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.

Stratospheric Memory and Skill of Extended-Range Weather Forecasts

We use an empirical statistical model to demonstrate significant skill in making extended-range forecasts of the monthly-mean Arctic Oscillation (AO). Forecast skill derives from persistent circulation anomalies in the lowermost stratosphere and is greatest during boreal winter. A comparison to the Southern Hemisphere provides evidence that both the time scale and predictability of the AO depend on the presence of persistent circulation anomalies just above the tropopause. These circulation anomalies most likely affect the troposphere through changes to waves in the upper troposphere, which induce surface pressure changes that correspond to the AO.

Insects, weather and climate

Applications of atmospheric science are relevant to a range of themes within science and society; application to entomology was the main focus of this meeting organised by Dr Curtis Wood (University of Reading). This meeting was held jointly with the Royal Entomological Society. The talks were designed to appeal to the broader scientific community by showcasing topics near the join of the two disciplines. The audience heard about exciting topics within weather and climate change, how they are applied to entomological science and how insects can be used to advance atmospheric science. The meeting included the 2009 Margary Lecture given by Prof. Philip Mellor from the Institute for Animal Health (IAH) at Pirbright.

Anthropogenic snowfall events in the UK: examples of urban weather modification?

Snow in the UK is generally associated with synoptic or mesoscale weather systems, thus snowfall during quiescent anticyclonic conditions is surprising and might not even be forecast. Consequently it could present a hazard. Snowfall during anticyclonic freezing fog conditions at Didcot and Hereford in December 2006 is investigated here. These two snowfalls seem to present circumstances in which anthropogenically-produced aerosols could have provided ice nuclei within the freezing fog, and therefore might provide characteristic examples of Anthropogenic Snowfall Events (ASEs).

Land surface anomaly simulations and predictions with a climate model: an El Nino Southern Oscillation case study

The ability of climate models to reproduce and predict land surface anomalies is an important but little-studied topic. In this study, an atmosphere and ocean assimilation scheme is used to determine whether HadCM3 can reproduce and predict snow water equivalent and soil moisture during the 1997–1998 El Nino Southern Oscillation event. Soil moisture is reproduced more successfully, though both snow and soil moisture show some predictability at 1- and 4-month lead times. This result suggests that land surface anomalies may be reasonably well initialized for climate model predictions and hydrological applications using atmospheric assimilation methods over a period of time.

How many facets are needed to represent the surface energy balance of an urban area?

We investigate the question of how many facets are needed to represent the energy balance of an urban area by developing simplified 3-, 2- and 1-facet versions of a 4-facet energy balance model of two-dimensional streets and buildings. The 3-facet model simplifies the 4-facet model by averaging over the canyon orientation, which results in similar net shortwave and longwave balances for both wall facets, but maintains the asymmetry in the heat fluxes within the street canyon. For the 2-facet model, on the assumption that the wall and road temperatures are equal, the road and wall facets can be combined mathematically into a single street-canyon facet with effective values of the heat transfer coefficient, albedo, emissivity and thermodynamic properties, without further approximation. The 1-facet model requires the additional assumption that the roof temperature is also equal to the road and wall temperatures. Idealised simulations show that the geometry and material properties of the walls and road lead to a large heat capacity of the combined street canyon, whereas the roof behaves like a flat surface with low heat capacity. This means that the magnitude of the diurnal temperature variation of the street-canyon facets are broadly similar and much smaller than the diurnal temperature variation of the roof facets. Consequently, the approximation that the street-canyon facets have similar temperatures is sound, and the road and walls can be combined into a single facet. The roof behaves very differently and a separate roof facet is required. Consequently, the 2-facet model performs similarly to the 4-facet model, while the 1-facet model does not. The models are compared with previously published observations collected in Mexico City. Although the 3- and 2-facet models perform better than the 1-facet model, the present models are unable to represent the phase of the sensible heat flux. This result is consistent with previous model comparisons, and we argue that this feature of the data cannot be produced by a single column model. We conclude that a 2-facet model is necessary, and for numerical weather prediction sufficient, to model an urban surface, and that this conclusion is robust and therefore applicable to more general geometries.

Simulation of the Earth's radiation budget by the European Centre for Medium-Range Weather Forecasts 40-year reanalysis (ERA40)

The radiation budget simulated by the European Centre for Medium-Range Weather Forecasts (ECMWF) 40-year reanalysis (ERA40) is evaluated for the period 1979–2001 using independent satellite data and additional model data. This provides information on the quality of the radiation products and indirect evaluation of other aspects of the climate produced by ERA40. The climatology of clear-sky outgoing longwave radiation (OLR) is well captured by ERA40. Underestimations of about 10 W m−2 in clear-sky OLR over tropical convective regions by ERA40 compared to satellite data are substantially reduced when the satellite sampling is taken into account. The climatology of column-integrated water vapor is well simulated by ERA40 compared to satellite data over the ocean, indicating that the simulation of downward clear-sky longwave fluxes at the surface is likely to be good. Clear-sky absorbed solar radiation (ASR) and clear-sky OLR are overestimated by ERA40 over north Africa and high-latitude land regions. The observed interannual changes in low-latitude means are not well reproduced. Using ERA40 to analyze trends and climate feedbacks globally is therefore not recommended. The all-sky radiation budget is poorly simulated by ERA40. OLR is overestimated by around 10 W m−2 over much of the globe. ASR is underestimated by around 30 W m−2 over tropical ocean regions. Away from marine stratocumulus regions, where cloud fraction is underestimated by ERA40, the poor radiation simulation by ERA40 appears to be related to inaccurate radiative properties of cloud rather than inaccurate cloud distributions.

