE, TI, and the corresponding Atmospheric Model Intercomparison Project (AMIP) atmosphere-only simulations, it is shown that the suppression of ENSO in TI is due to a doubling of the damping via heat flux feedback. Because the Bjerknes positive feedback is weak in both simulations, the KE simulation exhibits the right ENSO amplitude owing to an error compensation between a too weak heat flux feedback and a too weak Bjerknes feedback. In TI, the heat flux feedback strength is closer to estimates from observations and reanalysis, leading to ENSO suppression. The shortwave heat flux and, to a lesser extent, the latent heat flux feedbacks are the dominant contributors to the change between TI and KE. The shortwave heat flux feedback differences are traced back to a modified distribution of the large-scale regimes of deep convection (negative feedback) and subsidence (positive feedback) in the east Pacific. These are further associated with the model systematic errors. It is argued that a systematic and detailed evaluation of atmosphere feedbacks during ENSO is a necessary step to fully understand its simulation in coupled GCMs.

Orographic precipitation in the tropics: large-eddy simulations and theory

Recent radar and rain-gauge observations from the island of Dominica, which lies in the eastern Caribbean sea at 15 N, show a strong orographic enhancement of trade-wind precipitation. The mechanisms behind this enhancement are investigated using idealized large-eddy simulations with a realistic representation of the shallow trade-wind cumuli over the open ocean upstream of the island. The dominant mechanism is found to be the rapid growth of convection by the bulk lifting of the inhomogenous impinging flow. When rapidly lifted by the terrain, existing clouds and other moist parcels gain buoyancy relative to rising dry air because of their different adiabatic lapse rates. The resulting energetic, closely-packed convection forms precipitation readily and brings frequent heavy showers to the high terrain. Despite this strong precipitation enhancement, only a small fraction (1%) of the impinging moisture flux is lost over the island. However, an extensive rain shadow forms to the lee of Dominica due to the convective stabilization, forced descent, and wave breaking. A linear model is developed to explain the convective enhancement over the steep terrain.

Orographic precipitation in the tropics: experiments in Dominica

The “natural laboratory” of mountainous Dominica (15°N) in the trade wind belt is used to study the physics of tropical orographic precipitation in its purest form, unforced by weather disturbances or by the diurnal cycle of solar heating. A cross-island line of rain gauges and 5-min radar scans from Guadeloupe reveal a large annual precipitation at high elevation (7 m yr^{−1}) and a large orographic enhancement factor (2 to 8) caused primarily by repetitive convective triggering over the windward slope. The triggering is caused by terrain-forced lifting of the conditionally unstable trade wind cloud layer. Ambient humidity fluctuations associated with open-ocean convection may play a key role. The convection transports moisture upward and causes frequent brief showers on the hilltops. The drying ratio of the full air column from precipitation is less than 1% whereas the surface air dries by about 17% from the east coast to the mountain top. On the lee side, a plunging trade wind inversion and reduced instability destroys convective clouds and creates an oceanic rain shadow.

DTM generation from LIDAR data using skewness balancing

LIght Detection And Ranging (LIDAR) data for terrain and land surveying has contributed to many environmental, engineering and civil applications. However, the analysis of Digital Surface Models (DSMs) from complex LIDAR data is still challenging. Commonly, the first task to investigate LIDAR data point clouds is to separate ground and object points as a preparatory step for further object classification. In this paper, the authors present a novel unsupervised segmentation algorithm-skewness balancing to separate object and ground points efficiently from high resolution LIDAR point clouds by exploiting statistical moments. The results presented in this paper have shown its robustness and its potential for commercial applications.

Sampling uncertainty properties of cloud fraction estimates from random transect observations

[1] Cloud cover is conventionally estimated from satellite images as the observed fraction of cloudy pixels. Active instruments such as radar and Lidar observe in narrow transects that sample only a small percentage of the area over which the cloud fraction is estimated. As a consequence, the fraction estimate has an associated sampling uncertainty, which usually remains unspecified. This paper extends a Bayesian method of cloud fraction estimation, which also provides an analytical estimate of the sampling error. This method is applied to test the sensitivity of this error to sampling characteristics, such as the number of observed transects and the variability of the underlying cloud field. The dependence of the uncertainty on these characteristics is investigated using synthetic data simulated to have properties closely resembling observations of the spaceborne Lidar NASA-LITE mission. Results suggest that the variance of the cloud fraction is greatest for medium cloud cover and least when conditions are mostly cloudy or clear. However, there is a bias in the estimation, which is greatest around 25% and 75% cloud cover. The sampling uncertainty is also affected by the mean lengths of clouds and of clear intervals; shorter lengths decrease uncertainty, primarily because there are more cloud observations in a transect of a given length. Uncertainty also falls with increasing number of transects. Therefore a sampling strategy aimed at minimizing the uncertainty in transect derived cloud fraction will have to take into account both the cloud and clear sky length distributions as well as the cloud fraction of the observed field. These conclusions have implications for the design of future satellite missions. This paper describes the first integrated methodology for the analytical assessment of sampling uncertainty in cloud fraction observations from forthcoming spaceborne radar and Lidar missions such as NASA's Calipso and CloudSat.