Tripleclouds: an efficient method for representing horizontal cloud inhomogeneity in 1D radiation schemes by using three regions at each height

Radiation schemes in general circulation models currently make a number of simplifications when accounting for clouds, one of the most important being the removal of horizontal inhomogeneity. A new scheme is presented that attempts to account for the neglected inhomogeneity by using two regions of cloud in each vertical level of the model as opposed to one. One of these regions is used to represent the optically thinner cloud in the level, and the other represents the optically thicker cloud. So, along with the clear-sky region, the scheme has three regions in each model level and is referred to as “Tripleclouds.” In addition, the scheme has the capability to represent arbitrary vertical overlap between the three regions in pairs of adjacent levels. This scheme is implemented in the Edwards–Slingo radiation code and tested on 250 h of data from 12 different days. The data are derived from cloud retrievals using radar, lidar, and a microwave radiometer at Chilbolton, southern United Kingdom. When the data are grouped into periods equivalent in size to general circulation model grid boxes, the shortwave plane-parallel albedo bias is found to be 8%, while the corresponding bias is found to be less than 1% using Tripleclouds. Similar results are found for the longwave biases. Tripleclouds is then compared to a more conventional method of accounting for inhomogeneity that multiplies optical depths by a constant scaling factor, and Tripleclouds is seen to improve on this method both in terms of top-of-atmosphere radiative flux biases and internal heating rates.

Correlated observation errors in data assimilation

Data assimilation provides techniques for combining observations and prior model forecasts to create initial conditions for numerical weather prediction (NWP). The relative weighting assigned to each observation in the analysis is determined by its associated error. Remote sensing data usually has correlated errors, but the correlations are typically ignored in NWP. Here, we describe three approaches to the treatment of observation error correlations. For an idealized data set, the information content under each simplified assumption is compared with that under correct correlation specification. Treating the errors as uncorrelated results in a significant loss of information. However, retention of an approximated correlation gives clear benefits.

The frequency and dynamics of stratospheric sudden warmings in the 21st century

Changes to stratospheric sudden warmings (SSWs) over the coming century, as predicted by the Geophysical Fluid Dynamics Laboratory (GFDL) chemistry climate model [Atmospheric Model With Transport and Chemistry (AMTRAC)], are investigated in detail. Two sets of integrations, each a three-member ensemble, are analyzed. The first set is driven with observed climate forcings between 1960 and 2004; the second is driven with climate forcings from a coupled model run, including trace gas concentrations representing a midrange estimate of future anthropogenic emissions between 1990 and 2099. A small positive trend in the frequency of SSWs is found. This trend, amounting to 1 event/decade over a century, is statistically significant at the 90% confidence level and is consistent over the two sets of model integrations. Comparison of the model SSW climatology between the late 20th and 21st centuries shows that the increase is largest toward the end of the winter season. In contrast, the dynamical properties are not significantly altered in the coming century, despite the increase in SSW frequency. Owing to the intrinsic complexity of our model, the direct cause of the predicted trend in SSW frequency remains an open question.

Running climate models on grids using G-Rex

Compute grids are used widely in many areas of environmental science, but there has been limited uptake of grid computing by the climate modelling community, partly because the characteristics of many climate models make them difficult to use with popular grid middleware systems. In particular, climate models usually produce large volumes of output data, and running them also involves complicated workflows implemented as shell scripts. A new grid middleware system that is well suited to climate modelling applications is presented in this paper. Grid Remote Execution (G-Rex) allows climate models to be deployed as Web services on remote computer systems and then launched and controlled as if they were running on the user's own computer. Output from the model is transferred back to the user while the run is in progress to prevent it from accumulating on the remote system and to allow the user to monitor the model. G-Rex has a REST architectural style, featuring a Java client program that can easily be incorporated into existing scientific workflow scripts. Some technical details of G-Rex are presented, with examples of its use by climate modellers.

A new look at stratospheric sudden warmings. Part III. Polar vortex evolution and vertical structure

The evolution of the Arctic polar vortex during observed major mid-winter stratospheric sudden warmings (SSWs) is investigated for the period 1957-2002, using European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-40 Ertel’s potential vorticity (PV) and temperature fields. Time-lag composites of vertically weighted PV, calculated relative to the SSW onset time, are derived for both vortex displacement SSWs and vortex splitting SSWs, by averaging over the 15 recorded displacement and 13 splitting events. The evolving vertical structure of the polar vortex during a typical SSW of each type is clearly illustrated by plotting an isosurface of the composite PV field, and is shown to be very close to that observed during representative individual events. Results are verified by comparison with an elliptical diagnostic vortex moment technique. For both types of SSW, little variation is found between individual events in the orientation of the developing vortex relative to the underlying topography, i.e. the location of the vortex during SSWs of each type is largely fixed in relation to the Earth’s surface. During each type of SSW, the vortex is found to have a distinctive vertical structure. Vortex splitting events are typically barotropic, with the vortex split occurring near-simultaneously over a large altitude range (20-40 km). In the majority of cases, of the two daughter vortices formed, it is the ‘Siberian’ vortex that dominates over its ‘Canadian’ counterpart. In contrast, displacement events are characterized by a very clear baroclinic structure; the vortex tilts significantly westward with height, so that the top and bottom of the vortex are separated by nearly 180◦ longitude before the upper vortex is sheared away and destroyed.

Grid Remote Execution (G-Rex) of Climate Models

G-Rex is light-weight Java middleware that allows scientific applications deployed on remote computer systems to be launched and controlled as if they are running on the user's own computer. G-Rex is particularly suited to ocean and climate modelling applications because output from the model is transferred back to the user while the run is in progress, which prevents the accumulation of large amounts of data on the remote cluster. The G-Rex server is a RESTful Web application that runs inside a servlet container on the remote system, and the client component is a Java command line program that can easily be incorporated into existing scientific work-flow scripts. The NEMO and POLCOMS ocean models have been deployed as G-Rex services in the NERC Cluster Grid, and G-Rex is the core grid middleware in the GCEP and GCOMS e-science projects.