Although the links between stratospheric dynamics, climate and weather have been demonstrated, direct observations of stratospheric winds are lacking, in particular at altitudes above 30 km. We report observations of winds between 8 and 0.01 hPa (similar to 35-80 km) from October 2009 to April 2010 by the Superconducting Submillimeter-Wave Limb-Emission Sounder (SMILES) on the International Space Station. The altitude range covers the region between 35-60 km where previous space-borne wind instruments show a lack of sensitivity. Both zonal and meridional wind components were obtained, though not simultaneously, in the latitude range from 30 degrees S to 55 degrees N and with a single profile precision of 7-9 ms(-1) between 8 and 0.6 hPa and better than 20 ms(-1) at altitudes above. The vertical resolution is 5-7 km except in the upper part of the retrieval range (10 km at 0.01 hPa). In the region between 1-0.05 hPa, an absolute value of the mean difference <2 ms(-1) is found between SMILES profiles retrieved from different spectroscopic lines and instrumental settings. Good agreement (absolute value of the mean difference of similar to 2 ms(-1)) is also found with the European Centre for Medium-Range Weather Forecasts (ECMWF) analysis in most of the stratosphere except for the zonal winds over the equator (difference >5 ms(-1)). In the mesosphere, SMILES and ECMWF zonal winds exhibit large differences (>20 ms(-1)), especially in the tropics. We illustrate our results by showing daily and monthly zonal wind variations, namely the semi-annual oscillation in the tropics and reversals of the flow direction between 50-55 degrees N during sudden stratospheric warmings. The daily comparison with ECMWF winds reveals that in the beginning of February, a significantly stronger zonal westward flow is measured in the tropics at 2 hPa compared to the flow computed in the analysis (difference of similar to 20 ms(-1)). The results show that the comparison between SMILES and ECMWF winds is not only relevant for the quality assessment of the new SMILES winds, but it also provides insights on the quality of the ECMWF winds themselves. Although the instrument was not specifically designed for measuring winds, the results demonstrate that space-borne sub-mm wave radiometers have the potential to provide good quality data for improving the stratospheric winds in atmospheric models.

Winds measured by lidar from the Aeolus satellite are compared with winds measured by two ground-based radars - MARA in Antarctica (70.77 degrees S, 11.73 degrees E) and ES-RAD (67.88 degrees N, 21.10 degrees E) in Arctic Sweden - for the period 1 July-31 December 2019. Aeolus is a demonstrator mission to test whether winds measured by Doppler lidar from space can have sufficient accuracy to contribute to improved weather forecasting. A comprehensive programme of calibration and validation has been undertaken following the satellite launch in 2018, but, so far, direct comparison with independent measurements from the Arctic or Antarctic regions have not been made. The comparison covers heights from the low troposphere to just above the tropopause. Results for each radar site are presented separately for Rayleigh (clear) winds, Mie (cloudy) winds, sunlit ("summer") and non-sunlit ("winter") seasons, and ascending and descending satellite tracks. Horizontally projected line-of-sight (HLOS) winds from Aeolus, reprocessed using baseline 2B10, for passes within 100 km of the radar sites, are compared with HLOS winds calculated from 1 h averaged radar horizontal wind components. The agreement in most data subsets is very good, with no evidence of significant biases (<1ms(-1)). Possible biases are identified for two subsets (about -2ms(-1) for the Rayleigh winds for the descending passes at MARA and about 2ms(-1) for the Mie winds for the ascending passes at ESRAD, both in winter), but these are only marginally significant. A robust significant bias of about 7ms(-1) is found for the Mie winds for the ascending tracks at MARA in summer. There is also some evidence for increased random error (by about 1ms(-1) / for the Aeolus Mie winds at MARA in summer compared to winter. This might be related to the presence of sunlight scatter over the whole of Antarctica as Aeolus transits across it during summer.

