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Satellite sea-ice thickness observations are shown to be key to improving seasonal predictions of summer Arctic sea-ice concentration. The decrease in high-latitude surface albedo caused by the vanishing Arctic sea ice has a distinct signature on the Earth's radiative balance (Pistone I et al i ., [6]). The study shows that I in situ i observations play a critical role in initializing numerical weather forecasts during the winter season, while satellite microwave sounders are crucial during the summer season. [Extracted from the article]

Marine cold air outbreaks (MCAOs) are large-scale events in which cold air masses are advected over open ocean. It is well-known that these events are linked to the formation of polar lows and other mesoscale phenomena associated with high wind speeds, and that they therefore in some cases represent a hazard to maritime activities. However, it is still unknown whether MCAOs are generally conducive to higher wind speeds than normal. Here this is investigated by comparing ocean near-surface wind speeds during MCAOs in atmospheric reanalysis products with different horizontal grid spacings, along with two case-studies using a convection-permitting numerical weather prediction model. The study regions are the Labrador Sea and the Greenland-Iceland-Norwegian (GIN) Seas, where MCAOs have been shown to be important for air-sea interaction and deep water formation. One of the main findings is that wind speeds during the strongest MCAO events are higher than normal and higher than wind speeds during less severe events. Limited evidence from the case-studies suggests that reanalyses with grid spacings of more than 50 km underestimate winds driven by the large ocean-atmosphere energy fluxes during MCAOs. The peak times of MCAOs usually occur when baroclinic waves pass over the regions. Therefore, the strong wind episodes during MCAOs generally last for just a few days. However, MCAOs as defined here can persist for 50 days or more. [ABSTRACT FROM AUTHOR]

The impact of observations on analysis uncertainty and forecast performance was investigated for austral spring 2010 over the southern polar area for four different systems (NRL, GMAO, ECMWF and Météo-France) at the time of the Concordiasi field experiment. The largest multi-model variance in 500 hPa height analyses is found in the southern sub-Antarctic oceanic region, where there are rapidly evolving weather systems, rapid forecast-error growth, and fewer upper-air wind observation data to constrain the analyses. The total impact of all observations on the model forecast was computed using the 24 h forecast sensitivity-to-observations diagnostic. Observation types that contribute most to the reduction of the forecast error are shown to be AMSU, IASI, AIRS, GPS-RO, radiosonde, surface and atmospheric motion vector observations. For sounding data, radiosondes and dropsondes, one can note a large impact on the analysis and forecasts of temperature at low levels and a large impact of wind at high levels. Observing system experiments using the Concordiasi dropsondes show a large impact of the observations over the Antarctic plateau extending to lower latitudes with the forecast range, with the largest impact around 50-70°S. These experiments indicate there is a potential benefit from using radiance data better over land and sea-ice and from innovative atmospheric motion vectors obtained from a combination of various satellites to fill the current data gaps and improve numerical weather prediction analyses in this region. [ABSTRACT FROM AUTHOR]

Global numerical weather prediction skill over polar areas is assessed, mostly based on the European Centre for Medium-Range Weather Forecasts (ECMWF) system but also theMet Office, Japan Meteorological Agency (JMA), Environment Canada andNational Centers for Environmental Prediction (NCEP) analysis data. Polar forecast verification against analyses shows a similar trend of forecast improvement over the past 12 years compared with improvements at lower latitudes. These improvements are presumably due to increased model resolution and model sophistication, improved data assimilation methods and increased observational data coverage and better data quality. By comparing ECMWF's real-time forecast skill changes against those from reforecasts initialized from reanalyses, it is possible to quantify how much of the improvement is from system improvements and how much is attributable to weather variability. Ensemble skill also improved over time and, again, consistently across latitudes. The quality of analyses serving for forecast verification and initialization has been investigated further. An intercomparison of The Observing system Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) analyses and forecasts revealed substantial differences for surface parameters, but also at lower levels in the troposphere, wheremost of the physical processes relevant to weather in the short-to-medium range take place over the poles. The differences between the TIGGE analyses were generally much larger than differences between members of the ECMWF4D-Var ensemble of analyses generated internally at ECMWF. This suggests that neither the multi-analysis approach nor ensemble data assimilation may represent polar analysis uncertainty properly. This is particularly visible at the surface and lower levels in the atmosphere. Forecast spread and error match much better north of 65°N where less atmospheric variability prevails along the entire forecast range, while in areas of significant synoptic activity the spread also appears too low. [ABSTRACT FROM AUTHOR]

High-latitude climate change is expected to increase the demand for reliable weather and environmental forecasts in polar regions. In this study, a quantitative assessment of the skill of state-of-the-art global weather prediction systems in polar regions is given using data from the THORPEX Interactive Grand Global Ensemble (TIGGE) for the period 2006/2007-2012/2013. Forecast skill in the Arctic is comparable to that found in theNorthernHemispheremidlatitudes. However, relative differences in the quality between different forecasting systems appear to be amplified in the Arctic. Furthermore, analysis uncertainty in the Arctic is more of an issue than it is in the midlatitudes, especially when it comes to near-surface parameters over snow- and ice-covered surfaces. Using NOAA's reforecast dataset, it is shown that the changes in forecast skill during the 7-year period considered here can largely be explained by flow-dependent error growth, especially for themore skilful forecasting systems. Finally, a direct comparison between the Arctic and Antarctic suggests that predictions of mid-topospheric flow in the former region are more skilful. [ABSTRACT FROM AUTHOR]