Analysis of moisture variability in the European Centre for Medium-Range Weather Forecasts 15-year reanalysis over the tropical oceans

We compare European Centre for Medium-Range Weather Forecasts 15-year reanalysis (ERA-15) moisture over the tropical oceans with satellite observations and the U.S. National Centers for Environmental Prediction (NCEP) National Center for Atmospheric Research 40-year reanalysis. When systematic differences in moisture between the observational and reanalysis data sets are removed, the NCEP data show excellent agreement with the observations while the ERA-15 variability exhibits remarkable differences. By forcing agreement between ERA-15 column water vapor and the observations, where available, by scaling the entire moisture column accordingly, the height-dependent moisture variability remains unchanged for all but the 550–850 hPa layer, where the moisture variability reduces significantly. Thus the excess variation of column moisture in ERA-15 appears to originate in this layer. The moisture variability provided by ERA-15 is not deemed of sufficient quality for use in the validation of climate models.

A Proposed Modification to the Robert–Asselin Time Filter

The Robert–Asselin time filter is widely used in numerical models of weather and climate. It successfully suppresses the spurious computational mode associated with the leapfrog time-stepping scheme. Unfortunately, it also weakly suppresses the physical mode and severely degrades the numerical accuracy. These two concomitant problems are shown to occur because the filter does not conserve the mean state, averaged over the three time slices on which it operates. The author proposes a simple modification to the Robert–Asselin filter, which does conserve the three-time-level mean state. When used in conjunction with the leapfrog scheme, the modification vastly reduces the impacts on the physical mode and increases the numerical accuracy for amplitude errors by two orders, yielding third-order accuracy. The modified filter could easily be incorporated into existing general circulation models of the atmosphere and ocean. In principle, it should deliver more faithful simulations at almost no additional computational expense. Alternatively, it may permit the use of longer time steps with no loss of accuracy, reducing the computational expense of a given simulation.

MJO Simulation Diagnostics

The Madden–Julian oscillation (MJO) interacts with and influences a wide range of weather and climate phenomena (e.g., monsoons, ENSO, tropical storms, midlatitude weather), and represents an important, and as yet unexploited, source of predictability at the subseasonal time scale. Despite the important role of the MJO in climate and weather systems, current global circulation models (GCMs) exhibit considerable shortcomings in representing this phenomenon. These shortcomings have been documented in a number of multimodel comparison studies over the last decade. However, diagnosis of model performance has been challenging, and model progress has been difficult to track, because of the lack of a coherent and standardized set of MJO diagnostics. One of the chief objectives of the U.S. Climate Variability and Predictability (CLIVAR) MJO Working Group is the development of observation-based diagnostics for objectively evaluating global model simulations of the MJO in a consistent framework. Motivation for this activity is reviewed, and the intent and justification for a set of diagnostics is provided, along with specification for their calculation, and illustrations of their application. The diagnostics range from relatively simple analyses of variance and correlation to more sophisticated space–time spectral and empirical orthogonal function analyses. These diagnostic techniques are used to detect MJO signals, to construct composite life cycles, to identify associations of MJO activity with the mean state, and to describe interannual variability of the MJO.

Variability in the summer season hydrological cycle over the Atlantic-Europe region 1979-2007

Variability in aspects of the hydrological cycle over the Europe-Atlantic region during the summer season is analysed for the period 1979-2007, using observational estimates, reanalyses and climate model simulations. Warming and moistening trends are evident in observations and models although decadal changes in water vapour are not well represented by reanalyses, including the new European Centre for Medium Range Weather Forecasts (ECMWF) Interim reanalysis. Over the north Atlantic and northern Europe, observed water vapour trends are close to that expected from the temperature trends and Clausius-Clapeyron equation (7% K-1), larger than the model simulations. Precipitation over Europe is dominated by large-scale dynamics with positive phases of the North Atlantic Oscillation coinciding with drier conditions over north Europe and wetter conditions over the Mediterranean region. Evaporation trends over Europe are positive in reanalyses and models, especially for the Mediterranean region (1-3% per decade in reanalyses and climate models). Over the north Atlantic, declining precipitation combined with increased moisture contributed to an apparent rise in water vapour residence time. Maximum precipitation minus evaporation over the north Atlantic occurred during summer 1991, declining thereafter.

A new set of orthogonal patterns in weather and climate: Optimally interpolated patterns.

A new spectral-based approach is presented to find orthogonal patterns from gridded weather/climate data. The method is based on optimizing the interpolation error variance. The optimally interpolated patterns (OIP) are then given by the eigenvectors of the interpolation error covariance matrix, obtained using the cross-spectral matrix. The formulation of the approach is presented, and the application to low-dimension stochastic toy models and to various reanalyses datasets is performed. In particular, it is found that the lowest-frequency patterns correspond to largest eigenvalues, that is, variances, of the interpolation error matrix. The approach has been applied to the Northern Hemispheric (NH) and tropical sea level pressure (SLP) and to the Indian Ocean sea surface temperature (SST). Two main OIP patterns are found for the NH SLP representing respectively the North Atlantic Oscillation and the North Pacific pattern. The leading tropical SLP OIP represents the Southern Oscillation. For the Indian Ocean SST, the leading OIP pattern shows a tripole-like structure having one sign over the eastern and north- and southwestern parts and an opposite sign in the remaining parts of the basin. The pattern is also found to have a high lagged correlation with the Niño-3 index with 6-months lag.