A stochastic parametrization for deep convection, based on cellular automata, has been evaluated in the high-resolution (2.5 km) ensemble prediction system Hirlam Aladin Regional Mesoscale Operational NWP Ensemble Prediction System (HarmonEPS). We studied whether such a stochastic physical parametrization, whilst implemented in a deterministic forecast model, can have an impact on the performance of the uncertainty estimates given by an ensemble prediction system. Various feedback mechanisms in the parametrization were studied with respect to ensemble spread and skill, in both subgrid and resolved precipitation fields. It was found that the stochastic parametrization improves the model skill in general, by reducing a positive bias in precipitation. This reduction in bias, however, led to a reduction in ensemble spread of precipitation. Overall, scores that measure the accuracy and reliability of probabilistic predictions indicate that the net impact (improved skill, degraded spread) of the ensemble prediction system is improved for 6 h accumulated precipitation with the stochastic parametrization and is rather neutral for other quantities examined.

Because of the limited resolution of numerical weather prediction (NWP) models, subgrid-scale physical processes are parameterized and represented by gridbox means. However, some physical processes are better represented by a mean and its variance; a typical example is deep convection, with scales varying from individual updrafts to organized mesoscale systems. This study investigates, in an idealized setting, whether a cellular automaton (CA) can be used to enhance subgrid-scale organization by forming clusters representative of the convective scales and thus yield a stochastic representation of subgrid-scale variability. The authors study the transfer of energy from the convective to the larger atmospheric scales through nonlinear wave interactions. This is done using a shallow water (SW) model initialized with equatorial wave modes. By letting a CA act on a finer resolution than that of the SW model, it can be expected to mimic the effect of, for instance, gravity wave propagation on convective organization. Employing the CA scheme permits the reproduction of the observed behavior of slowing down equatorial Kelvin modes in convectively active regions, while random perturbations fail to feed back on the large-scale flow. The analysis of kinetic energy spectra demonstrates that the CA subgrid scheme introduces energy backscatter from the smallest model scales to medium scales. However, the amount of energy backscattered depends almost solely on the memory time scale introduced to the subgrid scheme, whereas any variation in spatial scales generated does not influence the energy spectra markedly.

The Barents Oscillation (BO) is an anomalous wintertime atmospheric circulation pattern in the Northern Hemisphere that has been linked to the meridional flow over the Nordic Seas. There are speculations that the BO has important implications for the Arctic climate; however, it has also been suggested that the pattern is an artifact of Empirical Orthogonal Function (EOF) analysis due to an eastward shift of the Arctic Oscillation/North Atlantic Oscillation (AO/NAO). In this study, EOF analyses are performed to show that a robust pattern resembling the BO can be found during different time periods, even when the AO/NAO is relatively stationary. This BO has a high and stable temporal correlation with the geostrophic zonal wind over the Barents Sea, while the contribution from the AO/NAO is small. The surface air temperature anomalies over the Barents Sea are closely associated with this mode of climate variability.

The coupled climate model EC-Earth2 is used to investigate the impact of direct radiative effects of aerosols on stationary waves in the northern hemisphere wintertime circulation. The direct effect of aerosols is simulated by introducing prescribed mixing ratios of different aerosol compounds representing pre-industrial and present-day conditions, no indirect effects are included. In the EC-Earth2 results, the surface temperature response is uncorrelated with the highly asymmetric aerosol radiative forcing pattern. Instead, the anomalous extratropical temperature field bears a strong resemblance to the aerosol-induced changes in the stationary-wave pattern. It is demonstrated that the main features of the wave pattern of EC-Earth2 can be replicated by a linear, baroclinic model forced with latent heat changes corresponding to the anomalous convective precipitation generated by EC-Earth2. The tropical latent heat release is an effective means of generating stationary wave trains that propagate into the extratropics. Hence, the results of the present study indicate that aerosol-induced convective precipitation anomalies govern the extratropical wave-field changes, and that the far-field temperature response dominates over local effects of aerosol radiative forcing.

