An analysis of observed daily maximum wind gusts in the UK

The greatest attention to the UK wind climatology has focused upon mean windspeeds, despite a knowledge of gust speeds being essential to a variety of users. This paper goes some way to redressing this imbalance by analysing observed daily maximum gust speeds from a 43-station network over the period 1980–2005. Complementing these data are dynamically downscaled reanalysis data, generated using the PRECIS Regional Climate Modelling system, for the period 1959–2001. Inter-annual variations in both the observed and downscaled reanalysis gust speeds are presented, with a statistically significant (at the 95% confidence interval) 5% increase across the network in daily maximum gust speeds between 1959 and the early 1990s, followed by an apparent decrease. The benefit of incorporating dynamically downscaled reanalysis data is revealed by the fact that the decrease in gust speeds since 1993 may be placed in the context of a very slight increase displayed over the longer 1959–2001 period. Furthermore, the severity of individual windstorm events is considered, with high profile recent events placed into the context of the long term record. A daily cycle is identified from the station observations in the timing of the daily maximum gust speeds, with an afternoon peak occurring between 12:00–15:00, exhibiting spatial and intra-annual variations

Assessing the suitability of statistical downscaling approaches for seasonal forecasting in Senegal

This work tests the suitability of statistical downscaling (SD) approaches to generate local seasonal forecasts of daily maximum temperature and precipitation for a set of selected stations in Senegal for the July-August-September season during the period 1979-2000. Two-month lead raw daily maximum temperature and precipitation from the five models included in the ENSEMBLES seasonal hindcast are compared against the corresponding downscaled predictions, which are obtained by applying the analog technique based on two different types of predictors: the direct surface variables and a combination of appropriate upper-air variables. Beyond correcting the large biases of the low-resolution raw model outputs, SD is found to add noteworthy value in terms of forecast association (as measured by interannual correlation), providing thus suitable (i.e. calibrated) predictions at the local-scale needed for practical applications, which means a clear advantage for the end-users of seasonal forecasts over the area of study. Moreover, a recommendation on the adequacy of surface (large-scale) predictors for SD of maximum temperature (precipitation) is also given.This study was supported by the EU projects QWeCI and EUPORIAS, funded by the European Commission through the Seventh Framework Programme for Research under Grant Agreements 243964 and 308291, respectively. The author is grateful to the free distribution of the ECMWF ERA‐Interim (http://www.ecmwf.int/en/research/climate‐reanalysis/era‐interim) data, as well as to the EU project ENSEMBLES, financed by the European Commission through the Sixth Framework Programme for Research under contract GOCE‐CT‐2003‐505539, for the seasonal simulations provided and to the Agence Nationale de la Météorologie du Sénégal for the observational data

A comprehensive sensitivity and uncertainty analysis for discharge and nitrate-nitrogen loads involving multiple discrete model inputs under future changing conditions

Environmental modeling studies aim to infer the impacts on environmental variables that are caused by natural and human-induced changes in environmental systems. Changes in environmental systems are typically implemented as discrete scenarios in environmental models to simulate environmental variables under changing conditions. The scenario development of a model input usually involves several data sources and perhaps other models, which are potential sources of uncertainty. The setup and the parametrization of the implemented environmental model are additional sources of uncertainty for the simulation of environmental variables. Yet to draw well-informed conclusions from the model simulations it is essential to identify the dominant sources of uncertainty. In impact studies in two Austrian catchments the eco-hydrological model Soil and Water Assessment Tool (SWAT) was applied to simulate discharge and nitrate-nitrogen (NO3--N) loads under future changing conditions. For both catchments the SWAT model was set up with different spatial aggregations. Non-unique model parameter sets were identified that adequately reproduced observations of discharge and NO3--N loads. We developed scenarios of future changes for land use, point source emissions, and climate and implemented the scenario realizations in the different SWAT model setups with different model parametrizations, which resulted in 7000 combinations of scenarios and model setups for both catchments. With all model combinations we simulated daily discharge and NO3--N loads at the catchment outlets. The analysis of the 7000 generated model combinations of both case studies had two main goals: (i) to identify the dominant controls on the simulation of discharge and NO3--N loads in the two case studies and (ii) to assess how the considered inputs control the simulation of discharge and NO3--N loads. To assess the impact of the input scenarios, the model setup, and the parametrization on the simulation of discharge and NO3--N loads, we employed methods of global sensitivity analysis (GSA). The uncertainties in the simulation of discharge and NO3--N loads that resulted from the 7000 SWAT model combinations were evaluated visually. We present approaches for the visualization of the simulation uncertainties that support the diagnosis of how the analyzed inputs affected the simulation of discharge and NO3--N loads. Based on the GSA we identified climate change and the model parametrization as being the most influential model inputs for the simulation of discharge and NO3--N loads in both case studies. In contrast, the impact of the model setup on the simulation of discharge and NO3--N loads was low, and the changes in land use and point source emissions were found to have the lowest impact on the simulated discharge and NO3--N loads. The visual analysis of the uncertainty bands illustrated that the deviations in precipitation of the different climate scenarios to historic records dominated the changes in simulation outputs, while the differences in air temperature showed no considerable impact.</p

Simulation modeling of phytoplankton dynamics in a large eutrophic river, Hungary – Danubian Phytoplankton Growth Model (DPGM)

