Alessio Bozzo

Impacts of parameterized orographic drag on the Northern Hemisphere winter circulation

Abstract A recent intercomparison exercise proposed by the Working Group for Numerical Experimentation (WGNE) revealed that the parameterized, or unresolved, surface stress in weather forecast models is highly model‐dependent, especially over orography. Models of comparable resolution differ over land by as much as 20% in zonal mean total subgrid surface stress (τtot). The way τtot is partitioned between the different parameterizations is also model‐dependent. In this study, we simulated in a particular model an increase in τtot comparable with the spread found in the WGNE intercomparison. This increase was simulated in two ways, namely by increasing independently the contributions to τtot of the turbulent orographic form drag scheme (TOFD) and of the orographic low‐level blocking scheme (BLOCK). Increasing the parameterized orographic drag leads to significant changes in surface pressure, zonal wind and temperature in the Northern Hemisphere during winter both in 10 day weather forecasts and in seasonal integrations. However, the magnitude of these changes in circulation strongly depends on which scheme is modified. In 10 day forecasts, stronger changes are found when the TOFD stress is increased, while on seasonal time scales the effects are of comparable magnitude, although different in detail. At these time scales, the BLOCK scheme affects the lower stratosphere winds through changes in the resolved planetary waves which are associated with surface impacts, while the TOFD effects are mostly limited to the lower troposphere. The partitioning of τtot between the two schemes appears to play an important role at all time scales.

Mitigating errors in surface temperature forecasts using approximate radiation updates

Due to computational expense, the radiation schemes in many weather and climate models are called infrequently in time and/or on a reduced spatial grid. The former can lead to a lag in the diurnal cycle of surface temperature, while the latter can lead to large surface temperature errors at coastal land points due to surface fluxes computed over the ocean being used where the skin temperature and surface albedo are very different. This paper describes a computationally efficient solution to these problems, in which the surface longwave and shortwave fluxes are updated every time step and grid point according to the local skin temperature and albedo. In order that energy is conserved, it is necessary to compute the change to the net flux profile consistent with the changed surface fluxes. The longwave radiation scheme has been modified to compute also the rate of change of the profile of upwelling longwave flux with respect to the value at the surface. Then at each grid point and time step, the upwelling flux and heating-rate profiles are updated using the new value of skin temperature. The computational cost of performing approximate radiation updates in the ECMWF model is only 2% of the cost of the full radiation scheme, so increases the overall cost of the model by only of order 0.2%. Testing the new scheme by running daily 5 day forecasts over an 8 month period reveals significant improvement in 2 m temperature forecasts at coastal stations compared to observations.

Impact of a spectral sampling technique for radiation on ECMWF weather forecasts

Radiation transfer computations are one of the most expensive components of atmospheric models integrations. Methods generally applied to reduce their computational cost, include less frequent update of the radiative fluxes or coarser spatial grids than those used for the rest of the model. In the operational configuration of the Integrated Forecast System (IFS) used at the European Centre for Medium Range Weather Forecasts (ECMWF), radiation accounts for ∼ 10% of the total computer time, and the radiative fluxes are computed with both a reduced temporal and spatial resolution. In this study, we show that these approximations have a negligible impact on the forecast errors when looking at the large-scale circulation but they lead to biases in the surface temperature. These are linked to inconsistencies in the representation of surface properties or to poorly sampled temporal evolution of the radiative fluxes. Here we explore an alternative method to alleviate such errors, based on a recently developed technique that estimates radiative fluxes from randomly-chosen subsets of spectral points designed to minimize the surface flux error. This approach introduces substantial uncorrelated noise in the radiative fluxes at the surface and heating rates, but it reduces the cost associated to the spectral integration. Therefore, it allows to update the radiative transfer computations more frequently and to perform them on a denser spatial grid. We evaluate how this approach applies to 5 days weather forecasts by testing different combinations of temporal and spectral sampling at high spatial resolution. We assess the impact of each radiation configuration on the forecast, by comparing the near-surface temperatures to observations from synoptic stations. The results show that a combination of spectrally sparse but temporally and spatially dense radiation calculations has the potential to reduce the forecast errors, particularly those associated with underresolved topography and stable nocturnal boundary layers, with affordable computational costs.