We examine the linearity of the Ensemble of Data Assimilations (EDA) technique with respect to the amplitude of the applied observation perturbations. We provide explicit examples to assess the linear relationship between such modifications of the observing system and the resulting changes in the EDA ensemble spread. The results demonstrate that, for a state-of-the-art numerical weather prediction (NWP) system, such linearity between the applied observation perturbations and the EDA ensemble spread holds well for temporal and spatial regimes relevant to global medium-range weather prediction: specifically, for forecast lead-times of up to approximately 5 days, in the vertical throughout the troposphere up to the lower and middle stratosphere and for broad horizontal scales.

A novel uncertainty quantification method is used to evaluate the impact of uncertainties of parameters within the icing model in the modeling chain for icing-related wind power production loss forecasts. As a first step, uncertain parameters in the icing model were identified from the literature and personal communications. These parameters are the median volume diameter of the hydrometeors, the sticking efficiency for snow and graupel, the Nusselt number, the shedding factor, and the wind erosion factor. The sensitivity of these parameters on icing-related wind power production losses is examined. An icing model ensemble representing the estimated parameter uncertainties is designed using so-called deterministic sampling and is run for two periods over a total of 29 weeks. Deterministic sampling allows an exact representation of the uncertainty and, in future applications, further calibration of these parameters. Also, the number of required ensemble members is reduced drastically relative to the commonly used random-sampling method, thus enabling faster delivery and a more flexible system. The results from random and deterministic sampling are compared and agree very well, confirming the usefulness of deterministic sampling. The ensemble mean of the nine-member icing model ensemble generated with deterministic sampling is shown to improve the forecast skill relative to one single forecast for the winter periods. In addition, the ensemble spread provides valuable information as compared with a single forecast in terms of forecasting uncertainty. However, addressing uncertainties in the icing model alone underestimates the forecast uncertainty, thus stressing the need for a fully probabilistic approach in the modeling chain for wind power forecasts in a cold climate.

A new scheme that solves the advection-diffusion equation for tracers in a spectral General Circulation Model (GCM) is presented. The main ideas are (1) using a monotonic and smooth functional of the tracer as prognostic variable to ensure positive definite concentrations and continuity of all derivatives and (2) defining an adjustable tracer-mass correction as a multiplication of the tracer in grid space, giving rise to an efficient correction in spectral space. Common standard benchmark tests for two-dimensional horizontal advection using deformational wind fields show that the new scheme is accurate and essentially not diffusive. A three-dimensional test is proposed in order to validate vertical transport. Additionally to standard error norms and global tracer mass, the entropy of mixing is introduced as another conservation constraint and utilized to determine the strength of the mass correction which is a free parameter. The transport scheme is applied in a mechanistic spectral GCM from the surface to the lower thermosphere. It is extended such that the mass correction takes the diffusion and other nonconservative effects explicitly into account. By this method we estimate the mean age of air along with its dependence on the turbulent horizontal Schmidt number.

A 350-year-long, well-dated delta O-18 stalagmite record from the summer rainfall region in South Africa is positively correlated with regional air surface temperatures at interannual time scales. The coldest period documented in this record occurred between 1690 and 1740, slightly lagging the Maunder Minimum (1645-1710). A temperature reconstruction, based on the correlation between regional surface temperatures and the stalagmite delta O-18 variations, indicates that parts of this period could have been as much as 1.4 degrees C colder than today. Significant cycles of 22, 11 and 4.8 years demonstrate that the solar magnetic and the El Nino-Southern Oscillation cycle could be important drivers of multidecadal to interannual climate variability in this region. The observation that the most important driver of stalagmite delta O-18 on interannual time scales from this subtropical region is regional surface temperature cautions against deterministic interpretations of delta O-18 variations in low-latitude stalagmites as mainly driven by the amount of precipitation.