Ecological models have often been used in order to answer questions that are in the limelight of recent researches such as the possible effects of climate change. The methodology of tactical models is a very useful tool comparison to those complex models requiring relatively large set of input parameters. In this study, a theoretical strategic model (TEGM ) was adapted to the field data on the basis of a 24-year long monitoring database of phytoplankton in the Danube River at the station of G¨od, Hungary (at 1669 river kilometer – hereafter referred to as “rkm”). The Danubian Phytoplankton Growth Model (DPGM) is able to describe the seasonal dynamics of phytoplankton biomass (mg L−1) based on daily temperature, but takes the availability of light into consideration as well. In order to improve fitting, the 24-year long database was split in two parts in accordance with environmental sustainability. The period of 1979–1990 has a higher level of nutrient excess compared with that of the 1991–2002. The authors assume that, in the above-mentioned periods, phytoplankton responded to temperature in two different ways, thus two submodels were developed, DPGM-sA and DPGMsB. Observed and simulated data correlated quite well. Findings suggest that linear temperature rise brings drastic change to phytoplankton only in case of high nutrient load and it is mostly realized through the increase of yearly total biomass

Examination of wind storms over Central Europe with respect to circulation weather types and NAO phases

The occurrence of wind storms in Central Europe is investigated with respect to large-scale atmospheric flow and local wind speeds in the investigation area. Two different methods of storm identification are applied for Central Europe as the target region: one based on characteristics of large-scale flow (circulation weather types, CWT) and the other on the occurrence of extreme wind speeds. The identified events are examined with respect to the NAO phases and CWTs under which they occur. Pressure patterns, wind speeds and cyclone tracks are investigated for storms assigned to different CWTs. Investigations are based on ERA40 reanalysis data. It is shown that about 80% of the storm days in Central Europe are connected with westerly flow and that Central European storm events primarily occur during a moderately positive NAO phase, while strongly positive NAO phases (6.4% of all days) account for more than 20% of the storms. A storm occurs over Central Europe during about 10% of the days with a strong positive NAO index. The most frequent pathway of cyclone systems associated with storms over Central Europe leads from the North Atlantic over the British Isles, North Sea and southern Scandinavia into the Baltic Sea. The mean intensity of the systems typically reaches its maximum near the British Isles. Differences between the characteristics for storms identified from the CWT identification procedure (gale days, based on MSLP fields) and those from extreme winds at Central European grid points are small, even though only 70% of the storm days agree. While most storms occur during westerly flow situations, specific characteristics of storms during the other CWTs are also considered. Copyright © 2009 Royal Meteorological Societ

New insights into North European and North Atlantic surface pressure variability, storminess and related climate change since 1830

The authors present initial results of a new pan-European and international storminess since 1800 as interpreted from European and North Atlantic barometric pressure variability (SENABAR) project. This first stage analyzes results of a new daily pressure variability index, dp(abs)24, from long-running meteorological stations in Denmark, the Faroe Islands, Greenland, Iceland, the United Kingdom, and Ireland, some with data from as far back as the 1830s. It is shown that dp(abs)24 is significantly related to wind speed and is therefore a good measure of Atlantic and Northwest European storminess and climatic variations. The authors investigate the temporal and spatial consistency of dp(abs)24, the connection between annual and seasonal dp(abs)24 and the North Atlantic Oscillation Index (NAOI), as well as dp(abs)24 links with historical storm records. The results show periods of relatively high dp(abs)24 and enhanced storminess around 1900 and the early to mid-1990s, and a relatively quiescent period from about 1930 to the early 1960s, in keeping with earlier studies. There is little evidence that the mid- to late nineteenth century was less stormy than the present, and there is no sign of a sustained enhanced storminess signal associated with “global warming.” The results mark the first step of a project intending to improve on earlier work by linking barometric pressure data from a wide network of stations with new gridded pressure and reanalysis datasets, GCMs, and the NAOI. This work aims to provide much improved spatial and temporal coverage of changes in European, Atlantic, and global storminess

New insights into North European and North Atlantic surface pressure variability, storminess, and related climatic change since 1830

The authors present initial results of a new pan-European and international storminess since 1800 as interpreted from European and North Atlantic barometric pressure variability (SENABAR) project. This first stage analyzes results of a new daily pressure variability index, dp(abs)24, from long-running meteorological stations in Denmark, the Faroe Islands, Greenland, Iceland, the United Kingdom, and Ireland, some with data from as far back as the 1830s. It is shown that dp(abs)24 is significantly related to wind speed and is therefore a good measure of Atlantic and Northwest European storminess and climatic variations. The authors investigate the temporal and spatial consistency of dp(abs)24, the connection between annual and seasonal dp(abs)24 and the North Atlantic Oscillation Index (NAOI), as well as dp(abs)24 links with historical storm records. The results show periods of relatively high dp(abs)24 and enhanced storminess around 1900 and the early to mid-1990s, and a relatively quiescent period from about 1930 to the early 1960s, in keeping with earlier studies. There is little evidence that the mid- to late nineteenth century was less stormy than the present, and there is no sign of a sustained enhanced storminess signal associated with "global warming." The results mark the first step of a project intending to improve on earlier work by linking barometric pressure data from a wide network of stations with new gridded pressure and reanalysis datasets, GCMs, and the NAOI. This work aims to provide much improved spatial and temporal coverage of changes in European, Atlantic, and global storminess. © 2008 American Meteorological Society