Trends of mean and extreme temperature indices since 1874 at low‐elevation sites in the southern Alps

We describe the recovery of three daily meteorological records for the southern Alps (Domodossola, Riva del Garda, and Rovereto), all starting in the second half of the nineteenth century. We use these new data, along with additional records, to study regional changes in the mean temperature and extreme indices of heat waves and cold spells frequency and duration over the period 1874–2015. The records are homogenized using subdaily cloud cover observations as a constraint for the statistical model, an approach that has never been applied before in the literature. A case study based on a record of parallel observations between a traditional meteorological window and a modern screen shows that the use of cloud cover can reduce the root-mean-square error of the homogenization by up to 30% in comparison to an unaided statistical correction. We find that mean temperature in the southern Alps has increased by 1.4°C per century over the analyzed period, with larger increases in daily minimum temperatures than maximum temperatures. The number of hot days in summer has more than tripled, and a similar increase is observed in duration of heat waves. Cold days in winter have dropped at a similar rate. These trends are mainly caused by climate change over the last few decades.

A Flexible and Efficient Radiation Scheme for the ECMWF Model

This paper describes a new radiation scheme ecRad for use both in the model of the European Centre for Medium‐Range Weather Forecasts (ECMWF), and off‐line for noncommercial research. Its modular structure allows the spectral resolution, the description of cloud and aerosol optical properties, and the solver, to be changed independently. The available solvers include the Monte Carlo Independent Column Approximation (McICA), Tripleclouds, and the Speedy Algorithm for Radiative Transfer through Cloud Sides (SPARTACUS), the latter which makes ECMWF the first global model capable of representing the 3‐D radiative effects of clouds. The new implementation of the operational McICA solver produces less noise in atmospheric heating rates, and is 41% faster, which can yield indirect forecast skill improvements via calling the radiation scheme more frequently. We demonstrate how longwave scattering may be implemented for clouds but not aerosols, which is only 4% more computationally costly overall than neglecting longwave scattering and yields further modest forecast improvements. It is also shown how a sequence of radiation changes in the last few years has led to a substantial reduction in stratospheric temperature biases.

A benchmark for testing the accuracy and computational cost ofshortwave top-of-atmosphere reflectance calculations in clear-skyaerosol-laden atmospheres

Accurate calculations of shortwave reflectances in clear-sky aerosol-laden atmospheres are necessary for various applications in atmospheric sciences. However, computational cost becomes increasingly important for some applications such as data assimilation of top-of-atmosphere reflectances in models of atmospheric composition. This study aims to provide a benchmark that can help in assessing these two requirements in combination. We describe a protocol and input data for 44 080 cases involving various solar and viewing geometries, four different surfaces (one oceanic bidirectional reflectance function and three albedo values for a Lambertian surface), eight aerosol optical depths, five wavelengths, and four aerosol types. We first consider two models relying on the discrete ordinate method: VLIDORT (in vector and scalar configurations) and DISORT (scalar configuration only). We use VLIDORT in its vector configuration as a reference model and quantify the loss of accuracy due to (i) neglecting the effect of polarization in DISORT and VLIDORT (scalar) models and (ii) decreasing the number of streams in DISORT. We further test two other models: the 6SV2 model, relying on the successive orders of scattering method, and Forward-Lobe Two-Stream Radiance Model (FLOTSAM), a new model under development by two of the authors. Typical mean fractional errors of 2.8 % and 2.4 % for 6SV2 and FLOTSAM are found, respectively. Computational cost depends on the input parameters but also on the code implementation and application as some models solve the radiative transfer equations for a range of geometries while others do not. All necessary input and output data are provided as a Supplement as a potential resource for interested developers and users of radiative transfer models.

The Numerics of Physical Parametrization in the ECMWF Model

The numerical aspects of physical parametrization are discussed mainly in the context of the ECMWF Integrated Forecasting System. Two time integration techniques are discussed. With parallel splitting the tendencies of all the parametrized processes are computed independently of each other. With sequential splitting, tendencies of the explicit processes are computed first and are used as input to the subsequent implicit fast process. It is argued that sequential splitting is better than parallel splitting for problems with multiple time scales, because a balance between processes is obtained during the time integration. It is shown that sequential splitting applied to boundary layer diffusion in the ECMWF model leads to much smaller time truncation errors than does parallel splitting. The so called Semi-Lagrangian Averaging of Physical Parametrizations (SLAVEPP), as implemented in the ECMWF model, is explained. The scheme reduces time truncation errors compared to standard first order methods, although a few implementation questions remain. In the scheme fast and slow processes are handled differently and it remains a research topic to find the optimal way of handling convection and clouds. Process specific numerical issues are discussed in the context of the ECMWF parametrization package. Examples are the non-linear stability problems in the vertical diffusion scheme, the stability related mass flux limit in the convection scheme and the fast processes in the cloud microphysics. Vertical resolution in the land surface scheme is inspired by the requirement to represent diurnal to annual time scales. Finally, a new coupling strategy between atmospheric models and land surface schemes is discussed. It allows for fully implicit coupling also for tiled land surface schemes.